Value-Focused Business Process Engineering : a Systems Approach: with Applications to Human Resource Management

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Value-Focused Process Engineering: a Systems Approach with Applications to Human Resource Management

INTEGRATED SERIES IN INFORMATION SYSTEMS Series Editors Professor Ramesh Sharda

Prof. Dr. Stefan Voß

Oklahoma State University

Universität Hamburg

E-BUSINESS MANAGEMENT: Integration of Web Technologies with Business Models/ edited by Michael J. Shaw VIRTUAL CORPORATE UNIVERSITIES: A Matrix of Knowledge and Learning for the New Digital Dawn/ Walter R.J. Baets & Gert Van der Linden SCALABLE ENTERPRISE SYSTEMS: An Introduction to Recent Advances/ edited by Vittal Prabhu, Soundar Kumara, Manjunath Kamath LEGAL PROGRAMMING: Legal Compliance for RFID and Software Agent Ecosystems in Retail Processes and Beyond/ Brian Subirana and Malcolm Bain LOGICAL DATA MODELING: What It Is and How To Do It/ Alan Chmura and J. Mark Heumann DESIGNING AND EVALUATING E-MANAGEMENT DECISION TOOLS: The Integration of Decision and Negotiation Models into Internet-Multimedia Technologies/ Giampiero E.G. Beroggi INFORMATION AND MANAGEMENT SYSTEMS FOR PRODUCT CUSTOMIZATION/ Thorsten Blecker et al MEDICAL INFORMATICS: Knowledge Management and Data Mining in Biomedicine/ edited by Hsinchun Chen et al KNOWLEDGE MANAGEMENT AND MANAGEMENT LEARNING: Extending the Horizons of Knowledge-Based Management/ edited by Walter Baets INTELLIGENCE AND SECURITY INFORMATICS FOR INTERNATIONAL SECURITY: Information Sharing and Data Mining/ Hsinchun Chen ENTERPRISE COLLABORATION: On-Demand Information Exchange for Extended Enterprises/ David Levermore & Cheng Hsu SEMANTIC WEB AND EDUCATION/ Vladan Devedžić INFORMATION SYSTEMS ACTION RESEARCH: An Applied View of Emerging Concepts and Methods/ edited by Ned Kock ONTOLOGIES: A Handbook of Principles, Concepts and Applications/ edited by Raj Sharman, Rajiv Kishore, Ram Ramesh METAGRAPHS AND THEIR APPLICATIONS/ Amit Basu and Robert W. Blanning SERVICE ENTERPRISE INTEGRATION: An Enterprise Engineering Perspective/ Cheng Hsu DIGITAL GOVERNMENT: E-Government Research, Case Studies, and Implementation/ edited by Chen et al TERRORISM INFORMATICS: Knowledge Management and Data Mining for Homeland Security/ edited by Chen et al

Value-Focused Process Engineering: a Systems Approach with Applications to Human Resource Management

Dina Neiger Swinburne University of Technology, Melbourne, Australia

Leonid Churilov National Stroke Research Institute and the University of Melbourne, Melbourne, Australia

Andrew Flitman Swinburne University of Technology, Melbourne, Australia

with the assistance of Kristian Rotaru

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Dina Neiger Swinburne University of Technology Melbourne, Australia [email protected]

Leonid Churilov National Stroke Research Institute and the University of Melbourne Melbourne, Australia [email protected]

Andrew Flitman Swinburne University of Technology Melbourne, Australia [email protected]

ISSN: 1571-0270 ISBN-13: 978-0-387-09520-2 DOI: 10.1007/978-0-387-09520-2

e-ISBN-13: 978-0-387-09521-9

Library of Congress Control Number: 2008933509 © 2009 by Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now know or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks and similar terms, even if the are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com

We live on shifting sands where all the signposts point in a disparate array. We are surrounded by so much ‘stuff’ – ideas, trends, inventions, auto-proliferating information. We need people who can make sense of all this by uncovering a common wholeness about it, by finding the point where they all converge. Freeman (2000, p. 229)

To our families

Contents 1 Introducing Value-Focused Process Engineering............................................1 1.1 Introduction: Motivation and Context..........................................................1 1.2 Value-Focused Process Engineering: an Integrated Approach to GoalOriented Process Design....................................................................................3 1.3 Systems Defined ..........................................................................................5 1.4 Systems perspective on Integration of Business Objectives and Business Processes............................................................................................................7 1.5 Scope and Contribution ...............................................................................8 1.7 Book structure............................................................................................10 2. Business Systems Modelling: Principles and Practices ................................15 2.1 Introduction................................................................................................15 2.2 Systems Modelling as Design: Fundamental Principles and Assumptions 16 2.3 Integration of Multiple Business Views through Systems Modelling .......19 2.4 Business Objectives Modelling..................................................................21 2.5 Business Process Modelling ......................................................................22 2.6 Exploring Systems Modelling Practices Through Wand and Weber Conceptual Modelling Framework ..................................................................26 2.7 Summary....................................................................................................28 3 Human Resources Management Context .......................................................31 3.1 Introduction................................................................................................31 3.2 Context and Background ...........................................................................32 3.3 Summary....................................................................................................48 4 Business Objectives Modelling ........................................................................50 4.1 Introduction................................................................................................50 4.2 Quantitative Objectives Modelling ............................................................50 4.3 Qualitative Objectives Modelling ..............................................................66 4.4 Desirable Properties of Objectives Models................................................71 4.5 Summary....................................................................................................74 5 Business Process Modelling with EPCs ..........................................................77 5.1 Introduction................................................................................................77 5.2 Business Process Modelling Context.........................................................78 5.3 Event-driven Process Chain.......................................................................79 5.4 Decomposition ...........................................................................................87 5.5 Extending EPC Script to Include Objectives .............................................92 5.6 Desirable Properties of Business Process Models from a Value-Focused Process Engineering Perspecitve .....................................................................94 5.7 Assessment of the EPC Modelling Environment.....................................103

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5.8 Summary.................................................................................................. 106 6 Requirements for a Value-Focused EPC: the “WHAT” Dimension.......... 109 6.1 Introduction ............................................................................................. 109 6.2 Review of Goal-Oriented Approaches..................................................... 110 6.3 Comparative Assessment of Modelling Methodologies .......................... 113 6.4 Analysis of Hereditary Property Requirements ....................................... 118 6.5 Linking Requirements ............................................................................. 124 6.6 Shared History Requirements .................................................................. 127 6.7 Emergent Properties Requirements.......................................................... 128 6.8 Summary.................................................................................................. 129 7 Building a Value-Focused EPC: the “HOW” Dimension ........................... 131 7.1 Introduction ............................................................................................. 131 7.2 Modifications to the VFT Model ............................................................. 131 7.3 Formalising the Link between the VFT and the EPC .............................. 138 7.4 Synchronized Decomposition .................................................................. 141 7.5 Setting up the Example............................................................................ 162 7.6 Flow Decomposition................................................................................ 166 7.7 Components Decomposition.................................................................... 168 7.8 Implementation Framework..................................................................... 171 7.9 Evaluation of the Combined Model......................................................... 173 7.10 Summary................................................................................................ 175 8 Application of Value-Focused Process Engineering to HRM Context ...... 177 8.1 Introduction ............................................................................................. 177 8.2 HRM Values, Fundamental and Means Objectives (Phases 1, 2 and 4a) 178 8.3 HRM Processes (Phases 3 and 4b) .......................................................... 186 8.4 Reconciling EPC and Objectives Structures (Phase 5) ............................ 201 8.5 Summary.................................................................................................. 205 9 Decision-Enabled e-EPC................................................................................ 207 9.1 Introduction ............................................................................................. 207 9.2 Decision vs Decision ............................................................................... 207 9.3 Relationship between Business Decision and Business Process Modelling Tools .............................................................................................................. 212 9.4 Integration Model .................................................................................... 215 9.5 Benefits of Decision-Enabling................................................................. 223 9.6 Summary.................................................................................................. 228 10 Conclusions and Future Directions............................................................. 231 10.1 Introduction ........................................................................................... 231 10.2 Desirable Properties of Business Objectives and Process Modelling .... 231 10.3 Properties of Value-Focused Process Engineering ................................ 234

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10.4 Linking Existing Models to Satisfy Requirements of Value-Focused Process Engineering.......................................................................................235 10.5 Application within the HRM Context....................................................236 10.6 Future Directions and Conclusion..........................................................237 References ..........................................................................................................241 Appendix 1. Decomposition of the means network.........................................253 Index ...................................................................................................................257

Abbreviations Used in the Text AML – ARIS – BPM – BPR – BSC – CL – CSF – DFD – EMPL – EPC – e-EPC – de-EPC – ERP – GED – GIM – HR – HRM – IDEF – IR – KPI – MCDA – MS – MAUT – OO – OR – RAD – RE – SD – SSM – SFD – UML – VFT –

ARIS Markup Language Architecture of Integrated Information Systems Business Process Modelling Business Process Re-engineering Balanced Scorecard Causal Loop Critical Success Factors Data Flow Diagram EPC Markup Language Event-driven Process Chain extended EPC decision enabled e-EPC Enterprise Resource Planning Goal-Exception-Dependency framework GRAI Integrated Methodology Human Resources Human Resource Management Integrated Definition Industrial Relations Key Performance Indicator Multi-Criteria Decision Analysis Management Science Multi-Attribute Utility Theory Object Oriented Operations Research Role Activity Diagram Requirements Engineering System Dynamics Soft Systems Methodology Stock and Flow diagram Unified Modelling Language Value-focused Thinking

Abbreviations Used in the Diagrams Ad DA HR HRM IR Max Min Mngt Obj(s) Org ROI

– – – – – – – – – –

Advertise, advertisement Decision analysis Human resources Human resource management Industrial relations Maximize Minimize Management Objective(s) Organization, organazational Return on investment

List of Figures Fig. 1.1 Book structure and contributions .............................................................13 Fig. 3.1 HR strategic goals (from Boxall 1999, p. 269, fig. 2)..............................35 Fig. 3.2 Objectives according to organizational types (from Baruch and Peiperl 2000, table VII)............................................................................................42 Fig. 4.1 Road map to the VFT concepts (based on Keeney 1992) ........................52 Fig. 4.2 VFT structure for a subset of objectives in DialAmerica example ..........57 Fig. 4.3 System dynamics model for DialAmerica example .................................65 Fig. 5.1 Functional vs Process view of a Recruitment Process .............................78 Fig. 5.2 Description of the e-EPC in the Control/Process View from Scheer (1999, p. 37, fig. 15)................................................................................................80 Fig. 5.3 EPC notation illustration..........................................................................84 Fig. 5.4 EPC hierarchical decomposition illustration............................................88 Fig. 5.5 An illustration of a 2-level business process EPC model.........................90 Fig. 5.6 Modelling levels adapted from Davis (2001, p. 244)...............................94 Fig. 5.7 Melao and Pidd taxonomy of how dimension........................................101 Fig. 6.1 EKD models from Rolland, Nurcan and Grosz (2000, p. 314, fig. 1)....112 Fig. 6.2 Property deficit.......................................................................................114 Fig. 6.3 Property overload...................................................................................115 Fig. 6.4a Completely redundant model ...............................................................115 Fig. 6.4b Property redundancy ............................................................................116 Fig. 6.4c Redundancy within the combined model .............................................116 Fig. 6.5 Property deficit analysis.........................................................................120 Fig. 6.6 Overlap properties..................................................................................122 Fig. 6.7 Revised deficit properties.......................................................................124 Fig. 6.8 Venn diagram of the relationship between VFT and EPC .....................127 Fig. 7.1 Illustration of a VFT framework goal model with modified means-ends network ......................................................................................................135 Fig. 7.2 Link between an EPC and a VFT...........................................................138 Fig. 7.3a Atomic link ..........................................................................................142 Fig. 7.3b Multiple objectives ..............................................................................143 Fig. 7.3c Hierarchically ranked function with direct links ..................................144 Fig. 7.3d Hierarchically ranked function with single objective ..........................145 Fig. 7.3e AND-join .............................................................................................146 Fig. 7.4a Sequence ..............................................................................................149 Fig. 7.4b Parallel split .........................................................................................150 Fig. 7.4c Null-branch ..........................................................................................152 Fig. 7.4d Exclusive choice (single instance) .......................................................153 Fig. 7.4e Exclusive choice (multiple instances) ..................................................153 Fig. 7.4f Multi-choice..........................................................................................155 Fig. 7.4g Arbitrary cycles....................................................................................156 Fig. 7.5 VAD chain .............................................................................................162 Fig. 7.6 Recruitment process e-EPC ...................................................................164

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Fig. 7.7 Retain and develop process e-EPC ........................................................165 Fig. 7.8 VFT structure for the recruitment process .............................................167 Fig. 7.9 Components decomposition...................................................................170 Fig. 7.10 Correspondence between original and synchronized structures...........171 Fig. 7.11 Implementation framework for Value-Focused Process Engineering with EPC and VFT.............................................................................................172 Fig. 8.1 Implementation framework in HRM context.........................................178 Fig. 8.2 Fundamental objectives hierarchy, HRM context..................................183 Fig. 8.3 High level means-ends network for HRM context (decomposition of means objectives is included in Appendix 1).............................................184 Fig. 8.4a Planning process e-EPC.......................................................................189 Fig. 8.4b Planning process objectives structure ..................................................190 Fig. 8.5a Staffing process e-EPC ........................................................................192 Fig. 8.5b Staffing process objectives structure ...................................................193 Fig. 8.6 Hierarchical decomposition of the staffing process ...............................194 Fig. 8.7a Training process e-EPC .......................................................................195 Fig. 8.7b Training process objectives structure ..................................................196 Fig. 8.8a Industrial Relations process e-EPC......................................................198 Fig. 8.8b IR process objectives structure ............................................................199 Fig. 8.9a Compensation process e-EPC ..............................................................200 Fig. 8.9b Compensation process objectives structure .........................................201 Fig. 8.10a HRM process objectives (relationships across processes) .................203 Fig. 8.10b HRM process objectives (means-ends decomposition) .....................204 Fig. 8.11 Complete Value-Focused Process Engineering structure ....................205 Fig. 9.1 Illustration of different decisions ...........................................................208 Fig. 9.2 Decision-enabled e-EPC conceptual model ...........................................216 Fig. 9.3 Decision view of a de-EPC adapted from Scheer (1999, pp. 34-35) .....217 Fig. 9.4 Recruitment causal loop.........................................................................221 Fig. 9.5 de-EPC of the “shortlist applicants” function ........................................222 Fig. 9.6 Benefits of decision-enabled processes (Mallach (2000, p. 22), Daellenbach (1994, p. 13); Davis (2001, p. 4), Scheer (2000, p. 7); Mallach (2000, p. 22), Sterman (1991))...................................................................223 Fig. 9.7 Framework for identification of decision-enabling potential of a business process .......................................................................................................227 Fig. A.1.1 Decomposition of the means objective “Ensure excellence in HR planning and research”...............................................................................253 Fig. A.1.2 Decomposition of the means objective “Ensure excellence in staffing” ...................................................................................................................253 Fig. A.1.3 Decomposition of the means objective “Ensure excellence in training and development” ......................................................................................254 Fig. A.1.4 Decomposition of the means objective “Ensure excellence in IR” ....254 Fig. A.1.5 Decomposition of the means objective “Ensure excellence in compensation” ...........................................................................................254

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Fig. A.1.6 Decomposition of the means objective “Ensure excellence in integration of technology infrastructure relevant to HR function” ............255

List of Tables Table 2.1 Taxonomy of business process models.................................................25 Table 3.1 HRM activities and goals (adapted from Schuler and Walker 1991, ch. 3) ..................................................................................................................47 Table 4.1 How to construct means-objectives networks and fundamentalobjectives hierarchies from Clemen and Reilly (2001, p. 49, fig. 3.3).........57 Table 4.2 Comparison of quantitative and qualitative objectives models.............73 Table 5.1 Interpretation of logical connectors within an EPC from Davis (2001, p. 119, table 7.3) ..............................................................................................89 Table 5.3 Evaluation of the EPC methodology against the desirable features of a process model ............................................................................................107 Table 6.1 Illustration of mapping categories. .....................................................117 Table 6.2 Results of the comparative assessment (property availability is denoted as “+”, partial availability is denoted as “±“, and absence of property is denoted as “−“) ..........................................................................................123 Table 6.3 Summary of requirements...................................................................130 Table 7.1 Mapping of goal-oriented business process model requirements to the structure of Chapter 7.................................................................................131 Table 7.2 Interpretation of logical connectors within the VFT model based on Haumer et al. (1998), Lamsweerde and Letier (1998), Rolland and Prakash (2000).........................................................................................................134 Table 7.3 Mapping summary by objective structure...........................................159 Table 7.4 Mapping summary by workflow structures ........................................160 Table 7.5 Components decomposition................................................................169 Table 7.6 Goal-oriented activity within the combined model.............................174 Table 9.1 Decision situation components ...........................................................210 Table 9.2 Recruitment process KPIs based on Fitz-enz and Davison (2002) .....219

Foreword Though the general public may not be aware of it, most business and public sector organisations could not function without decision and process models. Anyone who buys an airline ticket over the Internet will be aware that the price varies as the date of the flight draws closer. They may be less aware that the price offered also depends on how many seats have already been sold on the flight. The price of hotel rooms likewise changes, depending on the date and the success of the hotel in selling its rooms. This dynamic pricing depends on computer-based decision models that enable the airline or hotel to maximise its revenue and minimise its empty seats or bedrooms. The decision models must then be embedded in reliable e-business systems that enable ordinary people to book tickets and rooms online in an effective way. When we access and use such systems, we expect them to work every minute, every hour and every day and we expect the airline seat or hotel room to be available when we check-in. Designing systems to ensure that this happens each and every time is not straightforward and requires models of systems and business processes. These process models serve as test-beds for the real systems before they are implemented in practice. A systems approach and a process view are fundamental to improving organisational efficiency and effectiveness. This is as true of the public sector as the forprofit private sector. Organisations must continually search for better ways to meet the needs of their customers and clients. To do so, they must have ways of seeing the links between their activities so that they can plan those activities properly. This emphasis on efficiency and effectiveness in business process management and design needs, obviously, to be seen within the overall goals of an organisation. As a consequence, the idea of this book “Value-Focused Business Process Engineering: a Systems Approach with Applications to Human Resource Management” is to develop new business process engineering methodology that lead to the design of business process aligned with organisational values and objectives. The book focuses on the relationship between the models of business goals and processes. It encourages the identification of overlaps between those models and gaps that may exist between them. That is, it assumes that business process models can be targeted at business goals to maintain alignment with those goals. Alongside systems thinking, the book develops • evaluation frameworks for business processes based on a value-focused process engineering perspective; • a comparative assessment framework to support the systematic evaluation of any set of models as candidates for integration; • extensions to the objectives modelling capabilities of the value-focused thinking to enable the modelling of logical relationships and their linkage to an event-driven process chain methodology; • a framework for integrating an event-driven process chain modelling environment with a suite of decision modelling tools.

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This is done with particular reference to human resource management, which is a welcome recognition that organisations ultimately depend on the people who work in them. This book should be of interest to the process and decision modelling communities. Academics will find theoretical developments in the unification of process and decision models. These may be of value as a reference for graduate courses in IS, OR/MS, and business process management. Practitioners will be interested in the implementation guidelines for value-focused process engineering and its application in human resource management. Mike Pidd Lancaster University, 02 May 2008.

Acknowledgements We appreciate the contribution of the many people whose ideas and experiences influenced our thinking. While we can’t recognise all of them here, we would especially like to acknowledge the following people (listed in alphabetical order): W. M. P. van der Aalst

Eindhoven University of Technology, The Netherlands

J. W. Bourdeau

Cornell University, USA

P. Checkland

Lancaster University, UK

H. Daellenbach

University of Canterbury, New Zealand

R. Davis

BT Group, UK

R. L. Keeney

Duke University, USA

P. Kueng

Credit Suisse Holdings, USA

G. T. Milkovich

Cornell University, USA

M. zur Muehlen

Stevens Institute of Technology, USA

M. Pidd

Lancaster University, UK

M. Rosemann

Queensland University Technology, Australia

A.-W. Scheer

IDS Scheer, Germany

Y. Wand

University of British Columbia, Canada

R. Weber

Monash University, Australia

of

We acknowledge and thank the School of Business Systems, Monash University, Australia which was the original home of the authors and the place where ideas for this book came into existence. We would also like to acknowledge editors of the Integrated Series in Information Systems, Stefan Voss and Ramesh Sharda, for their encoragement.

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Acknowledgements

Kristian Rotaru has been instrumental in getting this book to its present form, his dedication, professionalism and sleepless nights are greately appreciated by the authors. Last but not least, the authors would like to specially thank our families for being exremelly patient, supportive and encouraging during the work leading to the creation and writing of this book.

1 Introducing Value-Focused Process Engineering Most organizations do a good job of establishing strategic objectives and of crafting a strategy that is designed to achieve these objectives. However, the question of whether the strategy, even if successfully implemented, can in fact yield the espoused objectives is rarely entertained...the question of whether the objective can be achieved has to do with whether the objective(s), strategy, and processes form an internally consistent set.

Richmond (1997, p. 131)

1.1 Introduction: Motivation and Context Business modelling defined as “the use of models and methods to understand and change business operations together with information systems in organisations” by Nilsson, Tolis and Nellborn (1999, p. 1), has been the focus of extensive effort within a variety of related fields of both research enquiry and practice. These fields include process and information modelling (within a broad Information Systems (IS) discipline) and decision analysis, business dynamics and quantitative modelling (within a broad discipline of Decision Sciences, often jointly referred to as Operations Research/Management Science (OR/MS)). In particular, use of appropriate models for superior business process design and execution remains a significant challenge for both researchers and practitioners. In January 2005, as a result of a survey of 1300 CIOs, Gartner Inc. (Gartner 2005) identified business process improvement as the top business priority with improvement interpreted as “doing things better, not just cheaper and faster” (Gartner 2005, p. 2). The following two Gartner reports based on the responses of 1400 CIOs and carried out in 2006 and 2007 respectively (Gartner 2006, 2007), re-emphasized business process improvement as a main priority, thus proving the long lasting nature of this trend. This means that the focus of business process engineering must be expanded from creating more efficient business processes (i.e. “doing things right”) to also making business processes more effective by “doing the right things” (Daellenbach 1994, p. 15). In order to do the right things, the design of business processes must be motivated by all of the strategic objectives of the business rather than just the efficiency gains (e.g. Muehlen 2004b). As stressed by Gartner Inc. (Gartner 2005, p. 2) “these pressures force a move towards business process improvement and integration”. The importance of ensuring that the business process is designed so that it fulfils organizational objectives has been highlighted by many authors (e.g. Hammer 1990, Harmon 2003, Jacobs and Holten 1995, Kueng and Kawalek 1997, Kuechler and Burg 1998, Muehlen 2004b, Rolland and Prakash 2000, Yu and Mylopoulos 1994). In 1990 Hammer (Hammer 1990, p. 108) noted that “Conventional process structures are fragmented and piecemeal, and they lack the integration necessary to maintain quality and service. They are breeding grounds for

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1 Introducing Value-Focused Process Engineering

tunnel vision, as people tend to substitute the narrow goals of their particular department for the large goals of a process as a whole” (emphasis added). The expression “paving of the cow path” (Yu et al. 1996, p. 16) is one of the more colourful references to the potential problems of ignoring business objectives when designing business processes. More mundane descriptions include “difficult alignment to specific requirements of the enterprise…[and] high possibility of cultural clash” (Rolland and Parkash 2000, p. 181), “workflow inflexibility” (Bider and Johannesson 2002, p. 1) and inability to meet both effectiveness and efficiency requirements of the organization (e.g. Ackermann et al. 1999, p. 195). As the process engineering discipline evolved, the discussion of process models that incorporated goal structures (referred to in the literature as goal-oriented (e.g. Yu and Mylopoulos 1994), goal-based (e.g. Kuechler and Burg 1998) or goaldriven (e.g. Jacobs and Holten 1995)) has moved from general understanding of their importance to descriptive specification of activities involved in goal-oriented process modelling (Kueng and Kawalek 1997) to formal definitions of goaloriented business process patterns (Andersson et al. 2005). These developments have highlighted the need for the goal-oritented process models to: • identify goals, corresponding constraints and measurement criteria; • decompose goals to the level of business activities represented graphically, including: – – • • • • •

decomposition of goals; graphical representation of different level of activities;

decompose goals to each level of business activity; derive activities from the set of goals defined; specify the flow of activities; identify roles responsible for execution of business processes; and identify objects transformed by the business process.

Existing models within the broadly defined Information Systems and Decision Sciences disciplines usually support only a subset of these activities. According to Bider and Johannesson (2002, p. 1), “most of the research and practical work in the domain of business process modelling is devoted to describing and formalising the order of activities rather than to the explication and representation of goals”. Business process and requirements engineering models within the IS discipline support specification of the process flow (e.g. Giaglis 2001) and identification of roles and decomposition of goals (e.g. Lamsweerde 2001) respectively. Decision models within the OR/MS discipline support identification of goals as well as corresponding constraints and measurement criteria (e.g. Keeney 1992, Olson 1996). However, none of the existing models combine all of these activities within a single integrated framework or modelling environment. Therefore, the original aim of goal-oriented business process engineering to develop business models that use business objectives to guide the design and evaluation of a business process while integrating representation of processes and

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objectives still remains to be achieved, and presents a considerable research challenge. This challenge partly stems from apparent differences between OR/MS and IS modelling paradigms that facilitate gains in either business efficiency (e.g. Eventdriven Process Chains (Scheer 1999; 2000)) or business effectiveness (e.g. ValueFocused Process Thinking (Keeney 1992)) respectively. As a direct consequence, the existing goal-oriented business process models (e.g. Dardenne et al. 1993; Kavakli 2002; Khomyakov and Bider 2001; Rolland et al. 1998, Yu 1999) are strongly aligned to the IS modelling paradigm and utilize goal models that are developed independently of the Decision Sciences field. Within the Human Resource Management (HRM) discipline these concerns are echoed in the recognized need for greater alignment of HRM processes with the overall organizational objectives (e.g. Boxall 1999, Lado and Wilson 1994, Pettigrew 1990, Koys 2000) and a lack of integration between the science of modelling of HR decisions (e.g. Bartholomew et al 1991) and HRM processes. Therefore, the breadth and depth of the HRM discipline provides a perfect application for a variety of concepts discussed in this book allowing for insight and illustration into some of the difficulties that may be faced by those who will apply the Value-Focused Process Engineering in practice. At the same time, the application of the Value-Focused Process Engineering to the HRM will help close some of the gaps that have been identified by researchers and pracititioners within the HRM discipline.

1.2 Value-Focused Process Engineering: an Integrated Approach to Goal-Oriented Process Design To date, the focus of most goal-oriented research is on development of new business process engineering models that lead to the design of business process in accordance with organisational values and objectives (e.g. Danesh and Kock, 2005). The original contribution of the Value-Focused Process Engineering research presented in this book is made by shifting the emphasis from this traditional focus to the establishment of the relationship between the existing models facilitating identification of overlaps between, articulation of gaps in, and a proposal for improvements to, existing business process and business objectives models while meeting requirements of goal-oriented business process modelling. The novelty of the Value-Focused Process Engineering is that there are no new business goal or process models introduced. Rather, the best aspects of what business process modelling and decision modelling have to offer is integrated within a single model in a way that preserves the strengths of the respective models while facilitating the emergence of new properties that satisfy goal-oriented business process modelling requirements. Value-Focused Process Engineering utilizes the following two models originating respectively within Business Process Modelling (and corresponding wider IS context) and Decision Sciences:

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1 Introducing Value-Focused Process Engineering

• An extended-Event Driven Process chain (e-EPC) model that is part of the Architecture of Integrated Information Systems (ARIS) framework developed by Scheer (1999, 2000). ARIS’s capability to seamlessly integrate many different views of the process Scheer (1999, 2000) has ensured extensive use of the eEPC for business process (re)-engineering and management (e.g. Mending et al. 2005, Scheer et al. 2006a, Scheer et al.. 2006b). This choice is consistent with the overwhelming support for the EPC methodology in practical applications of business process modelling (e.g. Scheer et al. 2006a, 2006b, Stein et al. 2003, Mendling and Nuttgens 2003a, IDS Scheer AG 2006) as well as increasing research within the academic process modelling community into the conceptual and theoretical properties of the EPC (e.g. Cuntz and Kindler 2004, 2005, Mendling and Nuttgens 2003b, Mendling et al. 2005, Mendling and Nuttgens 2005); and • Value-Focused Thinking (VFT) model that was developed by Keeney (1992) in order to facilitate identification and structuring of objectives in the context of decision making. By making explicit links from organizational values, to objectives, to alternatives, to decision-making methods Keeney (1992, p. viii) helps “articulate and use your fundamental values to guide and integrate all of your decision making activities”. Both models realize the need to integrate various aspects of business modelling within a single framework, however, the integration within these models is mainly limited by the boundaries of the respective disciplines (i.e. Information Systems and Decision Sciences) within which they operate. As a result, neither VFT nor eEPC meets requirements of goal-oriented business process model. In this book, we aim to show that by combining the two models into a Value-Focused Process Engineering (VFPE) model it is possible to extend their capabilities by taking advantage of their respective strengths in order to overcome their limitations and meet requirements of goal-oriented business process modelling. Business context where the design of business processes must be motivated by all of the strategic objectives of the business rather than just the efficiency gains provides the motivation for research into Value-Focused Process Engineering that is aimed at integrating business process modelling and objectives modelling in order to provide the methodology and tools for a more holistic approach to business process engineering. The need for an integrated and holistic approach to problem solving is the main impetus behind the systems view of the world. Therefore, it is logical to adopt a systems perspective when confronting the problem of integration. Fritjof Capra explains (in Schmidt 2004, p. 2) that Systems thinking involves shifting our attention from the parts to the whole, from objects to relationships, from structures to processes, from hierarchies to networks. It also includes shifts of emphasis from the rational to the intuitive, from analysis to synthesis, from linear to nonlinear thinking.

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Accordingly, the research described in this study uses synthesis of existing methodologies to develop a relationship between business process engineering and business objectives modelling. The systems view of the world described in the next section, suggests that as well as having business process and objectives models under “one roof” the integration of business processes and objectives modelling has the additional advantage of connecting “the notion of goal and the notion of business process” (Bider and Johannesson 2002, p. 1). This connection focuses “attention on what is to be achieved and the strategies required to achieve them” (Rolland and Prakash 2000, p. 181) thus enabling both effectiveness and efficiency requirements of an organisation to be met (Daellenbach 1994, pp. 13-15).

1.3 Systems Defined Everyday usage of the word system originates from two Greek words “syn” – together , and “histemi” – to set (Thatcher and McQueen 1980) and is currently used to describe “a complex whole; a set of connected things or parts; an organized body of material or immaterial things” (Tulloch 1997). This definition offers a glimpse into what is meant by the term system. However, it does not provide enough detail to understand the implications of viewing a particular subject or object as a system. For example, what is meant by the word whole? Is the whole the same as the sum of individual parts or is it something more? Is it possible to remove or add parts without affecting a whole? Can individual parts themselves be considered as whole? etc. These questions have been considered by researchers within various disciplines (e.g. Capra 1996, p. 17), resulting in a number of definitions (formal and informal) of the term system which provide more insight into the systems perspective. In the context of Information Systems, Wand and Weber (in Weber 1997) proposed a formal definition of a system that is founded in the Bunge’s ontological theory. According to them (Weber 1997, p. 44), a system is defined as a set of things where (a) each thing in the set is coupled to at least one other thing, and (b) it is impossible to partition the set of things such that the history of one partition is independent of the history of other partition.

The idea that “systems are greater than the sum of their parts” (Weber 1997, p. 46) is captured within the Wand and Weber framework (Weber 1997, p. 46, emphasis added) as “all systems possess at least one emergent property. In other words, all systems possess at least one property that is not a property of its components.” Two fundamental assumptions underpin this definition (Weber 1997, p. 34): • “things and their properties really exist in the world”; and • “we [only] know about things and their properties … via the models of things and their properties that we create”.

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1 Introducing Value-Focused Process Engineering

These assumptions as well as underltying theory and methodology are discussed in more detail in Chapter 2. Less formal definitions of a system have been adopted within Management and Decision Sciences as illustrated by a widely quoted definition of a system by Checkland (1999, pp. 317-318) as A model of a whole entity; when applied to human activity, the model is characterized fundamentally in terms of hierarchical structure, emergent properties, communication, and control. An observer may choose to relate this model to real-world activity. When applied to natural or man-made entities, the crucial characteristic is the emergent properties of the whole.

Checkland’s definition is a pointer to the Soft Systems Methodology (SSM) that has been developed in recognition of the multiple perceptions of reality that must be supported and reconciled in order to enable a more comprehensive understanding of businesses and their place in the world. Interestingly, both these definitions cross over traditional philosophical boundaries that separate research paradigms into quantitative based in natural sciences (also referred to as positivistic, Hussey and Hussey (1997, p. 47) and qualitative based in social sciences (also referred to as interpretivist or phenomenological by Hussey and Hussey (1997, p. 47)). To illustrate this, consider Daellenbach’s (1994, p. 27) definition of a system that concisely draws attention to elements relevant to this discussion: 1. A system is an organized assembly of components. ‘Organized’ means that there exist special relationships between the components. 2. The system does something, i.e., it exhibits a type of behaviour unique to the system. 3. Each component contributes towards the behaviour of the system and is affected by being in the system. No component has an independent effect on the system. The behaviour of the system is changed if any component is removed or leaves. 4. Groups of components within the system may by themselves have properties (1), (2), and (3), i.e. they may form subsystems. 5. The system has an outside – an environment – which provides inputs into the system and receives outputs from the system. 6. The system has been identified by someone as of special interest. Systems perspective on business process design, management and improvement facilitates the shift from reductionsist “task-breakdown” view (Clegg 2006, p. 416) to a wider perspective based on the understanding of the dynamic nature of business processes that is triggred by the changing values of stakeholders and environmental constraints (Fettke and Loos 2007, Valiris and Glykas 1999, van Ackere et al. 1993, Melao and Pidd 2000). In this book we further advance this view through the integration of business objectives and business process modelling within a systems framework. Having explained what is meant by the term system, the implications of adopting a systems approach for this research are explored next.

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1.4 Systems perspective on Integration of Business Objectives and Business Processes To explain how the system view influences the approach to integration of business objectives and business process modelling, each of the components of the Daellenbach’s system definition is discussed in the context of integration of business objectives and business process modelling and where appropriate related to the more formal Wand and Weber definition. The first point of the Daellenbach’s (1994) definition refers to two concepts: components and the relationship between them (this is referred to as a “coupling of things” in Wand and Weber’s definition). In the context of value-focused process engineering, the two components of interest are business objectives and business process modelling. Business objectives modelling is aimed at representing business values (e.g. Keeney 1992), while business process modelling is aimed at representing business operations (e.g. Davenport 1993). Some research into the relationship between business objectives and process modelling has been undertaken under the umbrella of goal-oriented business process modelling (e.g. Kueng and Kawalek 1997). However, since this research does not take a systematic view of the integration problem, it is usually focused on achieving certain modelling goals rather than on the relationship between the two components. Therefore, at this point in time, understanding the relationship between business process and objectives modelling is a research problem that is yet to be addressed. The second requirement of the Daellenbach’s (1994) definition is that “the system does something” in a way that is “unique to the system”. As discussed in the introduction, the aim of the integration between business objectives and business process modelling is to facilitate alignment of business processes and objectives. Neither business process modelling nor business objectives modelling are able to do this on their own. In other words, the actions of such a system are unique to that system. This element encompasses the definition of emergent properties of the system (by Wand and Weber (in Weber 1997, p. 37)) as properties that are not present in any of the system components. Similarly to Daellenbach, Wand and Weber (in Weber 1997, p. 46) specify that existence of emergent properties is one of the minimum requirements for the existence of the system. Therefore, integration of business process and objectives modelling must be shown to have properties that are not available separately within business process modelling or within business objectives modelling. The third element in Daellenbach’s definition is described more concisely by Wand and Weber (in Weber 1997, pp. 42-44) using the notion of shared history. The need for this requirement to be satisfied has been implicitly recognised within the goal-oriented business process modelling field as the call for research into goal-oriented business process patterns (e.g. Andersson et al. 2005). However, without explicit acknowledgment of the requirement, the existing research is unlikely to address it completely. As such the need to show that the integration of

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1 Introducing Value-Focused Process Engineering

business process and objectives modelling results in a shared history between these components remains to be addressed. The fourth element of the Daellenbach’s definition provides for the possibility that both business process and objectives modelling may be themselves considered as sub-systems (the definition of a sub-system is formalised by Wand and Weber (in Weber 1997, p. 44)). This means that the integration of business process and objectives modelling should not preclude them from being considered as being made up of related components that have a shared history. Nothing in the current research on the integration of business process and objectives modelling suggests that this cannot be the case. The fifth element refers to the environment of the system “which provides inputs into the system and receives outputs from the system” (the definition of system environment is also formalised by Wand and Weber (in Weber 1997, p. 45)). In the case of value-focused process engineering, a business is the environment within which an integrated business process and objectives model would operate by definition. The discussion of goal-oriented approaches to business process modelling (e.g. Kueng and Kawalek 1997; Bider and Johannesson 2002) highlights the organisational inputs that are required in order for the model to meet its objectives, as well as modelling outputs that are used by the organisation to meet its efficiency and effectiveness requirements. The last element of the Daellenbach’s definition has an impact on the paradigmatic assumptions for the research undertaken in accordance with the systems perspective. By identifying the system as being “of special interest” to someone, Daellenbach accommodates both objective and subjective views by acknowledging that while a business may exist in reality (i.e. objectively) the definition of a system within the business environment is dictated by the interest of the observer (i.e. subjectively) since the observer defines the boundaries that separate the system from its environment. This view is consistent with assumptions underlying Wand and Weber’s definition of the system that dictates that while “things” exist in the world “we know about [them]…only via the models … we create” (Weber 1997 p. 34).

1.5 Scope and Contribution This book unifies OR/MS and IS domains and as such will be of interest to academics and professionals in these domains and those concerned with the practice of HRM. The book will be of interest to the academic community because of the theoretical developments in unification of process and decision models and as a reference for graduate courses in IS, OR/MS, and business process management. The professional communities will be interested because of the implementation guidelines that are included in the book as well as many illustrations of the theory within the application domain of HRM. The discussion of the systems perspective on the integration of business objectives and process modelling highlighted outstanding research problems including:

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• understanding the relationship between business process and business objectives modelling; • the ability of business process and objectives modelling to share history within the integrated model; and • the creation of new properties as a result of integration of business process and objectives modelling. These research problems are addressed subject to the following scope considerations. Process modelling environment suggested in this book covers methodologies, techniques and tools that are being developed at the build time stage of the business process lifecycle, and do not cover such properties as “dynamic adaptability” that are normally developed at the run time stage of the business process lifecycle (Weske 2007, p. 311). Therefore, the scope of our research is limited to “Process Design” and “Process Implementation” phases of the business process lifecycle (Dumas et al. 2005, p. 12), which has been also defined in Weske (2007, p.12) as “Design & Analysis” and “Configuration” phases of business process lifecycle; and in van der Aalst et al. (2003a, p. 5) as “Process Design” and “System Configuration” phases. The business process Enactment phase (Weske 2007) lies beyond the scope of this book. Thus the scope of this research explicitly excludes the “run-time-only” aspects. Within this scope, the following research questions have been formulated in response to these research problems: Question 1. What are the desirable properties of business process and business objectives modelling? The answer to this question is required in order to enable comparative assessment (independent of disciplinary boundaries) of the suitability of existing business objectives and process models as candidates for the combined model. The analysis leading to the assessment and the assessment itself make the following contribution to the fields of business process and objectives modelling: • limitations and gaps of individual modelling techniques are identified; and • comprehensive and model-independent lists of desirable properties for business process and objectives models are created. Question 2. What properties must a value-focused process engineering model have? The answer to this question specifies the requirements for the combined model both as a system and as a model that is aimed at addressing both efficiency and effectiveness concerns of the business. The main contribution of this research to the field of modelling will be the application of the systems approach in order to develop a theoretically sound framework that enables any set of models to be assessed as candidates for integration. Question 3. How to link existing models to satisfy the requirements of a valuefocused process engineering model? By answering this question the following contributions are made:

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1 Introducing Value-Focused Process Engineering

• defining the relationship between business process and objectives modelling approaches within the combined model at the conceptual, formal and graphical levels; • building a combined model that satisfies both system requirements and the requirements of a model that is aimed at addressing the efficiency and effectiveness concerns of the business; • providing a step-by-step guide to value-focused process engineering; • enabling process and objectives modelling approaches to be integrated; and • demonstrating the benefits of the combined model within an area of application. The answers to these questions provide solutions to the stated research problems by linking business process and objectives modelling within an integrated modelling framework that contributes to knowledge through enhancements to existing modelling methodologies. Other contributions of the research include the development of: • evaluation frameworks for business process and objectives modelling properties that enable assessment of methodologies against desirable properties from a value-focused process engineering perspective; • a comparative assessment framework that enables a systematic evaluation of any set of models as candidates for integration; • extensions to the objectives modelling capabilities of the value-focused thinking framework that enable modelling of logical relationships and linking to the event-driven process chain methodology; and • a framework for integrating event-driven process chain modelling environment with a suite of decision modelling tools to facilitate simultaneous achievement of efficiency and effectiveness requirements of the business. Concepts discussed in the book are illustrated in the context of Human Resource Management. The illustrations provide insight into some of the difficulties that may be faced by those who will apply the combined model in practice while addressing some of the gaps that have been identified by researchers and practitioners within the Human Resource Management field.

1.7 Book structure In Chapter 2, the modelling methodology adopted in this research is discussed, including the theoretical foundations and practical considerations. The motivation for the choice of Human Resource Management (HRM) as the area of application is discussed in Chapter 3. Chapter 3 also provides context for numerous examples that are used throughout this book to illustrate the concepts and methodology of Value-Focused Process Engineering. The review of objectives and process modelling is provided in Chapters 4 and 5 respectively leading to the definition of the desirable properties of these models in response to the first research question. While the discussion of the desirable prop-

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erties in Chapters 4 is not limited to any particular methodology, the Event-driven Process Chain (EPC) (Scheer 1999, 2000) is the only process modelling methodology that is assessed against the desirable properties in Chapter 5. The call for improvement in the understanding and conceptual and formal representation of goals within business process modelling has been voiced by a number of authors (Kueng 2005, Bider and Johannesson 2002, p. 1), clearly demonstrating why the goal-modelling gap needs to be addressed. The objectives of Chapters 6-8 are about addressing the questions of what needs to be done to close the gap and how to go about it in the EPC environment. The answer to the question of what needs to be done is guided by the descriptions of desirable properties of integrated business process and objectives models referred to in the literature as either goal-oriented (e.g. Yu and Mylopoulos 1994), goal-based (e.g. Kuechler and Burg 1998) or goal-driven (e.g. Jacobs and Holten 1995). While acknowledging the strengths of the existing goal-oriented modelling approaches (reviewed in Chapter 6), the approach adopted in this study is distinctly different in that there are no new business goal or process models introduced. Instead, in the spirit of the systems view of the world, the best of what goal-modelling and process-modelling have to offer is integrated within a single model in a way that preserves the strengths of the respective models while facilitating the emergence of new properties that satisfy goal-oriented business process modelling requirements. This is achieved in two steps. Firstly, the issue of what needs to be done to preserve the strengths of the existing models within the combined model is addressed. This step is dealt with in Chapter 6 by developing a framework that enables evaluation of whether individual models are suitable candidates for the combined model. The lists of desirable properties of objectives and business process models (Table 4.2, Chapter 4 and Table 5.2, Chapter 5 respectively) are used as the basis for determining what components of the individual models should be included in the combined model. The application of the framework to the individual models discussed in Chapters 4 and 5 results in modifications to the VFT framework that allow all of the desirable properties of the objectives models to be combined within a single model in a way that can be combined with the EPC environment to achieve a complete and clear goal-oriented business process model. In this context, complete refers to the combined model having all of the desired objectives and process modelling properties, while clear implies that the combined model does not include components that use different representation mechanisms for the same concepts. Having established in Chapter 6 what new properties the combined model must possess in order to meet goal-oriented business process modelling requirements and what needs to be done to ensure that the desirable properties of the objectives and process models are preserved within it, the second step addressed in Chapter 7 is aimed at answering the question of how to link individual models so that the combined model meets these requirements. The links are first made at the syntactical level by (a) modifying the VFT model in accordance with the requirements identified in Chapter 6; (b) extending the EPC formal model to include the representation of goals in accordance with the modified VFT requirements; and (c) set-

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1 Introducing Value-Focused Process Engineering

ting out the rules that guide representation of goals and processes that is both synchronized and in accordance with their respective modelling rules. Together, these links provide a modelling framework for an EPC based goaloriented business process model. The modelling framework is complemented by the implementation framework that addresses the question of how to systematically use this modelling framework to steer business processes towards their goals by linking the models at the level of implementation. The contribution of these modelling and implementation frameworks is the enhanced goal representation within the EPC environment that meets requirements of goal-oriented business process modelling. In Chapter 8, the ability of the enhanced EPC environment to be used as a tool for increasing the understanding of the relationship between organisational objectives and processes is demonstrated in the HRM context as introduced in Chapter 3. Unlike the usual application of the VFT model to a real or made-up business example, Chapter 8 extends the VFT framework to enable its application to multiple (unrelated) situations based on the literature survey of industry practices and principles. By applying the methodology developed in Chapter 7 to the whole of the HRM area, the application of the methodology across a large number of complex situations is demonstrated and results in a high-level goal-oriented process model of the HRM context that explicitly links HRM objectives and processes. The limitations, contributions and future directions of the research are summarised in the final chapter of the book. The structure of the book is summarised in Figure 1.1 including the links between chapters and outputs of research.

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Book Structure

Chapter 1 Introduction

Chapter 2 Business Systems Modelling: : Principles & Practices

Chapter 3 Human Resource Management Context

Research Output and Contributions Chapter 4 Business Objectives Modelling Desirable properties of an objectives model

Chapter 5 Business Process Modelling with EPCs

Chapter 6 Requirements for a Value-Focused EPC: “WHAT” Dimension

Chapter 7 Building a Value-Focused EPC: “HOW” Dimension

Chapter 8 Application of Value-Focused Process Engineering to HRM context

Desirable properties of a business process model

Integration assessment framework System requirements for a goal-oriented business process model Modified VFT

Chapter 10 Conclusions and Future Directions

Fig. 1.1 Book structure and contributions

Objectives models fused Goal representation within the EPC environment enhanced

Modelling and implementation frameworks for EPC based value-focused process engineering

Process and objectives modelling integrated

High-level goal-oriented process model of HRM

Analysis capabilities of the EPC environment enhanced

Decision-enabled EPC modelling tool Chapter 9 Decision Enabled e-EPC

Gaps in the EPC and the VFT identified

HRM objectives and processes linked

2. Business Systems Modelling: Principles and Practices In science, we always deal with limited and approximate descriptions of reality. This may sound frustrating but for systems thinkers the fact that we can obtain approximate knowledge about an infinite web of interconnected patterns is a source of confidence and strength.

Capra (1996, p. 41)

2.1 Introduction This Chapter is dedicated to the discussion of modelling as a methodology chosen to achieve the study objectives formulated in the Chapter 1. This discussion spans from philosophical to practical considerations dealing with both generic features of modelling and specific issues of business objectives and business process modelling. The chapter also includes discussion of a conceptual framework suitable for modelling various real-world business phenomena. Adopting Checkland’s (1981) definition of methodology that is consistent with the systems view of the world introduced in Chapter 1 and further discussed in this Chapter, we refer to the methodology as a collection of problem-solving methods governed by a set of principles and a common philosophy for solving targeted problems. This choice of a definition is not unique among business process and decision modelling studies. For instance, Kettinger et al. (1997, p.58) uses this definition to provide a widely quoted reference framework to different approaches to business process modelling and reengineering. In discussing the choice of modelling as the methodology for this study, there is a need to closely examine the meaning of the nouns model and modelling (Pidd 1999, p. 119). Models are defined by Pidd (1999, p. 120) as “an external and explicit representation of part of reality as seen by the people who wish to use that model to understand, to change, to manage, and to control that part of reality in some way or other.” The term modelling refers to the (usually iterative) process of creating models that includes the following steps: problem awareness and articulation, suggestion or hypothesis, development and formulation, evaluation and testing, and recommendations, implementation and/or conclusions (Pidd 1985, p. 3, Smith 1989, p. 964, Sterman 2000, p. 87, Vaishnavi and Kuechler 2004, fig. 3, Winston 1994, pp. 2-5). Since modelling processes rely on a variety of methodologies and tools, depending on context, references to modelling in the remainder of this book may imply both activity and corresponding methods and tools. There is a general agreement in the modelling literature (Jick 1979, Pidd 2003, Smith 1989, Sterman 2000, Winston 1994 just to name a few) that an iterative modelling process that utilises both qualitative (inductive) and quantitative (deductive) methods (Hussey and Hussey 1997, p. 48) results in models that provide

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2. Business Systems Modelling: Principles and Practices

greater insight and understanding into real life problems. Since modelling as a methodology is able to accommodate both paradigms, for example by associating formal mathematical constructs with descriptive qualitative notions, it is appropriate for illuminating the phenomenon being studied, i.e. the relationship between business objectives and process elements. Business process and business objectives modelling do not fit neatly in any single discipline or school. Information Systems research is generally more concerned with business processes, how they should be supported and designed in order to meet business objectives. Decision Sciences (such as Operations Research and Management Science – often jointly referred to as OR/MS), on the other hand, are more focused on business objectives, generally limiting interest in processes primarily to decision processes. Furthermore, within each discipline, the focus may shift between various aspects within process and objectives modelling. It is therefore not surprising that an understanding of what is meant by business process or business objectives can vary substantially, depending on a researcher’s perspective. This multiplicity of views and approaches makes it paramount for the research into the areas of business process and objectives modelling to explicitly articulate the assumptions and definitions that have been adopted and their relationship to the existing paradigms in the relevant fields (e.g. Muehlen 2004b, pp. 12-18). The remainder of this Chapter is organized as follows: in section 2 we discuss the fundamental principles of Design Science that underpin modelling methodology and articulate philosophical assumptions adopted in this research and their relations to the systems view of the world. Section 3 summarizes modelling perspectives of businesses using interrogative pronouns what, why, how, who and when to enable an intuitive view of the differences in existing business modelling perspectives and better understanding of the respective business models’ strengths and limitations. A brief overview of business objectives and business process modelling has been provided to lay the foundation for in-depth discussion of these topics in Chapters 4 and 5. In the last section of this chapter we provide an overview of the Wand and Weber conceptual modelling framework that facilitates the unification of modelling activities across various disciplines. In Chapters 4 and 5, this conceptual modelling framework will be used to describe the VFT and e-EPC.

2.2 Systems Modelling as Design: Fundamental Principles and Assumptions Business objectives and business processes have been the subject of research within both qualitative and quantitative paradigms. In the research literature, the idea of combining qualitative and quantitative paradigms has been both advocated and criticised. Falconer and Mackay (1999) provide a comprehensive review of the literature in this area concluding (p. 293) that “researchers should focus on the nature of the phenomenon to be investigated, rather than the epistemology or on-

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tology, and select the method that can illuminate the phenomenon”. As discussed in the Chapter 1, in the context of this study the systems perspective dictates that the key phenomenon to be investigated is the relationship between business objectives and process elements. Therefore, a philosophical position that facilitates investigation of this phenomenon while accommodating both qualitative and quantitative approaches is required. The ways of combining different research methods, including those that are attributed to different research paradigms, are consistently described in Mingers (2001, 2006). The combination of two and more various views on reality as well as techniques to understand and gain knowledge about this reality, has proved to be effective not only as part of theoretical inquiry, but also in business projects (e.g. Pfahl and Lebsanft, 1999). In complete consistency with the adopted Checkland’s (1981) definition of the methodology, the nature of the research problem under consideration – i.e. understanding the relationship between business objectives and process elements – calls for an imaginative mix of problem-solving methods, a set of principles, and a common philosophy for solving this problem. While specific problem-solving methods are discussed in later sections of this Chapter, as well as later Chapters of this book, this section is dedicated to the discussion of principles and philosophical assumptions adopted in this research and their relations to the systems view of the world described in Chapter 1. When addressing these issues, it may be instructive to consider modelling as an instance of a wider notion of “design”. According to Merriam-Webster's Collegiate Dictionary (2003), to design means “to invent and bring into being”, referring to the process of creating new things that have not previously existed in nature. Design Science, a paradigm that was first clearly articulated by Simon (1996, first printed in 1969), “involves the analysis of the use and performance of designed artifacts to understand, explain and very frequently to improve the behaviour of existing systems” (Vaishnavi and Kuechler, 2001). It has attracted much attention in recent years (e.g. Love 2000, Gregor and Jones 2007, Vaishnavi and Kuechler 2007). It is important to note, that the concept of artifact within the Design Science paradigm incorporates the concept of a model (e.g. March and Smith 1995). Furthermore, the idea of the use of designed artifacts to understand, explain and improve the behaviour of existing systems as suggested by Design Science is remarkably similar to that of use of “…model to understand, to change, to manage, and to control … part of reality in some way or other” as expressed in Pidd’s (1999, p. 120) definition of a model cited earlier in this section. According to Hevner (2004) and vom Brocke and Buddendick (2006), Design Science addresses business needs by creating innovative and unique artifacts in a well-defined manner (Simon 1996, first printed in 1969, March and Smith 1995) that “deliver[ing] either more effective or efficient solutions for a solved business need” (vom Brocke and Buddendick 2006 p.282). This focus on efficiency and effectiveness is entirely consistent with the fundamental motivation for our study, i.e. that business process engineering must be expanded from creating more effi-

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2. Business Systems Modelling: Principles and Practices

cient business processes (by “doing things right”) to also making business processes more effective by “doing the right things”, as discussed in Chapter 1. Mainly applied when addressing specific problems that can be solved in the course of design (Hevner 2004), Design Science provides a utility-oriented methodology that aims at “fulfillment of identified business needs by an artifact” (vom Brocke and Buddendick 2006, p.581). These needs can be represented in terms of desirable artifact’s (e.g. model’s) specifications or, in other words, desirable properties that give a purposeful dimension to the process of artifact building (e.g. modelling). This feature of Design Science is clearly manifested in our study through formulating desirable properties for business objectives and business process models and then systematically evaluating individual and combined models against respective sets of desirable properties (Chapters 4-6). Having discussed the fundamental principles of design that underpin modelling methodology, it is important to articulate philosophical assumptions adopted in this research and their relations to the systems view of the world discussed in Chapter 1. Ontological assumptions (i.e. the assumptions about the nature of reality) within the adopted systems perspective need to acknowledge that while a business exists in reality (i.e. objectively), its definition as a system is dictated by where we (subjectively) define the boundaries that separate the system from its environment. Epistemological and axiological assumptions provide another dimension to the research paradigm by describing researcher’s relationship to the subject of that research by making assumptions about the nature of knowledge and knowledge validity (epistemological assumptions) and the role of values in the research (axiological assumptions). As demonstrated next, as with the ontological assumptions it is possible to avoid commonly accepted dualism between quantitative and qualitative disciplines by adopting a systems view of the epistemological and axiological assumptions. A business system that includes a researcher as one of its components (or as part of its environment) would accommodate the qualitative epistemological and axiological assumptions. These assumptions allow researchers to interact with and influence the subject of the research in the context of his/her values. On the other hand, the same business system may be considered by a different researcher as a system of special interest detached from the researcher, therefore accommodating epistemological and axiological assumption of an independent researcher, usually associated with quantitative and positivistic paradigms based in the natural sciences. By making explicit epistemological assumptions, it is possible to avoid confusion that may result from different interpretations of the systems concept - on the one hand in every day language system is used to refer to the world as if it is a system; on the other hand it is also used as an abstract concept “used to understand or create real-world systems” without making assumptions about the nature of real world (Checkland and Scholes 1990, p. 22). Consequently, the systemic way of inquiry that is adopted in this study is based on the epistemological representation of a system rather than regarding it as an ontological entity. According to Check-

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land and Scholes (1990, p. 277) “systemicity is shifted from the world to the process of inquiry about the world: ‘the system’ is no longer some part of the world which is to be engineered or optimized, ‘the system’ is the process of inquiry itself”. Thus, the philosophical position adopted in this research can be summarised as epistemological idealist/ontological realist combination using Muehlen’s classification of fundamental ontological and epistemological positions (Muehlen 2004b, pp. 13-14). This position assumes that a “real world exists; it cannot be observed independent of the mind of the observer” (Muehlen 2004b, p. 14, table 1-1), and, in addition, emphasizes the epistemological, rather than ontological, role of the systems worldview. Adopting this position and following the principles of Design Science, a multi-disciplinary and pluralistic view of business process and objectives modelling is enabled to facilitate the study into the relationship between them.

2.3 Integration of Multiple Business Views through Systems Modelling Earlier in this Chapter a brief account of modelling from philosophical and methodological perspectives was provided. The goal of this section is to provide an overview of how different views of business can be brought together through systems modelling. Modelling perspectives of businesses have been described by a number of authors (e.g. Daellenbach 2001, Giaglis 2001, Kavakli 2004, Wand and Weber 1990) using interrogative pronouns such as what, why, how, who and when to enable an intuitive view of the differences in perspectives. To avoid any misunderstanding as to what aspect of the business each pronoun represents, they are defined as follows: • models focusing on the what aspect of the business are concerned with representing products and services of the business; • models focusing on the how aspect of the business are concerned with representing the steps required to deliver business outputs, this applies to both business operations (how ( process) perspective), and business decisions (how (decision) perspective); • models focusing on the why aspect of the business are concerned with representing the business objectives, i.e. reasons for the business activities and output; • models focusing on the who aspect of the business are concerned with representation of roles, responsibilities and relationships between people in the business; • models focusing on the when aspect of the business are concerned with representing how the business changes over time, i.e. the dynamic aspect of business operations (e.g. Giaglis et al., 2005).

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2. Business Systems Modelling: Principles and Practices

Some models focus primarily on one aspect of the business (e.g. Daellenbach 2001; Pidd 2003). For example, hard Operations Research/Management Science (OR/MS) models (e.g. Winston 1994) are concerned with modelling how decision objectives can be optimally reached (i.e. how (decision) perspective). By comparison, soft OR/MS (e.g. Checkland and Scholes 1990) models focus on the objectives themselves (i.e. why perspective) and guidelines for how to arrive at these objectives. Other models combine multiple perspectives. For example, soft business process modelling (BPM) approaches (Melao and Pidd, 2000 p. 120) such as Requirements Engineering (RE) models (e.g. Lapouchnian et al. 2007, Lamsweerde 2000, Lee et al. 2001, Lamsweerde and Letier 2000) usually focus on both business objectives (why perspective) and people aspects of the business (who perspective) whereas hard BPM approaches (Melao and Pidd 2000, pp. 108, 113) approaches are concerned primarily with the outputs (what perspective) and steps required to achieve these outputs (how (process) perspective). Integrated and pluralistic models (Melao and Pidd 2000, pp. 122, 124) may combine the individual aspects of the soft and hard models and/or add another perspective. For example, System Dynamic (SD) models (e.g. Sterman 2000, van Ackere et al. 1993) within the OR/MS context cover the time perspective (i.e. when) with limited focus on the why dimension, whilst Multi-Criteria Decision Analysis (MCDA) models (e.g. Roy 1999, Brugha 2004) combine the why aspect strongly represented in the soft modelling approaches with the how (decision aspect) represented in the hard decision models. Note that by necessity modelling approaches that cover multiple perspectives represent relationships between these perspectives. It is important to note, though, that the relationship between these perspectives can sometimes fall into a gap that exists between disciplines due to the differences in focus. Disciplines that cover multiple perspectives can overcome this limitation. For example, the relationship between why, how (decision) and when (to some degree through integration of SD with other OR/MS tools) perspectives has been modelled in the integrated OR/MS models, whilst the relationship between what, how (process) and who perspectives is represented in the pluralistic BPM models. However, the relationship between the how perspective (especially the how (process) prospective) and the why perspective remains unclear as these aspects remain within the domain of their respective disciplines. This analysis highlights the appropriateness of the system modelling approach that facilitates integration of existing models into a combined model which has the desirable emergent properties as opposed to developing an entirely new model. By using existing models, the effort invested into their development is neither wasted nor duplicated but is built upon, with the strengths of each model complementing the weakness of the other models that are included in the system. In the case of value-focused process engineering the two components that are being considered for the combined model are business objectives and business

21

process modelling. The following two sections provide a brief overview of business objectives and business process modelling respectively.

2.4 Business Objectives Modelling Business objectives modelling is used to support decision analysis (e.g. Clemen and Reilly 2001), management science (e.g. Mooraj et al. 1999) and information systems disciplines (e.g. Rolland and Prakash 2000). However, unlike other supporting tools (e.g. statistical models, mathematical programming models, requirements engineering and business process models) business objectives modelling has not developed into a field of research in its own right. Consequently, there is no interdisciplinary discussion in the academic literature about the commonalities and differences in the definitions and properties of objectives modelling. It is therefore not surprising to find, that while there are substantial overlaps between objectives models from different disciplines, the methodology and focus of individual models is driven by the originating discipline. For example, in general management sciences, the interest in objectives is primarily from the point of view of measurement and evaluating performance of the business or its segments (e.g. processes, departments, individuals). Consequently, methodologies and tools such as Critical Success Factors (CSF) (Henderson et al. 1987, Rockart 1979) and Balanced Scorecard (BSC) (Kaplan and Norton 1996, 2001a, 2001b) have been developed to translate organisational objectives into key performance indicators that link “business operations with the overall strategy” (Mooraj et al. 1999, p. 482) and are used to measure and control business operations by providing a yes/no answer to the question of whether a particular objective has been achieved. In classical or hard decision modelling (e.g. Winston 1994), the focus is on finding the best alternative to achieve a quantitative expression of an objective subject to some constraints. In this context, the objective is often taken as given and must be narrowed down to a solvable mathematical expression. Consequently, business objectives are often considered in isolation from each other. The limitations of this approach have been discussed by many authors (e.g. Rosenhead 1989, Daellenbach 1994) and summarised by Keeney (1992, p. 41) as “separate decisions simply won’t make sense in the larger context of the organization’s affairs” unless they are linked to and guided by the strategic objectives. The need to link individual decision objectives to broader business values and vision has been the motivation behind the value-focused thinking framework (VFT) proposed by Keeney (1992) to facilitate identification, communication and structuring of business values and objectives to guide decision-making within an organisation. Once structured, the objectives are evaluated and combined with the help of Multi-Attribute Utility Theory (MAUT) that facilitates the quantification of the value judgements in the objectives structure (Keeney 1992, p. 132). While still strictly in the domain of decision science, the VFT methodology links qualitative approaches to modelling business objectives with the quantitative models that

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2. Business Systems Modelling: Principles and Practices

are aimed at finding an optimal path towards achieving these objectives as well as organizational performance measures such as those documented within BSC methodology (e.g. Kaplan and Norton 1996). Identification and structuring of business objectives have also been the major focus within the Requirements Engineering area of the Information Systems discipline. In this context, business objectives are usually referred to as goals which are used to elicit, elaborate, structure, specify, analyse, negotiate and modify requirements for the design and development of business information systems (Lamsweerde 2001, Kim et al. 2004). Due to this particular focus, requirements engineering is mainly concerned with qualitative modelling of objectives – identifying different objectives types, attributes and links between objectives and business processes. Another perspective on objectives modelling is highlighted in the definition of business objective as “a desired state of affairs” (e.g. Koubarakis and Plexousakis 2002, p. 302). The concept of state is used independently within OR/MS (e.g. Sniedovich 1992) and Information Systems (e.g. Weber 1997). This concept is used by Khomyakov and Bider (2001) to formally link business objectives and business processes by reducing high-level organizational values and objectives to “concrete and formal goals” that describe the process state. It is evident from this brief overview that the term objectives modelling means different but overlapping things to different research schools. In Chapter 4, these differences are explored in detail. To facilitate the discussion in Chapter 4, objectives models are classified into qualitative and quantitative. While this split oversimplifies the differences between objectives models, as most objectives models include qualitative and quantitative elements, this classification highlights the fundamental difference between the approaches to objectives modelling. The motivation of quantitative objectives modelling is the need to translate qualitative objectives statements into quantitative statements that can be used to evaluate and/or simulate the impact of business operations on its objectives. Conversely, qualitative objective modelling is motivated primarily by the need to understand and describe the context of the business that is being analysed. As such, the separation between objectives models into quantitative and qualitative does not refer to the nature of the mechanisms used to represent objectives, but to the ultimate purpose that the models have been designed for.

2.5 Business Process Modelling While the initial impetus for the explosion of activity in the area of business process modelling was the need to support enterprise wide information systems, unlike business objectives modelling, business process modelling has developed into a research field in its own right. Consequently, a number of classifications and comparisons of business process modelling approaches are available in the literature. For example, Pidd (2003, p. 21) includes business process models as a subclass of OR/MS models. This classification is debatable because most business

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process models have adapted modelling techniques and tools from disciplines other than OR/MS (for a comprehensive review refer to Peppard and Preece (1995) and Melao and Pidd (2000, p. 110). While some process models originate from manufacturing, industrial engineering or organizational theory, many have resulted from the research in the information systems discipline. Melao and Pidd (2000) proposed the classification of business process models from a similar perspective to the analysis of OR/MS models undertaken by Pidd in 1985 (Pidd 1985). As with OR/MS models, Melao and Pidd (2000) classify business process models into hard and soft. The classification of business process models into hard and soft models by Melao and Pidd (2000) is just one of a number of different approaches towards forming taxonomies of business process models. Other taxonomies are based on the ability of the model to represent different facets of a process (e.g. Curtis et al. 1992, Giaglis 2001, Peppard and Preece 1995) as well as classifications according to modelling requirements, capabilities or purposes (e.g. Aguilar-Saven 2004, Cheung and Bal 1998, Hommes and Reijswoud 2000, Katzenstein and Lerch 2000, Vernadat 1997, Yu and Wright 1997). The advantage of Melao and Pidd’s taxonomy (Melao and Pidd 2000), is that it puts various business process models in the context of generic research paradigms providing a common ground between business process and other business models, thus highlighting the potential for integration. The disadvantage of Melao and Pidd’s taxonomy, in the context of exploring the relationship between process and objectives modelling, is that it does not facilitate comparison of business process models according to their ability to represent business objectives. A taxonomy that meets this requirement was proposed by Katzenstein and Lerch (2000) who classified business process models into traditional, coordination, socio-technical qualitative and generic methodologies, in accordance with their ability to represent social context including goals. At a closer look, the two taxonomies are similar and can be combined to derive a taxonomy that uses both generic research paradigms and ability of business process models to represent business goals as classification criteria. Soft business process models are defined by Melao and Pidd (2000, p. 120) as “sense-making interpretive devices developed to generate debate and learning about how the process is being and should be carried out”. Melao and Pidd (2000, pp. 120-121) discuss three types of soft models: Soft Systems Methodology “used to represent a business process as a would-be purposeful human activity system consisting of a set of logically interconnected activities through which actors convert inputs into some outputs for customers”; socio-technical approach that is aimed at “integration of both social and technical needs through a participative approach”; and social constructionist models that recognise the political and cultural environment of the business. Katzenstein and Lerch (2000) limit their classification to socio-technical approaches represented by goal-exception-dependency framework (GED) (Katzenstein and Lerch 2000), action research methods such as Multiview and The Effective Technical and Human Implementation of Computer-

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2. Business Systems Modelling: Principles and Practices

based Systems (ETHICS) (e.g. Avison et al 1999), cognitive mapping (e.g. Eden 1992), and requirements engineering models such as i* (e.g. Yu 1999). Hard business process models (i.e. those other than soft models) are separated by Melao and Pidd (2000) into three classes: deterministic, dynamic and interacting models. Models in each class are based on the corresponding view of the process as a deterministic machine, a complex dynamic system, or interacting feedback loops. A common assumption behind the hard models is that business processes “exist in the objective and concrete sense” (Melao and Pidd 2000, p. 120) and should be designed in “logical and rational terms” (Melao and Pidd 2000, p. 117) reflecting positivistic paradigm. The deterministic models that describe business processes as static focus on “mapping and documenting the flow of items, the activities, their logical dependency and the resources needed” (Melao and Pidd 2000, p. 114) and tend to dominate business process modelling literature and applications. Katzenstein and Lerch (2000) separate theses models into two sub-classes: traditional and coordination models. Traditional modelssuch as flow charts, data flow diagrams (DFD) and Integrated Definition (IDEF) suite of models (e.g. Bosilj-Vuksic et al. 2000, Giaglis 2001) are called traditional as they have been traditionally used for the development of information systems and later adopted for business process modelling. Melao and Pidd (2000) describe these models as adopting a functional and structured approach. Generally these models do not include goals as a modelling construct, although recent developments in goal-oriented business process modelling have provided an indication that these models can be made more goal-friendly (e.g. Downs and Lunn 2002, Khomyakov and Bider 2001). Coordination models still take a static view of the process but they are better able to describe interactions between process elements than the traditional models. Petri-net based models (generally used for workflow modelling (van der Aalst 1998)), other workflow modelling languages (Muehlen 2004a), object-oriented (OO) business process models including Unified Modelling Language (UML) based models (Kueng et al. 1996), and others such as Rummler-Brache model (Rummler and Brache 1995) and Role Activity Diagrams (RAD) (Huckvale and Ould 1995) are included in this class by Katzenstein and Lerch (2000). Processoriented coordination models are more easily extended to include goal models and to facilitate goal-oriented business process modelling than traditional models (e.g. Kueng and Kawalek 1997). The dynamic and interacting models included in the Melao and Pidd classification (Melao and Pidd 2000) and represented (respectively) by simulation and system dynamic models are not discussed in the Katzenstein and Lerch classification (Katzenstein and Lerch 2000) probably because they are traditionally used as extensions to other types of hard business process models rather than business process modelling tools in their own right (e.g. Giaglis 2001). On the other hand, the Generic Methodologies class included in the Katzenstein and Lerch classification is not treated as a separate class in Melao and Pidd classification. Generic methodologies are described by Katzenstein and Lerch (2000) as more encompassing

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than business process models alone as they include other capabilities, for example: the Architecture of Integrated Information Systems (ARIS) methodology (Scheer 1999, 2000), in particular, incorporates a business process model (extended-eventdriven process chain), objectives diagram and BSC model (e.g. Davis 2001). Similarly, the GIM (GRAI Integrated Methodology) (e.g. Doumeingts et al. 1993) integrates the decision and process views of the business. Melao and Pidd (2000, p. 124) describe these types of models as “pluralistic and multidisciplinary modelling approaches” stressing that integration of different modelling paradigms is to be expected as these paradigms are “not independent of each other and that it is difficult to identify clearly where one perspective begins and the other ends” (Melao and Pidd 2000, p. 122). Furthermore, they emphasise the need for these modelling techniques to avoid “failure because the methodologies adopted are partial in their approach” (Melao and Pidd 2000, p. 124). The discussion above is summarised in Table 2.1 that combines the Melao and Pidd (MP) and Katzenstein and Lerch (KL) classifications into a single classification that is referred to as MPKL. As is evident from the discussion so far, business process modelling has a multidisciplinary and multifaceted nature that leads to a lack of a uniformly agreed set of desirable capabilities of a business process model. The objective of Chapter 5 is to review the common themes in the approaches to the classification of business process modelling techniques, with the aim of converging towards a set of desirable properties that have a level of acceptance across academic disciplines and in the real world. In accordance with the boundaries of the research defined in Chapter 1, the EPC (and its implementation environments) is the only process modelling methodology that is assessed against the desirable properties in Chapter 5. Table 2.1 Taxonomy of business process models MP categories Soft

MP subcategories

KL main catego- MPKL categories ries

SSM

Examples

Soft

Socio Technical Socio Technical Qualitative

SSM, GED, Multiview, i*

Social Constructionist Hard

Deterministic

Traditional

Deterministic Traditional

Flowcharts, DFD, IDEF

Coordination

Deterministic Coordination

Petri-nets, OO, UML, RAD

Dynamic

Simulation

Interacting

System Dynamics

Dynamic Interacting Pluralistic

Generic

Multidisciplinary ARIS, GIM

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2. Business Systems Modelling: Principles and Practices

2.6 Exploring Systems Modelling Practices Through Wand and Weber Conceptual Modelling Framework As discussed earlier in this Chapter, the ability to effectively represent relevant aspects of the real world is at the core of the systems modelling. Wand and Weber (2002) made significant progress towards unification of modelling activities by developing the conceptual framework for modelling the world in a way that addresses the requirements of the Information Systems discipline. This framework consists of four elements (Wand and Weber, 2002, p. 364): • conceptual modelling grammar as “a set of constructs and rules that show how to combine the constructs to model real-world domains”; • conceptual modelling method as “procedures by which a grammar can be used. Usually one major aspect of a method prescribes how to map observations of a domain into a model of the domain”; • conceptual modelling script as “a statement in the language generated by the grammar”; • context as “the setting in which conceptual modelling occurs and scripts are used”. A conceptual model is created by mapping the complexity of the real world phenomena into a set of constructs which represent only those characteristics that are of interest. Furthermore, the mapping of the real world to a conceptual model must ensure that by manipulating and analyzing the constructs valid conclusions about the behavior of the real world phenomena can be made. Differences and similarities between this definition of a conceptual model (based on the Wand and Weber conceptual modelling framework and its application to IS models) and the concept of a model within the OR/MS discipline is discussed next. This analysis aims to further highlight both the differences and similarities between modelling methodologies and lays the ground for in-depth discussion on e-EPC and VFT, originating in IS and Decision Sciences respectively, that is provided in Chapters 4 and 5. 2.6.1 Conceptual Modelling Grammar As a result of mathematical and computational origins of OR/MS, well-defined mathematical constructs are used by classical (or “hard”) OR/MS models to map the real world phenomena. Since these constructs and rules for using them (i.e. conceptual modelling grammar) are considered to be in scope of mathematics, statistics or computer science, the modelling grammar is rarely explicitly defined as part of the discussion of an OR model. This practice while originating in “hard” OR/MS has become an accepted approach in all OR/MS research including “soft” developments in OR/MS that do not rely on well defined mathematical or compu-

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tational constructs or rules (e.g. Value-Focused Thinking, Soft Systems Methodology, aspects of System Dynamics and Simulation). The lack of articulation of the modelling grammar constructs and rules within the OR/MS discipline contributes to the continuing segregation in OR/MS and IS modelling research despite obvious similarities especially between descriptive constructs used by both soft OR/MS and IS models such as objectives diagram or cognitive map (e.g. Pidd 2003) and entity-relationship diagram or state chart respectively (e.g. Giaglis 2001). 2.6.2 Conceptual Modelling Methodology While the OR/MS are mostly mute on the issue of modelling grammar, the need to articulate conceptual modelling methodology (i.e. procedures by which the mathematical and computational grammars can be used to describe the real world) is the primary requirement for an OR/MS modeler. This process of mapping real world observations into a model is referred to within the OR/MS context as modelling. A typical life cycle of an OR/MS approach to modelling usually includes four basic steps (e.g. Pidd 2003; Winston 1994): understanding a problem, representing the problem using modelling constructs, solving the problem and evaluating the solution. Out of these four steps, only the second step is in the scope of conceptual modelling, as defined by Wand and Weber, with the other steps referring to understanding a problem which can be solved with a conceptual model (step 1) and solving this problem using the conceptual model (step 3) satisfactorily (step 4). Therefore, from conceptual modelling point of view, OR/MS focuses on answering the question of how to represent real-world problems (e.g. investment decision, staff allocation or product mix) using models (such as a decision tree, a linear program or an multi-criteria decision model) that will help solve these problems. Finding and perfecting an answer to this question for a diverse range of real-life problems of varying degree of complexity is the Holy Grail of OR/MS (e.g. Daellenbach 2001, Winston 1994). As evidenced by Wand and Weber’s formulation of the conceptual modelling framework, the need to map real-world observations (e.g. relationship between organizational units) into modelling constructs (e.g. entities and relationships within an entity-relationship diagram) is also recognized within the IS context although it is rarely central to the discussion of an IS conceptual model. This is not surprising given that in the IS context, terminology used to define modelling grammar is often either directly or intuitively related to the domain being described. For example, a functional objective construct with an e-EPC model (defined as something that a business function is aimed at) is much easier to map to a real life observation of a business objective than a linear programming representation of the same objective as a mathematical function of decision variables.

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2. Business Systems Modelling: Principles and Practices

2.6.3 Conceptual Modelling Script and Context Both OR/MS and IS conceptual models are represented using the conceptual modelling script that may include a mathematical statement (in the case of hard OR models), a graphical representation (in the case of soft OR and IS models) and/or semantic representation (mostly in the context of functional IS paradigm e.g. Giaglis 2001, Hirschem and Klein 1989). The settings within which these scripts are used (i.e. modelling context according to Wand and Weber definition) vary from model to model. OR/MS models are generally used in the context of a decision-making or problem-solving setting. The context for IS models includes a variety of business representation settings with an emphasis on system development and process engineering.

2.7 Summary In this Chapter we concentrated on the discussion of modelling as a methodology to investigate the relationship between business objectives and process elements with the aim of designing processes that are better aligned with wider business objectives. As business process and business objectives modelling do not fit neatly in any single discipline or school, it is important for our research enquiry to accommodate both qualitative and quantitative paradigms. Modelling as a methodology is able to accommodate both paradigms, which leads to greater insight and understanding into real life problems. It is, therefore, appropriate for achieving the study goals. Design Science principles are consistent with the systems worldview including concepts (e.g. model as a designed artifact) and activities (e.g. use of designed artifacts to understand, explain and improve the behaviour of existing systems). An important characteristic of Design Science is its aim of designing artifacts according to desirable artifact’s specifications (desirable properties of a model) that provide a purposeful dimension to the process of artifact building (modelling). The philosophical position adopted in our study is summarized as epistemological idealist/ontological realist combination, assuming both the existence of the real world phenomena to be modelled and its inability to be observed independently of the mind of the observer. Also, the systemic way of inquiry that is adopted in this study is based on the epistemological representation of a system rather than regarding it as an ontological entity, thus following Checkland’s use of systems thinking without making assumptions about the systemic nature of real world phenomena being observed or modelled. A combination of this philosophical position and Design Science principles facilitates a multi-disciplinary and pluralistic view of business process and objectives modelling. Modelling perspectives of businesses have been summarized using interrogative pronouns such as what, why, how, who and when to enable an intuitive view of the differences in existing business modelling perspectives. This analysis highlighted the appropriateness of the system modelling approach that facilitates inte-

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gration of existing models into a combined model which has the desirable emergent properties as opposed to developing an entirely new model. A very important advantage of using existing models is that the effort invested into their development is neither wasted nor duplicated but is built upon, with the strengths of each model complementing the weakness of the other models that are included in the system. In the case of value-focused process engineering the two components that are considered for the combined model are business objectives and business process modelling. A brief overview of business objectives and business process modelling has been provided to lay the foundation for in-depth discussion of these topics in Chapters 4 and 5. In the last section of this chapter we provided an overview of the Wand and Weber conceptual modelling framework. In Chapters 4 and 5, this conceptual modelling framework will be used to describe the VFT and e-EPC as a prelude to their integration within the value-focused process engineering framework.

3 Human Resources Management Context 3.1 Introduction Human Resource Management (HRM) practices have a critical role in positioning an enterprise to achieve its corporate objectives (Gratton et al. 1999, Khoong 1996, Walker and MacDonald 2001, Zeffane and Mayo 1994). Traditionally, HR departments provided leadership in the areas of staff development, remuneration, industrial relations and performance management. The focus of the HR strategies in many organizations, over recent years, has evolved towards strategic workforce planning and equipping line managers with tools and support for managing their staff (bin Idris and Eldridge 1998). Having reviewed the origins and history of HRM Hendry and Pettigrew (1990, p. 22) identified that the “minimal, or core, specifications for HRM, however, was coherence of personnel practices one with another, and their adaptedness to the organization’s strategy”. Concerns about efficiency and alignment of personnel processes with organizational strategies and each other suggest HRM to be a suitable application for illustration of the research into relationships between business process modelling and business objectives modelling. Lado and Wilson’s definition of an HR system (1994, p. 701) as “as a set of distinct but interrelated activities, functions and processes that are directed at attracting, developing, and maintaining (or disposing of) a firm’s human resources” is an example of the approach to HRM from the process point of view, as it focuses primarily on specific personnel practices. This approach does not answer the question of why these activities should be undertaken as this question can only be addressed through the objectives dimension of the HRM. However, Koys (2000) demonstrated that in practice, the objectives dimension is rarely addressed since he found that “many HR departments have informal, unwritten strategies” that do not “force HR departments to show how they contribute to organization [sic] goals” (Koys 2000, p. 275). These observations are not surprising given that HR theory also tends to “side-stepped [sic] the issue of HR objectives … [with] …only a few HRM theorists [taking up] the challenge of specifying a schedule of strategic goals in HRM … [due to] …both practical and ideological difficulties” (Boxall 1999, p. 267) resulting from multidimensional nature of goals within the HR context and “different motivations in management actions” (Boxall 1999, p. 268). Despite this, HR objectives have been incorporated in many decision models that assist with selection, resource allocation and prediction of future HR requirements (e.g. Agrell and Steuer 2000, Bowlin 1998; Gardiner and ArmstrongWright 2000, Gass 1991). However, these models are usually concerned with narrow decisions that are not integrated into HRM processes. HRM therefore provides perfect context for the research into the relationship between business process modelling and objectives modelling as it incorporates both elements while currently lacking a clearly formulated framework and tools to

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3 Human Resources Management Context

enable the relationship between these elements to be articulated and enforced in practice. HRM is an extensive area of research with many nuances, conflicting opinions and extensive literature. It is not the purpose of this chapter to provide a comprehensive review of HR literature, there are experts in the field of HR who are much better suited for that task. Rather, the objective of this chapter is to provide a sample of approaches to desribe HRM objectives and processes in academic environment and in private and public organizations. The expectation is that the resulting overview will give the reader a strong sense of the types of objectives and processes that are the concern of the HRM field as well as illustrate the concepts of business objectives and processes and relationship between them within the HRM context.

3.2 Context and Background This review includes HRM literature sources that: • use different modelling approaches described by Gratton et al. (1999) as normative (e.g. Schuler and Walker 1991, Milkovich and Boudreau 1997), empirical (e.g. Kane et al. 1999, Keeney 1994) and conceptual or theoretical (e.g. Baruch and Peiperl 2000, Boudreau and Ramstad 2001, Guest 1997); • focus on specific HRM functions such as human resource planning (e.g. Walker 1989, Zeffane and Mayo 1994) or succession planning (e.g. Huang 2001); • describe HRM practices within different industry sectors (e.g. Keeney 1994; Selden et al. 2001); • compare HRM between different countries (e.g. Kane et al. 1999, Selden et al. 2001); • focus on an aspect of HRM modelling such as measurement (e.g. Fitz-enz and Davison 2002) or strategic development (e.g. Tyson 1997); • consider employee or employer perspectives (e.g. Beer et al. 1984); and • are part of academic research (e.g. Dyer and Reeves 1995), teaching texts (e.g. Cascio and Awad 1981; Schuler and Walker 1991) or application within industry (e.g. Keeney 1994). Within each source, parts that deal with HRM processes or objectives are quoted directly to retain the voice of the authors, thus highlighting different terminology used within different contexts. Where different levels of detail are included within a source, the higher level objectives and processes are included in the excerpt with examples provided of how lower level details are treated. Similar type sources are grouped together into five groups: 1) HRM strategy and performance, 2) HRM in public and private sectors and internationally, 3) HRM functions, 4) measuring HRM, and 5) HRM teaching texts. These grouping are formed solely as convenient way of providing logical flow to the review and are not aimed at providing a

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classification framework for the HR literature as most sources belong in several groups. 3.2.1 HRM Strategy and Performance Within this group, the focus is on the principles and practices of developing and evaluating HRM strategy and its impact on business performance. The strategy in Beer et al. (1984) is defined implicitly as a set of HRM actions aimed at achieving business outcomes. In later literature, the strategy becomes the focus, for example Dyer and Reeves (1995) discuss the impacts of HRM strategies on the outcomes, whilst Tyson (1997) uses interpretive techniques for strategy development. By the late 1990s, literature on HRM strategy and links to the business outcomes is diverse enough for Guest (1997) to advocate the need for theoretical approaches to HRM strategy development and for Boxall (1999) to undertake a meta-analysis of the field of strategic goal setting within the HRM context. This resulted in a proposal for a structure aimed at overcoming some of the difficulties observed in this area. Beer et al. (1984) define effectiveness within HRM context as (p. 69) “matching individual career needs and organizational requirements” while acknowledging “that employees’ perspectives may differ between cultures”. Within this context Beer et al. document the following HRM outcomes (Beer et al. 1984, p. 73): 1. Availability of the right number of personnel with the needed mix of competences in the short and long term. 2. Development of people needed to staff the organization in the future. 3. Employee perception of opportunity for advancement and development consistent with their needs. 4. Employee perception of relative security from termination due to factors beyond their control. 5. Employee perception that selection, placement, promotion, and termination decisions are fair. 6. The lowest possible payroll and people-processing costs possible to meet the objectives above. Further HRM outcomes are listed by Beer et al. (1984) in the context of managing the internal flow of employees. These include both organizational requirements that are implicitly linked to the above-mentioned outcomes and employee needs (pp. 77-78): • • • • •

satisfaction and commitment of employees; competence of employees; motivation of employees; congruence between work and personal life of employees; and costs associated with turnover, training, transfers, employee failure.

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3 Human Resources Management Context

Dyer and Reeves (1995) discuss the impact of human resource strategies on organizational effectiveness, concluding that (p. 665) “a good match between human resource strategies and organizational environments should produce core capabilities which would enhance an organization’s competitive position and, other things being equal, result in greater organizational effectiveness” where organizational effectiveness is measured by three outcomes (p. 661): 1. Human resource outcomes such as absenteeism, turnover and individual or group performance. 2. Organizational outcomes such as productivity, quality and service; and 3. Financial or accounting outcomes such as return on invested capital or return on assets. A fourth possibility, for publicly held firms, is stock-market performance as measured by stock value or shareholder return. Having reviewed strategic, descriptive, and normative HRM theories Guest (1997, p. 266) concludes that “we still lack a coherent theoretical basis for classifying HRM policy and practice” and proposes a theory (p. 268) “which links HRM practices to processes that facilitate high individual performance”. At the core of the theory is the assumption (Guest 1997, p. 269) that “improved performance is achieved through the people in the organization”. Within this context Guest (1997, p. 269) surmises “HRM practices should be designed to lead to HRM outcomes of high employee commitment, high quality staff and highly flexible staff.” Guest (1997) links these HRM outcomes to HRM strategies (differentiation, focus and cost), practices (selection, training, appraisal, rewards, job design, involvement, status and security), behaviour (effort/motivation, cooperation, involvement, organizational citizenship), performance (high productivity, quality, innovation and low absence, labour turnover, conflict, customer complaints) and financial (profits and ROI) outcomes. Tyson (1997) approaches human resource strategy as an interpretive process where the process is defined as “a mechanism for achieving a desired objective” (p. 278). The objectives of the HR strategy process are described by Tyson (1997, p. 277) as “typically concerned with devising ways of managing people which will assist in the achievement of organizational objectives” with the overriding purpose of HRM stated as (p. 281) “to achieve a ‘fit’ between the HR strategy and the overall strategy of the business”. As already discussed, Boxall (1999, pp. 267-268) suggests a multi-tiered structure for HR strategic goals in order to overcome the difficulties of HR goal specification. The multi-tiered structure proposed by Boxall (1999) allows trade-offs between goals to be taken into account with more specific goals set within the “activity domains” that contribute to the composite high-level objectives. It is illustrated in Figure 3.1.

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Workforce governance and executive leadership Employee competence and learning

Employee motivation and organisational commitment

Labour productivity, organizational flexibility and social legitimacy

Labour cost and workforce composition Fig. 3.1 HR strategic goals (from Boxall 1999, p. 269, fig. 2)

According to Boxall (1999, p. 267), “strategic HR goals form part of the systematic mix of strategic business goals which include objectives, whether explicit or implicit, for a range of critical areas: competitive positioning, structure, financing, information systems, operational style and HRM”. Boxall analyses (Boxall 1999, p. 267) the reasons why “only a few HRM theorists have taken up the challenge of specifying a schedule of strategic goals in HRM” concluding that “there are both practical and ideological difficulties”. According to Boxall, practical difficulties stem from the multidimensionality of HRM objectives, while ideological difficulties arise as a result of “different motivations in management actions” (p. 268). The multi-tiered structure proposed by Boxall and reproduced in Figure 3.1 is aimed at addressing the multidimensionality problem by identifying (Boxall 1999, p. 268) three key result areas: labor productivity, organizational flexibility, and social legitimacy. Labor productivity goals include objectives for short-term efficiency of labor use (such as sales per employee) as well as goals for longer-term effectiveness (such as building the loyalty of highly productive staff) (Koch and McGrath 1996, van Veldhoven 2005, Sheppeck and Militello 2000). Like labor productivity, organizational flexibility represents a composite set of outcomes including the ability to cope with radical industry change (Buford 2006, Zhang 2006). Social legitimacy consists of goals ranging from basic compliance with labor laws to outstanding citizenship of “model employer” goals (Lees 1997, Gibb 2004). Contributing to these composite outcomes are a set of activity domains in which more specific goals would need to be set. Boxall makes two important caveats for the discussion of HRM goals (Boxall 1999, pp. 268-269): 1) the level of detail of HR goals changes with some “highly specified while others… are simply generalized expectations, if not merely rhetoric”; and 2) “there are trade-offs among employment goals”.

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3.2.2 HRM in Public and Private Sectors and Internationally While papers reviewed in the previous section were focused on providing guidelines for how HRM should be done, this section focuses on how HRM is done. In this context, private sector HRM statements are quoted (Keeney 1994, 1996; DialAmerica Marketing 2004) as well as a public sector’s organizational guidelines for HRM (The Treasury Board of Canada 2001). These are complemented by surveys of public sector HRM activities in the USA (Selden et al. 2001) as well as an international comparison of HRM practices incorporating companies from Australia, Canada, NZ, UK, and USA (Kane et al. 1999). Keeney (1994) discusses the HRM objectives of a specific firm within the framework of Value-Focused Thinking, which is used to classify objectives into fundamental objectives (defined by Keeney (1994, p. 34) “as a statement of something that one wants to strive toward”) and means objectives (defined by Keeney (1994, p. 34) as “methods to achieve ends”). Keeney (1994) uses a private firm “Conflict Management, Inc” to illustrate the application of the VFT framework by identifying a number of strategic and means objectives that directly relate to HRM within that firm: Strategic objectives relating directly to HR (Keeney 1994, p. 36, table 2): Maximize well-being of employees: • • • • • •

have interesting work; allow professionals to do work of their choice; reduce required work time; reduce travel time; maximize compensation; ensure equity of compensation.

Means objectives directly relating to HR (Keeney 1994, p. 36, table 3): 1. Enhance management. • • • • • • • • 4.

Ensure excellence in recruitment. • •

5.

Free up time of staff; Use resources effectively; Enhance interconnections of different physical locations; Recognise management effectiveness as a contribution; Be less dependent on specific individuals for specific efforts; Continue toward more effective management; Improve the compensation system; …

Recruit first-rate staff; Enhance in-house breadth of expertise.

Maintain a quality staff.

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• • 6.

Reduce burdensome aspects of training. • • • •

9.

Reduce total training time by principals; Have skilled trainers who are not principals; Train by doing in addition to training by seminar; …

Spread conflict management ideas. • • •

13.

Develop staff skills; Keep quality professionals.

Put concepts in user-friendly form; Widely disseminate ideas of principled negotiation; …

Ensure availability of adequate funds: • •

Increase profits; Form equity.

Kane et al. (1999) analyses HRM practices based (p. 494) on a “sample comprised of 549 employees, managers and HRM staff across a wide range of types of organizations in Australia, New Zealand, the USA, the UK and Canada”. He concludes (Kane et al. 1999, pp. 510-511) that HRM is seen as effective (irrespective of the country within which organization is based) when it meets the basic criteria of both strategic and developmental perspectives. That is, HRM policies and practices must be long term in focus, integrated with one another and in line with the organization’s strategy and objectives as well as treating all employees fairly, increasing employee motivation, satisfaction and commitment, and helping all employees develop to their maximum potential.

The framework developed by the Government of Canada (The Treasury Board of Canada 2001) is an example of the application of HRM research within a public sector environment. Within the HRM framework, objectives are grouped into four result areas (p. 4):“leadership, a productive workforce, an enabling work environment and a sustainable workforce” that are complemented by the description (p. 13) of the “human resource management capacity”. Each of these categories is defined in terms of its overall objective with the desired outcomes and performance indicators providing information about different levels of HRM objectives. The first two levels are presented here: • Leadership is defined as (The Treasury Board of Canada 2001, p. 5) “the ability to establish necessary relationship, mobilize the energies and talents of staff, and manage for results, while respecting public service values and ethics” with the following desired outcomes (The Treasury Board of Canada 2001, pp. 5-6): –

Mission and Vision. The energies and talents of staff are mobilized to realize the vision and accomplish the mission.

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– – –

Managing for results. Human resources are in position to achieve operational objectives. Values and Ethics. Decisions and actions reflect respect for democracy, as well as professional, ethical and people values. Effective Relationships. Management works collaboratively with staff, employee representatives and other stakeholders to ensure that the organization delivers appropriate services to the Canadian public.

• A productive workforce is defined (The Treasury Board of Canada 2001, p. 7) as “one that delivers services in an efficient manner and continuously strives to improve” with the following desired outcomes (The Treasury Board of Canada 2001, pp. 7-8): – – – –

Service Delivery. Programs are designed and delivered to meet the needs of citizens. Clarity of Responsibilities. Roles, responsibilities and performance expectations are clearly defined, understood and accepted. Organization of Work. Work is organized and assigned to facilitate timely decision making and improvements in productivity. Employment Strategies. Strategies to attract skilled persons ensure good value for the money and are simple, timely and efficient.

• An enabling environment is defined (The Treasury Board of Canada 2001, p. 9) as “environment [that] provides the necessary support, tools, systems and equipment to enable employees to provide client-focused delivery while reaching their full potential” with following desired outcomes (The Treasury Board of Canada 2001, pp. 9-10); – – – –

Supportive Culture. The organization enables employees to attain their full potential and encourages a balance between work and personal life. Respect for the Individual. Individual rights are respected and the diverse nature of the workforce acknowledged. Communication. Information is obtained and disseminated so that everyone understands organizational goals, priorities and activities, and the sharing of ideas is encouraged. Well-being and safety. The work environment is safe and healthy.

• A sustainable workforce is defined (The Treasury Board of Canada 2001, p. 11) as “one in which the energies, skills and knowledge of people are managed wisely, and plans are in place to provide for organization’s viability” with following desired outcomes (The Treasury Board of Canada 2001, pp. 11-12) –

Human Resources Planning and Analysis. The organization’s human resources needs are a key consideration in strategic and operational planning.

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– – –

Learning and Development. Managers and employees have the competencies to keep the organization viable. Workload Management. Resources are sufficient to achieve the expected results. Compensation. The organization has a well-developed and properly administered compensation package.

• Human resource management capacity is defined (The Treasury Board of Canada 2001, p. 13) as “managers have access to tools, advice and support provided to them by their human resources professionals and use them in the management of their human resources” with the following desired outcomes (The Treasury Board of Canada 2001, p. 13): – – –

Support from Human Resources Specialists. Human resources expertise is available to managers. Appropriate Policies and Programs. Internal practices meet the organization’s needs. Tools and Techniques. Appropriate human resources tools and techniques are used by managers and staff.

Selden et al. (2001) take an academic approach to the analysis of public service HRM which is aimed to (p. 599) “identify trends and emerging practices in state government” within the USA context by focusing “on key personnel functions…general responsibility for personnel functions, workforce planning, selection, classification, performance evaluation, and reward systems”. The objectives of these key functions were defined by Selden et al. (2001) based on the HRM literature review as follows: • Workforce planning (p. 602): “to ascertain their [agencies] needs for and availability of human resources to meet their objectives ...maintaining a highly productive workforce”; • Selection process (p. 602): “supplies persons with specific knowledge skills and abilities needed to perform public services”; • Classification system (p. 604): “to allow to redeploy and reskill its [organisation’s] people”; • Performance evaluation and reward systems (p. 605): “performance-driven culture” that aligns “individual and team objectives with agency goals” resulting in “greater ownership of agency’s goals” by employees. The analysis by Selden et al. (2001, p. 601) is based on the review of 49 mission statements of human resource or personnel departments. The reported finding relating to the overall HRM objectives was that (p. 602) “the bureaucratic paradigm” is being replaced with a “new paradigm that emphasizes service, front-line workers, efficiency and results”.

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DialAmerica Marketing Inc. (2004) is an example of a private company that articulates its human resources values and objectives. In its corporate statement, the company describes itself as one of the nation’s top telemarketing agencies, [is] a leading provider of inbound & interactive and outbound telemarketing services for a range of industries: the book and magazine publishing, financial services, software, service contracts, energy services, cable, Internet and telecommunications industries.

Within this context, the following HR values have been defined on the company’s website (DialAmerica Marketing 2004): • People will always be the heart of our success. • DialAmerica is committed to the success of people. We provide: – – –

personal growth and recognition; integrity in the workplace; positive work environment;

• DialAmerica is committed to creating mutually beneficial business relationships. We provide: – – –

marketing services; innovative solutions; quality services for quality clients.

3.2.3 HRM Functions This section illustrates how the broad level activities and objectives discussed can be drilled down, highlighting the expansive nature of the HRM subject – the more you look, the more detail becomes apparent. Within this group, authors focus on specific objectives and HRM functions such as HR planning (Walker 1989, Zeffane and Mayo 1994; Idris and Eldridge 1998), career management (Baruch and Peiperl 2000), and succession planning (Huang 2001). The three papers on HRM planning highlight the differences in approaches to a single HRM function with Walker (1989) using his consulting experience to generalise the types of activities and objectives that (in his opinion) should be part of HRM planning, Zeffane and Mayo (1994) adopting an operations research approach to solving a comparatively narrow decision problem, and Idris and Eldridge (1998) taking a broad systems approach to link HRM planning activities with the objectives responsible for them. Papers by Huang (2001) and Baruch and Peiperl (2000) are examples of how the examination of lower level HRM functions can lead to generalisations about organizational types and philosophies highlighting the dependence of every level of HRM on the overall organizational objectives and policies.

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“Integrating business strategy and the management of people” is the slogan of the private consulting firm founded by Walker that specialises in HRM issues (Walker Group 2004). Accordingly, articles by Walker (e.g. Walker 1989) provide a complementary perspective to academic and public sector research in this area. According to Walker (1989, p. 233) “one issue is essential: how can the organization ensure it will have people of the right types and numbers, organized appropriately, managed effectively, and focused on customer satisfaction”. Within this context, Walker (1989) identifies a number of HR activities and corresponding objectives that are addressed within the scope of HR planning (Walker 1989, pp. 232-233): • achieving and sustaining cost competitiveness (personal costs, utilization, and downsizing; eliminating unnecessary work); • achieving competitive differentiation through service and product quality (productivity, customer satisfaction, and other components of total quality); • implementing organizational restructuring and mergers or acquisitions; • increasing delegation of authority and responsibility (streamlined approval processes, increased employee involvement, risk/reward compensation, and empowerment to act); • enhancing organizational effectiveness (team building, shared vision and values (culture), lateral relationships, etc.); • developing leadership (staffing, appraising, and developing managers); • enhancing work-force capability and motivation (staffing, retention, motivation and rewards, development, communications and involvement, work-life issues). Zeffane and Mayo (1994) adopt an operations research view of manpower planning with a focus on (p. 36) “monitor[ing] and manage[ing] the flow of people into, through, and out of the organization in order to achieve an equilibrium”. Within this context, they defined human resource planning as “a range of tasks designed to ensure that the appropriate number of the right people are in the right place at the right time”. A systems model of human resource planning developed by bin Idris and Eldridge (1998) incorporates both strategic and process definitions of human resource planning via a shared objective of (p. 345) “attainment of organisational or corporate objectives through the effective utilisation of human resources”. This objective is made more specific to the planning process by articulating the tasks of the planning process, namely (p. 347, fig.1): “1) analyses of environment factors, corporate objectives, and internal human resources; 2) forecasting supply; 3) forecasting demand; 4) matching supply with demand; 5) generation of human resource objectives and strategies” and corresponding objectives to (pp. 349-350): achieve compatibility between the human resource management system and external forces; … define the capabilities required to implement the organisation’s strategy, primarily focused on the capacity to act and change in pursuit of sustainable success;…identify human resource strengths and weaknesses; … gain knowledge about

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3 Human Resources Management Context future requirements in response to its [organisational] objectives;…expose a relevant combination of conflicting aspects and reflect a healthy dynamic for problem resolution arising from different perspectives and needs;… ensure successful alignment of an organisation’s strategy and the external environment;… define the organisation’s desired human resource position and the programmes necessary to move in that direction.

bin Idris and Eldridge (1998, p. 352) claim that the systems framework they propose for human resource planning “may lead to more informed human resource decisions” that will in turn stimulate “flexibility in the minds of managers necessary for building a competitive advantage in market oriented organisations”. Baruch and Peiperl (2000) provide an in-depth view of the “development of people’s careers” (p. 346) activity within the HRM resulting from a survey of “194 United Kingdom companies” (p. 346) and developing a “theoretical model of career processes” (p. 346). Within this model, the organizations are classified ( Baruch and Peirperl 2000, p. 358) according to “the level of sophistication of the OCM [organisational career management] practices and the level of involvement on the part of the organisation necessary to put them into use” into five overlapping types (p. 359, fig. 1): basic, active planning, active management, formal activities and multi-directional. As shown in Figure 3.2 and consistent with other authors, Baruch and Peiperl (2000, p. 362, table VII) find that objectives vary between different organisational types.

HR Strategic Goals

Basic Offer basic career system elements Satisfy employees' expectations Requires infrastructure

Active Management Maximize firm know ledge of employees Maximize employee know ledge of firm and options w ithin it

Formal Support internal labor market Provide stability Clarify options for career development w ithin the firm

Active Planning Make performance-career links explicit Offer personal and emotional support Provide for succession

Multi-Directional Maximize performance feedback Promote open culture Risks in small or "closed" organizations

Fig. 3.2 Objectives according to organizational types (from Baruch and Peiperl 2000, table VII)

A paper by Huang (2001) is an example of an empirical study in the field of HRM. Huang examines the “relationship between the succession program and human resource outcomes” (p. 742) by defining HR effectiveness against the outcome of “high employee commitment to organizational goals” as a composite of five indicators (p. 741): staff morale, organisational climate, staff turnover, organisational commitment and job satisfaction. These are measured by asking employees to compare their company with competitors. The aims of succession planning are defined by Huang (2001, p. 736) as “the process of ensuring a suitable supply

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for current and future key jobs successors, so that the career of individuals can be managed to optimize the organization’s needs and the individual’s aspirations”. 3.2.4 Measuring HRM While there is agreement that the HRM activities and objectives must be evaluated (e.g. Fleming and Wilson 2002, Veldhhoven 2005, Wright et al. 2005), there is no consensus as to how this should be achieved. In this section, the influence of the HRM measurement approach on the specification of the HRM objectives is illustrated. Walker and MacDonald (2001) adopt a balanced scorecard approach that links individual HRM objectives to the overall business objectives. Fitz-enz and Davison (2002) are more concerned with the evaluation of HRM activities linking them directly to performance measures, whereas Boudreau and Ramstad (2001) advocate an integrated approach that links activities, objectives and performance measures. Boudreau and Ramstad (2001, p. 10) focus on measuring HRM staffing by “systematically approach[ing] staffing from a decision-based and process-based perspective. This means that we explicitly consider the outcomes of the process, the key process steps, and then apply a framework that integrates them”. Within the framework, staffing is treated as a supply-chain of six processes (Boudreau and Ramstad 2001, p. 16, exhibit 9) being building/planning, recruiting, screening, selecting, offering/closing, and on-boarding. The outcomes are measured according to three broad categories (Boudreau and Ramstad 2001, pp. 10, 16): efficiency of activities (including cost, time and volume), effectiveness (including compliance/diversity and quality attributes of the talent) and impact (including customer/constituent satisfaction and value impact of talent). Walker and MacDonald (2001) apply and implement a balanced scorecard (BSC) model developed by Kaplan and Norton (Kaplan and Norton 1996) to the HR context as (Walker and MacDonald 2001, p. 365) “the means to monitor workforce indicators, analyze workforce statistics, diagnose workforce issues, calculate the negative financial impact, prescribe solutions, and track improvements”. Consistent with the BSC model, HR objectives and corresponding detailed performance measures are organized along four dimensions (Walker and MacDonald 2001, p. 369): financial, customer, operations and strategic. The focus of the financial dimension is on (Walker and MacDonald 2001, p. 368) adding “measurable financial value to the organization” with the high level financial objectives expressed by Walker and MacDonald (2001, p. 372, table II) as “max shareholder value, max human capital performance, min human resources costs”. These objectives are extended within the customer dimension to “develop best in class service delivery and increased employee value while ensuring a focus on cost and value” (p. 370) by ensuring that (p. 372, table II) the HRM area is a business partner i.e. it provides strategic support to the business that complies

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with world class standards, provides responsive quality service and minimizes costs. The operational and strategic dimensions address the need for employees (Walker and MacDonald 2001, p. 371) “to expand their skills and increase their productivity”. Walker and MacDonald (2001, p. 371) describe the strategic dimension as focusing on “the investments and initiatives designed to build employee capability” with strategic objectives distilled to (p. 372, table II) “capability (strategic competencies), talent (select, assimilate and train), performance based culture/climate, organizational integration (information for decision making), and leadership”. Simultaneously, the operational dimension is focused on (p. 371) “workforce solutions” including (p. 372, table II) “align[ment of] HR planning with business priorities, provide[ing] quality consultative advice, ensure[ing] a strategy focused workforce, develop[ing] and enhance[ing] world class programs, [and] optimise[ing] HR services through alternative delivery channels”. Walker and MacDonald (2001) support the statement that business and HRM objectives must be aligned by (p. 370) “a strategy map which illustrate[s] the cause and effect linkage between HR Strategy and business objectives” with this map used “as the guide …to evaluate the strategic objectives in terms of measures and outcomes”. These measures are in turn refined “into lagging measures (which tell how well a company has already done) and leading measures (which are indicators of future performance)”. Fitz-enz and Davison (2002) focus on the measurement of HRM performance by linking various HRM activities to the outcomes (defined as quantities that can be used to measure these activities (p. 26)), and through measuring customer satisfaction (p. 23). For example, the HRM activities of interviewing and making job offers are included within the HRM staffing function and are measured as hires, acceptances and rejections (Fitz-enz and Davison 2002, p. 26, fig. 3-3). These same activities are also assessed by Fitz-enz and Davison (2002, p. 79) in terms of “timeliness, completeness and quality” when viewed from the HRM customer perspective. In effect, Fitz-enz and Davison (2002), provide a contextual dictionary of measures that can be referred to when evaluating HRM objectives and activities. 3.2.5 HRM Teaching Texts To conclude the review, examples of sources that include comprehensive summaries of HR functions and objectives are discussed. Due to the comprehensive nature of the discussion in these texts, they are usually guided by an overriding framework that varies from author to author. For example, Cascio and Awad (1981) focus on the flow of HRM activities and their relationship to the business information systems – this is one of the earlier texts that stresses the importance of information systems for HRM. Ten years later, Schuler and Walker (1991) document their understanding of the practices and challenges facing HRM in the “in-

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formation age”. Milkovich and Boudreau (1997) approach HRM from a decision making perspective drawing “upon practices actually used by a wide variety of employers across the world”, while Stone (1998) uses Australasian material to compile a textbook which is aimed at linking objectives, functions and measurement of HRM and is suitable for undergraduate HRM courses. Cascio and Awad (1981) define HRM as (p. 3) “the attraction, selection, retention, development, and utilization of human resources in order to achieve both individual and organizational objectives”. Cascio and Awad (1981) highlight the fact (p. 4) that while “all managers who supervise others are personnel managers” (in a sense that they “have to be concerned with attracting, selecting, retaining, developing, and utilising human resources in order to achieve their own as well as organizational objectives”), HRM is “also a separate functional area within the organization” (p. 5) which “has two major functions…(1) to collaborate in the development and administration of the policies that affect the people who make up the organization; and (2) to help managers manage”. According to Cascio and Awad (1981, p. 16) these two major functions are characterised by four distinct activities: • Formulation of personnel policy – a top management responsibility. • Implementation of policies by line management – the service function. • Audit and control – the establishment of standards and procedures to see that organizational policies are maintained. • Innovation – research and development of new practices, procedures, and programs. Cascio and Awad subscribe (1981, pp. 30-31) to the “management by objectives approach to human resources department evaluation” that requires that “all the duties and goals of the human resources department are listed and described” which in turn, lead to the development of “standards for estimating the department’s effectiveness at the next evaluation period”. This includes (Cascio and Awad 1981, pp. 31-32) the specification of “means as well as ends – not only what results will be achieved, but also how”; “long- and short-range organizational goals”; and “goals and objectives that are consistent with and support overall organizational objectives”. Furthermore, according to Cascio and Awad (1981, p. 32), top management goals must be coordinated and harmonised “with the goals specified at successively lower levels” and “objectives that pertain to satisfaction and productivity” must be specified along with more easily quantifiable objectives such as “number of tests given”, “number of training programs conducted”, etc. Cascio and Awad also state (1981, pp. 202-203) that there are “two major approaches …to determine human resources objectives (defined as “desired end states, usually expressed in quantitative terms”): the individualised “bottom-up” approach and the policy “top-down” approach with the bottom-up approach deriving objectives “by analyzing each individual in his or her particular position in terms of performance, skills, and promotability in the organization” whilst the “top-down” ap-

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proach “relies on the organization’s overall goals and applies them in individual situations”. In addition, Cascio and Awad (1981) identify specific goals for individual HRM activities. For example, they state (1981, pp. 178, 206, 317) that “the overall objective of a job analysis is to define a job in terms of tasks or behaviours necessary to perform it”, the objective of human resource planning is “to anticipate future business and environmental demands on an organization and to meet the human resource requirements dictated by those conditions…to improve our ability to cope with change – technological, social, political and environmental”, whereas planning and administration goals are grouped into eight categories “adequacy, equity, security (and estate building), acceptability, cost control, balance, incentive, pay and effort bargain”. The textbook by Schuler and Walker (1991) is an example of a classical HRM textbook that discusses various HR activities and objectives in detail. HRM activities and corresponding goals as described by Schuler and Walker (1991) in Chapter 3 are summarised in Table 3.1. The descriptions in Table 3.1 are not “absolute” as each textbook adopts its own classification and terminology for the description of HRM activities and goals. Furthermore, changes in environment and organizational objectives also impact upon how HRM activities and goals are described (e.g. Schuler and Jackson 2004). Nevertheless, the activities in Table 3.1 remain relevant today. For example, in a more recent analysis of HRM practices and objectives, Schuler and Jackson (2004, Chapter 7, pp. 6-7) provide a more succinct model of HR activities consisting of four tasks: “managing employee assignments and opportunities, managing employee competencies, managing employee behaviours, managing employee motivation” with corresponding objectives being “organizations need to have the right number of people at the right place at the right time”, “ensuring that individuals have the needed skills, knowledge and abilities to perform successfully”, “ensuring that employees exhibit appropriate behaviors”, and “employees meet the firm’s motivational needs” in terms of effort, retention and reliability. However, even within this more recent and succinct framework (Schuler and Jackson 2004, Chapter 7, p. 8) “the major categories of HR policies and practices include: planning, recruitment and selection, training and development, performance management, compensation, health and safety, union-management relationships, organizational change and design” and therefore remain largely unchanged from those listed in Table 3.1. The textbook by Milkovich and Boudreau (1997) describes Human Resource Management practices and objectives within the framework of a diagnostic model (p. 14, exibit 1.7). The strength of this model is that it (Milkovich and Boudreau 1997, p. 13) explicitly recognises the ongoing nature of the HRM process and interrelations between various activities by including “continuous learning, flexibility, and feedback”. According to Milkovich and Boudreau (1997, pp. 3, 14) the overriding objectives of all HR practices within this model are efficiency and equity and HR practices include (at the broad level) planning, staffing, development,

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compensation and employee/union relationships. Within the HR context (Milkovich and Boudreau 1997, pp. 4-5) efficiency is understood as “an organization managing its employees efficiently”, “equity is the perceived fairness of both the procedures used to make HR decisions and the actual decisions”, and “balancing equity and efficiency is a constant challenge because both are required for an effective organisation”. Table 3.1 HRM activities and goals (adapted from Schuler and Walker 1991, ch. 3) Activity

Goals

Organization design To structure human resources so they can best accomplish an organizaand position manage- tion’s goals and mission ment Staffing

To facilitate or optimise the matching of people to jobs

Training and develop- The overall goal is to improve the fit of employees with their current or ment future jobs More specific goals are: removing current or anticipated performance deficiencies, increasing employees’ self-awareness of their strengths and weaknesses, improving decision-making and problem solving skills, increasing employee motivation and/or modifying employee values and attitudes Compensation and benefits

Adequate: having employees perceive that the organization’s pay and coverage or valued benefits are adequate; Equitable: having employees perceive that the organization’s compensation and benefit practices are fair; Motivating: structuring and administering compensation and benefits so as to motivate high levels of performance; Competitive: creating a compensation and benefit system that is competitive within relevant labour markets; Efficient: structuring and administering compensation and benefits in an effective manner;

Performance manage- The overall goal is to maximize organizational performance ment Specific goals are: increase motivation, identify and promote highpotential employees and managers, identify and rehabilitate or remove poorly performing employees and managers Labour relations

Overall goal: strategic labour relations management aims to create and maintain a union-management relationship that minimizes costs and improves productivity Sub goals: look out for employee needs, control or cooperate with the union

Personnel research

To give responsive, good quality assistance on request from clients To provide leadership either on its own or through other HR functions Generate new ideas and research based information to help an organization to meet its goals and discover strategic opportunities

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Milkovich and Boudreau (1997) include more specific objectives with the description of most HRM activities either directly or as part of the evaluation of the activity. For example, the description of the training and development process (Milkovich and Boudreau 1997, p. 408) directly defines the training objective as “an improved match between employee characteristics and employment requirements”, while recruitment objectives can be deduced from the list of measures for evaluation of recruitment (p. 215, Exhibit 6.15) that include statements such as “timely fashion”, “inexpensive”, “above-average performers”, “quality of employee hired (performance, turnover, attendance, etc)”. Stone (1998) adopts “a pragmatic approach to the study of human resource management” in the textbook titled “Human Resource Management”. While using slightly different terminology (consistent with the Australian and Asian focus of the book (Stone 1998, p. xiv)), the HR objectives and functions discussed by Stone are consistent with other similar texts in the area of HRM. For example, Stone’s list (1998, p. 22) of strategic objectives echoes those already discussed: • Cost containment. HR objectives and activities will focus on cost reduction via reduced headcount, improved expense control, improved productivity, reduced absenteeism and labour turnover; • Customer service. HR objectives will focus on achieving improved customer service through recruitment and selection, employee training and development, and rewards and motivation; • Social responsibility. HR objectives and activities will focus on legal compliance and improvements in areas such as equal opportunity, occupational health and safety, minority training and development programs; • Organizational effectiveness. HR objectives and activities will focus on organizational structure, job design, employee motivation, employee innovation, adaptability to change, flexible reward systems and employee relations. The more recently published research papers and surveys on HRM (e.g. Lee 2007, Jorgensen et al. 2007, Becker and Huselid 2006, de Ceri et al. 2007, Buford 2006) prove to be consistent with the list suggested by Stone (1998) and overall with the body of knowledge presented in this chapter.

3.3 Summary There is no doubt from this HRM literature review that HRM activities should be aligned to organizational objectives. While some informal descriptions of HRM processes identify links to HR objectives and vice versa (e.g. Boudreau and Ramstad 2001), the currently available formal modelling mechanisms for processes and objectives do not facilitate such integration. Boxall (1999, p. 269) noted that while “management in each firm needs to develop its own goal classification [it] does not imply that managers in all firms have a highly specified schedule of HR goals”. This observation is confirmed by Koys (2000, p. 265) who concluded as a result of an analysis of 530 business and human

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resource strategy statements that “generally, different business goals did not produce different elements on the HR statements”. The overview of HR literature provided in this chapter confirms that, fundamentally the HRM literature agrees that HRM activities are aimed at filling the gap between business HR needs and capabilities in an efficient and effective way. Nevertheless, there is a lack of agreement on the formal definitions of objective, process or their relationship. In other words, there are partial (and sometimes contradictory) answers to questions such as: which HRM functions contribute to which HRM and business objectives, do HR processes meet required objectives and how to (re-)design HRM processes to facilitate achievement of the objectives. A modelling framework that will enable these questions to be answered in a consistent and integrated manner would address these gaps perceived in HRM modelling.

4 Business Objectives Modelling 4.1 Introduction An objective model~ is often used to facilitate business effectiveness, where business effectiveness is defined by Daellenbach (1994, p. 14) as “how well the goals or objectives of entity or activity are achieved”. In this context, an objective model is required to facilitate: • identification, communication and structuring of business objectives; and • measurement of the level of success in achieving objectives. There is no disagreement about these general statements, but despite this, individual modelling methodologies focus primarily on selected aspects of objectives representation and measurement. For example, an HRM planning objective may be treated by an operations research analyst as an optimisation objective, by a decision analyst as a set of related criteria, by a management consultant as a set of performance indicators, and by a requirements engineer as a network of interrelated objectives. The purpose of this chapter is to relate terminology and concepts from various perspectives to each other in order to identify overlaps between them and to develop a set of desirable properties of an objectives model that is not limited by a single discipline or paradigm. To provide a structure for this discussion, a review of objectives models is split along the qualitative (Section 4.2)/quantitative (Section 4.3) lines reflecting the primary purpose of the modelling methodologies discussed (as defined in Chapter 2). Based on the review, desirable properties of objectives modelling are identified and defined in Section 4.4. The chapter is concluded with a summary.

4.2 Quantitative Objectives Modelling Quantitative objectives models are motivated by the need to translate qualitative statements of objectives into quantitative statements which can be used to evaluate and/or simulate the impact of business operations on its objectives. To illustrate the different dimensions of quantitative objectives modelling, the following methodologies are considered: • the value-focused thinking (VFT) framework (Keeney 1992) that uses values and objectives as drivers for understanding decision situations in order to develop a set of alternatives that are required by quantitative decision models within classical decision analysis and modelling disciplines; • the business performance management model as represented by a Balanced Scorecard (BSC) (Kaplan and Norton 1996) that uses business objectives to provide a performance management framework for the business; and

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• the system dynamics (SD) causal loop models (Sterman 2000) that recognise and model feedback mechanisms between business objectives, assist with the development of a set of alternatives for multiple objectives, and facilitate a choice among these alternatives through the use of simulation techniques. Unlike other models, the VFT framework provides an explicitly articulated and systematic approach for identification and structuring of objectives and linking them to the quantitative choice model based on the Multi-Attribute Utility Theory (MAUT). The generic nature of the objectives models within the VFT means that it can (and has been) easily linked to any other decision analysis technique concerned with making a choice amongst a set of alternatives. Because of its generic nature, the VFT framework is of greater interest in the context of this research than the other two models, and it is therefore described in much greater detail. Conversely, since the BSC methodology (originating in the business performance management domain) can be considered a special case of the VFT framework (with the emphasis on linking business objectives to a broader business management context) only a very brief overview of the BSC methodology is provided. Classical decision analysis models are sometimes based on unrealistic assumptions that ignore dynamic and feedback relationships between objectives. SD models overcome this limitation through the use of stock and flow diagrams and causal loops that are linked to quantitative and visual simulation models. However, SD models do not include objectives identification mechanisms that are available within the VFT framework, and as such are best used to complement the VFT framework by assisting with the development of a set of alternatives for multiple objectives and facilitating a choice among these alternatives through the use of simulation techniques. 4.2.1 Value-Focused Thinking Framework The representation of objectives in the decision analysis context has been driven by the need to consider and balance multiple and sometimes conflicting objectives that arise when making a decision about selecting from a set of alternatives (Olson 1996, Leon 1999). Consequently, objectives models within the decision analysis context are aimed at supporting decision making through one or more of the following activities (e.g. Keeney 1992, 1994): • • • •

identification of business objectives; understanding the relationships between business objectives; identification of alternatives; and solving the decision problem.

In the value-focused thinking framework, Keeney extends the Multi-Attribute Utility Theory (MAUT) (e.g. Keeney and Raiffa 1976, Belton and Stewart 2002) by further developing the concept of objectives and relationships between objec-

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4 Business Objectives Modelling

tives. The MAUT theory supports decision making for selection problems requiring “balancing of multiple, often conflicting objectives, criteria, or attributes” (Olson 1996, preface) which makes it one of the key methods in the toolbox of MultiCriteria Decision Analysis (MCDA). As a result, the VFT and MAUT share the same basic concepts as other MCDA methods. Figure 4.1, provides a road map to the concepts of the VFT framework that are discussed next. strategic

Values - what we care about are represented as

are compared to derive

characterization of the higher-level objectives

fundamental

Objectives - qualitative statements of something that one wants to strive towards

means

Alternatives - choices available to the decision maker

natural

are transformed into Criterion- the prefered direction of movement on an attribute Constraint(s)- limit choices to acceptable alterntives

value judgments for fundamental objectives

judgement about facts for means objectives

Trade-offs - assessments of relative importance of objectives

Attribute(s) - a measure of the degree of achievement for each objective are evaluated as

constructed proxy

Consequences - a vector of attribute values

are integrated as Objective Function (Value Model) - assigns a number to each consequence to * indicate the relative desirability of the consequences * be used to derive preferences for alternatives

measurable value function (if no uncertainty about the consequences of chosen alternatives) utility function (if uncertainty about the consequences of chosen alternatives)

Fig. 4.1 Road map to the VFT concepts (based on Keeney 1992)

As there is no uniformly agreed set of terms used to describe these concepts, definitions by Keeney (1992) and Keeney and Raiffa (1976) have been followed where possible and appropriate. The discussion of these definitions is accompanied (where applicable) by a brief overview of deviations from these definitions and alternative terms used within the MCDA context. 4.2.1.1 Values In a general sense Keeney defines value as a qualitative statement of “what we care about” (Keeney 1992, p. 3). For example, in the case of DialAmerica company (DialAmerica Marketing 2004) one of the core values is commitment to success of people.

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The mathematical meaning of the term value, defined by the Oxford dictionary (Tulloch 1997) as “the amount denoted by an algebraic term or expression” is referred to by Keeney (1992, p. 60) as a level or standard. To differentiate between the two meanings Keeney’s definition of the term value will be implied when the term value is used together with words such as organizational, business or decision-maker (e.g. organizational values) or when referring to value function or value model. In all other contexts (unless otherwise specified) the term value is used according to its mathematical definition. In business literature the term value can also be used in a sense of accounting or market value of a business (e.g. Kim 2004) and whilst this may be the main thing that the business cares about, it still represents only a subset of a broader concept of business values. The main premise of the VFT framework (Keeney 1992, p. 3) is that “focusing early and deeply on values (in a sense of “what we care about”) when facing difficult problems will lead to more desirable consequences, and even to more appealing problems than the ones we currently face” resulting in better solutions for the business as a whole. 4.2.1.2 Objectives and Alternatives While values represent general “principles used for evaluation of actual or potential consequences of action and inaction” (Keeney 1992, p. 6), objectives are more concrete propositions of what we are aiming for or in Keeney’s words (1994, p. 34) “[qualitative] statements of something that one wants to strive towards”. Returning to the DialAmerica example (DialAmerica Marketing 2004), the value of “commitment to success” may be translated into specific fundamental objectives of “maximizing personal growth and recognition” and “maximizing integrity in the workplace”. Within other contexts, terms such as purpose, goal and sometimes criterion are used to mean what Keeney defines as objective. To avoid confusion, the term purpose will be used in its general sense to differentiate between decision objectives and objectives of modelling techniques, systems, etc. The terms criterion and goal will be discussed later in this section. Keeney separates between two classes of objectives: fundamental and means objectives. Although, “when the contexts of two decision problems are related, the fundamental objectives in one context may be means objectives in another context” (Keeney 1992, p. 87). Fundamental objectives “concern the ends that decision makers value in a specific decision context” (Keeney 1994, p. 34) and are therefore closely related to business values as they are important “just because” rather than as a means to achieve some other objective. Fundamental objectives are described by Keeney (1992, pp. 31-34) as:

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4 Business Objectives Modelling

• qualitatively articulating values that one cares about as well as consequences of concern in the decision context; • the basis for any interest in the decision being considered; • providing guidance for action; and • providing a foundation for any quantitative modelling or analyses that may follow. A subset of fundamental objectives that corresponds to the strategic decision context is defined by Keeney (1992, p. 41) as strategic objectives – representing the highest level of abstraction. To ensure that individual decisions make sense, Keeney recommends that strategic objectives are “carefully defined and communicated” and used to guide all other decision objectives. Unlike fundamental objectives, means objectives are of interest only “because of [their] implications for the degree to which another (more fundamental) objective can be achieved” (Keeney 1992, p. 34). Means objectives can be simply defined as means of achieving fundamental objectives providing a link from decision alternatives to the business values. For example, means objectives for the “maximizing personal growth and recognition” fundamental objective, may include “using internal candidates as the first choice when vacancies arise”, “providing adequate training to staff”, “implementing a performance management system”, etc. Alternatives are not explicitly defined by Keeney, but from classical decision theory are usually taken to refer to as a set of possible actions or choices available to the decision maker within a particular decision situation. So in the context of training, internal and external training may be considered as two alternatives. Generally speaking, alternatives are quantified using a set of decision variables (e.g. cost of training, staff preference rating for each training type, etc) that could be discrete or continuous, and could take on positive, negative or integer values depending upon a specific decision situation. Usually, decision theory assumes that the decision alternatives are known, with the focus on selecting the best alternative as defined by the values and objectives of the decision maker. The VFT framework turns this approach to decision making upside down by making values (rather than alternatives) “the fundamental notion in decision making” (Keeney 1992, p. 3) and providing a structure that, as well as facilitating a choice among alternatives, encourages the modeller “to create better alternatives than those already identified” (Keeney 1992, p. 4). The VFT framework addresses the issues of “identifying the overall (highest-level) fundamental objectives for the decision situation, relating objectives on different levels of a structure, and stopping the structuring process” (Keeney 1992, p. 77). 4.2.1.3 Relationship between Objectives The first step in the identification of objectives within the VFT framework is the identification of the overall fundamental objective that “characterizes the reason

55

for interest in the decision situation and defines the breadth of concern” (Keeney 1992, p. 77). Once this objective is identified, it can be drilled down into other (lower level) fundamental objectives that help articulate the dimensions of the higher level objective by answering the question “what aspects of the high level objective are important”. Keeney (1992, p. 79) suggests the use of categories to assist with the identification of more specific fundamental objectives. For example, to explain what is meant by maximizing growth and recognition, two categories may be considered being skills and rewards. These categories provide the general sense of what is meant by the higher level objective and can be expanded further to clarify the meaning of the higher level objective. For example, the skills category may include personnel management skills, client service skills, and financial management skills. Fundamental objectives are structured as a strict hierarchy (Keeney 1992, p 6971). Consequently, Keeney (1992, p. 98) specifies that lower level objectives within the fundamental objectives hierarchy “should be mutually exclusive and collectively should provide an exhaustive characterization of the higher-level objective. There should be at least two lower-level objectives connected to any higher-level objective”. The identification process is iterative. As lower level objectives are identified, higher levels of the hierarchy may need to be modified to avoid duplication and to ensure that all the important dimensions are covered. This may also result in revisions to the overall fundamental objective. The completeness within the fundamental hierarchy is achieved when all high level objectives are fully specified (i.e. can be measured) without double counting (Keeney 1992, p. 85). The relationships between the levels of the fundamental objectives hierarchy are not causal (i.e. financial skills do not cause growth but explain what is meant by growth) since the links drill down to what is meant by the objective. Means objectives have the role of explaining what causes the fundamental objectives to be achieved (Keeney 1994, p. 34). For example, mentoring will cause an increase in client management skills thus causing the fundamental objective to be achieved. Therefore, means objectives are structured as an objectives network at the next level of abstraction within the VFT framework. Keeney uses the term means-ends objectives network to describe the means objectives network. The means-ends objectives network does not aim to be a “collectively exhaustive representation of the means to the high-level ends” as some causes are outside of the scope of business operations (e.g. weather, war, interest rates, etc). Since this network does not form a hierarchy, it is unnecessary to enforce a minimum of two lower level objectives per each high level means objective (Keeney 1992, pp. 78-79). Furthermore, unlike a hierarchical structure, the network structure allows “complex interrelationships” between objectives (Keeney 1992, p. 78). Just like with the fundamental objectives hierarchy, the process of identifying means objectives is also iterative, with a potential to influence the fundamental objectives hierarchy as means objectives and relationships between them are iden-

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4 Business Objectives Modelling

tified. Within the means-ends network, completeness is defined by Keeney (1992, p. 80) as the complete specification of “all the means to achieve that [overall fundamental] objective” thus providing an explicit or implicit set of alternatives for the fundamental objectives hierarchy. One of the benefits of clearly identifying means objectives is to avoid duplication in the fundamental objectives hierarchy caused by either “double-counting the possible impacts of alternatives” or “double-counting the values of those impacts”. These problems are usually caused by including means objectives in the fundamental objectives hierarchy (Keeney 1992, p. 85). For example, performance management is likely to have an impact on both growth and recognition and therefore should not be included in the hierarchy, however if it is included it will have to be duplicated in both growth and recognition branches of the fundamental hierarchy. Within the VFT framework, the structure and measurement of the means objectives is primarily aimed at assisting development of the structure and forming an understanding of the fundamental objectives hierarchy as well as providing a link between alternatives and fundamental objectives. Therefore, the means-ends network contains only a limited representation of logical or influencing relationships between means objectives. For example, it is assumed that all lower level means objectives need to be satisfied in order for the parent objective to be satisfied. Temporal relationships within the means network are also not represented, that is there is no mention within the framework as to whether these objectives must be satisfied simultaneously or within a certain time period. Further insight into the relationship between objectives within the VFT framework is provided by the identification procedures. Keeney (1994, p. 34) explains that the process of identification consists of a number of steps. At first, he suggests to hold a “discussion with relevant decision makers and stakeholders”. Keeney warns (1994, p. 34) that objectives ascertained in this step “will contain many items that are not really objectives. It will include alternatives, constraints, and criteria to evaluate alternatives.” To convert these initial statements into objectives, it is necessary to identify “a decision context, an object, and a direction of preference” for each of the statements. Keeney (1994, p. 36) stresses that the aim of the objectives identification is “to get a list of objectives that are deeper than “motherhood and apple pie” objectives, which everyone can agree to, but which provide little strategic guidance”. To assist with the identification of objectives within the VFT framework, Clemen and Reilly (2001, p. 49) provided a set of simple questions (Table 4.1) that can be used to build the structure in a top-down or bottom-up manner. Once the objectives are stated, these questions are used to distinguish between fundamental and means objectives and to relate objectives to each other (Keeney 1994, p. 34).

57 Table 4.1 How to construct means-objectives networks and fundamental-objectives hierarchies from Clemen and Reilly (2001, p. 49, fig. 3.3) Fundamental Objectives

Means Objectives

to move: downward in the hierarchy: What do you mean by that? ask:

away from fundamental objectives:

to move: upward in the hierarchy:

toward fundamental objective:

ask:

How could you achieve this?

Of what more general objective is this an Why is that important? aspect?

In situations where there are multiple stakeholders, Keeney suggests (1994, p. 36) that the objectives are first structured for each individual and then integrated “into a common set of objectives”. The integrated list can be broader than either of the initial lists, but “a consensus that these strategic objectives [which are] appropriate for the organization” must be reached (Keeney 1994, p. 37). The resulting structure (illustrated for a subset of objectives in the DialAmerica example in Figure 4.2) clearly differentiates between abstract and causal relationships between objectives (Hurri 2000, pp. 31-32). People will always be the heart of our success

Value

Fundamental Hierarchy

Safe & healthy work environment

Means-Ends Network

Equitable policies

…

…

Integrity in the workplace

Fair & flexible pay & conditions

Integrity in staffing

Comply with legal EEO/OHAS & industrial laws

Ensure respect for ethical & people values

Integrity in training

Staff understand their rights & responsibilities

Equitably achieve training targets

EEO recruitment target met

Fig. 4.2 VFT structure for a subset of objectives in DialAmerica example

The link between the two structures can be made at the highest level of the objectives hierarchy (i.e. the collective achievement of means objectives results in the achievement of the highest level fundamental objectives) or between individual lower level fundamental objectives and the means objectives that provide the

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4 Business Objectives Modelling

means to achieving them (Keeney 1992, p. 91). For example, Keeney (1994, p. 37) illustrates the interrelationships between objectives with arrows “from one objective to another indicat[ing] that achieving the former objective has a major influence on (i.e., is a means to) achieving the latter”. Multiple means objectives in the structure can contribute to one fundamental objective and each means objective can contribute to multiple fundamental objectives as long as these links are able to “be quantified based on available factual data and judgement” (Keeney 1992, p. 91). In situations with many objectives, the structure can quickly become unwieldy. Keeney avoids this problem (Keeney 1994, p. 37) by only including the top level means and fundamental objectives in the graphical representation of the structure. The decision alternatives that have been constructed as a result of thinking through the values, fundamental objectives and the means of achieving them reside at the bottom abstraction level of the VFT framework. This level is not always explicitly articulated in the structure and is sometimes implied through the means-ends network. While objectives are not the sole stimuli for creating alternatives, the objectives structure nevertheless plays an important role in removing “inappropriately narrow anchors for creating alternatives by increasing the breadth of concern” Keeney (1992, p. 204). As discussed, the process of deriving the VFT structure is iterative and is referred by Keeney (1992, p. 46) as the push-pull process of moving upwards in the means-ends network from the specific decision context and downwards from the strategic objectives until “a set of fundamental objectives for the given decision is achieved and the initial rough-cut of decision alternatives is broadened to increase the range of potential alternatives, and to match the decision context to fundamental objectives.” While Keeney (1994, p. 37) chooses to use upward movement as the direction for arrows connecting objectives within the structure, downward arrows from one objective to another can also be used to indicate that the former objective is influenced by the achievement of the latter objectives. The importance of the resulting VFT framework is that it forms a bridge between qualitative decision making which is “formulated in general terms, dependent on uncertain assumptions and subject to change” while taking into account “cognitive attitudes such as knowledge, beliefs, desires, goals and intentions” and drawing on the classical decision theory capabilities to “conceptualize[s] a decision as a choice from a set of alternative actions” (Dastani et al. 2005, pp. 762763). Value focused thinking facilitates the identification of alternatives in a way that links them to the strategic objectives of the business (through means objectives) overcoming “the shortcoming of alternative focused thinking” by expanding the focus from “solving the problem” to “problem finding” in other words from a reactive to proactive approach to decision making (Keeney 1992, pp. 44-47). Once identified, the alternatives provide a link to decision theory methods that allow optimal or satisficing solutions to be found (e.g. Olson 1996, p. 5; Dastani et al. 2005). The multi-attribute utility theory (MAUT) that Keeney links to the VFT is “the most theoretically accepted approach” (Olson 1996, pp. 5-6) in the context

59

of “reasoning about multiple objectives, which may conflict” (Dastani et al. 2005, p. 16). The MAUT provides the means for quantitative evaluation of the qualitative statements of fundamental objectives made within the VFT framework by: • quantifying the objectives with the help of attributes; • using the attributes to define consequences of our choices and criteria for meeting the objectives; • facilitating comparison among conflicting objectives by defining the trade-offs between the objectives; and • combining attributes, trade-offs and consequences in a value-model that provides a general structure to enable the choice of the best alternative to meet the fundamental objectives within the VFT framework. The definitions of these concepts are included in the description of the VFT framework as they provide the quantitative expressions to the qualitative statements of objectives within the VFT framework. However, the strength of the VFT framework is that it (without being limited to a single decision making theory) provides a generic framework for identifying and structuring business objectives in a systematic and logically consistent way (Keeney 1992, pp. 77-82). 4.2.1.4 Attributes, Consequences and Criteria The qualitative statement of objectives is sufficient to start identifying decision alternatives but it is not precise enough to enable evaluation of the consequences of our choice. To allow such evaluation, two further terms are defined: attribute and criterion. Attribute measures the degree to which an objective is achieved (Keeney 1992, p. 100) and can also be referred to in the literature as measure of effectiveness, measure of performance (e.g. key performance indicator) and sometimes criterion. For example, the attribute of the “integrity in the workplace” objective may be quantified by the number of harassment and unfair treatment complaints that have been made. The term attribute, in its more general sense (i.e. a quality or a feature) is used in the business process modelling context (e.g. Davis 2001). The formal model definition of the attribute within the context of Information Science (Weber 1997, pp. 34-35) as a representation of a property is similar in concept to Keeney’s definition since measurement is a way of representing the performance. Keeney (1992, pp. 101-110) identified three types of attributes being natural, constructed and proxy. Natural attributes are defined by Keeney (1992, p. 101) as “those in general use that have a common interpretation to everyone”. For example, dollar profit value may be considered as a natural attribute of maximizing profit objective. In many cases such attributes are not available or carry an implied value judgment that may be considered inappropriate in a specific decision context. In these cases, a constructed attribute designed specifically for the purpose of

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measuring the objective of interest, may be more appropriate. For example in the case of the integrity in the workplace objective, a constructed attribute may be a measure of staff opinion about the integrity in the workplace derived from a specially designed questionnaire. Keeney (1992, p. 102) notes that attributes that have been initially constructed for a specific objective may, after years of use, take on the features of natural attributes. The proxy attribute is the attribute of last resort as it provides an indirect measure of the objective, therefore compromising on the ability to adequately evaluate the consequences of decisions against that objective (as is the case with the number of complaints attribute). However, in practice, direct measures are not possible to construct in many situations due to costs, ethical or other valid considerations. In those circumstances, an indirect measure is required in order to provide an indication of the level of achievement of the objective of interest. Irrespective of the attribute type, an unambiguous measurement scale must be associated with each attribute to ensure that it is measurable, operational and understandable (Keeney 1992, p. 112). Attributes play an important role in the structure of the VFT framework as the specification of the fundamental objectives is only considered complete once “reasonable attributes can be found” for these objectives (Keeney 1992, p. 80). Furthermore, Keeney (Keeney 1992, pp. 130-131) recommends that the analysis should be performed at the highest level of the hierarchy at which the attributes are specified irrespective of the level of decomposition of the fundamental hierarchy. By defining a consequence as a vector of attribute values that is achieved as a result of choosing a particular alternative, the consequences of decisions can be quantitatively evaluated and compared. For example, if the number of complaints is reduced by 50% as a result of an awareness program, that program can be considered as successful. The term criterion draws on the concept of attribute and is defined as an expression of the value with respect to a specific attribute of an objective for the orientation of preferences provided for that objective. For example, an integrity criterion may be set as a maximum of 10 complaints a year. Unlike most authors within decision analysis (e.g. Olson 1996, p. 9), Keeney (1992, p. 60) uses this definition to describe the term goal. Keeney’s approach is not followed in the remainder of the book to avoid confusion that may result from interchangeable use of the term goal with the term objective in many contexts. The concept of the constraint is defined (Keeney 1992, p. 60) as a reciprocal of a criterion in a sense that constraints are designed to eliminate unacceptable alternatives (e.g. in excess of 50 complaints a year) rather than assist with the selection of the best alternative from a set of acceptable alternatives. Within the VFT framework, the measurement discussion is focused on the measurement of fundamental objectives, as these are the objectives that reflect value judgments. Consequently, Keeney (1992) discusses how to measure the achievement of fundamental objectives using attributes that are specified by the fundamental objectives in detail, but leaves open the question of how to ensure

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that these attributes are reflected in the measurement functions of the contributing means objectives (Keeney 1992, p. 99). 4.2.1.5 Trade-offs Attributes, criteria and consequences provide much information about the differences in outcomes from the various alternatives. However, in the context of multiple objectives this information may be insufficient to select a single alternative that satisfies all of the objectives. In this case, the relative importance of objectives must be assessed and quantified. Within decision analysis the term trade-off is often used to describe the result of this process and is understood to be the amount of change required in the value of one attribute in order to achieve a specified change in the value of another attribute (e.g. Winston 1994). For example, the trade-off between profit and integrity which may only be a 50% increase in integrity may be achieved as a result of 1% decrease in profit. Keeney (1992, pp. 180-182) differentiates between two types of trade-offs – factual and value tradeoffs: The former concerns the “facts”: how much of one attribute must I give up to get a specified amount of another attribute. The latter concerns values: how much of one attribute would I be willing to give up [in order] to get a specified amount of another attribute.

The value trade-offs are made as a result of individual (in case of individual decision-maker) or combined (in case of multiple decision-makers) value judgments about the relative importance of fundamental objectives (e.g. max profit vs max integrity in the workplace). The discussion of individual techniques and tools aimed at assisting with making and quantifying these judgments can be found in Keeney (1992, ch.5 and ch. 8). The factual trade-offs, on the other hand, are used to evaluate the impact of means objectives on the fundamental objectives. 4.2.1.6 MAUT Value Model Whereas attributes provide a measure of achievement of individual objectives, the purpose of the value model is to provide a “general structure to combine the various attributes in some proper manner” (Keeney 1992, p. 131) in order to “clarify many complex and intertwined issues about values” (Keeney 1992, p. 129). The need for such clarification is especially evident when it is not possible to make a choice that will yield the best possible consequence for each of the decision objectives or when the issues of risk are present in the decision situation thus requiring an assessment of whether the potential benefit of a particular action is worth the risk of an undesirable outcome associated with that action. As those two scenarios cover most of the real-life decision situations, the value model is required to ensure that the consequences of our decisions concord with “what we care about”.

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Keeney uses the term objective function interchangeably with the term value model. In order to differentiate between the concept of the value function and a more narrow concept of a quantity to be optimised (usually referred to as objective function within the OR community (e.g. Winston 1994)), the term objective function is not used in the context of the MAUT. Keeney (1992, p. 129) defines the value model as a model that assigns a number to each consequence such that the number assigned indicates the relative desirability of the consequences and can be used to derive preferences for alternatives. Depending on the decision situation, the value model may be either a measurable value function or a utility function. A measurable value function simply assigns higher numbers to preferred consequences and can only be used to derive preferences for alternatives in situations where there is no uncertainty and each consequence can be linked to a single alternative. While Keeney only refers to measurable value functions in the contexts when uncertainty is not present, the form of this function is consistent with a belief-ranking function (that incorporates uncertainty in a qualitative way by ordering preferences) within the scope of qualitative decision theory (e.g. Dastani et al. 2005, p. 6). In circumstances where there is uncertainty and more than one consequence can arise from an alternative, Keeney defines (1992, p. 132) a utility function as a function constructed in a manner such that, if one accepts a set of logical principles (see, for instance, von Nuemann and Morgenstern, 1947; Savage, 1954; and Pratt, Raiffa, and Schlaifer, 1964), the expected utility derived for each alternative is an indication of its relative desirability.

Olson (1996, p. 4) describes the simplest form of the utility function as follows: K

Utility =

∑w a

k ik

, where for alternative i of N alternatives, K is the number of

k =1

objectives (assumed to be single-attribute), wk is the relative weight of objective k (indicating the value trade-off) and aik is the value of the attribute of the objective k when alternative i is selected. A more generic form of a utility function was defined by Dastani et al. (2005, p. 5) as follows (note that the definition has been modified to be consistent with the terminology adopted in this chapter): Let A stand for a set of alternative actions (or alternatives). With each action, a set of consequences is associated. Let W stand for set of all possible consequences. Let U be a measure of consequence value that assigns utility U(w) to each consequence w ∈ W , and let P be a measure of probability of consequences conditional on actions (alternatives), with P(w|a) denoting the probability that consequence w comes about after taking action (choosing alternative) a ∈ A in the situation under consideration. The expected utility EU(a) of an action (alternative) is the average utility of the consequences associated with the action (alternative), that is EU( a ) =

∑

w∈W

U( w )P( w | a ) .

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A number of methods are available that assist with the identification of the relative utilities of each attribute and quantifying trade-offs. Olson (1996) provides an overview of these methods and corresponding software tools. Belton and Stewart (2002, p. 9) identify three broad categories of MCDA models: value measurement models (already discussed); goal, aspiration or reference level models; and outranking models;. While within the VFT framework the link between qualitative objective structure and quantitative decision making is made through the value model, a similar link can be easily made to other types of MCDA models. The VFT structure provides the framework of objectives that are then evaluated and compared using the MCDA methods discussed. The importance of measuring means objectives is acknowledged by Keeney (1992) however the details of how this should be done are not discussed in the context of linking VFT framework and MAUT. 4.2.2 Balanced Scorecard Within the Business Performance Measurement context, the VFT structure can be used to link the operational KPIs (or attributes) to the strategic objectives of the business. For example, the Balanced Scorecard (BSC) approach to business performance measurement introduces a generic objectives structure for the fundamental objectives which are then linked to operational measures (i.e. attributes of means objectives) of the business. The BSC approach proposed by Kaplan and Norton (1996) to “translate strategy into action” can be used to provide a link from a strictly decision making context to a wider Business Management context using the VFT framework. In its essence, the BSC is an approach to measuring business performance by linking the individual measures to the strategic objectives of the business with the aim of assisting the business to “align their resources and their efforts to the overall strategy, in turn driving the organization towards fulfilment of its future goals” (Jarrar 2004, p. 1). It is widely acknowledged (e.g. Belton and Stewart 2002) that one of the major contributions of the BSC to the measurement and control of business operations is in the expansion of business focus from traditional financial performance measures to corporate non-financial measurements grouped into four strategic categories (referred to as perspectives by the founders of the BSC Kaplan and Norton (1996)): financial, customer, internal-business-process, and learning and growth. These categories are expanded according to the business type (e.g. private or public) and the operational stage of the business (e.g. growth, sustain or harvest). According to Belton and Stewart (2002, p. 323) BSC “is essentially a generic multicriteria model at a strategic level, built to support ongoing measurement and management.” It is also consistent with the VFT framework of structuring fundamental objectives within the categories, and as such, the BSC can be considered as a special case of the VFT where the fundamental objectives structure is con-

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strained to the four perspectives. While the fundamental hierarchy is explored within the BSC framework, its focus is more on identifying and measuring means objectives and their links to the strategic objectives through cause-and-effect relationships, thus providing a useful tool for further insight into the means-ends objectives network and its connection to the fundamental objectives hierarchy. Since the publication of the BSC framework by Kaplan and Norton in 1992, it has been implemented in a wide range of business systems and adopted by organizations within public, private and non-profit sectors (e.g. Belton and Stewart 2002, IDS Scheer AG 2006, Kaplan and Norton 2001b). Therefore, by using the VFT as the foundation for the formulation of the BSC both the BSC and the VFT frameworks would benefit – BSC from the theoretically sound and well-defined links to the MCDA and the VFT from the access to a wider business audience and tools for measurement of the means objectives network within the framework. 4.2.3 System Dynamics When dealing with real-life business systems, most people think about complexity in terms of the number of components in a system or the number of combinations one should consider for effective decision making – so-called, combinatorial complexity (Sterman, 2000). Sophisticated algorithms of combinatorial optimisation nature are designed for a number of classes of problems, including scheduling, assignment, transportation, etc., where the complexity lies in finding the best solution out of an astronomical number of possibilities (Nemhauser and Wolsey, 1988). As indicated by Sterman (2000), dynamic complexity can arise in simple systems with low combinatorial complexity, due to the interactions among agents over time. The famous Beer Game (Sterman 2000) provides an excellent example of such situations as most real-life business systems are dynamic, tightly coupled, governed by feedback, often nonlinear, history dependant and policy resistant, and, almost always, counterintuitive by their very nature. They appear in the class of dynamically complex systems (often in addition to being combinatorially complex). The dynamic complexity is generally well appreciated by those applying SD methods to organizational planning and problem solving. In particular, Rosenhead (1989, p. 10) states that “...optimal solutions to individual problems cannot be added to find an optimal solution to the whole mess: the behaviour of the mess will depend on how the solutions to its various parts interact.” Identifying the dynamics of the relationships between business objectives is the first step towards understanding the “mess”. SD (referred to as business dynamics when applied to solving business problems (Sterman 2000) is used both for decision making (Clemen and Reilly 2001) and structuring business processes (Giaglis 2001) using causal loops and stock and flow diagrams. Causal loops (CL) represent the “interdependencies and feedback processes” (Sterman 2000, p. 191) within the business, while stock and flow (SF)

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diagrams represent “the state of the system and generate the information upon which decisions and actions are based” (Sterman 2000, p. 192). The causal relationships are pictured by linking the quantities with arrows and noting the sign of the relationship (as positive or negative). Arrows start at the quantity that causes changes and end at the quantity that changes in response. The stocks are represented as rectangles while the flows come in or out of the stock with the rate of the flow represented as a tap. A simple systems dynamic model for the DialAmerica (DialAmerica Marketing 2004) example is illustrated in Figure 4.3.

development rate +

stock of skills

separation rate -

% salary inc rease -

$ spent on training Fig. 4.3 System dynamics model for DialAmerica example

In this illustration, the separation rate is used as an attribute of the staff satisfaction objective, the development rate is used as an attribute of the staff development objective and salary increase is used as an attribute of staff recognition objectives. The stock and flow diagram in Figure 4.3 illustrates and quantifies causal relationships between these objectives. For example, as more resources are spent on development, fewer resources are available for recognition thus causing a decrease in staff satisfaction. By using simulation techniques available within the SD frameworks, the impact of changes in the levels of lower level objectives on the higher level objective can be evaluated. This approach to business modelling allows for representation of time delays and non-linearities inherent in the dynamic nature of a business whilst the rigorous mathematical foundation for system dynamics makes it possible to develop a seamless link from a business model to a simulation model for quantitative evaluation of “what if” scenarios. Furthermore, the strong emphasis on causality within the system dynamics framework provides solid foundations for decision analysis by highlighting causal and feedback mechanisms within the organization and its wider environment. There are no separate objectives models within SD since objectives are represented within both CL and SF diagrams as “concrete targets”. These targets (also commonly referred to as Key Performance Indicators (KPIs) guide corrective action if the actual performance of the system falls short of a satisfactory outcome.

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KPIs are derived by quantifying organizational objectives with aim of reducing complexity associated with solving optimisation problems and in accordance with the principle of bounded rationality (Sterman 2000, ch. 15). In the context of the VFT framework definitions, KPIs represent attributes of business objectives and therefore provide a link from a dynamic structure of CL or SF diagrams to the VFT objectives structures. In doing so, a framework is provided that is able to describe complex interactions and feedback mechanisms between business objectives while supporting the use of quantitative and qualitative decision modelling (Sterman, 2000). As well as being important decision support tools in their own right, SD tools have been used in combination with highly prescriptive mathematical models (such as Markov chains or Multiple Criteria Decision Analysis) in order to overcome the limitations of an individual modelling technique so that better decision support can be provided within overall business or social context (for example, Brans et al. 1998, Gregoriades and Karakostas 2004, Santos et al. 2001).

4.3 Qualitative Objectives Modelling The objectives models discussed so far focused primarily on the understanding of business objectives within the context of decision making based on quantitative information. Within business context business objectives models also play an important part as a qualitative guide for the design of business systems and processes they support (e.g. Kavakli 2004, Rolland and Prakash 2000). These objectives models have been developed independently from and with a different focus to decision analysis objectives models (and often independently from each other) and use their own terminology and tools that are reviewed next. 4.3.1 Requirements Engineering Supporting organizational change resulting from transition between “as is” and “to be” process models is considered to be “the overriding purpose of requirements development for business processes” (Loucopoulos 2003, p. 2). Even though there is no “uniform notion of goal in RE” (Yu and Mylopoulos 1998, p. 1), definitions of goals within RE usually refer to statements of intention that are consistent with the definitions of the objectives previously discussed. For example, Kueng and Kawalek (1997, p. 20) define goals as “statements which declare what has to be achieved or avoided by a business process”. Yu and Mylopoulos (1998, pp. 15-16) also identify goals as “an important construct in a number of different areas of RE” and include (among others) the following uses of goals within the RE context: “elicitation and elaboration of requirements”, “relating requirements to organizational and business context”, “clarifying requirements”, “dealing with competing demands and conflict”, “connecting requirements to design”, and

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“traceability of rationales, management of change, verification of achievement requirements, and process support”. The variety of purposes for modelling objectives, alluded to above, results in a variety of objectives models and frameworks that incorporate these models into a broader organizational model. However, as noted by Kavakli (2004, p. 1340) in most cases a goal model is represented as an organized structure of goals, typically forming a tree or network describing the ‘causal transformation’ of general goals into one or more subgoals that constitute the means of achieving desired ends.

The overlap between objectives models that originate in different disciplines is clearly evident in this statement. Without referring to objectives modelling in the context of decision analysis, this description of objectives structures mirrors the means objectives structures discussed within the scope of the VFT framework. If there was any doubt left about the similarities between these structures, the framework for identification of business goals proposed by Kavakli (2004) confirms these conclusions. Kavakli (2002, p. 238) reviews a number of RE approaches (including ORDIT, i*, ISAC, F3, GDC, KAOS, GBRAM, NFR, GQM) that “implicitly or explicitly, represent the goals of individuals, groups or organizations, in order to describe organisational behaviour” without describing the structure of the goal models themselves in detail. Lamsweerde (1998, 2001), on the other hand, focuses his reviews on the elements of goal models within the RE discipline using the terms objectives and goals interchangeably. Lamsweerde (2001, p. 251) lists the following dimensions for objectives classification according to objectives type: functionality, verification, temporal, system state and objectives level. Within the functionality dimension the objectives are divided into functional objectives that refer to “services that the system is expected to deliver” including the ability of a system to satisfy requests and provide required information; and non-functional objectives that refer to “expected system qualities such as security, safety, performance, usability, flexibility, customizability, interoperability and so forth”. The verification dimension is concerned with whether objective “satisfaction can be established through verification techniques”. If it can, then the objective is referred to as a hard goal, otherwise the objective is categorised as a soft goal, (e.g. Soffer and Wand, 2005). Temporal behaviour of the objective is classified into three classes: achieve (or cease) objectives that “require some target property to be eventually satisfied in some future state (resp. denied); maintain (or avoid) objectives that “require some target property to be permanently satisfied in every future state (resp. denied)”; and optimise objectives that favour behaviours “which better ensure some soft target property”. Similarly, system state and objective level dimensions classify objectives according to desired system states and goal levels.

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According to Lamsweerde (2001, p. 251) name, specification, priority, utility and feasibility are the five objectives attributes that can also be used to characterise objectives within the requirements engineering context. Requirements engineering goal models cater for a variety of goal modelling structures using different types of links to “relate goals (a) with each other and (b) with other elements of requirements models” (Lamsweerde 2001, p. 251). AND/OR refinement graphs are widely used to describe logical relationships between objectives. Refinement is referred to as a set of sub-goals that either positively or negatively support a parent goal, in other words, an influencing relationship is implied with the refinement concept. AND-refinement describes situations where “satisfying all subgoals in the refinement is a sufficient condition for satisfying the [parent] goal” (Lamsweerde and Letier 1998, p. 55), whereas OR-refinement means that “satisfying one of the refinements is a sufficient condition for satisfying the [parent] goal” (Lamsweerde and Letier 1998, p. 55). A refinement of a “maximizing personal growth and recognition” objective (DialAmerica Marketing 2004) into “maximizing personal growth” and “maximizing recognition” objectives is an example of an ANDrefinement. Whereas a refinement of an objective “maximizing personal growth” into “provide external training” and “provide internal training” objectives is an example of an OR-refinement, since this is a situation where either one or both types of training are sufficient to satisfy personal growth objectives. Within this context a conflict link (i.e. a negative influence link) is introduced by Lamsweerde (2001, p. 252) for situations when “satisfaction of one of them may prevent the other from being satisfied”. For example, if spending resources on training may result in insufficient resources for recognition, a conflict link will have to be used between these two objectives when they are linked to the high level objectives. While these definitions refer to objective satisfaction, Lamsweerde (2001) provides alternative definitions in terms of objectives satisficing guided by the principle of bounded rationality. In addition to AND/OR refinements, Rolland and Prakash (2000, p. 182) defined an exclusive-OR relationship between goals (i.e. XOR-refinement). According to that definition, “in cases of an exclusive OR relationship” goals are bundled into alternatives that are applicable in a given situation, as opposed to “one or several being applicable in a given situation”. Note that the term alternative is used here in a sense of exclusive choice rather than according to the definition of the alternative provided in the VFT context. An example of an exclusive choice relationship is when a choice has to be made between external and internal training if there are insufficient resources to deliver both or they deliver the same outcomes. It is evident from these examples that unlike the VFT, the RE goal models do not clearly separate between abstract relationships (depicted within the fundamental objectives hierarchy in the VFT framework) and causal relationships (depicted within the means ends network in the value-focused framework). As a consequence, functional and non-functional goals cannot be related to each other without some confusion (Hurri 2000, p. 34). For example, training and resource objec-

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tives are treated in the same way as the growth and reward objectives in the RE goal model. On the other hand, an RE model provides a much more comprehensive structure for the objectives network by: • including logical and temporal relationships that are not explicitly represented within the VFT framework (e.g. within the VFT model it was not possible to separate whether the training types are complementary or alternative); and • linking objectives models to business systems and processes represented within the RE through models of operations, scenarios, objects, agents and organizational policies (Hurri 2000, Lamsweerde 2001). An example of potential benefits of such links is a three-dimensional framework S3 proposed by Loucopoulos (2003) that is aimed at integrating the modelling of “business system and the factors that influence decisions according to which the service levels of the support system are set”. This framework is one of the few examples where RE models are linked to decision analysis models (in this case with the help of SD tools), although in this case the link to objectives is not made clear. Nishit (2002, p. 66), on the other hand, makes a suggestion to link objectives models to quantitative evaluation mechanisms with the help of the MCDA tools (similar to the VFT/MAUT link). While the focus of most RE goal models is on business goals in relation to the system specification and design (Kavakli 2002, p. 239, table 1), the features of the generic goal models provide a comprehensive objectives modelling framework that is not limited to the RE application (e.g. El-Gizawy and El-Sayed 2002). This will be evident from the discussion in the next section. 4.3.2 Business Process Modelling A number of business objectives models have been developed outside the RE field to complement business models aimed at describing, automating, analysing and designing business processes for the purposes of improving efficiency and effectiveness of business operations. In this context, the primary driver for objectives modelling is alignment of business processes and business objectives (e.g. Neiger and Churilov 2006, Richmond 1997). Historically, business process models were concerned primarily with business automation (workflow modelling) consequently the emphasis on goal-oriented approaches to business process modelling is relatively recent. For example, the 2002 Workshop on Goal-Oriented Business Process Modelling (e.g. Bider 2002) and the 2005 special issue of Business Process Management Journal on Goal-Oriented Business Process Modelling (e.g. Kueng 2005, Nurcan et al. 2005, Heinrich 2005) highlight the increasing recognition, within the business process modelling community of the importance of goal models for effective business process modelling and of the need for a common understanding of the concept of goal or objective within business process modelling context.

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At the workshop, two types of business goals which can be associated with a business process were distinguished (Bider 2002, p. 1): • strategic goals that explain “why the process exists/should exist in the organization, and why it should be driven in a certain way”; and • operational goals that “concern process instances, and … show when a given process instance can be considered as finished”. Similarly to fundamental and means objectives, strategic and operational goals describe a different dimension of the same process and therefore must be clearly differentiated within a single process. Mirroring the VFT (but replacing the decision context with a process level), strategic goals of a lower level process may be considered as operational goals of a higher level process creating a network of objectives. In the context of Business Process Modelling, a number of approaches have been suggested that include goal models (e.g. Rolland and Prakash 2000; Khomyakov and Bider 2001, Rummler and Brache 1995, Yu and Mylopoulos 1994, Yu et al. 1996). These approaches have been developed independently of each other and do not necessarily comply with the above definitions of goal. What they do have in common, is the recognition of the importance of goals for efficient and effective business processes and the consequent focus on synchrnonizing business processes with their goals (Neiger and Churilov 2004, 2006). The representation of goals, however, varies substantially from descriptive or formal representation of goals within existing process models to comprehensive frameworks “that capture the intentional dimension of organizational work” (Yu and Mylopoulos 1994, p. 2). For example, Rummler and Brache (1995, pp. 20-25) introduce four levels of goals reflecting different organizational perspectives: • organazational goals that are part of the business strategy; • process goals that are derived either from organizational goals or the needs of internal customers; • functional goals that represent a subset of process goals but are limited to the contribution that each function needs to make to the key processes; and • job goals that reflect outputs and standards of individual jobs supporting the processes. While providing a useful insight into the different types of goals within the organization, Rummler and Brache (1995) do not include a formal or graphical representation of goals. Often, business process modelling approaches that do include formal goal representations draw on the established Requirements Engineering goal modelling techniques to represent and define goals (e.g. Bleistein et al. 2006, Robinson and Pawlowski 1999, Katzenstein and Lerch 2000, Koubarakis and Plexousakis 2000, 2002, Kueng and Kawalek 1997, Rolland and Prakash 2000, Yu and Mylopoulos 1994). Some of these goals were developed in the context of representing social context of business processes (e.g. Katzenstein and Lerch 2000) through recognising and relating objectives of competing interests within an

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organization (e.g. strategic, operational, individual, customer). Others are more concerned with linking objectives to the actors responsible for achieving them (e.g. Yu and Mylopoulos 1994) or actions representing means of achieving these objectives (e.g. Kueng and Kawalek 1997). The EKD framework (Bubenko et al. 1998) developed as part of the ESPRIT program integrates a goal model (based on a generic RE model) with a suite of other business models including a business process model. Within this context, a business goal is defined as “a desired state of affairs that needs to be attained” that “may be expressed as a measurable set of states, or as general aims, visions or directions” (Bubenko et al. 1998, p. 25). A distinctly different approach to goal modelling utilises the concept of a goal as an intermediate of the final state of a business process where the business process is defined as a “flow of activities” and is viewed as a “trajectory in the space of all possible states” Khomyakov and Bider (2001, p. 2). While it is possible to develop a formal concept of a goal within this context (e.g. Weber 1997), business process modelling draws on RE goal models and does not provide independent goal models that enable the relationship between goals to be described (Koubarakis and Plexousakis 2000, 2002).

4.4 Desirable Properties of Objectives Models Discussion in the previous sections highlighted the multidimensional nature of the objective concept, with each of the objectives modelling techniques having its own emphasis. A complete objective model would ideally include all of these perspectives. In “A study on Goal-Oriented Business Process Modelling”, Nishit (2002) identified the following elements as being important to represent business objectives: objective concept, abstraction levels, relationships between objectives including logical, causal and influencing relationships, and an evaluation mechanism to enable an assessment of the level of achievement of different objectives. While the first element – objective concept – may seem redundant, the differences in the definitions discussed in the previous section highlight the need to explicitly include this element in the framework. Within the quantitative models discussed, the concept of the objectives is precisely defined within the VFT/MAUT framework with the SD and BSC concepts consistent with these definitions. The presence of abstraction levels (separate from the relationship structure of the objectives model) is necessary in order to differentiate the high level objectives reflecting organizational values from the lower levels means of achieving these objectives. These levels are clearly separate within the VFT framework, whereas within RE goal models they are not. These abstraction levels reflect the basic sub-ordinate relationship between the levels of objectives and are referred to as refinements within the RE context (Lamsweerde 2001). However, within these models separation between strategic and operational objectives is not clearly articulated. The nature of the abstract relationship can be considered from three angles: logical, causal and influencing.

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Logical relationships are links between the levels of abstraction that are defined using logical connectors AND/OR/XOR. Within the RE objectives models, these relationships are clearly defined based on the satisfaction requirements of the higher-level objective by the lower level objectives. For example, if all lower level objectives must be satisfied in order for the high level objective to be satisfied and the satisfaction of the lower level objectives is sufficient for the satisfaction of the high level objective, the two refinements must be linked with an AND connector (Haumer et al. 1998, Lamsweerde and Letier 1998, Rolland and Prakash 2000, Yu and Mylopoulos 1994). The OR connector is often referred to as a choice relationship between the objectives refinements, as it occurs when satisfying one of the refinements is sufficient for satisfying the parent objective (Haumer et al. 1998, Lamsweerde and Letier 1998, Yu and Mylopoulos 1994). Objectives within an OR-refinement are often characterised by having the same intention but different non-mutually exclusive means of achieving it (Rolland and Prakash 2000, p. 182). An XOR refinement is not usually used within the RE objectives models and is more common in the process models to differentiate between the alternative (but not equivalent) paths. This definition can also be applied to the objectives structure where the XOR connector is defined as a different and mutually exclusive means of achieving the high level objectives. These definitions of connectors are based on a concept of satisfaction. More relaxed definitions using concepts of bounded rationality and satisficing may be more appropriate in some circumstances (Lamsweerde 2001, Simon 1979). Quantitative models do not generally define logical relationships as part of their objectives structures, relying instead on the quantitative relationships expressed through quantitative models (e.g. tradeoffs in MAUT). Causal relationships are a generalisation of the logical relationships where the causal link between the abstraction levels indicates that the achievement of lower level objectives leads to the partial or complete achievement of the high level objective (Nishit 2002, p. 48) without specifying the nature of achievement required at the lower level. The logical relationships, as defined above, are causal relationships, therefore any model that includes logical relationships represents the causality as well. Both quantitative and qualitative models include causal relationships, either through logical connectors (RE Model) or separate models aimed at identifying causal relationship (SD model, means-ends network). Influencing relationships are a special case of causal relationships defined by Nishit (2002, p. 49). In this case the achievement of a certain objective does not directly lead to the achievement but influences some other objective in a positive or negative way. The ability to represent these types of relationships is important to uncover causal loops and feedback mechanisms within the objectives structure. Influencing relationships can be explicitly represented within the RE model using special objectives links to indicate the type of influence. Within quantitative models the direction of influence is expressed quantitatively with the help of MCDA tools or qualitatively using SD tools.

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An additional type of relationship that is not represented in Nishit’s framework is the temporal relationship. While this relationship may not be of great importance within a purely objectives context, this relationship becomes important when considering objectives in a time-dependent business operations context. As discussed, the RE model is able to represent limited temporal relationships, however temporal relationships are usually ignored within the quantitative class of models except in the simulation applications of SD. The evaluation mechanism provides the last element of Nishit’s framework. In the context of the definitions discussed, this element must include KPIs/attributes, criteria and constraints and an appropriate quantitative decision model (such as an MCDA model) to allow evaluation of whether a particular objective has been achieved. Within the quantitative dimension, the BSC is an effective tool for monitoring and controlling business performance. The qualitative models usually limit the coverage of the evaluation dimension to the mention of measurement criteria without exploring the nature of these criteria or developing mechanisms for combining the criteria into performance measures suitable for evaluation of high level objectives. Table 4.2 Comparison of quantitative and qualitative objectives models Objective Model Dimension Concept of objective

Relationship

Abstraction level

Model Type Quantitative (VFT model with Qualitative (generic RE links to MAUT, SD and BSC) model) Present and complete Present but not clearly articulated Present and complete

Present but not clearly identified

Logical

AND relationship implied

Present

Causal

Present

Present

Influencing

Absent in VFT, available through the MAUT link

Present

Temporal

Absent

Present

Evaluation

Can be derived using MAUT Present but weak and concepts of attribute, goal and constraint

Implementation

Intuitive presentation, not read- Present in many business sysily available through business tems systems

Formal representation

Absent in VFT, available through the MAUT link

Present through links to Information Systems formalism

Two other elements that were not included in the Nishit’s framework, but are essential characteristics of a good model are usability and formal representation (e.g. Pidd 1999, p. 120). As usability is often difficult to assess objectively, the in-

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tegration of an objective model with existing business systems is used as a proxy for this criterion. Formal representation becomes important in order to link the objectives models to other models (such as process or business models) and to facilitate system design. The “ideal” properties of the objectives model described above represent the different perspectives of the objective concept and will be considered as desirable dimensions of a generic objectives model. A complete objectives model will be able to represent all of these elements either directly or through links to other modelling methodologies. A summary of the comparison between the quantitative and qualitative models against these properties is presented in Table 4.2.

4.5 Summary In this chapter, an overview of the desirable features of an objectives model was provided and two types of objectives models were discussed in detail: the quantitative models linked to the business decision-making requirements and the qualitative models linked to the information systems specification requirements. The need to describe different types and levels of business objectives and to use different types of relationships to structure objectives is highlighted within both model types. The strength of each model reflects the purpose these models were built to address. The benefits of applying the VFT in real world organizations have been welldocumented (e.g. Clemen and Reilly 2001, Keeney 1992, 1994, Moussa and Mousseau 1999) and include a “simple structure for decision makers to understand and use” (Moussa and Mousseau 1999, p. 1400), “uncovering hidden objectives, guiding strategic thinking, improving communication” (Keeney 1992, p. 24), “high benefit-to-effort ratios” (Keeney 1994, p. 41), etc. While the VFT framework was developed by Keeney to link the narrow objectives of individual decision problems to a wider organizational context it has not been widely implemented within business systems. Nevertheless, the generic nature and intuitive representation of the framework facilitates its use within a wider organizational context. The many examples provided by Keeney and others (e.g. Keeney 1992, 1994; Clemen and Reilly 2001) in the application of this framework to real-life businesses also assist with the implementation and use of the framework. The link to the BSC ensures that the usability element is addressed, while links to Decision Analysis make formal representation of objectives available. Given the natural links between the RE goal models and the information systems designed to meet these goals, the usability of the RE model is assured. Similarly, the formalism of the RE goal models utilises the formal representations available within the Information Systems discipline (e.g. Lamsweerde and Letier 1998, Weber 1997). To summarise, the VFT framework was built primarily to expand the horizons of decision making from the narrow context of a single decision to a broader organizational context whereas RE objective models are more concerned with a de-

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tailed description of objectives for the purpose of system specification. Links between these models and corresponding tools from related disciplines (e.g. decision models for the VFT and information systems formal concepts and tools for the RE objectives models) expand the scope of both models to provide a more complete model for the concept of objective. However, even taking into account these links, neither model has all of the desirable properties of an objectives model: the VFT provides better guidelines for identification of objectives and separation between abstract and causal relationships, whereas the RE objectives model provides a more complete representation of the types of complex and interrelated relationships that exist between business objectives. Having provided the bridge between disciplinary boundaries of objectives modelling in this chapter, the next chapter considers business process models.

5 Business Process Modelling with EPCs 5.1 Introduction While objectives modelling aims to make business more effective, research evidence suggests (e.g. Ackermann et al. 1999, Balasubramanian and Gupta 2005, Rolland and Parkash 2000, Yu et al. 1996) that, in practice, process modelling primarily focuses on improving efficiency of the business. Daellenbach (1994, pp. 13-15) refers to efficiency as “doing things right” or more formally as “using a given set of inputs or resources to produce the maximum level of output, or alternatively, producing a given level of output with the minimum amount of inputs or resources” (technical efficiency). Therefore, a process model is required to facilitate: 1. Identification and structuring of business processes. 2. Automation of business transactions. 3. Execution of business processes to minimize costs while maximizing returns. The purpose of this chapter is twofold: firstly, to provide the background to the EPC methodology and secondly, to identify the desirable properties of a generic business process model from a value-focused process engineering perspective. To provide the context for the discussion in this chapter, a brief overview of the history of business process modelling is provided in Section 5.2. In Section 5.3, the EPC model is discussed using the conceptual modelling framework introduced in Chapter 2. This is followed by a detailed discussion of the decomposition properties (5.4) and extension of the EPC syntax to include objectives constructs (5.5). A large number of techniques and tools have been developed to represent business processes. Hommes and Reijswoud (2000) identified in excess of 350 business process modelling tools that were available at that time. When it comes to classifying this myriad of tools there is no uniformly accepted classification framework for even widely accepted business process modelling techniques (e.g. Rosemann 2003). However, there are some common themes in the approaches taken in the classification of business process modelling techniques and some convergence towards a set of desirable properties of a process model that has a level of acceptance across academic disciplines and in the real world. This convergence makes it possible to derive a comprehensive and internally consistent set of desirable properties by analysing and synthesising existing reviews and classifications of business process modelling techniques. The results of this analysis are documented in Section 5.6 and are used to identify the gaps in the EPC environment in Section 5.7.

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5.2 Business Process Modelling Context The importance of understanding and modelling business processes to meet business goals became evident in early 1990s as the drive for business process improvement gathered momentum (e.g. Hammer 1990). In his seminal book “Process innovation: reengineering work through information technology” Thomas Davenport (Davenport 1993, p. 5) explains that a process view of the organisation “represents a revolutionary change in perspective: it amounts to turning the organization on its head, or at least on its side”. Prior to this, the dominant perspective was a functional view (some times described as a “stove pipe” view) of organizational processes which was limited by the boundaries of organizational structure. The approach suffered from poor communication and hand-over between functional units leading to inefficiencies. Davenport (1993) advocated a more integrated view of the organization based on business processes that crossed functional borders. This move has been motivated by many authors, and summarised by Vanhaverbeke and Torremans (1999, p. 44) as follows: The fast-changing demands for the business environment create an urgent need for organizations to break away from the traditional organizational model. Among the most important external forces were the globalization in many industries, the shortening of product life cycles, the convergence of technologies and the associated blurring of industry boundaries, the massive deregulation in industries and the necessity to build up and renew technological capabilities to succeed in knowledge-intensive industries. Since economic growth slowed in the 1970’s and 1980’s and competition intensified, customers became more demanding. To become customer oriented was one of the major business challenges. Companies tried different strategies within the existing organizational paradigm to cope with the new challenges of the external environment but they were only modestly successful: matrix organizations, decentralization, increased customer involvement and other corporate tune-ups did not give satisfactory bottom-line improvements. The basic reason for the relatively poor results of these tune-ups is that the organizations’ structures are basically not changed; most organization structures are based on function or product or a combination of both. Instead of starting from what could add value for the customer and work backward from there, traditional structured companies still attribute customers only a secondary role in shaping the way how the company organizes its activities…..Process-centred companies have the ability to overcome this problem, since processes bring by definition, the customer to the fore.

Analysis section

Recruitment

Staffing & Development section

inputs

HR Planning section

Stove Pipes

Fig. 5.1 Functional vs Process view of a Recruitment Process

Process

outputs

79

In Figure 5.1, the two views are illustrated in the context of recruitment. More formally, Davenport (1993, p. 5) defines a process as a structured, measured set of activities across time and place, with a beginning, an end, and clearly identified inputs and outputs which are designed to produce a specified output for a particular customer or market. While this is not the only definition of a business process (see for example Lindsay et al. 2003, Silvestro and Westley 2002, Muehlen 2004b for a review of process definitions in the literature), it is the one that is principally used by authors in the area of business process management, reengineering and integration (e.g. Sandoe et al. 2001, p. 6) because (according to Davenport, 1993, pp. 5-9), it incorporates the key concepts associated with the business process, namely: • • • • • •

a dynamic view of how the organization delivers value; measurement of cost, time, output quality and customer satisfaction; a focus on how work is done; process ownership and process relationship to the organizational structure; an ability to draw on multiple functional skills; the use of information to improve the flow and integration across functions and interfaces between functional or product units; and • applicability across industries. Therefore, when referring to business process, Davenport’s definition of this term (with the accompanying concepts) is implied unless otherwise specified. Note that the term business process modelling is often used synonymously with terms such as business process engineering or business process re-engineering (BPR), enterprise resource planning (ERP), and business improvement. These terms are not equivalent to business process modelling but often incorporate business process modelling within their scope. For example, BPR involves creating models of existing and desired processes i.e. business process modelling; similarly successful ERP implementations involve business process modelling (e.g. Grant 2002). Since not every business process modelling technique is suitable for ERP or BPR, it is necessary to gain an understanding of the properties of a business process modelling methodology in order to ascertain the suitability of the methodology for various uses.

5.3 Event-driven Process Chain Davis (2001, p. 111) defines an EPC as “a dynamic model that brings together the static resources of the business (systems, organization, data, etc) and organizes them to deliver a sequence of tasks or activities (‘the process’) that adds business value”. Scheer (1999, 2000) developed the concept of an extended EPC (eEPC) to include business flows and corresponding classes of objects to represent a consolidated Business Process Model.

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ARIS framework (Scheer 1999) provides a complete description of the e-EPC through different views of the process such as Control/Process View (shown in Figure 5.2), Data View, Function View, Output View, and Organization View.

Fig. 5.2 Description of the e-EPC in the Control/Process View from Scheer (1999, p. 37, fig. 15).

The concept of views avoids the complexity of an “all-in-one” meta-business process model without the loss of information that would have been inevitable if the model was subdivided into simpler but separate sub-models. In addition, the Control/Process view enables modelling of the dynamic aspects of the business process flows. An e-EPC can be formed at various levels of the business process. Using the eEPC model to describe the process, Davis (2001, p. 229) defines two types of decomposition: horizontal segmentation of the e-EPC model into “manageable chunks which link together”; and hierarchical decomposition required for complex processes to enable modelling at different levels of details. ARIS (through the eEPC conceptual model) enables the flow of the business process to be linked to other process models (e.g. organizational hierarchy, information model, a simple objectives model etc). The detailed discussion of the decomposition is included in Section 5.4 using the conceptual modelling framework representation of the EPC introduced in this section.

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In the context of value-focused process engineering only the aspects of the eEPC that relate to the process flow and its links to business objectives are of interest while the links to other elements of the process (refer to Scheer 1999; 2000) are taken as given and are not discussed further in this paper. The conceptual modelling framework introduced in Chapter 2 (Section 2.6) is used next to describe the e-EPC model. 5.3.1 Modelling Grammar The following constructs are relevant for the description of process flow and business objectives within an e-EPC conceptual model: • event (e.g. applications received or candidates shortlisted); • function also referred to as activities (e.g. advertise position or shortlist candidates); • hierarchically ranked function (e.g. if the “shortlist candidates” function is decomposed into individual activities involved in checking eligibility and applying selection criteria, it is considered to be a hierarchically ranked function); • process (e.g. the whole sequence of activities involved in recruitment); • rules including AND-, OR- and XOR- rules; • objectives (or functional goals such as “select the best 5 candidates” for the “shortlist candidates” function) ; and • links including control flow link, process decomposition link and objective assignment link. Detailed rules that govern the e-EPC modelling grammar are described in detail by Scheer (2000), Davis (2001) and others. For the purpose of this discussion, it is sufficient to note that: • events, functions (including hierarchical functions and process signs) and rules are linked together into a sequence (or chain) of the form: event, function, event; • multiple events can be linked to a single function (and vice versa) via a logical rule such as AND, OR, XOR; and • hierarchically ranked functions and process signs are used as links between process decomposition levels. Within an e-EPC, objectives (usually referred to as goals in this context) can be only linked to functions in a way that: • enables functions to support multiple goals; and • higher level processes can inherit the association between functions and goals. Within the ARIS framework, the suggestion that goals should be linked with one another by means of a directed network is the only reference to the rules for structuring goals.

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5 Business Process Modelling with EPCs

5.3.2 Modelling Methodology Events within the EPC represent the “changing state of the world as a process proceeds” (Davis 2001, p. 111) and trigger functions that represent “activities or tasks that are carried out as part of a business process ” (Davis 2001, p. 112). Functional objectives also referred to as operational goals are usually loosely defined as something that a function is aiming to achieve (e.g. Regev et al. 2005). Rules objects are introduced to the EPC to allow “parallel branches, decisions, multiple triggers and complex flows” (Davis 2001, p. 118). The interpretation of the operators varies slightly depending on whether they are following or preceding a function (Davis 2001, p. 119). For example, recruitment process can be started only after a position becomes vacant and there is ongoing funding available for the position. This is an example of event decomposition represented in the model by the logical operator AND preceding the function. Operators following functions indicate a decomposition of the process flow. For example, applications may be received by post or email will require different initial processing, splitting the process into two non-mutually exclusive paths by the logical connector OR. These paths may converge later (e.g. once applications are processed they are forwarded to the selection panel) or they may never converge (e.g. the HR staff may realise that an application is incomplete and send a rejection note to the application without consultation with the selection panel). Davis (2001, p. 218) provides a generic list of things to consider when mapping real world business process to the e-EPC model. For functions this includes identifying triggers and outcomes, key decision points, branches and links to other processes, relevant attributes, data input and outputs of each function, systems, organizations and resources that support each function, etc. While functional objectives are not explicitly mentioned in this list, Davis (2001, p. 3) makes clear that “ideally, the business objectives come first; the processes are designed to achieve them, and systems, organisation, data, etc. should support the process”. Therefore, in a similar manner to the VFT methodology, an objective instance can be identified for each function by asking a question of “What functional objective is this activity aimed at?” Van der Aalst et al. (2003a) developed a model independent methodology for mapping known observations in the business process domain into a workflow or process model. This methodology captures elementary aspects of process control as workflow patterns such as sequence, parallel split, synchronization, exclusive choice etc.). Each pattern is described in the real-world context e.g. sequence pattern is described as “an activity in a workflow process is enabled after the completion of another activity in the same process” (Aalst et al. 2003a, p. 10) and includes procedures for mapping them into various process and workflow models including an EPC (e.g. Mendling et al. 2005, Neiger and Churilov 2006). For example, the split of the recruitment process into either paper or email applications processing is described by van der Aalst et al. (2003, pp. 13-14) as a multi-choice

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pattern defined as a point in the workflow process where, based on a decision or workflow control data, a number of branches are chosen. The generic patterns described by van der Aalst et al. (2003a) are combined into higher level processes by asking the question “What process is this activity a part of?” Similarly, higher-level processes can be decomposed into lower level activities by asking the question “What activities is this process composed of?” 5.3.3 Modelling Script and Context The name of the model “event-driven process chain” epitomises the representation of business processes as a sequence (chain) of events, functions (also referred to as activities or tasks), and rules (also referred to as logical connectors) with the events being function triggers and the results (i.e. process drivers) (Aalst 1999; Davis 2001, Keller and Teufel 1998, Klaus et al. 2000, Nuttgens et al. 1998; Scheer 1999, Scheer 2000). Functions are represented as rounded rectangles, events as hexagons and logical connectors as circles that include the rules labelled V

XOR

V

as (AND), (OR) and (XOR) . Solid lines with arrows on the line destination are used to show the control flow of the process and are sometimes referred to as arcs or control flow links. In addition to graphical symbols, verbs are used to help with the communication of business processes with EPC models, with active verbs used to describe activities and passive verbs to describe events. The resulting intuitive graphical representation of the business process by the EPC model, has been used for (Loos and Allweyer 1998, p. 3): • • • • • •

business process re-engineering (BPR); definition and control of workflows; configuration of standard software; software development; activity-based costing (ABC); and quality-related documentation of processes according to the requirements of ISO900x.

In Figure 5.3, the EPC notation is illustrated by a simple process structure with a single event triggering two activities undertaken in parallel which result in two separate outcomes. Various process structures that can be represented with the EPC model have been documented by van der Aalst et al. (2003a) and are referred to as workflow patterns.

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5 Business Process Modelling with EPCs

Application received

V Notify section

Find applicant’s address

Section notified

Address known

Send letter of acknowledgement

Applicant notified

Fig. 5.3 EPC notation illustration

Initially EPC representation was limited to informal graphical representations discussed in this section. However, since initial introduction of the EPC methodology, a number of EPC formalisms (scripts) have been proposed including the semantics of Nuttgens and Rump discussed by Aalst et al. (Aalst et al. 2002, p. 71) and EPC Markup Language (Mendling and Nuttgens, 2005). As noted by Keller and Teufel (1998, p. 158) the formal description of the EPC (discussed in the next section) “is particularly important for the development of modelling tools” and therefore required for the development of Value-Focused Process Engineering models later in this book. Keller and Teufel (1998, ch. 4.3) defined a script based on the declarative description of the syntax of EPC models by describing elements of the EPC graph and characteristics of a correct model (Keller and Teufel 1998, p. 158). Following Keller and Teufel’s description of EPC (Keller and Teufel 1998, p. 159), a generic 7-tuple g tI = I t ,ν t ,κ t ,τ t ,τ tκ ,α t ,α tκ is defined as follows: • t is type of the model being described by the tuple (e.g. e is used for an EPC). • It is a unique identifier of a model type t. • ν t is the non-empty, finite set of nodes of a model type t. • κ t is the link relationship, which describes the connections between the various types of nodes, κ is defined as κ ⊆ ν ×ν . • τ t ,τ tκ are representations that assign a type to every node or link. • αt ,α tκ are representations that assign attributes to every node or link type.

85

Representations τ ,τ κ defined in (5.1) are used to define nodes in (5.2) and links in (5.3). ⎧function, event, process sign, AND connector, ⎫ ⎪ ⎪ τ e : ν e → ⎨OR connector, XOR connector, hierarchically ⎬ ⎪ ranked function ⎪ ⎩ ⎭ ⎧control flow link, ⎫ τ eκ : κ e → ⎨ ⎬ ⎩ process decomposition link ⎭

(5.1)

Ie = {u ∈ν e τ e ( u ) = Id of an EPC}

(5.2)

E = {u ∈ν e τ e ( u ) = event}

F = {u ∈ν e τ e ( u ) = function} , F ≠ ∅

FH = {u ∈ν e τ e ( u ) = hierarchically ranked function} P = {u ∈ν e τ e ( u ) = process sign}

B1 =F ∪ FH ∪ P B2 =FH ∪ P

O P = {u ∈ν e τ e ( u ) = process goal}

J AND = {u ∈ν e τ e ( u ) = AND connector} J OR = {u ∈ν e τ e ( u ) = OR connector}

J XOR = {u ∈ν e τ e ( u ) = XOR connector} J=J AND ∪ J OR ∪ J XOR BJ =B1 ∪ J (u,v) ∈ κ is a link from node u to node v

(5.3)

K R = {(u,v) ∈ (BJ × E) ∪ (E × BJ ) ∪ (J × J)} (u,v) ∈ K R :⇔ τ eκ ( (u,v) ) = control flow link K O = {(u,v) ∈ (B1 × O P ) ∪ (O P × B1 )}

Keller and Teufel (1998, p. 159) use the concepts of positive and negative adjacency lists, input and output degrees and number, positive and negative incidence lists and the number of incidence nodes (5.4) to define start and end events of an EPC (5.5). These constructs can be also used to define events preceding and fol-

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5 Business Process Modelling with EPCs

lowing hierarchically ranked functions (5.6) as well as connector nodes that follow a function (5.7) thus indicating a horizontal decomposition of a process flow. Adjacency lists of a node v are the sets:

{ adj (v,w)= {u ∈ν (u,v) ∈ κ

(5.4)

} ∧ τ κ ( (u,v) ) = w}

adj+ (v,w)= u ∈ν (v,u) ∈ κ t ∧ τ tκ ( (v,u) ) = w -

t

t

Output and input degree of node v:

γ + (v,w)= adj+ (v,w) and γ − (v,w)= adj- (v,w) . Incidence lists for node v:

{ inz (v,w)= {(u,v) ∈ κ

} ( (u,v) ) = w}

inz + (v,w)= (v,u) ∈ κ t τ tκ ( (v,u) ) = w -

t

κ

τt

Number of incidence nodes for node v: i + (v,w)= inz + (v,w) and i- (v,w)= inz- (v,w)

{ = {u ∈ E γ

} } ,E

Es = u ∈ E γ − (u,w)=0 ∧ γ + (u,w)=1, where w ∈ K R , Es ≠ ∅ Ee

{ = {u ∈ E

−

(u,w)=1 ∧ γ + (u,w)=0, where w ∈ K R

} ∧ u ∈ adj (v)}

E ps = u ∈ E ∃v, v ∈ B2 ∧ u ∈ adj+ (v) E pe

∃v, v ∈ B2

-

e

(5.5)

≠∅

(5.6)

87

{

}

FCAND = u ∈ J AND ∃v, v ∈ B1 ∧ v ∈ adj- (u)

{

}

(5.7)

FCOR = u ∈ J OR ∃v, v ∈ B1 ∧ v ∈ adj- (u)

{

}

FC XOR = u ∈ J XOR ∃v, v ∈ B1 ∧ v ∈ adj- (u) FC=FCAND ∪ FCOR ∪ FCXOR

To complete the description of an EPC model, it is necessary to introduce the C concept of a path. The notation u ⎯⎯ → v is adopted from Keller and Teufel (1998, p. 160) to describe a path defined as a connection from node u to node v by a chain of other nodes and connectors, where C represents the series of nodes and connectors included in the path. Notation uÆv is used to describe the chain links between the adjacent nodes u and v. For any two nodes on an EPC path the following is true: u,v ∈ C ∃ (u,v) ⇔ (u,v) ∈ K R . The script defined above is sufficient to describe a single EPC model and local consistency criteria (Keller and Teufel 1998, pp. 161-165). By complying with local consistency criteria, the correctness of the individual EPC is ensured. For example, compliance with the criteria ensures that functions and events are connected with each other or logical connectors with only one control flow link, i.e. the control flow cannot be split without application of one of the logical rules. Notwithstanding the limitations with respect to workflow correctness which are discussed later in this chapter (e.g. Aalst 1999, Aalst et al. 2002), the formal model described in this section is sufficiently generic to ensure consistency of business process with the overall business objectives as it enables a description of:

• static objects (such as goals, functions, events, logical connectors, etc) and links between objects (including assignment, flow and decomposition links); and • relationships between levels of process and objectives decomposition structures. The need for “an intuitive graphical business process description…targeted to describe processes on the level of their business logic, not necessarily on the formal specification level, and to be easy to understand and use by business people” (van der Aalst 1999, p. 4) was one of the key motivations for the development of the EPC and it continues to provide the context for the application of the e-EPC conceptual model.

5.4 Decomposition Each activity within an EPC model can itself be described as a process and in turn be represented by an EPC model creating a hierarchical decomposition structure of EPC models that can be used to simplify and clarify presentation of complex

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business processes. To illustrate, consider “find applicant’s address” and “send letter of acknowledgment” functions in Figure 5.3. These functions can be combined into a more complex function “acknowledge application” that is then represented with the aid of a lower level EPC that contains the abovementioned functions as illustrated in Figure 5.4. Application received Application received

V Notify section

Acknowledge application

Section notified

Applicant notified

Find applicant’s address

Address known

Send letter of acknowledgement

Applicant notified

Fig. 5.4 EPC hierarchical decomposition illustration

Process decomposition can be motivated by many reasons. Differences in motivation could result in substantial differences between the decomposition structures of a process (Davis 2001, p. 259). Common drivers for process decomposition include clarity of activities, understanding of resource allocation (Gordijn et al. 2000), the needs of business analysis (IDS Scheer 2000), the need to ensure that processes produce the required results (Curtis et al. 1992), etc. A process model may aim to meet two or more of those needs within the same decomposition structure. Curtis et al. (1992, p. 83) summarise the issues involved with the decomposition structure as The granularity issue involves the size of the process elements represented in the model. The pressure for greater granularity is driven by the need to ensure process precision - the degree to which a defined process specifies all the process steps needed to produce accurate results ... In many domains, descriptions for process scripts are presented to humans at too high a level of abstraction and they do not provide sufficient detail for guiding actual execution… If human agents do not possess sufficient process knowledge, it may be desirable to model finer-grained process steps with approaches that represent alternative steps and sequences and can encode guidance in how to choose among them.

Process decomposition using an EPC model is defined by Davis (2001, p. 229) as either horizontal segmentation into “manageable chunks which link together” or hierarchical decomposition required for complex processes, to enable model-

89

ling at different levels of detail. Levels within the hierarchical decomposition are linked using directional process decomposition links. Within the horizontal segmentation, the functional flow is decomposed using the rules (also referred to as logical operators) summarised in Table 5.1. Table 5.1 Interpretation of logical connectors within an EPC from Davis (2001, p. 119, table 7.3) Connector Graphic rep- Interpretation for a functional flow split resentation

XOR AND

V XOR

V

OR

one or more possible paths will be followed as a result of the decision described by the function immediately preceding the connector one, and only one, of the possible paths will be followed process flow splits into two or more parallel paths

When a function is hierarchically decomposed, it can be described within an EPC model as either a hierarchically decomposed function or a process sign (e.g. Davis 2001, p. 244, Keller and Teufel 1998, p. 162). Irrespective of how the hierarchically decomposed function is represented, the predecessor and successor events of this function are required to be the start and end events (respectively) of the subordinate EPC. Figure 5.5 illustrates a 2-level business process model for function 1 (f1) and its predecessor and successor events (e1 and e2 respectively). The model at the second level includes a horizontal decomposition into parallel branches using the logical connector OR. The formal representation of the EPC by Keller and Teufel (1998, p. 167) stopped short of extending the individual EPC model with the global consistency criteria that are required to ensure a correct business process model. In order to define global consistency criteria, the EPC formalism discussed in the previous section is now extended to include the space of all EPC tuples within a business process model (5.8), process decomposition links between EPCs (5.9), and a function Φ (5.10). This function operates on that space of EPCs to allow selection of all objects of the same nature (e.g. links, nodes, etc) from a given EPC through enabling the selection of elements from a given tuple. Tuple elements are numbered from one to seven as they appear in the initial tuple description geId with the 1st element being an EPC identification, the next two elements being the sets of nodes and links respectively, and the remaining four elements referring to each of the four representations in the order of their appearance in the tuple.

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1st level o1

e1

o2

f1

e2

f2

o4

o3

e1

e6

f3

e3

f4

V

o5

e4

e5

f5

f6

o6

V 2nd level

e2

Fig. 5.5 An illustration of a 2-level business process EPC model

Gt =

∪g

I

t

, I ∈ Ie

(5.8)

I

K H = {(u,v) ∈ (B2 × Ge )}

(5.9)

(u,v) ∈ K H :⇔ τ eκ ( (u,v) ) = process decomposition link K EPC =K R ∪ K O ∪ K H

Φ : {Gt } × {1, 2,3, 4 ,5, 6, 7}

(

(5.10)

)

Φ gtI ,n := nth element of gtI

The above equations allow four global consistency criteria to be defined as follows:

91

D1. Process decomposition links cannot be used to connect nodes within an EPC to that EPC.

(

)

(

)

(5.11)

∀i ∈ I e ,u ∈ Φ gei , 2 ⇒ ∃/ u,gei ∈ K H

D2. If an EPC is a subordinate of another EPC it cannot also be its higher level EPC.

(

)

(

)

(

)

∀i, j ∈ I e ,u ∈ Φ gei , 2 ∩ B2 ,v ∈ Φ gej , 2 ∩ B2 , u,gej ∈ K H

(

)

(5.12)

⇒ ∃/ v,gei ∈ K H

D3. A process sign does not include events in its adjacency lists. ∀i ∈ I e ,u ∈ Φ( g ei ,2) ∩ P,

{

(5.13)

}

w ∈ Φ( gei ,2) ∩ adj+ (u,w) ∪ adj- (u,w) ⇒ w ∉ E

D4. The start event of a subordinate EPC corresponds to the predecessor event of the hierarchically ranked function that is linked to that EPC using process decomposition links. Similarly, the end event of a subordinate EPC corresponds to the successor event of the hierarchically ranked function linked to that EPC using process decomposition links. ∀i, j ∈ I e ,i ≠ j,u ∈ Φ( g ei , 2 ) ∩ FH ,es ∈ Φ( g ei , 2 ) ∩ E ps ,

(5.14)

ee ∈ Φ( gei , 2 ) ∩ E pe, ( es ,u ),( u,ee ) ∈ K R , ( u,gej ) ∈ K H ⇒ es ∈ Φ( gej , 2 ) ∩ E s ,ee ∈ Φ( g ej , 2 ) ∩ E e

The global consistency criteria D1-D4 describe the necessary characteristics of a multi-level EPC business process model. While it is possible to describe an EPC model of a small and straightforward process with pen and paper, this type of implementation would not realise the full potential of the EPC model in a complex business environment. The wide adoption of the EPC model for the representation of business processes is due to the strong support of the model by the market dominating SAP ERP system and business process modelling system ARIS (e.g. Scheer et al. 2006a, 2006b, Mendling and Nuttgens 2003a, p. 1, Mendling and Nuttgens 2004, p. 28).

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5.5 Extending EPC Script to Include Objectives ARIS architecture is widely accepted within the business community and is considered to be one of the most comprehensive methodologies for process modelling (Davis 2001, Klaus et al. 2000, Scheer et al. 2006a, 2006b). It “provides integrated tools for designing, implementing and controlling business processes” (IDS Scheer AG 2006) that includes methods, tools and content. This is achieved by consolidating business models through different views of an extended Eventdriven Process Chain (e-EPC) thus avoiding the complexity of an “all in one” meta-business process model whilst retaining all relevant information (Scheer 2000). In the ARIS modelling paradigm, the terms “function” and “process” can be used interchangeably and synonymously. Processes are generally described as complex functions that can be divided into sub-functions to reduced complexity (e.g. Davis 2001; IDS Scheer 2000). Functions are defined as “a technical task or action performed on an object to support one or more company goals” (IDS Scheer 2000, p. 4-1). The lowest level functions are defined as “functions which cannot be divided any further for the purpose of business analysis” (IDS Scheer AG 2000, p. 4-2). The e-EPC process model has expanded the scope of the EPC model to incorporate comprehensive descriptions of objects and flows associated with the process and integrated a number of different business modelling tools under “one roof”. The integration is achieved through seamless links between graphical symbols which represent elements of business processes and database objects which are then themselves shared by models representing different aspects of business process (e.g. organizational structure, information flow, flow of activities, etc). This ensures “consistency and reusability of data and models” (IDS Scheer AG 2006) and allows a large range of models to be integrated within the single conceptual framework of ARIS. ARIS architecture utilises five views (Function, Organization, Data, Output and Control/Process) to reduce complexity of an “all-in-one” meta-business process model without the loss of information that would have been inevitable if the model was subdivided into simpler but separate sub-models (Scheer 1999, p. 36). Models included in Function, Organization, Data and Output views are limited to those objects and connections that are relevant to the corresponding view. For example, the objective diagram and function tree models are available for modelling goals and functions (respectively) within the Function View. Entity relationship and data warehouse models are among models that are available to model data process environment within the Data View. Organizational chart and network diagrams provide capability for modelling the hierarchical organizational structure and assignment of responsibilities and resources within the Organization View. Product allocation and input/output diagrams can be used to model physical and non-physical input and output included in the Output View. The models included in the Control/Process view, on the other hand, are aimed at modelling “relationships among the views as well as the entire business proc-

93

ess”. For example, the “value-added chain” (Davis 2001, pp. 263-264) model is included in the process view as it represents the high level of the business process model. The EPC and e-EPC models are used to represent the details of individual processes with the e-EPC having the additional capability to link the objects from other views to the relevant process with the help of control flow and assignment links. The EPC script discussed in the previous section can be easily extended to incorporate the additional objects and flows included in the e-EPC model. For example, to include a “goal” object and assignment link (that links the goal to the activity responsible for it) τ ,τ κ representations defined in (5.1) must be modified to include a process goal and goal assignment link (as shown in equation 5.15) and the corresponding objects and links must also be defined (as shown in equation 5.16): ⎧function, event, process sign, AND connector, ⎫ ⎪ ⎪ τ e : ν e → ⎨OR connector, XOR connector, hierarchically ⎬ ⎪ ranked function, process goal ⎪ ⎩ ⎭ ⎧control flow link, goal assignment link,⎫ τ eκ : κ e → ⎨ ⎬ ⎩ process decomposition link ⎭

(5.15)

OP = {u ∈ν e τ e ( u ) = process goal}

(5.16)

(u,v) ∈ K O :⇔ τ eκ ( (u,v) ) = goal assignment link

The implementation of the business process decomposition within ARIS tools is illustrated in Figure 5.6. Business processes that add value to the organization are included at the top level of the hierarchy (using value-added chain representation), while elementary functions that do not require any further decomposition are included at the bottom of the function tree and in the detailed models. Schallert (2001) provides a comprehensive list of modelling techniques recommended for each view of the ARIS with new models being added to the ARIS Toolset on an ongoing basis.

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5 Business Process Modelling with EPCs

Company models

Overview models

Rough models

Detail models

Level 1

Value added chain diagram

Level 2

Value added chain diagram

Function tree

Scenario eEPC

Detail eEPC

Level 3

Level 4

Fig. 5.6 Modelling levels adapted from Davis (2001, p. 244)

A proprietary XML interchange format, ARIS Markup Language (AML), is available within ARIS to facilitate the export of ARIS Toolset models and extension of the ARIS Toolset with other models (Mendling and Nuttgens 2004). Having described the e-EPC model and its environment provided by ARIS, the next task is to establish whether the EPC environment is suitable for modelling different aspects of the business process as referred to by Davenport (1993).

5.6 Desirable Properties of Business Process Models from a Value-Focused Process Engineering Perspecitve In order for the process view of an organization to be implemented and to be of benefit to a business, it is necessary to employ models to understand, change, manage, and control business processes (e.g. Rosemann 2003). Since the nature of the model depends on its objectives and context (e.g. Hirschheim and Klein 1989, Iivary 1991), the discussion of business process modelling is highly segregated between various disciplines. For example, Becker et al. (2000), Hommes and Reijswoud (2000) focus on the elements of model quality (e.g. correctness, relevance, economic efficiency, clarity, comparability, systematic design, etc) while others (such as Curtis et al. (1992) and Giaglis (2001)) are more concerned with the coverage of the modelling techniques. Furthermore, in process modelling review literature, the separation between the methodology, technique and tool is not always made. For example, Cheung and Bal (1998) use the terms techniques and tools interchangeably but separate between software-supported and software-enabled tools. In this chapter, the following definitions of these terms are adopted from Kettinger et al. (1997, p. 58):

95 …methodology is defined as a collection of problem solving methods governed by a set of principles and a common philosophy for solving targeted problems (Checkland 1981)… The term technique is defined as a set of precisely described procedures for achieving a standard task... tool is defined as a computer software package to support one or more techniques (Palvia and Nosek 1993).

The combination of methodology, techniques and tools will be referred to as the process modelling environment. As discussed in Section 1.5, the notion of process modelling environment in this book explicitly excludes the “run-timeonly” aspects of the business process life-cycle. While there is little agreement on the terms used between the disciplines, in essence, business process modelling classification frameworks are all based on the same core properties. These are the ability of the model to be used for different modelling purposes, the representation capabilities of the model, the capability of the model to capture different aspects of the process referred to as content, and the technical features of the models and accompanied tools. Various frameworks for the classification and evaluation of business process modelling environments are reviewed in this section with the aim of describing the desirable features of a business process model for each of the four properties discussed. In the systems thinking context, every model has its own purpose, consequently the appropriateness of representation capabilities, content and technical features properties of a particular model will be highly dependent on the purpose of that model. On the other hand, representation capabilities, content and technical features of a model will determine the extent to which that model is suitable for a particular purpose. Since the aim of this section is to provide a comprehensive review of the properties, this interdependency is not emphasised. 5.6.1 Purpose

The adoption of a model such as an EPC is in practice dependent on its technical qualities (described in Section 5.3), but also on the acceptance of the broader methodology by the modelling community, the availability of user-friendly techniques and the characteristics and marketing of the tools used to support it. The importance of each element depends, amongst other factors, upon the resources available for process modelling, the complexity of the process being modelled and modelling purposes/requirements. To minimize confusion with the goals and objectives of the actual process, the term purpose is used when referring to the reasons for the creation of a model. The terms objectives and goals are used to describe the outcomes that the process being modelled is aimed at. While not all process modelling classifications have an explicit purpose dimension, those authors who do include it argue that it is useful for two reasons: • there are a number of purpose built process modelling techniques that are aimed at addressing a particular business need, therefore in classifying and

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evaluating different types of techniques it makes sense to include a purpose dimension to facilitate a meaningful comparison; • when practically applying classification frameworks to decide on the best modelling tool to use in a particular situation, the presence of the purpose dimension facilitates the choice of the technique or tool “fit for purpose” as well as encouraging one to consider of what is the purpose of modelling prior to making decisions about the best modelling techniques or tools. The following five broad categories of purpose property characteristics were extracted from the literature (refer to Table 5.2 for an illustration of how these broad categories map to a selection of literature sources). • Description (D) – incorporating the modelling purposes of describing and documenting business processes and improving understanding of, communication and coordination between business processes (e.g. Davis 2001, Giaglis 2001, Rosemann 2000, Rosemann 2003). • Analysis (A) – according to Dowson (1993, p. 57) “Models may also be constructed for the purposes of analysing processes for particular properties such as concurrency or robustness, abstracting only the features relevant to determining these properties”. In a more general context, models constructed for the purpose of analysis are aimed at providing decision support, problem solving and diagnosis. One of the purposes of these models may be to provide measurements against the relevant features or criteria and to assist with the management of business processes (e.g. Powell et al. 2001, p. 64). • Improvement (I) – referring to the purpose of improving business processes with the aim of improving business efficiency and/or effectiveness through improvements to “cost, quality, service and speed” (Hammer and Champy in Ackermann et al. 1999, p. 159). Ackermann et al. (1999, p. 159) note that “in practice a focus on cost (efficiencies) tends to drive the redesign of processes rather than overall effectiveness”. • Development (DT) – business process modelling techniques are created primarily to assist with the development of new business processes for a business or part thereof (e.g. Aguilar-Saven 2004). For example, within the software engineering discipline, business process modelling techniques are widely used to guide the process of developing new software (e.g. Rolland 1998). • Execution (E) – automation and control of business processes are some of the key purposes of business process modelling, models that are aimed primarily at supporting process execution are used widely within workflow applications (e.g. Mentzas et al. 2001, Muehlen 2004b). A generic business process modelling methodology would ideally be able to be used for each of the abovementioned purposes. Table 5.2 Purpose categories in classification of business process modelling techniques).

97

Author

Purpose categories

Category code

Giaglis (2001)

Understanding and communication

D

Process improvement

I

Process management

A/E

Process development

DT

Process execution

E

Descriptive models for learning

D

Aguilar-Saven (2004)

Descriptive models for decision support to process de- A velopment and design Enactable or analytical models for decision support during process execution, and control Enactment support models to Information Technology Rolland (1998)

PERFECT consortium (1997)

Dowson (1993)

E/A DT

Descriptive

D

Prescriptive

DT

Explanatory

A

Understanding

D

Communicating

D

Improving

I

Automating

E

Descriptive

D

Analytical

A

Preceptive (guide, support or enforce the performance A/E of a process) Kettinger et al. (1997)

Rosemann (2000)

Project management

A

Project solving and diagnosis

A

Customer requirement analysis

A

Process capture and modelling

D/DT

Process measurement

A

Process prototyping and simulation

A/I

IS systems analysis and design

A/DT

Business planning

D/I/A

Creative thinking

D/I/A

Organizational analysis

A

Change management

A/D

Document

D

Analyse

A

Improve

I

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5 Business Process Modelling with EPCs

Author Gordijn et al. (2000)

Purpose categories

Category code

How value-creating activities are carried out

D

Creation of a common approach for work to be carried DT out Incremental improvement of processes (e.g. efficiency) I Support of process by workflow management systems E Analysis of properties of a process (e.g. deadlock free) A Fatohali et al. (2007)

Mansar and Reijers (2007)

Process planning

D/A

Process analysis

A

Process management

A/E/I

Business process decomposition

D/A

Process (re)design improvement

I

Process performance in terms of cost/quality, time and I/A flexibility Deriving new process designs

DT

Fettke and Loos (2007) Holistic description of business components

D/I

Barros (2007)

Process innovation

I

Business process design facilitation through business process patterns

D/DT

Encapsulating complex business decision logic

A

Business process design

D/A

Business process improvement

I

Business process management

A/E

Initiation

D

Diagnosis

A

Design

D/A

Implementation

E

Clegg (2006)

Adesola and Baines (2005)

Luo and Tung (1999)

Valiris and Glykas (2004)

Process management

A/E/I

Formality

D/A/I

Scalability

D/A/I

Enactability

E

Ease of use

I

Establishing the vision and objectives

D

Business modelling

DT

Business analysis

A

Redesign

D/A

Continuous improvement

I

99

5.6.2 Representation

As discussed by Dowson (1993, p. 57) “irrespective of the intent of a process model, it must be represented in some notation, language or formalism”. Theoretically, there are a number of desirable representation qualities that a generic process model must have. For example, Hommes and Reijswoud (2000, p. 4) discuss theoretical concepts of comprehensibility, coherence, completeness and expressiveness that stem from the quality requirements of generic models and can be used to evaluate the representation notation and formalism for a specific modelling technique. However, in practice, such assessment is very time consuming and requires an in-depth understanding of each technique being evaluated as well as an independent user assessment of each technique. As a result, most reviews either avoid this question (e.g. Giaglis 2001) or provide an assessment against simpler criteria such as existence of a formal model and the availability of user-friendly graphical representation (e.g. Curtis et al. 1992). Accordingly, the availability of a formal model and a graphical representation are taken as the two desirable characteristics of the representation property. A formal model facilitates analysis of a business process modelling methodology in order to help with the understanding of the relationships between different types of modelling techniques and systematic verification of individual models (Curtis, Kellner and Over 1992, Rolland 1998). On the other hand, an intuitive and expressive graphical representation facilitates effective communication and allows a lay user to be able to utilise the technique and benefit from process modelling activities. This in turn is likely to result in wider acceptance of the technique in a non-academic environment (e.g. Curtis et al. 1992). 5.6.3 Content

Having discussed the method of representation, the next step is to determine what aspects of the process are capable of being represented by a particular process modelling technique. As previously discussed, the complete definition of the process incorporates many different perspectives of the process such as time, costs, outputs, flows, use of resources (including information and human resources), organizational structure etc. These perspectives can be summarised into three broad dimensions: what is to be accomplished, how is it to be accomplished, why is it to be accomplished. This approach is adapted from Curtis et al. (1992, p. 3) who use words such as “what”, “why”, “who”, “where” and “when” to describe various process perspectives. This is motivated by an intuitive representation of the “information that people ordinarily want to extract from a process model” in a question form for example “who is going to do it”, “how and why will it be done”, “who is dependent on its being done”, etc. A similar approach is adopted by Reijers et al. (2005). The content of the three dimensions is described next.

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What dimension

A business process model that includes this dimension facilitates identification and representation of outputs of a business process (e.g. Dowson 1993, Rolland 1998). Some classifications do not include the ability of a model to separately identify and represent business process outputs as a desirable property. For example, Curtis et al. (1992, p. 3) and later Giaglis (2001) include the outputs of the process together with the inputs and process objectives in the informational perspective described as “information entities produced or manipulated by a process”. Similarly, Gordijn et al. (2000), Vernadat (1997), PERFECT consortium (1997), Reijers and Liman Mansar (2005) combine process outputs and inputs into a single dimension. These types of frameworks make it difficult to determine whether the process modelling technique facilitates the description and analysis of the outputs that are often of most interest and importance to the organization in their own right (e.g. for market share analysis and/or as an influence on the structure of organizational processes such as a delivery process). How dimension

This dimension is very broad in scope as it includes the capabilities of process modelling techniques to cover a number of business process elements and flows of these elements, as well as the ability to capture relationships between these aspects. As discussed in Chapter 2, Melao and Pidd (2000) suggest classifying business process modelling techniques according to four themes that reflect different views of the organizations and processes. Business process modelling techniques within each theme approach the how aspect of business process modelling from a different paradigmatic perspective. A detailed analysis of the influence of various philosophical paradigms on process models can be found in Peppard and Preece (1995). The classification proposed by Melao and Pidd (2000) is more efficient than those based on a single-faceted review paradigm (e.g. Curtis et al. 1992, Giaglis 2001, Gordijn et al. 2000, Rolland 1998, PERFECT consortium 1997, Vernadat 1997, Wetter 2003) as it takes into account the ability of each technique to address the complexity of how a process as a whole is modelled as well as the ability of the technique to cover individual elements and flows of the process. The key characteristics of each perspective as described by Melao and Pidd (2000) and in the case of soft models expanded up on by Katzenstein and Lerch (2000) have been summarised in Figure 5.7. Some of these characteristics (greyed out in Figure 5.7) are expanded in more detail elsewhere (e.g. ability to cater for process decomposition is discussed in the next section).

101

structure (tasks, activities & areas of responsibility) procedures (constraints & rules of the work to be performed) what

goals (nature of the output to be obtained) internal relationships (interactions between subsystems: people, tasks, structure, technology, etc) external relationships (interactions between subsystems and business environment)

features

decomposition

flows (rates) of resources (physical or non-physical) from outside process boundaries stocks (levels) representing accumulations (e.g. materials) or transformations (e.g. raw material to finish product) actions and information why

policies (decisions) representing explicit statement of actions to be taken in order to achieve a desired result

psychological elements (motivation or frustration)

deterministic machines

static rational (mainly efficiency)

complex dynamic systems

dynamic (no intrinsic control) rational (elements of efficiency & effectiveness)

interacting feedback loops

dynamic (with intrinsic control) rational (elements of efficiency & effectiveness)

social constructs

sociological elements (relationships or conflicts) interacting socio-technical systems why

purposeful whole

sense-making interpretative subjective (mainly effectiveness)

Fig. 5.7 Melao and Pidd taxonomy of how dimension

Note that interpretation of the how dimension presented in Figure 5.7 includes a number of perspectives that are described by other authors as what, when, and who perspectives. For example, Reijers and Liman Mansar (2005, p. 293) describe functional, behavioural and organizational perspective (that are all included in the how perspective in Figure 5.7) as what, when, and who respectively. Why dimension

The capability of a business process model to represent this dimension is necessary in order to “bridge the gap between organizational needs and ERP functionality” (Rolland and Prakash 2000, p. 180), “help us ‘know why’ a process operates as it does” (Katzenstein and Lerch 2000, p. 391), avoid “workflow inflexibility” (Bider and Johannesson 2002, p. 1) and ultimately ensure that organizational “structure and its behavior contribute to the organization’s goal” (Muehlen 2004b, p. 31). In other words, when present, this capability enables the effectiveness concerns of a business to be addressed through process modelling. As discussed in the previous chapter, the representation of objectives within business process models is usually referred to as “goal-modelling” and (if it in-

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cludes the desirable properties of objectives models listed in Table 4.2) it facilitates a more holistic view of the business process by linking individual activities to the strategic and operational objectives of the business (Melao and Pidd 2000, Rolland 1998, Rolland and Prakash 2000). 5.6.4 Features and Tools

The features and tools of business process modelling environment are usually discussed from the perspective of quality and usability of the model (Becker et al. 2000) and the desirable features of process modelling tools supporting these techniques (documented in detail by Cheung and Bal (1998)). Very few tools have been evaluated at the level of detail described by Cheung and Bal (1998), instead the following broad level capabilities have been used: • enactment support including the ability to modify the model in response to changes in the process and exceptions handling (e.g. Katzenstein and Lerch 2000, Aguilar-Saven 2004); • checking of syntactical and logical correctness (e.g. Becker et al. 2000, Cheung and Bal 1998); • representing process at various levels of detail (i.e. hierarchical and logical decomposition) and from different views (e.g. Rolland 1998, Davis 2001); • integration with other business analysis tools Cheung and Bal (1998) and Katzenstein and Lerch (2000); and • user friendliness (Cheung and Bal 1998). The list of desirable properties of a business process modelling environment is summarised in Figure 5.8 (note that implementation and presentation properties of objectives models are included within the features and tools dimension). The presence of these properties within the e-EPC methodology and ARIS environment is discussed next.

103 Desirable Properties purpose analysis description development execution improvement

content

representation graphical representation formal model

what how

why

* structure * procedures * internal relationships * external relationships * flows (rates) * stocks (levels) * activities & information * psychological elements * sociological elements * interacting socio-technical * systems * goal concept * abstraction level * goal relationship * logical * causal * influencing * temporal * evaluation mechanism

features & tool support

enactment support user-friendliness syntactical & logical correctness checks integration with other business tools representation of process at various levels of detail

Fig. 5.8 Summary of desirable properties for process modelling environment).

5.7 Assessment of the EPC Modelling Environment 5.7.1 Purpose

The graphical easy to understand nature of the EPC and its implementation in ARIS suggests that it is suitable for the purposes of describing and developing business processes within a business environment. While the improved understanding of the business process through the use of the EPC model facilitates improvements in business efficiency, the model itself does not have capabilities for behavioural analysis or execution support (Dehnert 2002, p. 51). The wide-spread use of the EPC model has facilitated research into “making EPCs fit for workflow management” (Dehnert 2002, p. 51) focusing on overcoming inconsistencies present in the EPC formal and informal semantics which had compromised the use of the EPCs as workflow applications (Aalst et al. 2002, Mendling and Nuttgens 2003a). So while the EPC may not be the best modelling tool for the purpose of execution support, it can and has been used for this purpose with the help of the non-proprietary modelling language, EPC Markup Language (EPML), and the ARIS modelling language (AML) (Mendling and Nuttgens 2004, Mendling and Nuttgens 2005). Similarly, if the main purpose of business process modelling is business analysis, the EPC would not be the logical choice. However, in cases where analysis is part of a wider strategy for process improvement involving understanding and re-

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design of business processes, the EPC makes a valuable addition to the modelling toolset. 5.7.2 Representation

The analysis of the AML language by Mendling and Nuttgens (2004, p. 31) from the point of view of transforming the ARIS toolset models to other formats has highlighted the following weakness of the AML: …as soon as these models have to be moved to other applications or have to be used in a different context, non-trivial transformations are needed. The limited readability of AML, then, is a road block for developing transformation programs.

An alternative interchange format proposed by Mendling and Nuttgens (2004) that overcomes these problems is briefly discussed in the next section. 5.7.3 Content

The EPC model, when isolated from the ARIS toolset, is concerned only with elements of the how dimension. However, when integrated with other models within the ARIS framework, the scope of the EPC is extended to include other dimensions. For example, the what dimension is explicitly included in the ARIS house as the output view providing links from the outputs of the process to the activities responsible for the delivery of this output. The how dimension within the EPC is limited to the control flow of business functions. However, an e-EPC within ARIS also includes other business flows such as organizational, target, control, output, human and information flows and corresponding classes of objects, such as organizational units, goals, functions, events, messages, outputs, data and resources. The function view combines the function, goal and application software components of the business model within ARIS. The link between goal and function is obvious; however the link between application software and function is less apparent. Scheer’s argument (Scheer 1999, p. 36) is that the software defines the processing rules of a function and therefore should be included in the function view. The organizational unit, human output, machine resource and computer hardware are responsible for the execution of the function and are therefore included in the organization view. The data view includes environmental data as well as events and messages triggering or triggered by functions. The remaining model entities represent all physical and non-physical inputs to and outputs from the function and are part of the output view. The control/process view complements the other four views by modelling the dynamic aspects of the business process flows. The meta-business process model, which forms the basis of the

105

consolidated business model as developed by Scheer (1999, 2000) is illustrated in the control/process view of Figure 5.5. An e-EPC is essentially an activity-centred descriptive model that represents the business process as a deterministic machine with a limited capacity to represent relationships or feedback mechanisms which are required to view the business as a complex dynamic system with interacting feedback loops. Similarly, representation of psychological and sociological aspects of the business and interaction between socio-technical systems is also limited. Models of roles and hierarchical structures provided within the requirements definition level of the system lifecycle (Schallert 2001, p. 7) “do not capture social relationships” that need to be included in a business process model that represents social constructs (Katzenstein and Lerch 2000, p. 387). However, as demonstrated by the integration of a simulation model with the ARIS Toolset (IDS Scheer 2006), the ability of the ARIS Toolset to be easily extended (either using the proprietary AML or tool-independent EPML) to integrate with other models indicates its potential to be developed to add the currently absent aspects of the what dimension. Similarly, while currently available methodologies and tools do not include an objectives model that meets the requirements discussed in Chapter 2 (the oversimplified objectives model and the BSC model are not linked to each other nor do they facilitate design of the business process from the “purposeful whole” perspective) the potential to include such a model is there. 5.7.4 Features and Tool Support

The main criticism of the implementation of the EPC methodology within the ARIS environment has been associated with the limitation of the ARIS interchange format (AML). However, more recent advances in “specification and standardization efforts” (Mendling et al. 2004b, p. 4) of Business Process Modelling Tools encouraged the development of an open-source EPC tool set (based on the EPML) that facilitates the interchange of data and models between “heterogenous Business Process Modelling tools” (Cover Pages 2002). Mendling and Nuttgens (2004, pp. 27, 32) explain that “EPML is a XML-based tool-neutral interchange format for EPC business process models” that has been developed in accordance with the principles of “readability, extensibility, tool orientation and syntactical correctness” (Mendling et al. 2004a, p. 55). The development of the EPML has been motivated by the “heterogeneity of proprietary data formats” and “the lack of commonly accepted interchange format” that would allow for data models to be migrated between tools (Mendling and Nuttgens 2004, pp. 27, 32). As such, it is not a replacement for ARIS methodology but an alternative format for representing the EPC. The script for transforming ARIS Toolset AML to EPML by Mendling and Nuttgens (2004) allows models developed in ARIS to be easily made available to

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other tools, facilitates an automated information extraction from ARIS process models (Mendling and Nuttgens 2004, p. 28) and overcomes the technical limitations of AML. For example, with the help of the AML to EMPL script, an ARIS EPC Model can be checked for cyclic definitions that may result from non-local semantic properties of the EPC (Aalst et al. 2002, p. 73), deadlocks or contact situations indicating a bad design of an EPC (Cuntz and Kindler 2004, pp. 22-23). The EPC Tools developed by Cuntz and Kindler (2004) can be used to achieve this. The EPC community is also being encouraged to extend EPC Tools into “an open source EPC tool suite” (Cuntz and Kindler 2004) that has the potential to further “leverage EMPL as a tool-neutral interchange format for EPCs” (Mendling and Nuttgens 2004, p. 37) as demonstrated by EPM2SVG (mapping of EPC models to Scalable Vector Graphics (SVG) and websites (Mendling et al. 2004a)). The combination of ARIS Toolset and EMPL results in a comprehensive set of methodologies, techniques and tools for business process modelling with EPC models.

5.8 Summary The discussion in this chapter provided an overview of complementary perspectives on business process modelling (not limited by the boundaries of individual disciplines) and applied the resulting framework for the evaluation of the EPC methodology as summarised in Table 5.3. The framework of desirable features summarised in Table 5.3 can be used as: • an interdisciplinary guide to business process modelling features that can form a starting point for identifying features required for a specific application; • a comprehensive business check list for the selection of business process modelling methodologies, techniques and tools; and • the basis for the selection criteria in an MCDA model (discussed in the previous chapters) to provide a quantitative comparison of business process models. Even a clearly formulated evaluation framework requires subjective judgment by the user of the framework. However, while being subjective, the judgement must be internally consistent and convincingly motivated. For example, Table 5.3 summarises the authors’ views of the suitability of the EPC methodology for business process modelling purposes motivated by the discussion in Section 5.7. Based on the evaluation summarised in Table 5.3, it is concluded that the EPC methodology (enhanced through the development of EPML) is suitable for most business process modelling purposes with the potential for further development through integration with other modelling tools.

107 Table 5.3 Evaluation of the EPC methodology against the desirable features of a process model Desirable features category Purpose

Desirable features characteristics

EPC

Description

Suitable

Improvement Development Analysis

Representation

How

Content

Execution Graphical Formal What deterministic machine complex dynamic system interacting feedback loop social constructs Why

Features and Tool Support

User-friendliness Integration with other business tools Enactment support

Potential for further development Being refined Suitable Being refined Suitable Suitable Potential for further development Not available Potential for further development Potential for further development Suitable Being refined Suitable

Syntactical and logical correctness checks Representation of process at various levels of detail

In later chapters, a methodology (both at the conceptual and implementation levels) is proposed to link the EPC business process modelling environment and business objectives models. Such a link would provide an opportunity to overcome limitations of the EPC goal representation (e.g. Jacobs and Holten 1995) and address the gaps in the analysis capabilities of the EPC.

6 Requirements for a Value-Focused EPC: the “WHAT” Dimension 6.1 Introduction Recall from the first chapter that the definition of a system has five key elements (Daellenbach 1994, Weber 1997): • system components; • properties of the system that have been inherited from components (hereditary properties); • links between components; • shared history between components; and • properties of the system that are not properties of any of the components (emergent properties). Using systems thinking to describe goal-oriented business process modelling methodology requires that a goal-oriented business process model is defined in terms of the five elements above. In this context, a goal-oriented business process model is viewed as a set of linked goal and process models with a common history and two types of properties: those inherited from the component models and those which are the result of these models being linked together into an integrated whole. This is a distinctly different perspective on how to “clarify the connection between the notion of goal and the notion of business process” (Bider and Johannesson 2002, p. 1) compared to existing approaches to goal-oriented business process that are discussed in the next section (Section 6.2). The advantage of the systems perspective is that it encourages the best of what goal-modelling and process-modelling has to offer integrated into a single model in a way that preserves the strengths of the respective models (as the hereditary properties inherited by the combined model) while facilitating the emergence of new properties that satisfy goal-oriented business process modelling requirements. To enable integration of multiple models (that may come from different disciplinary domains) into a single coherent model - the properties of the individual models must be comparatively assessed to determine which components of these models should be combined to provide a “complete, clear description of the domain being modelled” (Weber 2003, p. 11). The framework for such comparative assessment is proposed in Section 6.3. This framework is then used to guide the definition of requirements for a valuefocused EPC methodology within systems context including analysis of hereditary properties requirements (Section 6.4), linking requirements (Section 6.5), shared history requirements (Section 6.6) and emergent properties requirements (Section 6.7). In Section 6.7 these requirements are combined to provide the list of what

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6 Requirements for a Value-Focused EPC: the “WHAT” Dimension

needs to be done in order to address the goal-modelling gap of the EPC methodology.

6.2 Review of Goal-Oriented Approaches Generic guidelines for the development and evaluation of goal-oriented business process models were proposed by Kueng and Kawalek (1997) as a set of four steps. The first step combines three activities: identification of goals and corresponding constraints and measurement criteria, decomposition of goals “until they can be transformed into activities which have to be carried out within the process” (Kueng and Kawalek 1997, p. 23), and graphical representation of goals as “Goal/Means-Hiearchy”. Kueng and Kawalek (1997, p. 23) acknowledge that the hierarchy does not reflect the fact “that goals have a different relationship to each other: some are contradictory, others are interdependent, and several are complementary. Furthermore, we have to deal with the fact that different goals get different priorities.” In the second step, Kueng and Kawalek (1997, p. 23) identify the key difference between goal-oriented business process modelling and other types of business process modelling: “the activities have to be derived from the Goal/MeansHierarchy and each leaf from this hierarchy has to be transformed into at least one activity” while ensuring that (p. 24) “every input that is needed by an activity [is] produced by another activity” and “every output, produced by a certain activity [is] delivered to customer [sic] or used as an input by another activity”. Furthermore, the resulting model must be able to model the flow of activities that represents “logical and temporal dependencies between activities and define which activities can be carried out sequentially, alternately or concurrently” (Kueng and Kawalek (1997, p. 24) refer to this as “execution order”). Importantly, Kueng and Kawalek (1997, p. 23) acknowledge that the process of deriving activities from the objectives structure may have to be iterative until inconsistencies (p. 18) are resolved and each leaf from the objectives structure is “transformed into at least one activity”. Steps 3 and 4 address representation of the role and object (respectively) aspects of business processes. These steps do not impact on the integration of process and objectives modelling as they represent other (than the objective) dimensions of process modelling. In addition to the generic guidelines discussed, a number of specific goaloriented business process models have been developed. These can be classified into two major classes: State-Flow based models developed by Bider and coauthors (e.g. Khomyakov and Bider 2001, Andersson et al. 2005) and Requirements Engineering - based models including models developed by Dardenne et al. (1993), Hurri (2000), Jacobs and Holten (1995), Kavakli (2002), Nishit (2002), Rolland et al. (1998),Yu (1999) among others).

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6.2.1 State-Flow Approach to Goal-Oriented Business Process Modelling

At the basis of the state-based approach to goal-oriented business process modelling, is the proposal by Khomyakov and Bider (2001, p. 1) to develop workflow models starting from chaos and then “introducing some means to restrict it”. This leads to representation of a process models as a “trajectory in the space of all possible states” (Khomyakov and Bider 2001, p. 1) and representation of goals as final states in the flow of a process’ states. Within this context, the process can be decomposed into a set of independent sub-processes each leading to achievement of sub-goals as well as interlinked processes with one process delivering the means required by another process to achieve its goal. Andersson et al. (2005, p. 6) argue that for “loosely structured processes, like decision making” the stateflow models may be more appropriate than the traditional business process models as “a sequence of activities for such processes is difficult, or sometimes impossible to establish”. However, the disadvantage of the approach is that the resulting model “says nothing about who is performing activities, and how things are done in the real world” (Andersson et al. 2005, p. 8) thus making it unsuitable in process execution and automation context. Andersson et al. (2005, p. 12) claim that the state-flow view is “the most promising direction” for a “task of annotating business processes in a way that the similarity between two business processes could be established based on comparing their annotations” and may “also be useful for other type of tasks, e.g. analysis and synthesis (design)” although “its validness [sic] for practical purposes” is yet to be proven. 6.2.2 Requirements Engineering Approach to Goal-Oriented Business Process Modelling

Unlike the state-flow models, the goal-oriented models within RE such as GDC, ISAC, i*, the NFR framework, KAOS, GBRAM and goal-scenario coupling are well-established and tested in practice (Kavakli 2002). The maturity of goaloriented methodology within the RE field is also evidenced by the availability of goal driven metamodels (e.g. Jacobs and Holten 1995; Kavakli 2002, Rolland et al. 2000), as well as a number of unifying and evaluation frameworks for the multitude of goal-oriented approaches available in the field (e.g. Hurri 2000, Kavakli 2002, 2006, Nishit 2002, Mylopoulos et al. 1999, Yu and Mylopoulos 1998). Within RE, business process modelling is part of requirements elicitation activities aimed at “describ[ing] current organisational behaviour” and requirements specification activities aimed primarily at “linking business needs and objectives to system functional or non-functional components”(Kavakli 2002, p. 238). For example, Rolland and co-authors (e.g. Rolland and Prakash 2000, Kaabi et al. 2004) claim to be (in the words of Rolland and Prakash 2000, p. 180) “bridging the gap between organisational needs and ERP functionality” by creating the Map

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- “a process model expressed in intentional terms” (Rolland and Prakash 2000, p. 181) and linking it to an SAP enterprise model at the level of “organisational goals-strategies and SAP goals-strategies” (Rolland and Prakash 2000, p. 193). In this context, business process modelling becomes a means for “representing actions carried out by actors to achieve goals” (Kavakli 2002; Yu and Mylopoulos 1994) with primary modelling objectives being representation of “strategic dependences among actors” (Kavakli 2002, p. 238) and relationships between goals (Kaabi et al. 2004, Lamsweerde 2001, Rolland and Parkash 2000). This means that even when business process and business objectives models are linked, they remain at different levels of the modelling framework (e.g. refer to the illustration of the EKD model by Rolland et al. (2000) replicated in Figure 6.1). As a consequence, RE goal-oriented modelling frameworks are more suited to support “the same reasoning and communication … as cognitive maps and as the more specific goal-based causal reasoning” (Katzenstein and Lerch 2000, p. 401) than workflow automation activities.

Fig. 6.1 EKD models from Rolland, Nurcan and Grosz (2000, p. 314, fig. 1)

This review illustrates that existing goal-oriented business process modelling approaches do not take advantage of some of the properties available within specialised goal and/or process models. This is not surprising given that within the existing goal-oriented business process modelling frameworks either goal or process or both models are developed from scratch thus making it difficult, if not impossible, to be able to take advantage of the strengths of the existing models. While acknowledging the strengths of existing goal-oriented modelling methodologies, the approach adopted in this research is distinctly different from them in that there are no new business goal or process models introduced, rather in the spirit of the systems view of the world, the best of what goal-modelling and process-modelling has to offer is combined into a single model in a way that preserves the strengths of the respective models (as the hereditary properties inherited by the

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combined model) while facilitating the emergence of new properties that satisfy goal-oriented business process modelling requirements.

6.3 Comparative Assessment of Modelling Methodologies In order to create the VFPE model using a systems approach (discussed in Chapter 1), a methodology is required for comparing existing models and determining what components of these models should be combined to derive a new model which is both complete and clear. The concept of complete is defined as the maximum desirable hereditary properties coverage by the combination of candidate models (consistent with the definition of completeness by Wand and Weber (1993, p. 221)). Similarly, the concept of clear representation is defined as the minimum overlap in the representation of the same concept by the candidate models (consistent with the definition of clarity by Wand and Weber (1993, p. 223)). This task of comparing models that are candidates for a combined model can be done using set mapping principles (e.g. the Mathematical Dictionary at thesaurus.maths.org), where the sets being mapped are: • VFT modelling properties discussed in Chapter 4; • e-EPC modelling properties discussed in Chapter 5; and • combined set of desirable properties for objective and process modelling derived in Chapters 4 and 5 respectively. By defining the mapping domain to correspond to the combined set of candidate model properties and the mapping range to correspond to the set of desirable properties for an “ideal” VFPE model it is possible to assess whether individual models (each with a set of properties) are suitable candidates for the combined model with a predetermined list of desirable properties. Mapping categories that reflect different ways the objects in the mapping domain are paired with objects in the mapping range are used to help with the assessment of which candidate models should be included in the combined model. There are many different terminology conventions to describe mapping categories, in this book we adopt naming conventions used by Wand and Weber (e.g. Weber 2003, p. 11) in the context of ontological modelling. Property deficit exists when one or more of the desirable hereditary properties are not represented via a candidate model. Obviously a candidate model that does not have any of the desirable hereditary properties should not be considered as a candidate for the combined model. Conversely, a candidate model that does not have any property deficit would not need to be combined with any other models. In all other cases, for each of the desired properties in the combined model, the candidate that does not have a deficit of that property should be included in the combined model. Once all of the desired properties are represented in the combined model, no other candidates should be added to it. For example, in Figure 6.2, there are four desirable properties (1, 2, 3 and 4) and three candidates for the

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combined model (A, B and C). The property deficit for each of the candidates is: properties 1 and 2 are not represented in B and C, property 3 is not represented in A and C, property 4 is not represented in A and B. Model A must be combined with models B and C to ensure that all of the desired properties are included in the combined model. Desirable properties

1

Candidate models

A

2

B

3 C 4

Fig. 6.2 Property deficit

Property overload exists when a property within a candidate model maps to two or more desirable hereditary properties. Property overload results in a blending of properties within the combined model that ideally should be separate. As illustrated in Figure 6.3, property overload results in a deficient representation of the desirable properties within the combined model as the candidate model A does not have the mechanism to separately represent the desirable properties 1 and 2. It is, therefore, preferable to select a candidate model that does not have property overload (i.e. candidate model A in Figure 6.2 is preferable to candidate model A in Figure 6.3).

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Desirable properties

Candidate models

A

1 2

B

3 C 4

Fig. 6.3 Property overload

Property redundancy exists when desirable hereditary property is present in two or more of the candidate models. A candidate model that has all of its properties redundant with one other candidate for the combined model should not be included in the combined model and will be referred to as completely redundant. Conversely, a model that has no redundant properties when compared to other candidates should be included in the combined model. Desirable properties

1

Candidate models

A

2

B

3 C 4

Fig. 6.4a Completely redundant model

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Desirable properties

1

Candidate models

A

2 3

B

4 5

C

Fig. 6.4b Property redundancy

Fig. 6.4c Redundancy within the combined model

In all other cases, as each model is considered for the combined model, it should be assessed against the properties already present in the combined model and should be added to the combined model only if it is not completely redundant compared to the combined model. For example, model B in Figure 6.4a is completely redundant as its only desirable property is already included in model A. On

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the other hand, none of the models in Figure 6.4b are completely redundant when compared to each other, however once models A and B are included in the combined model, model C becomes completely redundant compared to the combined AB model (Figure 6.4c) and, therefore, does not need to be added to the combined model. Note that in Figure 6.4c, property redundancy cannot be eliminated from the combined model without creating property deficit. Situations like this will be referred to as property overlap. More formally, property overlap is defined as the situation when models with one or more properties in common are included in the combined model. If the property is represented differently within the two models, then either one of those representations must be selected or a hybrid representation must be developed that integrates the two representations. The mapping discussed above will be referred to as the comparative assessment framework with mapping categories referred to as assessment criteria for the remainder of the book. To illustrate the application of the framework, assume that there are two candidate models for the combined model: a goal model and a business process model; and two desirable properties for the combined model (based on the list of the desirable properties discussed in Chapter 4): representation of causal relationships between objectives and representation of abstract relationships between objectives. Table 6.1 summarises results of the assessment in this scenario. Table 6.1 Illustration of mapping categories. Assessment cri- Assessment results teria Property deficit A goal model that represents causal relationships between objectives but not abstract relationships. Property overload

A goal model within which both causal and abstract relationships are represented in the same way without delineation.

Property redun- Both the goal model and process model represent causal relationships bedancy and over- tween objectives (not necessarily using the same representation mechalap nism).

Assessments in Table 6.1 assist with the selection of the candidate models so that the coverage of the combined model is maximized and highlight the areas where the candidate models need to be modified to minimize the overlap – in other words, the criteria within the framework provide the means for assessment of the hereditary properties of the candidate models in relation to the desirable hereditary properties of the combined model. Whether the candidate models can be combined into a single model within which the properties of individual models are linked is the next question that needs to be answered. The link between any two candidate models can be made if they have (a) a common element (that will be referred to as link element) and (b) there exists a common representation mechanism for this element that can be util-

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ised by both candidate models. The common representation mechanism is required in order to minimize confusion that may result from combining candidate models with overlapping properties with disparate representation. The candidate model whose representation mechanism is used as a basis for representing the common element will be referred to as the primary model. To ensure a uniform representation mechanism within the combined model, the primary model must be able to represent all properties of the combined model irrespective of their origin. Where necessary, the representation mechanism of the primary model can be modified to facilitate representation of the desirable properties not included in the primary model. Therefore, the choice of the primary model can be based either on the number of desirable properties present within it or features of the representation mechanism. For example, in Figure 6.4b, model B can be chosen as the primary model on the prima facie basis of it having more desirable properties. However, if model A can be easily modified to include the missing desirable properties from model B, while significant modifications would be required to model B to incorporate the two missing properties of model A, then model A would be a more appropriate choice as the primary model. This approach circumvents the confusion that may result from combining candidate models with overlapping properties.

6.4 Analysis of Hereditary Property Requirements Value-focused process engineering with EPCs implies that the EPC methodology is included in the combined model. Furthermore, as the EPC environment has been designed with the aim of integrating various modelling methodologies within the single framework and, therefore, can be easily modified to include additional models and/or properties, it is the natural choice as the primary model. This simplifies the assessment task of the candidate models. Instead of having to assess each model against a complete list of desirable properties, the candidate models need only be assessed against the property deficits of the EPC methodology. The property deficits of the EPC methodology were identified in Chapter 5 (Table 5.3) as: • analysis of business processes; • representation of business processes as complex dynamic systems, interactive feedback loop and social constructs; and • goal-modelling. The fact that most of the properties that have been identified as deficit properties are partially included in the EPC environment is addressed in the property overlap analysis that follows deficit, overload and redundancy analysis. In addition to the EPC, two other models are considered as candidates for the combined model based on the review of models in Chapter 4: the VFT and the RE goal models. The discussion in Chapter 4 has highlighted the respective strengths of these goal models in the area of business objectives representation. To recap,

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the VFT framework developed by Keeney (1992) represents a Decision Analysis approach to goal-modelling with the focus on quantitative models and capability for qualitative objectives modelling, while RE goal models (e.g. Lamsweerde 2001, Rolland and Prakash 2000) specialise in qualitative goal models with capability to represent some aspects of business processes. 6.4.1 Property Deficit Analysis of business processes. In Chapter 5 the analysis of business processes property was summarised as the ability of the model to provide decision support, problem solving and diagnosis. The EPC environment facilitates qualitative analysis of business processes but lacks the ability to provide quantitative decision support. RE models are similar to the EPC in this respect. On the other hand, the VFT model incorporates the quantitative analysis tools through its links to the MAUT and other Decision Analysis models. Representations of business processes. As discussed in Chapter 4, the KPIs included in the VFT model as attributes of business objectives can be used as a link element to link the VFT objectives structure to system dynamic models that are able to represent business processes as dynamic systems with interactive feedback loops. On the other hand, the RE models are qualitative models that provide a greater degree of social construct representation than is available within the EPC environment (Katzenstein and Lerch 2000). Goal-modelling. Recall that the review of goal modelling literature in Chapter 4 identified the following desirable properties of a goal model: concept of objective, abstraction level, relationship between objectives, evaluation, implementation, and formal representation. The comparison of the VFT and RE models with respect to these desirable properties (Table 4.2, Chapter 4) identified the strengths of the VFT framework in the areas of the concept of objectives, abstraction level and evaluation while, the properties of relationship between objectives, implementation and formal representations were considered to be better represented in the RE goal models. The summary of the property deficit analysis is provided in Figure 6.5.

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6 Requirements for a Value-Focused EPC: the “WHAT” Dimension Combined Model

Candidate Models VFT link to DA & OR/MS models

Quantitative analysis Representation EPC properties Goal modelling

dynamic system feedback loop

objectives identification & structures

social constructs

objective, abstraction, evaluation

RE qualitative approach

implementation, formal representation relationships between objectives

links to IS objectives structures

Fig. 6.5 Property deficit analysis

6.4.2 Property Overload

Based on the representation in Figure 6.5, there are two possible cases of property overload: (a) a link to DA and OR/MS models within the VFT structure, and (b) an objective structure within the RE model. The first case is not a property overload as the links between the VFT model and other quantitative models are clearly delineated within the VFT model. On the other hand, the abstract relationships are not clearly delineated within the RE models, making the RE goal model a less preferred candidate for the combined model because of the resulting property overload. Nevertheless, the RE model is not completely redundant, so it should not be dismissed as a candidate for the combined model. 6.4.3 Property Redundancy and Overlap

As can be seen from Figure 6.5, neither the VFT nor RE models are completely redundant compared with the EPC environment and, therefore, should be considered for the inclusion in the combined model. The overlap between goal modelling, representation, analysis and EPC properties indicates that elements of these properties are included in the EPC environment, even though as a whole, these properties can be considered in deficit within the EPC environment. As is clear from the discussion in Chapters 3 and 5, all three models (VFT, RE and EPC) are concerned with the representation of elements of business processes and objectives to varying degrees. This means that when these models are combined, some properties will overlap to some degree. Overlap in the combined

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model can come either from the overlap in business process representation (excluding representation of objectives) or in business objectives representation. Overlap in business process representation. Representation of business process is not of a direct concern within the VFT framework. Business activities are discussed only as alternative ways of achieving means objectives (Keeney 1992, p. 205). For example, means objectives of increasing efficiency and effectiveness of the staffing function may be addressed by outsourcing specialised or large-scale recruitment activities to a recruitment agency (e.g. Milkovich and Boudreau 1997, p. 557). This approach to representing business processes provides a link between business objectives and business functions but fails to represent the process in a way that can be used to determine the flow of business activities or information, assignment of resources, responsibility for the delivery of process outputs or the hierarchy of business processes. In addition to the common property of representing objectives and linking them to functions, both the VFT and EPC environment have the capacity to be linked to the simulation tools for quantitative analysis of the process. However, the weak links to the process within the VFT would make it difficult to provide meaningful analysis of the process. The RE models are directly concerned with representing business processes. Depending on the model, various aspects of business processes may be represented with the focus usually being more on the relationships between agents, objectives and functions than on the sequential aspects of the process. In other words, RE models effectively represent behavioural and organizational aspects of the processes (e.g. Yu 1999) but are less effective than the EPC environment in representing functional and information aspects of the process (Giaglis 2001). Both the RE and EPC models have well-developed formalism and graphical representation of business processes and have been successfully implemented as process modelling tools in practice. Therefore, with the exception of the social construct representation properties, business process modelling properties available within the RE models are also available within the EPC environment. Overlap in business objectives representation. As discussed in Chapter 4, business objectives fall into two classes: fundamental (including strategic) and means. The key criterion for business objectives modelling is the ability of the objectives model to clearly separate between these classes while relating them to each other. The qualitative and quantitative models of fundamental objectives are the main focus of the VFT framework. The tools for separating and relating fundamental and means objectives are also provided. However, the capability for modelling means objectives is limited within the VFT framework. The network structure of the means-ends hierarchy, while providing the causal links, is not sufficient to identify the logical or temporal relationships and can quickly become unwieldy when attempting to link even a moderately comprehensive set of objectives for a subset of business operations (e.g. HRM). The lack of well-defined levels within the means-ends network is an impediment both to easy navigation between objectives and an understandable decomposition structure. Means objectives are also omitted from the quantitative model within the VFT framework, although the BSC

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model (which can be considered as a special case of the VFT) provides some guidance on how means objectives can be measured. In contrast, the RE goal models do not separate between fundamental and means objectives but provide comprehensive structures that incorporate logical and temporal relationships and (similarly to the VFT model) decomposition structures for all business objectives. Being qualitative models, the RE goal models are not concerned with quantifying business objectives. Within the EPC environment, the objectives are linked to functions that are in turn represented in sequential order of their execution. This link allows temporal relationships between objectives to be identified. The availability of the BSC model within the EPC environment enables modelling of the “cause-and-effect” relationships between business objectives, links to the functions responsible for these objectives and KPIs for measuring them (IDS Scheer AG 2000, pp. 9-14, 915, 9-20). While this environment is more suitable for linking objectives and functions responsible for them, without integration to the VFT framework the BSC model has limited capabilities for representing objectives. For example, the links to other decision models (e.g. VFT to VAUT in Keeney 1992) are not made and the fundamental objectives structure is not articulated. Furthermore, similar to the VFT means-ends network, the logical and decomposition structures of objectives are not articulated. Property overlap between EPC, VFT and RE models is summarised in Figure 6.6. EPC implementation, formal representation, elements of process representation, temporal relationships between objectives

link to BSC link to simulation tools objective, objective-function link

VFT

RE

network structure

Fig. 6.6 Overlap properties

While all three models represent objectives, only the VFT model distinguishes between the levels of abstraction and provides links to OR/MS decision models. The EPC and VFT models have the link to the BSC model in common. However, the link within the EPC is focused on the link between the objective and function, while the link within the VFT is focused on identification and structuring of objectives. The VFT and RE models both use network structure to represent objectives.

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While the RE objectives structure is more comprehensive in terms of representing different dimensions of objectives and types of relationships between the objectives, it suffers from a lack of delineation between fundamental and means objectives. Some of the relationships between objectives that are absent from the VFT structure (e.g. temporal relationship), are present in the EPC model as a result of direct links between objectives and functions responsible for them. 6.4.4. Results of the Comparative Assessment

Results of the comparative assessment are summarised in Table 6.2 and Figure 6.6. As can be seen from Table 6.2, RE models have two desirable properties that are only partially available within the EPC environment and the VFT framework: representation of social context and logical relationships between objectives. Table 6.2 Results of the comparative assessment (property availability is denoted as “+”, partial availability is denoted as “±“, and absence of property is denoted as “−“) Desirable properties

Quantitative analysis Goal Objective, abmodelstraction, ling evaluation

± ±

VFT framework − + +

Logical relationships between objectives Dynamic system, feedback loop

−

±

+

−

+

−

Social constructs

±

±

+

EPC properties

Representation of contents

EPC environment +

RE models ± − ±

A representation of social context is partially available within both VFT and the EPC environments. The VFT framework enables identification and representation of different types of objectives (e.g. organizational, individual, operational, etc) whilst the EPC environment enables the identification and representation of organizational structure and roles and responsibilities within it at the level of both people and positions. On this basis, property deficit analysis summarised in Figure 6.5 can be revised as shown in Figure 6.7.

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6 Requirements for a Value-Focused EPC: the “WHAT” Dimension Combined Model

Candidate Models VFT

Quantitative analysis

link to DA & OR/MS models

Representation EPC properties Goal modelling

dynamic system feedback loop

objectives identification & structures

social constructs RE

objective, abstraction, evaluation logical relationships between objectives

objectives structures

Fig. 6.7 Revised deficit properties

The revised analysis highlights that the need to represent social context within the EPC environment will be addressed once the properties of the VFT framework are integrated in the EPC environment. With this in mind, the only desirable property of the RE model that remains is the ability to represent logical relationship between objectives. The results of the comparative assessment are used in the next section to define requirements for linking the desirable properties of the three models within a single coherent model.

6.5 Linking Requirements Given that both the VFT and RE models represent the goal dimension of the combined model, it makes sense to firstly create a model that includes all desirable properties of a goal model and then link this model to the EPC environment to create a goal-oriented business process model. As discussed in Chapter 4, from goal-modelling point of view, the logical relationships between objectives must be added to the VFT framework in order to complete it. The fundamental objectives hierarchy within the VFT framework already includes an implicit logical relationship described within the RE goal models as an “AND-reduction”– all lower level fundamental objectives must be satisfied in order for the high level objective to be satisfied (e.g. Haumer et al. 1998, p. 1042). The similarity of objectives structures between the VFT and RE models allows other refinement types (e.g. OR-refinement or XOR-refinement) available within the RE model to be easily added to the VFT model. Once the VFT framework is modified to include logical relationships (requirement 1), RE models become completely redundant with respect to the combined model (refer to Figure

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6.6) and the task of linking reduces to establishing the link between the VFT framework and the EPC environment. As discussed in Section 6.3, two conditions must be met in order to link any two models. Models must have a common element and be able to be represented using a common representation mechanism. It was demonstrated in Section 6.4.3 (Figure 6.5) that objectives and links between objectives and functions are represented within both EPC and VFT methodologies and can, therefore, be used as the link between them. On the basis that the EPC model was selected as the primary model, the formal language of the EPC environment must be modified to enable representation of the VFT framework (requirement 2). Due to the generic nature of the EPC formal modelling language developed by Keller and Teufel (1998) such modifications are easily made to represent any network or directed graph such as the VFT objectives structure. As well as modifying the formal modelling language of the EPC to represent the VFT objectives structure, it is also necessary to reconcile conceptual differences that may exist between the EPC and VFT methodologies. These are discussed next. 6.5.1 Objectives Structure

There is only limited information available within the EPC environment about the conceptual representation of objectives. The goals are implicitly defined within the EPC environment through the definition of the function as “a technical task or action performed on an object to support one or more company goals” (IDS Scheer AG 2000, p. 4-1). This approach assumes that company goals and objectives are known to the modeller in advance and are supported by functions (Scheer 2000). The discussion of how these goals are to be derived and related to the overall goals is limited to the suggestion (Scheer 2000, p. 22) that “goals can be derived by critical success factors as developed by Rockart.” As critical success factors are also derived from organizational goals (Rockart 1979), this does not resolve where organizational goals come from, how they should be structured and what their relationship is to process and functional goals. Process modelling rules for structuring goals corresponding to business processes and functions can be summarised as follows (Scheer 2000, p. 22): 1. goals can be linked with one another by means of a directed network; 2. functions are able to support multiple goals; and 3. the association between functions and goals can be inherited by higher levels. Only the first of the above rules relates directly to the objectives structure, the other two rules deal with the links between functions and objectives. As discussed in Chapter 4, the objectives structure within the VFT framework is non-directional in order to enable the top-down and bottom-up approaches to

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identification and structuring of objectives. To satisfy the conceptual requirements of the EPC methodology, the VFT objectives structure must be represented as a directed network (requirement 3). To maintain the bi-directional property of the VFT framework, it is proposed that the objectives network is directed from the high level objectives down to lower level objectives solely for the purpose of illustrating the top-down flow of the objectives decomposition and not for the purpose of dictating the order of objectives’ identification. 6.5.2 Link between Objectives and Functions

Within the EPC environment, each business process is decomposed into a set of functions and can itself be a function within a higher-level process. Even at the top of the functional tree within the EPC environment, processes and functions are identically defined. This implies that both functions and processes must have at least one business goal associated with them. Furthermore, these goals are linked directly to the activities responsible for them. Within the VFT framework, means objectives can be linked directly to the activities responsible for them (Keeney 1992, p. 205), while the fundamental objectives are linked to activities only via the network of means objectives. From this it is concluded that the only objectives that are common to the VFT and the EPC environment are means objectives (requirement 4). This conceptual relationship between the VFT framework and EPC environment is illustrated with the help of a Venn diagram in Figure 6.8. The following conclusions can be made with respect to conceptual links between functions and objectives within the VFT and EPC methodologies: • fundamental objectives are not directly linked to processes and functions and can only be associated with the business processes and functions via the means objectives; and • functional objectives within the EPC environment can be represented as the means objectives within the VFT framework.

127 means objectives

functional goals

fundamental objectives

U functions & processes

VFT framework

EPC environment

Fig. 6.8 Venn diagram of the relationship between VFT and EPC

These conclusions imply that means objectives that are also functional objectives must link directly to the functions responsible for them. Given the rules for linking functions and objectives within the EPC environment, the following conceptual requirements must be incorporated into the combined model: • ability to link multiple means objectives to one function (requirement 5). This requirement is consistent with the Keeney’s description of the VFT model (Keeney 1992, p. 204-205) that implies that each activity is able to influence multiple means objectives, and • a means objective linked to a lower level function can also be linked to a process within which that function is included, in other words, process objective must be able to inherit all objectives linked to functions within that process (requirement 6). This relationship is also part of the shared history requirements that are discussed next.

6.6 Shared History Requirements Recall, that Weber (1997, p. 44) describes common history as meaning that “it is impossible to partition the set [of things] into two subsets such that the history of the things in one subset is independent of the history of the things in the other set”, where history is defined (p. 42) as “the sequence of states through which it [thing] passes over time”. How does this inform the requirements for a goal-oriented business process model? The EPC environment and the VFT model (modified according to the requirements specified already) are the only two “things” under consideration. The sequence of states through which these models pass, is represented as the levels within their respective structures. Therefore, the definition above can be reworded as follows: “the decomposition structure of the business process model and the ob-

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jectives model cannot be independent of each other”. In order to satisfy this definition, the two structures must be synchronized. Two types of synchronization must be considered: (a) flow synchronization defined as: functional objectives derived from the EPC structure are sufficient to meet the high level objectives (taking into account aspects of high level objectives that are not process driven); and (b) component synchronization defined as: each attribute of a (process related) means objectives is inherited by at least one of the means objectives contributing to it. When process and objectives models are completely synchronized, their histories become intertwined satisfying the “shared history” requirement. Furthermore, the synchronization, as it has been defined here, ensures that the functions inherit objectives from the process that they are participating in as is required by the EPC methodology. This synchronization is achieved by firstly creating an objective refinement corresponding to each function and then connecting these refinements into a logical structure that reflects the logical structure of the process flow. The logical structures are described with the help of the patterns referred to by Andersson et al. (2005, p. 2) as high-level annotations of the business process. In the context of goal-oriented business processes Andersson et al. (2005) defined a set of requirements that the patterns must satisfy (requirement 7) (p. 2): 1. The annotation should be formulated in a neutral terminology independent of the language and methodology used in the process descriptions. This neutral terminology would enable searchers resulting in a minimization of false hits and a maximization of good hits. 2. The annotation, in one way or another, should represent the goal of the process, as naturally the processes we are looking for should have the same goal as the one we want to substitute. 3. The annotation, as long as possible, should not refer to the order of activities in the process. The process we want to substitute may be unsatisfactory just because it has a “wrong” order of activities. 4. The annotation, as long as possible, should not refer to in what way and by whom the activities are performed in the process. The process we want to substitute may be unsatisfactory just because a “wrong” way of performing some activities. 5. There should be a way to make annotation and comparison completely formal so that an automatic retrieval could be introduced at some stage. To achieve synchronization, the patterns of the process model must be mapped to the objective model patterns (requirement 8) (or vice versa).

6.7 Emergent Properties Requirements Kueng and Kawalek (1997, pp. 18, 25-27) argue that the ultimate objective of goal-oriented business process modelling is to “create business processes which fulfil all goals” while at the same time satisfying the requirements of a “good”

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business process model. They suggest (Kueng and Kawalek 1997, p. 19) that this can be achieved through the use of goals to “structure the design”, “evaluate the design”, “evaluate the operating process”, “help the modeller to better understand the broader implications of design, beyond those of the business process itself”. These ideas are echoed by Bider and Johannesson (2002, p. 1) who clarified the nature of the emergent properties of goal-oriented business process modelling as “the connection between the notion of goal and the notion of business process” which included but was not limited to: • • • • • • •

goal classification (strategic, operational, etc.); conceptual and formal representation of goals; methods of goal identification; engineering of business processes according to the strategic goals; impact of the process environment on the strategy of reaching the goals; goal-oriented business process patterns; and methods of steering business process instances towards their goals.

The first three of the above properties are inherited from an objectives model. Similarly, Kueng and Kawalek’s (1997) reference to the ability of a goal-oriented business process model to develop a “good” process model is a reference to a process modelling hereditary property. Other properties discussed by these authors such as “engineering business processes according to its goals”, “using goals to evaluate the design”, “goal-oriented business patterns” etc are emergent properties as they are the direct result of integration of goal and process modelling and are not found in either of these components. The implementation of the combined model must be shown to have the emergent properties defined above (requirement 7).

6.8 Summary Requirements for a goal-oriented business process model articulated in this chapter are summarised in Table 6.3. As well as articulating the requirements for a goal-oriented business process model in a systematic manner, the contribution of this chapter included the clarification of the relationship between objectives and process model that provides the foundations for value-focused process engineering with EPCs. Through the use of set mapping suitability of individual models as candidates for an integrated model that is known to have a set of desirable properties has been assessed in this chapter. The chapter also answered the question of what properties of the candidate models should be included in the integrated model. The question of what needs to be done to address the gaps within the EPC environment has been also answered in this chapter. The next chapter addresses the question of how to achieve this.

130 Table 6.3 Summary of requirements Requirements categories

Modifications to the Modifications to the Combined model VFT model EPC model

Hereditary properties Add logical relationship (req. 1) Linking

None required

Directed network (req. Formal modelling lan2) guage to represent VFT objectives structure (req. 3)

NA Functional objectives are a subset of means objectives (req. 4) Multiple means objectives link to one function (req. 5) Process objective must be able to inherit all objectives linked to functions within that process (req. 6)

Shared history

NA

NA

Process patterns are mapped to objectives patterns and satisfy Andersson et al. (2005) criteria (req. 7)

Emergent properties

NA

NA

Implementation of the combined model has emergent properties as defined by Kueng and Kawalek (1997) and Bider and Johannesson (2002) (req. 8)

7 Building a Value-Focused EPC: the “HOW” Dimension 7.1 Introduction In the previous chapter, the requirements for creating a combined value-focused thinking model were specified. These requirements are addressed in this chapter. The mapping between the structure of this chapter and goal-oriented business process requirements specified in Table 6.3 is provided in Table 7.1. Table 7.1 Mapping of goal-oriented business process model requirements to the structure of Chapter 7 Section

Requirement

7.2 Modifications to the VFT model

1: Adding logical relationships to the VFT framework 2: Adding directional links to the VFT framework 3: Using EPC formal language to represent the VFT model

7.3 Formalising the link between the VFT and 4 and 5: Linking EPC and VFT the EPC 7.4 Synchronized decomposition

6: Shared history requirement including illustration of synchronized decomposition

7.5 Setting up the example 7.6 Flow decomposition 7.7 Components decomposition 7.8 Implementation framework

7: Emergent properties including proposal for the implementation framework

7.9 Evaluation of the combined model

7.2 Modifications to the VFT Model Modifications required to the VFT model to meet goal-oriented process modelling requirements are relatively minor once the VFT model is described using the same conceptual modelling framework (introduced in Chapter 2) that is used within the

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7 Building a Value-Focused EPC: the “HOW” Dimension

IS modelling paradigm. This formulation facilitates addition to the VFT goal model the required properties already present in goal models which are strongly aligned to the IS modelling paradigm (e.g. Dardenne et al. 1993, Kavakli 2002, Khomyakov and Bider 2001, Rolland et al. 1998, Yu 1999) as well as enabling seamless integration with the EPC model as shown in Section 7.3. Keeney (1992) and many others (e.g. Olson 1996, Winston 1994) explain in detail the mechanisms for addressing the decision objectives once they are formulated. The key difference between VFT framework and other OR/MS conceptual models (as discussed in Chpater 4) is in its focus on identification and structuring of business objectives by focusing firstly on the fundamental values of the business and then, on the means of achieving these values. This aspect of the VFT framework is of particular relevance in the context of developing the VFPE framework as it provides a link from high level business objectives to business activities (i.e. means of achieving these objectives). Accordingly, in describing the VFT framework using elements of the Wand and Weber conceptual modelling framework, we will focus on the identification and decomposition of objectives, taking the links to decision models and decision models themselves as given. Articulation of decision models using Wand and Weber conceptual modelling framework is beyond the scope of this book. 7.2.1 Modelling Grammar

As discussed in Chapter 4, the VFT framework includes the following grammatical constructs that are necessary for identification and decomposition of business objectives: • • • • •

a fundamental objective; a means objective; a link between fundamental objectives; a link between means objectives; and a link between means and fundamental objectives.

The grammatical rules (Keeney 1992) that govern these constructs can be summarized as follows: • the links between fundamental objectives follow the rules of a hierarchy (referred to as the hierarchy of fundamental objectives); • the links between means objectives follow the rules of a network (referred to as means-ends network); • the links between means and fundamental objectives follow the rules of a network; • all links are non-directional; and

133

• all links are implicitly connected with an AND connector that implies that all lower level objectives must be met in order to achieve the higher level objective. Although the AND-rule was the only rule implicitly available for connection of objectives in the original VFT framework, the means-end network can be extended to include OR- and XOR- rules based on the logical objectives structures present in Requirements Engineering goal models (e.g. van Lamsweerede 2001). These extensions facilitate decomposition of objectives into structures that are suited to a realistic business scenario of equally valid alternative means for achieving a higher level objective. Without loss of generality, the direction of the objectives decomposition can be made consistent with the direction links within the EPC environment (as per requirement 2 in Table 7.1). To do that, a top-down approach is adopted so that the objectives structure is decomposed from general objectives down to lower level means goals. Within the means-ends network structure the relationship between objectives is many-to-many, this means that each means objective can have multiple incoming and outgoing arrows. In a fully decomposed “value-focused thinking” framework of objectives each means objective is ultimately linked to one of the fundamental business objectives through the means-ends network and the hierarchy of fundamental objectives. As well as ensuring that individual objectives are linked to the overall business objectives, this approach facilitates identification of conflicting objectives and development of value trade-offs required for quantitative decision modelling (Keeney 1992). 7.2.2 Modelling Methodology

Keeney (1992) and Chapter 4 of this book devote a substantial part of the VFT framework to the question of how to map real-life organizational objectives and values into the constructs within the framework including procedures for moving up and down the objectives structure. Adding logical connectors to the VFT framework takes advantage of the logical structures present in the RE goal models while maintaining the benefits of the Decision Sciences methodologies, thus resulting in a goal model that is better suited for linking to the generic business process modelling frameworks. While AND and OR connectors are defined in detail in the RE goal models (Chapter 4), the modelling methodology for the XOR connector provided in Chapter 4 (based on the definition by Rolland and Prakash 2000) needs to be further explained. As stated by Rolland and Prakash (2000, p. 182), the XOR-refinement is a special case of the OR-refinement: whereas within the OR-refinement one or more lower level goals must be fulfilled in order for the higher level goal to be fulfilled (Haumer et al. 1998), the XOR-refinement limits the choice to just one of the

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7 Building a Value-Focused EPC: the “HOW” Dimension

goals in the refinement. This situation may arise when the means objectives correspond to two mutually exclusive alternatives. For example, a means objective of “filling a vacancy with a quality applicant” may be decomposed into two lower level objectives: “filling a vacancy with a quality external applicant” and “filling a vacancy with a quality internal applicant”. The OR-refinement is not appropriate in this situation as it allows both objectives to be met simultaneously, which is not possible if only one vacancy exists. By using the XOR connector to link the two lower level objectives to the higher level objective, the mutually exclusive nature of the objectives is made clear. The modelling methodology of the extended set of the logical connectors is summarised in Table 7.2. Table 7.2 Interpretation of logical connectors within the VFT model based on Haumer et al. (1998), Lamsweerde and Letier (1998), Rolland and Prakash (2000) Connector Graphic rep- Interpretation for a objectives split resentation

XOR AND

V

objective preceding the connector can be achieved by different non-mutually exclusive means

XOR

means objective preceding the connector can be achieved by different mutually exclusive means

V

OR

objective preceding the connector can be split into more than one objective, all of which have to be met before the process can proceed

Arrows connecting objectives should be interpreted as follows: the objective at the end of the arrow contributes to the fulfilment of the objectives from which that arrow originates as well as to the higher level objectives. 7.2.3 Modelling Script and Context

The objectives’ structuring part of the VFT framework adopts a graphical network script to represent the objectives structure as illustrated in Figure 7.1. The graphical network script has been modified slightly by changing the nature of links within the means-ends network to directional links. An example of a fundamental objectives hierarchy with the directional links and logical connectors added to the means network is provided in Figure 7.1. Within this example, there is one fundamental objective that can be decomposed into two lower objectives by asking a question “what do you mean by that?” (Clemen and Reilly 2001, p. 49). The means objectives are derived from the fundamental objectives hierarchy by asking the question “how can you achieve this?” (Clemen and Reilly 2001, p. 49), with each level of the means objectives derived from the previous level by asking the question “how can you better achieve this?” (Keeney 1992, p. 71).

135

Fundamental Hierarchy

d1

d2

d3

m2

m1

V m3

m4

m5

V Means Ends Network

m6

m7

Fig. 7.1 Illustration of a VFT framework goal model with modified means-ends network

For example, in Figure 7.1, the fundamental objective d1 may refer to “maximizing quality of staff”, where quality is understood as years of experience (d2) and academic qualifications (d3). Having asked the question of how these objectives can be achieved, the decision maker arrives at two means objectives: “recruiting quality staff” (m1) and “maximize quality of existing staff” (m2). While both objectives m1 and m2 contribute directly to the fundamental objectives, objective m2 can be also said to contribute to objective m1 because a business with a reputation for commitment to staff development and rewarding excellence is more likely to attract high quality staff. The AND-refinement of objective m2 is used to illustrate how the quality of existing staff may be improved: e.g. by “effectively rewarding qualifications” (m3), “effectively rewarding experience” (m4), and “using quality development programs to develop skills of existing staff” (m5). With objective m5 able to be achieved either through “quality in-house training” (m6) or “quality external training” (m7) or both. To enable development of tools based on methods from different knowledge domains, it is essential to accompany the intuitive model with a formalism that allows seamless transition between these domains. Accordingly, the EPC formalism discussed in Chapter 5 is used to represent the VFT framework in the next section. Adopting the EPC formalism discussed in Chapter 5, objects and links within the VFT framework are described with the 7-tuple g oId = Ido ,ν o ,κ o ,τ o ,τ oκ ,α o ,α oκ

tion 5.1) as follows:

by modifying τ ,τ κ representations (from equa-

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7 Building a Value-Focused EPC: the “HOW” Dimension

⎧fundamental objective, means objective, ⎫ ⎪ ⎪ : τ 0 ν 0 → ⎨ AND connector, OR connector, ⎬ ⎪XOR connector ⎪ ⎩ ⎭ ⎧means decomposition link, ⎪

(7.1)

⎫ ⎪

τ 0κ : κ 0 → ⎨fundamental decomposition link, ⎬ ⎪means decomposition subset link ⎪ ⎩ ⎭

Using these representations, the sets of fundamental objectives, means objectives and logical connectors are defined in (7.2). Sets of fundamental decomposition links that are used to connect the fundamental objectives with each other and to means-ends network; and means decomposition links that connect means objectives and logical connectors are defined in (7.3). The means decomposition subset link is described in Section: 7.4.2. O m = {u ∈ν o τ o ( u ) = means objective}

(7.2)

Od = {u ∈ν o τ o ( u ) = fundamental objective} J AND = {u ∈ν o τ e ( u ) = AND connector} J OR = {u ∈ν o τ e ( u ) = OR connector}

J XOR = {u ∈ν o τ e ( u ) = XOR connector} J=J AND ∪ J OR ∪ J XOR (u,v) ∈ κ is a link from node u to node v

(7.3)

K M = {(u,v) ∈ (OM × O M ) ∪ (OM × O J ) ∪ (OJ × OM )} (u,v) ∈ K M :⇔ τ oκ ( (u,v) ) = means decomposition link K D = {(u,v) ∈ (O D × O D ) ∪ (O F × O D ) ∪ (O D × O F )}

(u,v) ∈ K D :⇔ τ oκ ( (u,v) ) = fundamental decomposition link K VFT =K M ∪ K D

C Path u ⎯⎯ → v is defined within the VFT model u,v ∈ C ∃ (u,v) ⇔ (u,v) ∈ K VFT where C represents as: a series of nodes and connectors included in the path

(7.4)

137

Similarly to an EPC model, there are a number of local consistency criteria (corresponding to local consistency criteria defined in Keller and Teufel (1998, pp. 161-165)) that apply to the VFT model and are defined using the constructs introduced in Chapter 5 (Section 5.3.3): O1: Means objectives can have several inbound and/or several outbound means objective decomposition links. Means objectives at the top level of the means objectives network do not have any inbound means objectives decomposition links and similarly means objectives at the bottom of the means objectives network do not have any outbound means objectives:.

(

) (

∀u ∈ OM ,v ∈ K M : i- (u,v)=0 ∨ i- (u,v) ≥ 1 ∧ i + (u,v)=0 ∨ i + (u,v)

(7.5)

O2: There are no loops. ∀ u ∈ Om ∪ Od : (u,u) ∉ K VFT

(7.6)

O3: Connectors have one input and several outbound decomposition links.

(

)

∀ u ∈ J: i - (u,m)=1 ∧ i + (u,m)>1

(7.7)

O4: Connections between connectors are acyclical. C ∀ u ∈ J: u ⎯⎯ →v ⇒ u ≠ v

(7.8)

O5: Each means objective is part of a path that starts at a fundamental objective. C ∀ u ∈ OM ∃v ∈ O D : v ⎯⎯ →u

(7.9)

As a single VFT goal-model describes a fully decomposed network of objectives for a business, global criteria do not need to be defined within the scope of a business. The representation of objectives in the decision analysis context has been driven by the need to consider and balance multiple and sometimes conflicting objectives (Olson 1996, p. 2) that arise when making a decision about selecting from a set of alternatives (Olson 1996, p. 1). This context of supporting business decision making through modelling of objectives is the traditional setting for VFT conceptual modelling (e.g. Keeney 1992, 1993, 1994).

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7 Building a Value-Focused EPC: the “HOW” Dimension

7.3 Formalising the Link between the VFT and the EPC Aalst et al. (2003a, p. 6) state that “activities in elementary form are atomic units of work, and in compound form modularize an execution order of a set of activities”. Similarly, objectives in elementary form are atomic units of intention, and in compound form modularise an achievement order for a set of objectives. EPC and VFT structures are (respectively) activities and objectives in the compound form. The VFT and the EPC methodologies are linked through common elementary constructs of function and intention, i.e. each unit of work (i.e. function) has at least one intention (i.e. functional objective). As previously discussed, functional objectives are a subset of means objectives that are themselves atomic units of the VFT structure. For example, as illustrated in Figure 7.2, activity f1 that is an atomic unit of an EPC is linked to objectives o1 and o2 (this link is discussed in detail in Section 7.4) that are an atomic unit of a means-ends network (note that according to Figure 7.2, links between other functions and the objectives in the means-ends network are not yet made, hence functional objectives o3-o5 do not appear in the VFT structure).

Fig. 7.2 Link between an EPC and a VFT

In the graphical representation of the objective network, functional objectives are represented using EPC notation in order to differentiate between means objectives that are functional objectives and other means objectives. Figure 7.2 illus-

139

trates this by representing objectives o1 and o2 (that are known functional objectives) differently from other objectives in the means-ends network. Recall (Chapter 5, equation 5.15) that objectives are included in an e-EPC model by modifying τ ,τ κ representations to include objectives (referred to as process goal) and objectives assignment links (referred to as goal assignment link) that connect objectives and functions that are aimed at achieving them (7.10). These representations are then used to define script for the additional nodes and links (7.11-7.12). : ⎧function, event, process sign, AND connector, ⎫ ⎪ ⎪ τ e : ν e → ⎨OR connector, XOR connector, hierarchically ⎬ ⎪ ranked function, functional goal ⎪ ⎩ ⎭ ⎧control flow link, goal assignment link,⎫ τ eκ : κ e → ⎨ ⎬ ⎩ process decomposition link ⎭ OP = {u ∈ν e τ e ( u ) = functional goal}

K O = {(u,v) ∈ (B1 × O P ) ∪ (O P × B1 )}

(7.10)

(7.11) (7.12)

(u,v) ∈ K O :⇔ τ eκ ( (u,v) ) = goal assignment link

In the context of goal-oriented business process modelling, each function is linked to its goal(s) with the goal assignment links: ∀u ∈ B1 ∃v ∈ Op : (u,v) ∈ K O

(7.13)

Similarly to the EPC model, the VFT model must also be modified to allow for explicit representation of functional objectives as follows: 1) the set of functional objectives does not intersect with the set of fundamental objectives for the same business; 2) functional objectives are linked to each other and other means objectives with means decomposition links; and 3) fundamental objectives are linked to functional objectives with fundamental decomposition links. These statements are formally expressed in (7.14): OP ⊆ OM OP ∩ OD = ∅

(7.14)

140

7 Building a Value-Focused EPC: the “HOW” Dimension C ∀ u,v ∈ O P ∃ u ⎯⎯ → v ⇔ ∀ x, y ∈ C , (x, y ) ∈ K M C ∀ u ∈ OP , v ∈ OM \ OP ∃ u ⎯⎯ → v ⇔ ∀ x, y ∈ C , (x, y ) ∈ K M C ∀ u ∈ OP , v ∈ OD if ∃v ⎯⎯ → u ⇔ ∀ x, y ∈ C,

(x, y ) ∈ K VFT ∧ ∃x, y ∉ J , (x, y ) ∈ K D

Within an EPC structure, a single function can be either an elementary function or a hierarchically ranked function (Chapter 5, equation 5.1 and 5.2). Elementary functions are at the bottom of the EPC functional tree. Accordingly, objectives that are linked to them should not be decomposed further as the elementary function represents the smallest unit of work that is aimed at achieving the intention expressed by that functional goal. This is formally expressed as follows: K OF = {(u,v) ∈ (F × O P ) ∪ (O P × F)}

(7.15)

∀u ∈ O M ,v ∈ K M : if u ∈ O P ∧ ∃x ∈ F: (u,x) ∈ K OF ⇒

(i (u,v)=0 ∨ i (u,v)=1) ∧ (i -

-

+

(u,v)=0

)

Similarly, since hierarchically ranked functions at the top of the functional tree represent the highest composite unit of work that is aimed at achieving business objectives, they must be linked to at least one objective within the highest level of the means-ends network.

(

)

∀g ei ∈ L HE , v ∈ Φ( g ei ,2) ∩ B2 : ∃u ∈ ( L HM ∩ O P ) ∧ (v,u) ∈ K O

where

{

L HE = g ie ∈ Ge ∀j ∈ I e ∃ v ∈ Φ( g ej ,2) ∩ B2 : (v,g ie ) ∈ K H

{

L HM = u ∈ OM ∃ v ∈ O M : (v,u) ∈ K M

}

(7.16)

}

To ensure that each means objective is able to be achieved, means objectives that are not directly linked to functions must be linked to functional objectives with the help of the objectives refinements discussed in Section 7.2.1. ∀u ∈ ( O M \ O P ) , n ≥ 2 ∃v1 ,..., v n ∈ O P :

(7.17)

C ∀i = 1...n,n ∈ N 0 ∃vi ⎯⎯ →u

Similarly, functional objectives linked to the hierarchically ranked functions must be linked to lower level functional objectives.

141

( )

∀o ∈ OP , f ∈ FH , g ie ∈ G t : ( o,f ) ∈ K O ∧ f,gie ∈ K H

(

)

(7.18)

C ∃u ⎯⎯ → o ∀u ∈ Φ gie , 2 ∩ OP

Having defined the links at the atomic level of the constructs, the next step is to define the modelling methodology for deriving congruent EPC and VFT structures.

7.4 Synchronized Decomposition The requirement of shared history, discussed in the previous chapter, dictates that the combined model must include the atomic link and rules that enable two components of the model (EPC and VFT) to be constructed in tandem. As can be seen from Figure 7.2, the compound structures are built up from constructs of atomic elements. The number of constructs within each structure is limited. For example, the means-ends network has only three constructs (described in the previous section): AND-refinement, OR-refinement and XOR-refinement. An EPC, on the other hand, may have as many as 26 constructs with different levels of complexity (Aalst et al. 2003a). In addition, each function within an EPC may itself be decomposed into another (lower-level) EPC (Davis 2001). Without loss of generality, rules for synchronized decomposition can be split according to the EPC construct type into: (a) rules for modularising intentions corresponding to a single activity, or in other words, rules for deriving objectives constructs from an atomic EPC construct; and (b) rules for mapping the execution order of activities to the order of achievement of objectives, or in other words, rules for deriving objectives constructs from EPC activities in compound form. Consistent with the terminology used in the formal models of the VFT and the EPC, the term elementary function is used to refer to atomic work activities; hierarchically ranked function or process are used to refer to functions that are decomposed into lower level functions within the EPC structure; and functional goal is used to refer to the intention of a function. 7.4.1 Rules for Modularising Functional Goals of a Single Function

The convention for pattern description used by Aalst et al. (2003a) that includes pattern name, pattern description, pattern treatment and pattern illustration, is adapted to describe the relationship between the e-EPC and means-ends network constructs. Pattern name is used to provide a short description of the pattern, e.g. elementary function linked to single objective. All patterns in this section are abbreviated to SP that stands for Single function Pattern with a unique id number for each pattern. So that, for example, a single function linked to a single objective is

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7 Building a Value-Focused EPC: the “HOW” Dimension

referred to as SP1, a single function linked to multiple objectives is referred to as SP2, and so on. The pattern description heading is used to describe the e-EPC construct, while the pattern treatment heading is used to describe the rules for deriving the means-ends network construct corresponding to the e-EPC construct. Both constructs are illustrated graphically and with an example under the pattern illustration heading. SP1 – elementary function linked to single objective (fig. 7.3a) Pattern Description: A single goal corresponds to a single elementary function. Pattern Treatment: This pattern is included solely for the purposes of completeness; no rules are required as the link is made at the atomic level within both the EPC and VFT structures.

Fig. 7.3a Atomic link

Illustration: A low level function within an HR context may be to send rejection letters to the unsuccessful applicants with the only objective for this function to be “to minimize the time delay between the decision and notification of applicants”.

SP2 – elementary function linked to multiple objectives (fig. 7.3.b) Pattern Description: Multiple goals corresponding to a single elementary function. Pattern Treatment (AND): As the elementary function is aimed at meeting multiple goals, the function cannot be said to have achieved its objectives unless all of these goals are met. Therefore, these goals can be represented within the VFT framework as an AND-refinement of a high-level means objective that is indirectly linked to the elementary function. In some cases, goals corresponding to a single function may be in conflict with each other (e.g. cost and quality). Treatment of conflicting goals is discussed in detail in Chapter 4, illustrated in Section 7.7 in the context of synchronized decomposition and formalised in Chapter 9.

143

Fig. 7.3b Multiple objectives

Illustration: Minimizing cost while maximizing the quality of an outcome is an example of two objectives often assigned to a single business activity. Both objectives must be met in order to meet the overall objective of that activity. The overall objective may be worded as minimizing costs while maximizing quality or simply as satisfaction of f1 objectives.

SP3 – hierarchically ranked function directly linked to lower level functional objectives (fig. 7.3c) Pattern Description: A hierarchically ranked function is decomposed into a set of lower level functions each with their own lower level objectives with the hierarchically ranked function linked to each of these lower level objectives. Pattern Treatment (AND): This pattern is similar to the SP2 pattern. In the SP2 pattern, a single elementary activity had two objectives, whereas in this pattern (SP3), the function is decomposed into two activities that are each responsible for one of those objectives. In both cases, an AND-refinement should be used within the means-ends network to connect lower level objectives to an implied overall objective of the function.

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7 Building a Value-Focused EPC: the “HOW” Dimension

o1 e1

o1 f1o1

V

Process P1 f1o2

f1o1

f1o2

e2

Functional tree f1

f1o1

f2

f1o2

Process P1

Fig. 7.3c Hierarchically ranked function with direct links

Illustration: For example, a shortlist applicants function may have two objectives: minimize number of applicants to be interviewed and maximize quality of applicants. The function can be decomposed into two separate activities: develop shortlisting criteria and interview referees, the first function is aimed primarily at maximizing the quality of applicants through ensuring that the criteria are closely matched to job requirements and the second function is aimed primarily at minimizing the number of applicants to be interviewed by using referees to establish how closely each applicant meets the criteria.

SP4 – hierarchically ranked function linked to single objective that implies the achievement of lower-level functional objectives (fig. 7.3d) Pattern Description: A hierarchically ranked function is linked to a functional goal that is different from the lower-level functional goals. Pattern Treatment (AND): This pattern is another variation on the SP2 pattern with the main difference being that the overall functional objective is explicitly stated (rather than implied) within the EPC structure. Consequently, the ANDrefinement is used to link the lower level functional goals to the goal of the hierarchically decomposed function. This treatment is consistent with requirement 6, identified in the previous chapter. This requirement states that all lower level objectives must be able to be inherited by the higher level functions. The ANDrefinement stipulates, that in order for the high-level functional objective to be met, all lower level objectives must be met, which is equivalent to the high level function inheriting all lower level objectives. Therefore, each level of the process hierarchy must be reflected in the objectives hierarchy as an AND-refinement with

145

the highest level of the means-ends network corresponding to the highest level of the process hierarchy (equation 16 in this Chapter).

Fig. 7.3d Hierarchically ranked function with single objective

Illustration: Refer to example in SP3.

SP5 – hierarchically ranked function linked to multiple objectives that imply achievement of lower-level functional objectives (fig. 7.3e) Pattern Description: A function with multiple objectives is hierarchically decomposed into lower-level functions. Pattern Treatment (AND): Case 1: if hierarchical decomposition does not introduce any additional objectives, then the SP1 pattern should be used to represent multiple objectives of the high level function. Case 2: if each of the lower-level objectives contributes to no more than one of the high level objectives, then the SP2 pattern should be used to link lower level objectives to each of the high level objectives and the SP1 pattern should be used to link the high level objectives together (in cases where only one lower level objective contributes to the higher level objective, the AND-connector may be omitted). Case 3: if some or all of the lower level objectives contribute to more than one of the higher level objectives an AND-join refinement is used to link them. Within the AND-join refinement the AND connector is used to both merge the hierarchically ranked function’s objective and as an AND-refinement for the lower level objectives. To simplify the presentation of the objectives network, it is recom-

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7 Building a Value-Focused EPC: the “HOW” Dimension

mended that higher level function’s objectives are combined into a single objective and the SP4 pattern is used for the combined objective. PO e1

PO Po1

V

Process P1 Po2

Po1

Po2

e2

V Functional tree f1

o1 o1

o2

Process P1 f2

o2

Fig. 7.3e AND-join

Illustration (Case 3): Consider a process of selecting a recruitment agency with multiple objectives of “maximizing quality of applicants” and “minimizing recruitment costs”. This high level process “select recruitment agency” may be decomposed into two functions: “interview representatives from the recruitment agencies” with the objective of maximizing the knowledge about the recruitment process used by an agency and “decide on the best agency” with the objective minimize the gap between the organizational requirements and agency capabilities. Both these functions have objectives that contribute to the quality of applicants and recruitment costs objectives of the high-level function. Therefore, the AND-join pattern should be used. In an alternative representation, the two higher level objectives are first combined into a single objective of efficient and effective recruitment (SP1) and the two lower level objectives are represented as an AND-refinement of that objective (SP2). The five patterns discussed allow any combination of objectives linked to a single function to be composed into a single objective that corresponds to that function. This simplifies the discussion of the rules that map the execution order of activities to the order of achievement of objectives as it is now possible to assume (without loss of generality) that each function within the EPC (irrespective of whether it is elementary or hierarchically decomposed) is linked to a single functional objective. Once the execution mapping is complete, composite objec-

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tives of single functions can be easily decomposed into elementary objectives using the rules described in SP1-SP5. 7.4.2 Rules for Mapping the Execution Order of Activities to the Order of Achievement of Objectives

The execution order of activities has been described by Aalst et al. (2003a) as a set of workflow patterns grouped into the following categories: Basic Control Patterns, Advanced Branching and Synchronization Patterns, Structural Patterns, Patterns Involving Multiple Instances, State-based Patterns and Cancellation Patterns. These patterns are used to annotate the order of activities within the process and have been represented in Petri-net based notation and in neutral terminology (Aalst et al. 2003a). The analysis of how these patterns support EPCs was undertaken by Mendling et al. (2005) resulting in extensions to the EPCs that are supported by the EPML and include (p. 13) “introduction of the empty connector, the inclusion of a multiple instantiation concept for both simple functions as well as for hierarchical functions and process interfaces; and the inclusion of a cancellation concept”. Neither the generic patterns introduced by Aalst et al. (2003a) nor extensions for the EPCs introduced by Mendling et al. (2005) reference business goals. The contribution of this section is the mapping of the generic patterns introduced by Aalst et al. (2003a) to the means-ends network structure in order to link the execution order of activities to the order of achievement of objectives. Where applicable, independently of Mendling et al. (2005), the generic patterns have been represented using EPC notation to facilitate synchronized decomposition of objectives and activities within the combined model. The mapping of the workflow pattern to the corresponding goal pattern is structured similarly to the structure of the single function patterns with the following exceptions: • the abbreviation WP that stands for Workflow Pattern reflects the fact that the execution flow is being mapped to the goal structure; • the pattern description does not discuss how to link multiple objectives corresponding to a single function (as already mentioned each function is assumed to have one objective linked to it), rather a brief definition of the workflow pattern from Aalst et al. (2003a) is quoted; • the pattern analysis heading is added to provide an analysis of the pattern from the goal modelling point of view; and • the pattern treatment describes the objectives pattern corresponding to the workflow pattern under consideration. Some general remarks with respect to workflow patterns and their relationship to goal modelling need to be made before individual patterns are discussed.

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Remark 7.1

Goal patterns are not aimed to mirror the process flow but rather to reflect the relationships between objectives, taking process structure into account. This means that: • some workflow patterns do not need to be mapped to goal patterns. For example, empty process branches (i.e. do nothing branches) that do not include a function (e.g. Davis 2001, pp. 125-126) do not have to be reflected in the goal pattern; and • different workflow patterns may correspond to the same goal pattern (e.g. compare sequence and parallel split patterns discussed later). Remark 7.2

Similar to SP patterns, in case of the WP patterns it is assumed that that there exists an objective O that corresponds to a set of functions (f1, …, fn) that are structured in a pattern P, where for each i, there exists an objective oi such that fi is aimed at achieving oi with an exception of a do nothing function or empty process branch that may have a null objective represented in the means-ends structure by a full stop symbol “z”. Remark 7.3

In consideration of the linking rules defined in Section 7.2, it is acknowledged that: • objectives other than those relating to the process can also contribute to the goal pattern but are not included in the mapping as they are not relevant to the relationship between functional and objectives structure; • the network nature of the objectives structure implies that individual objective can contribute to more than one pattern; and • functional objectives do not have to be unique to the specific function although identical objectives may result in a “trivial” objectives pattern consisting of a single objective. By following identification rules discussed in Chapter 4, it is possible to refine most functional objectives so that they are not identical to functions within the same level of the process, making the goal-pattern structure easier to read and reconcile with the workflow pattern. Remark 7.4

Goal constructs are considered in the context of process decomposition. In this context only single inputs, multiple outputs operators are included (i.e. only those that decompose the process). In other words, workflow patterns corresponding to

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process mergers and synchronization are not included in the goal patterns, with the only exception to this being an orphaned merger which is defined as mergers within the process that do not have a corresponding split earlier in the process (e.g. processes having multiple start events requiring action before the process flow is merged). The mapping can be easily reversed to make it suitable for a bottom-up approach to business process design. This bidirectional nature of the relationship between the workflow and objectives patterns is discussed in Section 7.8 and illustrated in Chapter 8. WP1 – Sequence (fig. 7.4a) Workflow Pattern Description (Aalst et al. 2003a, p. 10): An activity in a workflow process is enabled after the completion of another activity in the same process. Analysis: The overall objective O of this pattern P cannot be achieved unless all functions within the pattern have been executed and, therefore, individual objectives of these functions have been completed. This is consistent with the definitions of AND-refinement in Table 7.2. Objectives Pattern (AND): Similar to the definition of the OR-refinement pattern by Haumer, Pohl and Weidenhaupt (1998), this objective pattern is formally defined as follows: to fulfil O all subgoals o1, o2, …,on must be fulfilled. PO e1

PO Po1

V

Process P1 Po2

Po1

Po2

e2

V Functional tree f1

o1 o1

Process P1 f2

Fig. 7.4a Sequence

o2

o2

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Illustration: The Interviewing Applicants process consists of two activities “schedule interviews” and “conduct interviews” which are executed sequentially. The objective of this process to “select best applicant efficiently” is achieved when both “minimize scheduling costs” and “select best applicants that satisfy business requirements” (corresponding to the two functions) are achieved.

WP2 – Parallel Split (fig. 7.4b) Workflow Pattern Description (Aalst et al. 2003a, pp. 10-11): A point in the workflow where a single thread of control splits into multiple threads of control which can be executed in parallel thus allowing activities to be executed simultaneously or in any order. The process can be split following or preceding functions. Analysis: Activities corresponding to each thread within the pattern must be completed before the process can continue and is consistent with the definition of the AND-refinement in Table 7.2. Objectives Pattern (AND): The objective structure corresponding to these workflow patterns is the same as the objectives structure in WP1. e1 i) preceding functions

V f1

f2

o1

e2

o2

e3

O O

V ii) following a function

V e1

f1

e2

o1

o2

f2

O e3

Fig. 7.4b Parallel split

e4

o1

o2

151

Illustration: The AND workflow split may be triggered by an AND connector either preceding two or more functions or following a function as illustrated in Figure 7.4b. In both cases, the corresponding goal-pattern is identical to the one illustrated in WP1. Preparing for Interview process, commences with “the names of shortlisted applicants notified”. Once the names are known two activities are undertaken in parallel: “schedule interviews” and “select interview panel” objectives of “minimizing scheduling costs” and “maximizing the quality of the panel” must be both met in order to “facilitate the best outcome from the interview process”. Alternatively, the shortlisting process may result in two outcomes: “list of applicants shortlisted for interview”, that triggers the action of “schedule applicants for interviews”, and “list of dates available for interview” that triggers the action of “book interview rooms”, the two actions can be undertaken in parallel, and the objectives of “minimizing scheduling costs” is achieved only if both the costs of scheduling applicants and booking of rooms are minimized.

WP3 – Synchronization As discussed in Remark 7.4, this workflow pattern (Aalst et al. 2003a, p. 11) does not need to be mapped to goal-pattern that is synchronized with process decomposition. WP4 – Exclusive Choice (fig. 7.4c, fig. 7.4d, fig. 7.4e) Workflow Pattern Description (Aalst et al. 2003a, pp. 11-12): A point in the workflow process where, based on a decision or workflow control data, one of several branches is chosen. Analysis: When mapping the concept of exclusive choice to a goal pattern it is important to separate between the following types of EPC constructs: XOR-“do nothing” function: in some cases an XOR may split a process into branches one of which will not have its own objective (e.g. in a do/don’t do scenario, the don’t do branch does not need an explicit objective). XOR-single instance: single instance process where the choice is made between mutually exclusive alternatives so that within a single execution the process only ever goes through one of the XOR paths (e.g. a vacancy is filled either with an external or an internal applicant). This is consistent with the definition of the XORrefinement in Table 7.2. XOR-multiple instance: multiple instance process where within a single execution the process would go through an XOR split more than once, following different paths depending upon the outcome of the decision. In this case the XOR-split operates on mutually exclusive (non-empty) sets of instances. However, all alternatives paths will be utilised at some point in the process. To accommodate the XOR-multiple instance split, the means decomposition subset link was added to

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the definition of τ 0κ in equation 7.1. This link is used to indicate that both objectives are met for a subset of process instances. A goal pattern that includes this are link is mapped to the exclusive choice process structure. The funnel links used to represent the means decomposition subset link in the graphical representation of the goal pattern. An XOR-connector that is used to define a loop (refer to WP10), is an example of this structure with another example being when a process applies to multiple subjects (e.g. job applicants) and at some point the process flow is split according to some characteristic of the subjects (e.g. local versus overseas applicants, accepted versus rejected applicants etc). Objectives Pattern (“do nothing” function): In these cases, the split should still be represented in the goal pattern. However, the branch corresponding to the function without its own objective should have a null objective rather than the functional objective.

XOR

Fig. 7.4c Null-branch

Illustration (XOR-null branch): In the performance management process, following the process evaluation, staff with performance problems must undergo performance counselling, whereas no action is required for other staff at this stage of the process. Objectives Pattern (XOR-single instance): XOR-refinement is used when the pattern objective O can be achieved by mutually exclusive means o1, o2, …, on and only one of those means is evoked within a single execution of the process.

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XOR

e1

e2

O

XOR

f1

o1

o2

f2 o1

O e3

o2

e4

Fig. 7.4d Exclusive choice (single instance)

Illustration (XOR-single instance): Two alternative activities are considered for developing an induction program: “to outsource induction program development” or “to develop an induction program in-house”. The objective of an “effective and efficient induction program” is, therefore, achieved either through “effective and efficient management of the external agency” or “effective and efficient development of the program internally”. Objectives Pattern (XOR-multiple instance): means decomposition subset links are used describe the situation where a pattern objective O applies to multiple instances within a single process, and is achieved by objectives o1, o2, …, on that operate on mutually exclusive sets of these instances. Note that the XOR-multiple instance split applies to multiple records and multiple cases, in other words, it also applies when multiple runs of the single-instance process are expected.

Fig. 7.4e Exclusive choice (multiple instances)

Illustration (XOR-multiple instance): Depending on the outcomes of the skills assessment, some staff may be required to complete an internal training course while others may be recommended for external training. The objective of “minimizing the gap between required and available skills” will therefore be achieved

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as a result of “minimizing the gap between required and available skills using internal courses” for some staff and as a result of “minimizing the gap between the required and available skills using external courses” for others. WP5 – Simple Merge As discussed in Remark 7.4, this workflow pattern (Aalst et al. 2003a, p. 12) should not be mapped to a goal pattern that is synchronized with process decomposition, with the exception of an orphaned merger construct. In order to reflect objectives corresponding to the functions within an orphaned merger, the goals of the functions within the merge pattern are treated as if the functions were in a split with the same connector used to merge them. For example, a selection process may start with two events: application received and vacancy identified. Once the application is received, it needs to be entered into the applicants’ database, similarly, once the vacancy is identified it needs to be entered into the vacancy database. After these activities are completed, the applicants and the vacancies can be matched as part of the interview shortlisting exercise. Within this process, the objectives corresponding to entering data into the databases are part of the merge pattern that does not have a corresponding split. However, since both these activities must be completed before the process flow is merged, an AND split connector is implied. Accordingly, two objectives are treated in accordance with the WP2 (parallel split) pattern. If, on the other hand, the two functions were connected within an OR operator implying a Multi-Choice split, then the two objectives should be treated in accordance with the WP6 (multi choice) pattern. WP6 – Multi-Choice (fig. 7.4f) Workflow Pattern Description (Aalst et al. 2003a, pp. 13-14): A point in the workflow process where, based on a decision or workflow control data, a number of branches are chosen. Analysis: According to the workflow pattern description, branches can be executed in parallel or individually with any combination of branches allowable (i.e. they are not mutually exclusive). Haumer et al.’s definition (1998, p. 7) of the ORrefinement, as at least one goal among the refinement must be fulfilled, comes closest to the description of the relationship between objectives within this pattern and is consistent with the OR-refinement definition in Table 7.2. Note that in order to avoid an ambiguous pattern definition, the definition of the OR-refinement can be further clarified as the same intention but different means (Rolland and Prakash 2000, p. 192). As with the workflow pattern, the OR-refinement within the objectives pattern can be represented by a combination of AND- and XORrefinements and null objectives (e.g. Davis 2001; Aalst et al. 2003a).

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Objectives Pattern (OR): OR-refinement is used when the pattern objective O can be achieved by at least one of the o1, o2, …, on objectives.

Fig. 7.4f Multi-choice

Illustration: Staff training needs can be satisfied either via internal training, external training or both. The objective of “minimizing the gap between the required and available skills” can, therefore, be met either through “minimizing the gap between the required and available skills using internal training” or “minimizing the gap between the required and available skills using external training” or using both sources of training.

WP7 and WP8 - Synchronising Merge and Multi-Merge As discussed in Remark 7.4, these workflow patterns (Aalst et al. 2003a, pp. 14-17) do not need to be mapped to a goal pattern that is synchronized with process decomposition. WP9 – Discriminator Workflow Pattern Description (Aalst et al. 2003a, pp. 17-19): The discriminator is a point in a workflow process that waits for one of the incoming branches to complete before activating a subsequent activity. From that moment on, it waits for all remaining branches to complete and “ignores” them. Once all incoming branches have been triggered, it resets itself so that it can be triggered again (which is important otherwise it could not really be used in the context of a loop). Analysis: This pattern determines the sequence of the flow at the merging of the branches and, therefore, does not apply to the objectives diagram. Note that this pattern is supported by a minority of workflow applications and is not supported by the EPC (Mendling et al. 2005).

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WP10 – Arbitrary Cycles (Loops, Interactions, Cycles) (fig. 7.4g) Workflow Pattern Description (Aalst et al. 2003a, pp. 20-21): A point in a workflow process where one or more activities can be done repeatedly. Analysis: Depending on the activity within the loop and the nature of the exit decision, the exit decision may be treated as a null-objective. For example, if the exit decision function is to add 1 to the counter, it does not have to have its own objective, provided that the objective of the activity being repeated in the loop articulates the repetitive nature of that activity. If the exit decision does have its own objective then that objective has to be included for both the loop and the rest of the process objectives branches (refer to Figure 7.4g) as the count function appears in both paths. Within the workflow pattern, the loop is represented using an XOR connector with the join occurring before the corresponding split to allow activities to be repeated (refer to Figure 7.4g). Since the order and timing of the join are not relevant to goal-patterns (Remark 7.1), the workflow loop pattern can be represented using an XOR-multiple instance goal-pattern. Objectives Pattern (XOR-multiple instance): The XOR-multiple instance pattern as discussed in WP4.

Fig. 7.4g Arbitrary cycles

Illustration: Rather than representing the administration of an interview process as a multiple instance process, it can be represented within the workflow using the loop with the decision function used to determine whether all available applicants have been interviewed. If the answer is yes, then the loop exits and execution proceeds to the next activity, for example, “take minutes of the panel discussion”. If

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the answer is no, then the “accompany the next scheduled applicant to the interview room” becomes the next activity. The overall objectives of the interview administration process may be to “ensure smooth running of the process”. This is achieved when “the gap between interviews is minimized” (objective of the loop activity function), “the accuracy of the minutes is maximized” (objective of the remainder of the process or loop exit function) and “all applicants are accounted for” (objective of the decision function). However, only two of the three objectives can be achieved within a single instance: either “the gap between interviews is minimized” and “all applicants are accounted for” are achieved; or “all applicants are accounted for” and “the accuracy of minutes is maximized” are achieved. Therefore, the XOR-multiple instance pattern must be used to represent the mutually exclusive nature of these instances. The shared (decision) objective is represented at the lower level of the objectives structure with the AND-goal pattern. WP11 – Implicit Termination Workflow Pattern Description (Aalst et al. 2003a, p. 22): A given sub-process should be terminated when there is nothing else to be done… Most workflow engines terminate the process when an explicit Final node is reached. Any current activities that happen to be running at that time will be aborted. Analysis: This pattern refers to an event within the process rather than a flow of functions, therefore, it does not need to be mapped to the goal pattern as the termination of the process is implied in the goal-pattern: when all goals in the goal pattern are achieved the corresponding process can be terminated.

WP12 – Multiple Instances without Synchronization Workflow Pattern Description (Aalst et al. 2003a, pp. 23-24): Within a context of a single case (i.e., workflow instance) multiple instances of an activity can be created, i.e., there is a facility to spawn off new threads of control. Each of these threads of control is independent of other threads. Moreover, there is no need to synchronise these threads. There are three implementation mechanisms:

1. Use of the loop and the parallel split construct as long as the workflow engine supports the use of parallel splits without corresponding joins and allows triggering of activities that are already active. 2. Some workflow languages support an extra construct that enables the designer to create a sub-process or a subflow that will “spawn-off” from the main process and will be executed concurrently.

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3. In most workflow management systems, the possibility exists to create new instances of a workflow process through some API. This allows for the creation of new instances by calling the proper method from activities inside the main flow. Note that this mechanism works. However, the system maintains no relation between the main flow and the instances that are spawned off. Although SAP R/3 Workflow does not support this pattern, it is supported by most workflow management systems. Analysis: The implementation option 1 can be mapped to an objective pattern using AND-refinement and XOR-multiple instance patterns. The other implementations can be also mapped to this pattern for the purposes of representing objectives. This pattern is not illustrated as it is derived directly from other patterns already discussed. WP13, WP14, WP15 – Synchronization of concurrent threads As discussed in Remark 7.4, these workflow patterns (Aalst et al. 2003a, pp. 24-29) do not need to be mapped to goal-pattern that is synchronized with process decomposition. WP16, WP17, WP18 – State-based Patterns (deferred choice, interleaved parallel routing, milestone) These patterns (Aalst et al. 2003a, pp. 30-38) are concerned with the order and timing of the activities. From a goal-pattern point of view all objectives must be met (taking into account OR and XOR structures in the workflow model) irrespective of the order or timing of the activities. The temporal dimension is expressed through the link between the objectives and functions and is not explicitly represented in the objectives structure. Therefore, these patterns do not need to be mapped to a goal pattern. WP19, WP20 – Cancellation Patterns (cancel activity (single instance), cancel case (all instances of each activity)) As long as the process includes an activity and a corresponding objective it should be included in the goal pattern irrespective of whether these activities may be cancelled for some instances. If all instances are cancelled resulting in so-called “shadow activities” within non-null objectives (Aalst et al. 2003a, pp. 38-39), then an XOR decision structure can be used to include the goal of an activity if it has not be cancelled and a null-objective if it has.

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7.4.3 Mapping Summary

To simplify the use of the mapping, two look-up tables have been constructed: Table 7.3 provides the summary of the mapping by the objectives structures; and Table 7.4 provides the summary of the mapping by the workflow structures. Table 7.3 includes all objectives structures including two additional structures that were introduced to avoid ambiguity - AND-join (SP5, Case 3) and XOR-multiple instance (WP4, WP10) – and those workflow structures that map to them. Table 7.3 Mapping summary by objective structure Objective structures

AND – refinement

Process scenarios Single function

Horizontal decompo- Hierarchical decomsition position

multiple objectives (SP2)

sequence (WP1)

hierarchical decomposition SP3 , SP4, SP5

parallel split (WP2) loops (WP 10) AND-join

hierarchical decomposition with multiple functional objectives SP5

OR – refinement

multi-choice (WP6)

XOR – single instance

exclusive choice (WP4) in cases where there is only one execution

XOR – multiple instance

exclusive choice (WP4) in cases where more than one record or case are executed loops (WP10)

In Table 7.3, process constructs were combined into three categories: single function, horizontal decomposition that includes workflow patterns defined by Aalst et al. (2003a) and hierarchical decomposition that includes constructs corresponding to the decomposition of a function into lower level functions. Table 7.3 is a useful look-up table to assist with consideration of how functions corresponding to an objective structure may be organized within a process structure. Whereas Table 7.4 is useful to assist with the construction of an objective structure for an existing EPC since it includes all workflow structures defined by Aalst et al. (2003a) and corresponding objectives structures types (without drilling down to

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the additional objectives types included in the previous table). The use of the mapping is illustrated later in this chapter. Table 7.4 Mapping summary by workflow structures Workflow structures

Objective structure AND-split OR-split XOR-split None

WP1 – sequence

9

WP2 – parallel split

9

WP3 –synchronization

9

WP4 –exclusive choice WP5 –simple merge

9 9

9

orphaned merge WP6 –multi-choice

9

WP7 – synchronising merge

9

WP8 – multi-merge

9

WP9 – discriminator WP10 – arbitrary cycles

9 9

9

WP12 – multiple instances without synchroniza- 9 tion

9

WP11 – implicit termination

9

WP13, WP14, WP15 – synchronization of concurrent threads

9

WP16, – deferred choice

9

WP17 – interleaved parallel routing

9

WP 18 – milestone

9

WP19, WP20 – cancellation patterns (with shadow activities) WP19, WP20 – cancellation patterns (without shadow activities)

9 9 9

7.4.4 Evaluation of the Decomposition Framework against Shared History Requirements

Recall, that goal-oriented business process requirement 7 relating to goal-oriented patterns consisted of five criteria defined by Andersson et al. (2005, p. 2): 1. The annotation should be formulated in a neutral terminology independent of the language and methodology used in the process descriptions. This neutral terminology would enable searchers resulting in a minimization of false hits and a maximization of good hits.

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2. The annotation, in one way or another, should represent the goal of the process, as naturally the processes we are looking for should have the same goal as the one we want to substitute. 3. The annotation, as long as possible, should not refer to the order of activities in the process. The process we want to substitute may be unsatisfactory just because it has a “wrong” order of activities. 4. The annotation, as long as possible, should not refer to in what way and by whom the activities are performed in the process. The process we want to substitute may be unsatisfactory just because a “wrong” way of performing some activities. 5. There should be a way to make annotation and comparison completely formal so that an automatic retrieval could be introduced at some stage. It has been already shown by Andersson et al. (2005) that the approach for defining workflow patterns adopted by Aalst et al. (2003a) and utilised in this chapter is “language neutral” and, therefore, satisfies the first of the above mentioned criteria. Similarly, being a formal approach that “focuses on ordering of activities” the work-flow based approach proposed by Aalst et al. also satisfies criteria four and five. By utilising the synchronized decomposition rules, process control flow is mapped into a goal space thus resulting in a set of goal patterns that reflect the control flow of the business. The resulting set of goal patterns is supplemented with patterns that are goal-specific and are not reflected in the workflow patterns in order to produce a complete set of goal patterns for a business process that remains language neutral and easily formalised using EPC formal language. This set can be used to compare business processes based upon their goals without introducing the specific order of activities or other aspects of the business process (i.e. consistent with criteria two, three and four in Andersson et al. (2005)). Therefore, the set of goal-patterns corresponding to the workflow patterns developed by Aalst et al. (2003a) satisfies the goal-oriented pattern requirements set out by Andersson et al. (2005) and is suitable for the purpose of comparing business processes. Furthermore, the mapping between the goal-oriented and the workflow-pattern introduced in this chapter harmonises these two patterns thus taking advantage of the analysis, design and workflow support capabilities of the latter, which are not available in other goal-oriented patterns such as the state-flow pattern discussed by Andersson et al. (2005). This provides an opportunity to use the goal-oriented pattern to “steer business processes towards their goals” (Bider and Johannesson 2002, p. 1) as discussed later in this chapter. To illustrate the links between the EPC and the VFT environments the same situation of having one fundamental objective of maximizing quality of staff (objective d1 in Figure 7.1), which is achieved by the means of recruiting quality staff (objective m1 in Figure 7.1), and maximizing quality of existing staff (objective m2 in Figure 7.1) is considered in more detail in the next section.

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7.5 Setting up the Example According to the definitions of links between the objectives and process structures (equation 7.16), the means objectives at the top of the means-ends network specify the top level processes within the process structure. In the example that is being considered, there are two high level means objectives, one relating to the recruitment of new staff and the other relating to the retention and development of existing staff. Two HRM processes that are likely to support these objectives are recruit staff and train and develop staff. As discussed in Chapter 5, the processes at the top level of the process hierarchy are organized in a value-added chain, with the logical sequence being recruitment of the staff followed by retention and staff development (Figure 7.5).

Fig. 7.5 VAD chain

Note that the arrow from recruit staff process to retain and develop staff process in the VAD chain diagram simply indicates the order in which these processes are undertaken within the organization (unlike the arrow from m1 to m2 in Figure 7.1, which indicates the decomposition of objectives and in this case is interpreted as m2 (i.e. high quality staff) contributing to the achievement of m1 (i.e. max quality of recruitment)). Naturally staff need to be firstly recruited before they can be developed and retained, however, as common sense would suggest, once staff are within the organization, the process of retaining and development is ongoing while recruitment may only happen from time to time. Having determined the high level processes that are responsible for the achievement of the high level means objectives, the process modelling rules described in Chapter 5 can be used to describe these processes. The process description can be based on existing processes or an “ideal” process that the organisation decides should be followed (also referred to as “reference models” (e.g. Scheer 1999, pp. 61-63)). For illustrative purposes, in this section, fictitious recruitment and retention and development processes will be used. A systematic application of the methodology will be demonstrated in the next chapter within the HRM context. The description of the recruitment process is loosely based on the Fitz-enz and Davison (2002) description of recruitment activities and corresponding KPIs. The link between the activities and the corresponding KPIs provided by Fitz-enz and Davison (2002) facilitates illustration of both flow synchronization and component synchronization (defined in the Chapter 7) and reconciliation of the resulting

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structures by applying the VFT logic to the structuring of functional objectives. The description of the retention and development process, on the other hand, is driven by the already available objectives structure provided in Figure 7.1. The purpose of this approach is to illustrate some of the inconsistencies that may arise as a result of aligning organizational objectives and processes and to present ways of overcoming these inconsistencies. 7.5.1 Recruitment Process

The recruitment process is triggered by a request for recruitment from one of the business units. Once the request for recruitment is received by recruitment staff, they decide whether they are going to undertake the recruitment internally or employ a recruitment agency. If the recruitment is undertaken internally, a file is opened, selection criteria are agreed upon, advertisements are written and placed in appropriate media, application queries are dealt with and a shortlist of applicants for the interviews is selected. If it is decided to outsource recruitment to an agency, then an appropriate agency is selected and briefed with the agency then becoming responsible for advertising and shortlisting of applicants for interview. In either case, the interviews are scheduled and conducted internally, with the interview panel making recommendations and the recruitment staff responsible for referring potentially suitable applicants to the requesting area. Once requesting area approves the selection, the offers are made and rejections are notified by the recruitment staff. Based on the description above, the recruitment process is represented with a two-level e-EPC (Figure 7.6). The first level consists of the recruitment function and events that trigger and arise from it. Within the process design context, it was decided that the objective of that process is to “maximize efficiency and effectiveness of recruitment process”. Within the second level, the recruitment process is decomposed into the following activities: “decide on advertising method”, “advertise”, “contact recruitment agency”, “schedule interviews”, “interview”, “refer”, “make offer”, “notify rejections”. These activities are combined into an e-EPC model that includes an objective that each function is aimed at achieve (according to the EPC environment methodology discussed in Chapter 5), as illustrated by Figure 7.6.

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Fig. 7.6 Recruitment process e-EPC

7.5.2 Retention and Development Process

A simplified version of a process concerned with retaining and developing staff is presented in Figure 7.7. Functions that encourage retention of high quality staff through rewards and deliver training that may be in-house or external are suggested by the objective structure.

165

Level 1, e-EPC

Max quality of existing staff

Staff recruited

Retain and develop staff

Staff retained and developed

Level 2, e-EPC Staff recruited Effectively reward qualifications Encourage retention of high quality staff

Effectively reward experience

Staff retained

Assess development neds

V

Quality in-house training

Needs met via in-house training

Needs met via external training

Deliver inhouse training

Facilitate external traning

Quality external training

V

Staff developed

Fig. 7.7 Retain and develop process e-EPC

In addition, a function that facilitates assessment of organizational and individual needs has been included in the “retain and develop” process e-EPC as a means of achieving training effectiveness (e.g. Milkovich and Boudreau 1997, ch. 10). Having described processes and functions that are required to achieve the fundamental objectives, it is now possible to illustrate the decomposition framework described in Section 7.4.

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7.6 Flow Decomposition

As discussed, the 1st level objective and function reflect the relationship between the high level means objectives network and the process responsible for achieving it. In this case, the high level means objective from which this process originates is stated as “efficient and effective” recruitment process. An objective structure can be easily derived from the process structure using the mapping between workflow patterns and goal-oriented patterns summarised in Tables 7.3 and 7.4. In the recruitment example, the following patterns apply: • • • •

SP4: to link the objectives between the two levels; SP2: to link multiple objectives of the “brief agency” function; WP6: to link objectives within the multiple-choice functional pattern; WP1: to link the remaining objectives within the 2nd level.

The resulting objectives model is presented in Figure 7.8. Guidelines for identification of means objectives discussed in Chapter 4 (Table 4.1) were used to create means objectives that are not explicitly represented on the EPC pattern, but are required for the objectives pattern (e.g. to link two objectives within the WP6 pattern to other 2nd level objectives, an additional means objective is required). Additional means objectives are represented as rectangles on the objectives diagram. Note that combined objectives can be expressed as headings for the lower level objectives rather than explicit objectives in their own right, provided that the lower level objectives fully explain them. While in this fictitious example, the resulting objective structure perfectly matches the process structure without any revisions, this may not be the case in reality for a number of reasons. Firstly, there may be an independent means structure already present that may have been used to help define functional objectives but was found to have a different structure, and/or be incomplete, or have objectives that do not correspond to any function within the process. Secondly, if the means objectives structure has been derived from the process flow, it may also be found to be incomplete from the point of view of satisfying high-level objectives. In both cases, the iterative process recommended by Keeney (1992) should be used until the inconsistencies have been resolved, either through modifications to the objectives structure or process structure or both (as illustrated later in this chapter).

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Max efficiency & effectiveness of recruitment process

V Training objs achieved

Optimize ad methodology

V Max quality of applicants

Ad objs

V Min source cost

Max number of applicants

Fig. 7.8 VFT structure for the recruitment process

Application of the decomposition framework to the recruitment process example highlighted a set of steps that guide the flow decomposition approach. At each step it may be necessary to review earlier steps and repeat the process with the revised structure. Step 1: Use objectives in the highest level of the means-ends network to derive the first level of the process structure by asking the question “what activities are aimed at this?” (e.g. the recruitment process is derived from the recruitment objective). Step 2: Decompose high level processes into lower level functions by asking the question “what activities is this process composed of?” (e.g. recruitment process is described as consisting of a number of activities including “decide on advertising method”, “advertise”, “brief recruitment agencies”, etc). Step 3: Use EPC methodology to link lower functions together into an EPC (refer to Figure 7.7). Step 4: Identify objectives for each of the lower level functions by asking the question “what objectives will be fulfilled by this activity?” Use VFT principles for stating means objectives (e.g. function “advertise” is aimed at “minimizing source costs” and “maximizing number of applicants”). Step 5: Identify all workflow patterns within a process (starting from the smallest pattern) that need to be translated into goal-oriented patterns as follows:

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7 Building a Value-Focused EPC: the “HOW” Dimension

• identify splits and joins (e.g. in Figure 7.7 the first OR-connector is a split, while the second one is a join, two objectives corresponding to the “advertise” function represent an AND split); • identify orphaned joins (e.g. in Figure 7.7 there are no orphaned joins); • identify branches without functions (e.g. in Figure 7.7 there are no branches without functions); Step 6: Connect objectives linked to functions within each valid split (i.e. the one that has two or more branches with functions) to a higher level means objectives using the appropriate goal-oriented pattern (e.g. objectives of the “advertise” function are connected to a higher level means objectives “advertising objective”). In case of nested splits, start at the lowest level split and work upwards to the highest-level split (e.g. in Figure 7.7 objectives of the “advertise” function are nested within the OR split, therefore (as illustrated in Figure 7.8) these two objectives are linked to a higher level objective first, and then that higher level objective is used in the OR split). Repeat this step until objectives within all splits have been linked (e.g. the “source objective” in Figure 7.8 is a result of objectives in the recruitment process EPC being linked within each of the process splits of that EPC). Effectively within this step, functions within each workflow split are treated as if they have been combined into a higher level function that is required to meet al.l objectives of that split thus reducing the EPC to a workflow sequence pattern. Step 7: Use the AND-connector to link the remaining unconnected means objectives (i.e. those corresponding to the high level functions within the sequence pattern of the reduced EPC) to the high-level process objective (in Figure 7.8, after Step 6 is completed there are two remaining non-linked objectives: “source objective” and “optimise advertising methodology” – they are now linked to the higher level means objective “max efficiency and effectiveness of the recruitment process”). Remember that lower level functions can contribute to multiple subprocess objectives (i.e. crossed links in the objectives diagram are allowed). While the resulting objective structure may appear to be complete, it needs to be assessed from the organizational objectives’ point of view. Does it comply with the organizational understanding of this process’ objectives? Are there organizational objectives that have not been met as a result of this process? Do objectives of this process contribute to objectives of other processes? These questions are considered next.

7.7 Components Decomposition As discussed in Chapter 4, Keeney (1992) stipulates that depending on a decision situation, means objectives can themselves be considered as fundamental objectives. In other words, the higher level means objective can be decomposed into dimensions (that will be referred to as components) that explain different aspects of the objectives that are then in turn linked to the business activities. These components represent attributes of the high level means objective and must be satisfied

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in order for the high level objective to be achieved. It is, therefore, necessary to understand the relationship between functions, functional objectives and the components of the high level objectives. In the recruitment example, Fitz-enz and Davison (2002, p. 26) identify the following attributes of the efficient and effective recruitment: min response time, min time to fill, max hit rate, min cost per hire, max quality of hire. Each recruitment activity must be examined to establish whether it contributes to this component. The results of such analysis are presented in Table 7.5 with the tick sign (9) in the table indicating whether the activity influences the objective (note that the influence may be positive or negative). For example, every activity in the recruitment process contributes to the total cost of recruitment. Therefore, within an efficient recruitment process, the costs per hire are minimized by minimizing the costs of each activity. On the other hand, only “interview” and “refer” activities influence the quality of hire as they determine which applicants are hired. Table 7.5 Components decomposition Recruitment Activities

Components of the “efficient and effective recruitment process” objective Max quality Min cost per Min reMin time to Max hit of hire hire sponse time fill rate

Decide on advertising method

9

9

Advertise

9

9

9

Contact recruitment agencies

9

9

9

Schedule interviews

9

9

9

Interview

9

9

9

Refer

9

9

9

Make offer

9

Notify rejections

9

9 9

In order to link components to the individual functions, means-ends identification procedures are applied so that each component objective is decomposed into a set of objectives, with the number of objectives in the set corresponding to the number of activities in the process that contribute to the component (as illustrated in Figure 7.9).

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7 Building a Value-Focused EPC: the “HOW” Dimension

Max efficiency & effectiveness of recruitment process

Min response time Max hit rate

··· ··· ······

··· ······ ···

Min time to fill

Max quality of hire

Max interview quality Max quality of referral documentation

··· ···

Min cost per hire

···

Min decision costs Min advertising costs Min agency costs Min scheduling costs Min referral costs Min offer costs Min rejection costs

Fig. 7.9 Components decomposition

This decomposition allows tracking of the contribution of the individual functions to each of the high level objectives. Furthermore, by establishing the relationship between the component objectives and the activities within the process, the link between performance of the process and achievement of process objectives is made explicit (by utilising a quantitative model within the VFT). The drawback of this approach is that in a complex process with complex objectives, functions may inherit many objectives, some of which may be conflicting or secondary to the function’s primary objective. Furthermore, it is neither practical nor useful to have tens or even hundreds of objectives linked to each function in an e-EPC or other process diagram. To overcome these drawbacks, it is proposed that when the components method is deployed, only functional objectives that are derived from the components based on the primary purpose of the function are used, with other objectives included as constraints or additional KPIs. There are two alternative ways of creating functional objectives from the component objectives: 1. a functional objective derived from a composite objective of the individual components (composite); or 2. a functional objective that is one of the individual components (exact). In the case of the functional objective being the composite of the individual components, decision analysis methods such as MCDA, discussed in Chapter 4, can be used as a measurement tool for the function. In the recruitment example, “optimise advertising methodology” is an example of a composite functional objective as it implies the different dimensions that must be taken into account when making a choice regarding advertising methodology. On the other hand, the “max quality of applicants” objective of the “briefing recruitment agency” function is an objective that inherited one of the components (max hit rate) without change. In this case, the other relevant components must be used as constraints or additional KPIs for the functional objective in order to en-

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sure that the overall objective is met (as each of the components to which this function contributes is an attribute of a higher level objective). If required, multiple components can be translated into multiple functional objectives that are all fulfilled by the function. For example, the “advertise” function inherited two of the three components to which it contributes: min cost per hire (“min source cost”) and max hit rate (“max number of suitable applicants”). The response time component will remain as a constraint on both functional objectives. The resulting structure links objectives components to functional objectives that are in turn structured into an objectives structure that is mapped to the corresponding process structure. Together they provide a single coherent framework for goal-oriented business process modelling.

7.8 Implementation Framework As discussed in Section 7.5 the “retain and develop” process was constructed from an organizational objectives structure illustrated in Figure 7.1. Functions in the resulting process model (Figure 7.8) are relatively high level (i.e. they do not provide sufficient detail to perform each task) and will have to be decomposed further in order for the model to be workable. The flow decomposition approach discussed in Section 7.5.2 can be used to decompose the objective structure accordingly. Even though the objectives for each function in the retention and development process were sourced directly from the means-ends network, the process of synchronization between the workflow and the objectives structures is likely to result in revisions to the original structure as demonstrated in Figure 7.10, with revisions highlighted in grey in the objectives structure.

Fig. 7.10 Correspondence between original and synchronized structures

There are two revisions illustrated in Figure 7.10. The revision with respect to the reward objectives is a better reflection of the workflow structure, but is logi-

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7 Building a Value-Focused EPC: the “HOW” Dimension

cally equivalent to the original structure of the reward objectives. The other revision is the addition of the objective corresponding to the “assess development needs” function. This revision illustrates the incompleteness of objectives structures which are constructed without consideration for workflow and process requirements. Having reconciled the objectives for this process, the objectives structure now needs to be considered within the overall organizational context. As discussed earlier (refer to Figure 7.1), objectives of the retention and development process contribute to the achievement of recruitment objectives. However, in the original objectives structure, illustrated in Figure 7.1, the recruitment process objective is worded differently from the recruitment process objective in Figure 7.8. Either or both may be valid within an organizational context. Synchronization of the two structures highlighted the need for a systematic approach to the construction of a goal-oriented business process model to ensure common understanding of objectives and processes within an organization. Such an approach is proposed in Figure 7.11. What do you mean by that?

Hierarchy of Fundamental Objectives

2

Of what more general objective is this an aspect?

What objectives is this aimed at?

1st level Means Objectives

3

Network of Means Objectives

What objectives is this aimed at? 5 What activities are aimed at this?

4b

Of what more general process is this a part?

Why is this im portant?

How can you achieve this?

Detail

4a

Value Added Chain or 1st level Hierarchical Process Decomposition What activities is this process composed of?

What activities are aimed at this?

Means of Achieving

Decision Makers Values

Why is this im portant?

How can you achieve this?

Company Values

1

Hierarchy of Decomposed Processes

Fig. 7.11 Implementation framework for Value-Focused Process Engineering with EPC and VFT

The framework introduced in Figure 7.11 is discussed in the context of the topdown approach (i.e. following the downward arrows in Figure 7.11), the upward arrows in Figure 7.11 illustrate an equivalent bottom-up approach to goal-oriented business process modelling. Phase numbers are shown in Figure 7.11 as rounded rectangle numbers that are placed between the arrows. As has been illustrated, the process of building a goal-oriented business process model is iterative (irrespective of whether a top-down or bottom-up approach is adopted) and will require re-

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visions of earlier steps as each new phase provides additional insight into the earlier structures. It has already been established that business processes are guided by business goals within the combined model, in as so far as the fundamental hierarchy determines the means objectives network (Phase 2) and the highest level of the means objective network determines the business processes at the highest level of the process structure (Phase 3). In order to take advantage of strengths of the EPC to facilitate the design of an efficient business process, the decomposition of the high-level business process should be guided by the EPC methodology (Phase 4b). The mapping of the goaloriented patterns to the workflow patterns allows the requirements of a goaloriented model to be met through reconciliation of the process and objectives structures (Phase 5). Consequently, Phases 1 and 2 are contained within the VFT framework and are aimed at, firstly, converting organizational values into specific fundamental objectives and subsequently, using the fundamental objectives to derive the first level of the means objectives. The first level of means objectives usually corresponds to the first level of the process model (Phase 3) that describes the relationships between broad organizational processes referred to as a Value Added Chain within the EPC environment. The next two phases (4a and 4b) can be performed in parallel to enable decomposition within the means network and process (respectively), taking into consideration other decomposition drivers such as level of detail required, resource allocation considerations etc (e.g. Gordijn et al. 2000). The components decomposition of means objectives should be considered in Phase 4a. The synchronization of the objectives and process structures occurs in Phase 5 with the help of flow decomposition methodology. When each of the means objectives within the network of means objectives are linked (directly or through other means objectives) to at least one function within the process model, it can be said that, from the goal-oriented process modelling point of view, the business process model contains a sufficient level of detail. As a result of revisions to the process and objectives models that are made in Phase 5, congruence between business strategic objective and business process models is achieved. Having completed the links between the EPC and the VFT models at the formal and implementation level, it is now possible to integrate the combined model against the emergent properties of a goal-oriented business process model.

7.9 Evaluation of the Combined Model Kueng and Kawalek (1997, pp. 23-25) were the first to propose a generic set of activities for designing and steering business processes towards their goals so that the emergent properties of a goal-oriented business process model are met. These activities included:

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7 Building a Value-Focused EPC: the “HOW” Dimension

1. Identification of goals, corresponding constraints and measurement criteria, decomposition of goals to the level of business activities and graphical representation of goals. 2. Derivation of activities from the set of goals defined and specification of the flow of activities. 3. Identification of roles responsible for the execution of business processes. 4. Identification of objects transformed by the business process. To illustrate how methodologies and tools within the value-focused process model proposed in this chapter support the goal-oriented business process modelling activities specified by Kueng and Kawalek (1997), each of these steps was mapped to the fitting methodology within the combined model in Table 7.6. Table 7.6 Goal-oriented activity within the combined model Activity (Kueng and Kawalek 1997, pp. 23-25 )

Methodology within the combined model

Book reference

Identification of goals, corresponding constraints and measurement criteria

VFT methodology for identifica- Chapter 4 Section 4.2.1 tion of goals, corresponding constraints and measurement criteria

Decomposition of goals to the VFT methodology for derivation Chapter 4 Section 4.2.1 level of business activities repre- of the means-ends network and its sented graphically graphical representation Derivation of activities from the set of goals defined

Implementation framework for goal-oriented business process modelling with VFT

Chapter 7 Section 7.8

Specification of the flow of activi- EPC environment methodology Chapter 5 Section 5.6 ties for designing business processes Identification of roles responsible EPC environment methodology Chapter 5 Davis (2001) for the execution of business for designing business processes processes Identification of objects transformed by the business process

EPC environment methodology Chapter 5 Davis (2001) for designing business processes

As can be seen from Table 7.6, each of the goal-oriented activities specified by Kueng and Kawalek (1997) is enabled within the combined model either as a result of linking the EPC and the VFT models or inherited directly from these models. Therefore, each of the emergent properties that were specified by Kueng and Kawalek (1997) as a result of these activities is present within the combined model. For example, using goals to “evaluate the design” property that is expressed by Bider and Johannesson (2002, p. 1) as “steering business process instances towards their goals” is enabled through the links between business activities in the e-EPC model and KPIs that are sourced from the synchronized objective structure. Similarly, “engineering of business processes according to the strategic goals” (Bider and Johannesson 2002, p. 1; Kueng and Kawalek 1997, p. 19) is achieved by deriving business activities from the objectives structure (Phase

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3 in Figure 7.11) and synchronising process and objectives structures (Phase 5 in Figure 7.11). By meeting the emergent properties requirements, the need for the integration of efficiency and effectiveness concerns (e.g. Hammer 1990, Daellenbach 1994) is addressed as the ability of an e-EPC to represent multiple business process perspectives (with the aid of the ARIS framework). This ensures that process efficiency concerns are addressed in the integrated model while the use of the VFT framework to guide the design of the business process and to structure process objectives ensures that the broader business objectives and values are expressed in the integrated process model allowing effectiveness concerns to be addressed. In addition, as a result of combining the capabilities of the EPC, the VFT and the RE models, a superior business objectives modelling environment is enabled that encourages greater understanding of business objectives and discovery of objectives and links between them that may have been previously overlooked. This minimizes the risk of increasing efficiency of one function at the expense of the efficiency of the overall process. The ability to link each function within the process model to business fundamental objectives provides the effectiveness context to business operations. It enables decision support modelling tools to be linked to the relevant functions in order to address effectiveness as well as efficiency concerns, thus decision enabling the EPC environment. The formalism for linking the e-EPC to decision models is presented in Chapter 9, accompanied by discussion of the benefits of decision enabling and the potential of an individual process for decision enabling.

7.10 Summary In the previous chapter the requirements for a goal-oriented business process model were articulated, the contribution of this chapter has been the articulation of how these requirements can be met with the help of the existing business process and objectives modelling tools. To summarise: • modifications to the VFT framework have been introduced. The resulting objectives model is a fusion of objectives and goal models in the fields of Decision Science and Requirements Engineering – establishing a firm connection between these two areas that have much in common but have been rarely brought together because of the disciplinary boundaries between them; • the common formalism between the VFT and EPC model has been proposed. This formalism allows seamless transition from one domain to the other thus breaking the artificial barriers between the disciplines and facilitating the use of complementary methods to deliver a more holistic approach to business process modelling; • the goal-oriented business process patterns using VFT structures have been introduced and mapped to the workflow patterns. The patterns and the mapping enables synchronization decomposition between the VFT and EPC models, thus enabling integration of the two models within a single framework; and

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7 Building a Value-Focused EPC: the “HOW” Dimension

• the application of the combined model was illustrated with examples facilitating the development of the implementation framework that provides a systematic approach to goal-oriented business process modelling with the EPC and the VFT. This implementation framework will be illustrated within the HRM context in the next chapter

8 Application of Value-Focused Process Engineering to HRM Context 8.1 Introduction As discussed in Chapter 3, and highlighted in the recent review of HRM by Brewster (2004), there are many models and theories of HRM that reflect a variety of cultural, organizational and legislative settings. The objective of this chapter is not to propose a new HRM model but rather to illustrate the application of the ValueFocused Process Engineering methodology proposed in Chapter 7 to simultaneously address efficiency and effectiveness concerns of HRM. Instead of choosing a particular view and set of assumptions about the nature of HRM, a synthesis approach is adopted in this chapter to illustrate how the proposed methodology can be used as an identification and integration mechanism. The resulting framework of HRM objectives and processes is not claimed to be universally applicable since it does not draw on the systematic and in-depth overview of each area of HRM (that would be impossible to do within a scope of one or even two chapters). Nor is it based upon a case-study approach that would have made it applicable to a small number of specific situations. Instead, the framework developed in this chapter should be viewed primarily as a companion to the implementation framework developed in the previous chapter, aimed at providing additional guidance for the practical implementation of the combined model. The contribution to the field of HRM is made by providing a methodology that is shown to have the necessary attributes to enable articulation of HRM objectives and their integration with the relevant processes which has been lacking to date in both the practice (Koys 2000) and theory (Boxall 1999) of HRM. Accordingly, the overview of HR literature provided in Chapter 3 is first used to identify and structure fundamental and means objectives in Section 8.2 (Phase 1, Phase 2 and Phase 4a of the Implementation Framework). The top level of the means objective structure is used to identify top-level HR processes (Phase 3 of the Implementation Framework) that are described (at a high level) using EPC methodology in Section 8.3 (Phase 4b of the Implementation Framework). Phase 5 of the Implementation Framework is illustrated in Section 8.4. In this section, the principles of flow decomposition are used to construct an objectives structure for each process described in Section 8.3 and the principles of component synchronization are then utilised in order to complete synchronization of process and objectives structures. The chapter is concluded with a brief summary.

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8 Application of Value-Focused Process Engineering to HRM Context

Hierarchy of HRM Fundamental Objectives

2

1 Of what more general objective is this an aspect?

What objectives is this aimed at?

1st level HRM Means Objectives

3

Network of HRM Means Objectives

What objectives is this aimed at? 5 What activities are aimed at this?

4b

Of what more general process is this a part?

Why is this important?

How can you achieve this?

Detail

4a

HRM Value Added Chain or 1st level Hierarchical Process Decomposition What activities is this process composed of?

What activities are aimed at this?

Means of Achieving

Decision Makers Values

Why is this important?

How can you achieve this?

HRM Values

What do you mean by that?

Hierarchy of Decomposed HRM Processes

Fig. 8.1 Implementation framework in HRM context

8.2 HRM Values, Fundamental and Means Objectives (Phases 1, 2 and 4a) As discussed in Chapter 3, Boxall (1999, pp. 267-268) suggests that a multi-tiered structure, which allows for trade-offs between goals to be taken into account, is required for the specification of HRM objectives. The VFT framework provides the objectives model that satisfies these requirements as it incorporates a fundamental objectives hierarchy reflecting the multidimensional nature of HR objectives, the MAUT providing the theoretical framework for evaluating multiple motivations with the help of trade-offs, and the means objectives network facilitating the identification of the “more specific goals” (Boxall 1999, p. 268). The application of the VFT framework in the HRM context has been limited to a specific enterprise or decision context (e.g. Clemen and Reilly 2001, Gregory and Keeney 1994, Sheng et al. 2007, Keeney 1993, 1994, 1996). Boxall (1999, p. 269) noted that while “management in each firm needs to develop its own goal classification [it] does not imply that managers in all firms have a highly specified schedule of HR goals”. This observation is confirmed by Koys (2000, p. 265) who concluded as a result of analysis of 530 business and human resource strategy statements that “generally, different business goals did not produce different elements on the HR statements”. This suggests that a generic HRM VFT model derived from the HRM literature, while may not be universally applicable will, nev-

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ertheless, provide a useful illustration of how the VFT framework can be used to extract and relate fundamental and means objectives of an HRM system in order to facilitate a more strategic approach to HRM advocated by many (e.g. Beer et al. 1984, Boxall 1992, 1999, Evans 1986, Gratton et al. 1999, Guest 1997, Walker and MacDonald 2001, etc.). 8.2.1 Constructing the Generic VFT Framework within the HRM Context

The objectives of constructing the generic HR VFT framework are twofold: (a) to illustrate the application of the VFT model within the business context (Phases 1, 2 and 4a of the Implementation Framework in Figure 8.1); and (b) to illustrate how existing objectives statements can be modified to fit with the VFT requirements. For these purposes, the methodology suggested by Keeney (1992, 1994 and 1999) was modified so that the HR objectives were formulated on the basis of HRM literature, rather than in actual consultation with HRM specialists and stakeholders. The resulting framework is a synthesised objectives structure that may not be applicable to any single organization but is useful for illustrative purposes and can be used as a source of HR objectives with an understanding that: • any generic framework of HRM objectives will have its limitations and a useby date especially since the nature of the business environment and business objectives change over time as do the HRM objectives and priorities; and • among HRM practitioners and academics there are multiple and sometimes contradictory theories on the aims of HRM policies and practices (as already discussed). The generic framework is not aimed at reconciling these different views, but rather seeks to accommodate them within the single structure in appreciation of the diverse and contradictory nature of stakeholders and decision makers in HRM that occur in real life. The three-step process of identifying, formulating and structuring HR objectives is proposed for the development of the generic VFT framework of HR objectives. Within each step the VFT framework discussed in Chapter 4 is adapted to enable completion of the implementation phases described in Figure 8.1 using HRM literature rather than consultations with stakeholders. 8.2.1.1 Identification of Objectives According to Keeney (1992, pp. 56-57), eliciting and structuring objectives begins with obtaining a written list of objectives from each of the individuals with an interest in and knowledge about HRM. In a business context, this activity would involve consultation with the key decision makers and stakeholders who are “interested in and knowledgeable about” HRM (Keeney 1992, p. 56). In the case of

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8 Application of Value-Focused Process Engineering to HRM Context

synthesising literature sources, it is proposed that HRM objectives are extracted from the HRM literature with each literature source (e.g. those discussed in Chapter 3) being treated as a proxy individual. As recommended by Keeney (1992, p. 57), the lists in this phase should be compiled without any attempt to eliminate redundancy, reconcile contradictions or reformulate the wording to ensure that these statements satisfied objectives statement requirements. It is, therefore, expected that objectives on the lists may not be applicable to every organization as some objectives need to be refined to fit in with a specific organizational context. It is also expected that objectives may need to be added to the lists in order to completely describe the HRM goals within a specific organization. The complete list of objectives identified in this phase is provided in Chapter 3. To illustrate how this list is used in the other phases, objectives identified from Beer et al. (1984, pp. 73, 77) are renumbered and replicated here: 1. Availability of the right number of personnel with the needed mix of competences in the short and long term. 2. Development of people needed to staff the organization in the future. 3. Employee perception of opportunity for advancement and development consistent with their needs. 4. Employee perception of relative security from termination due to factors beyond their control. 5. Employee perception that selection, placement, promotion, and termination decisions are fair. 6. The lowest possible payroll and people-processing costs possible to meet the objectives above. 7. Satisfaction and commitment . 8. Competence . 9. Motivation . 10.Congruence . 11.Costs .

8.2.1.2 Classification into fundamental and means objectives Consistent with Keeney’s (1994) expectation of the initial list of objectives, many objectives on the initial list are not really objectives. For example, statement 6 on Beer’s list of objectives: “the lowest possible payroll and people-processing costs possible to meet the objectives above” is a criterion, while “the organization has a well-developed and properly administered compensation package” is an alternative identified by the Treasury Board of Canada (2001, p. 12). Nevertheless, these statements provide insight into the means of achieving the HR objectives. Those sources discussed in Chapter 3 that included strategic HR objectives were used as a guide for separation between fundamental and means objectives.

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For example, an objective statement “HR services are aimed at meeting strategic organizational objectives” has been identified by Tyson (1997) as a high-level strategic HR objective and, therefore, should be included in the fundamental hierarchy. This objective is reflected in most HR sources, although the wording may vary from source to source. For example, Kane’s (1999, p. 511) statement “HRM policies and practices must be long term in focus, integrated with one another and in line with the organization’s strategy and objectives” is another expression of the same sentiment. Analysis of the sources identified two other high level fundamental objectives: “social legitimacy” (Boxall 1999, p. 269) and “maximize wellbeing of employees” (Keeney 1996, p. 36). It was found that all other fundamental objectives are explanatory of these three high level fundamental objectives. For example, statements 5 and 7 in Beer’s list explain that the well-being of employees is maximized when employees perceive that selection, placement, promotion, and termination decisions are fair and employees are satisfied with their jobs. Similarly, statement 11 implies that in order to meet organizational objectives, the costs of HRM processes must be minimized. Beer’s list can be also used to drill further in the objectives hierarchy, for example, motivation of employees (statement 9 in Beer’s list) can be used as an indicator of job satisfaction. Once key fundamental objectives are identified, other objectives are more easily classified into either means or fundamental objectives depending upon whether they clarify what is meant by the high level fundamental objectives or describe the means of achieving the fundamental objectives. For example, the first two statements in Beer’s list clearly specify the means of how fundamental objectives can be achieved: (a) through making available the “right number of personnel with the needed mix of competencies in the short and long term” and (b) “development of people”. Having classified the objectives, the next step is to reformulate them to ensure that the fundamental objectives include a clear decision context, object and a direction of preference. Out of the three top level fundamental objectives that have been identified, only the one proposed by Keeney satisfied this definition. The HRM objective (Tyson 1997, p. 281) “to achieve a ‘fit’ between the HR strategy and the overall strategy of the business” did not clearly articulate the direction, whilst the “social legitimacy” objective identified by Boxall (1999, p. 269) did not explicitly state the subject or the direction. Accordingly, these objectives were reformulated as “max value of HR services to meet organisational objectives” and “max social legitimacy of HR services”. Some objective statements incorporated multiple objectives, for example objective statement “HR management is the attraction, selection, retention, development, and utilization of human resources in order to achieve both individual and organizational objectives” (Cascio and Awad 1981) was reformulated into multiple objectives including “maximizing the value of HR services to meet organisational objectives” and “maximizing well-being of employees”. In some cases, the objective’s nature was not immediately clear from the authors’ description. In these cases the overall context in which objectives are stated

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is used to help determine the nature of the objectives. For example, consider Huang’s (2001) statements: • “ensuring a suitable supply for current and future key jobs successors, so that the career of individuals can be managed to optimize the organization’s needs and the individual’s aspirations” (p. 736); and • “high employee commitment to organization goals” (p. 742) that is a composite of five indicators (p. 741): “staff morale, organizational climate, staff turnover, organizational commitment and job satisfaction”. The following individual goals are derived from the first statement: “ensure suitable supply for current job successors”, “ensure suitable supply for future job successors”, “optimise organisational needs”, and “optimise individual’s needs”. To determine which of these goals are fundamental and which are means, the question of “why is this important” is asked. Words “so that … to…” are taken as an indicator of Huang’s opinion that ensuring a suitable supply is important because it is a means towards achieving optimisation objectives. No reasons for optimisation are given. In other words, they are considered important “just because”. According to the VFT principals, this means that the “supply” objectives are means objectives, while the “optimisation” objectives are fundamental objectives. Huang (2001) discusses employee commitment in the context of the “relationship between succession planning and HR outcomes of firms” (p. 745). This relationship can be interpreted as a means relationship i.e. succession planning is a means to “high employee commitment to organizational goals” where as the goals of succession planning are to ensure supply. In other words, the “supply” objectives as well as contributing to the “succession plan” objective, also contribute to another objective “high employment commitment”. Why is it important to have “high employee commitment to organizational goals”? The context of the article suggests that this provides the means towards optimising organizational and personal outcomes. In other words, the commitment objective is itself a means objective to the two fundamental objectives already identified. As a result of this analysis, two new fundamental objectives are identified: optimise organizational outcomes, optimise personal outcomes. Comparing these objectives to the previously identified high level fundamental objectives clearly shows that they are equivalent, with the earlier formulations of “maximizing value of HR services to meet organizational objectives” and “maximizing well-being of employees” being more consistent with the definitions of the fundamental objectives. Reformulating the objectives involves subjective judgement on the meaning of the objectives based upon the context of the source from which the original statement was extracted, as well as a general understanding of the HRM context and issues. In acknowledgement of the fact that another person may have reformulated the initial statements differently or chose to use different sources altogether, the contribution of this chapter is not claimed to be a universal HRM objectives struc-

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ture but, rather, an illustration of the value-focused business process engineering methodology, discussed in the previous chapters, within the HRM context. 8.2.1.3 Removal of Redundancy and Structuring of Objectives To remove redundancy from the initial list, similar objectives are reformulated into a single objective and the final list of non-redundant objectives is constructed. Each objective in this list is classified into either a fundamental or means objective according to the principles reviewed in Chapter 4. The objectives are then structured according to the rules of the fundamental objectives hierarchy and means ends network which is also described in Chapter 4. For example, HRM objectives and strategies are driven by the overall organizational objectives (e.g. Clemen and Reilly 2001, Khoong 1996, Walker 1989), therefore, “maximizing the value of HR services to meet organizational objectives” is important “just because” rather than to meet some other HR objective. The objectives of “maximizing efficiency of HR practices” (reformulated from Beer et al. 1984, Boudreau and Ramstad 2001, Kane et al. 1999) and “maximizing effectiveness of HR practices” (reformulated from Beer et al. 1984; Boudreau and Ramstad 2001; Kane et al. 1999, Schuler and Walker 1991, Selden et al. 2001) represent aspects of the value of HR services and are, therefore, treated as lower level fundamental objectives. In removing redundancy, objectives that are aimed at optimising organizational outcomes (e.g. Boxall 1999, Huang 2001) are treated as equivalent to “maximizing the value of HR services to meet organizational objectives”, with the latter formulation chosen because it clearly articulates the direction of the objective. Similarly, objectives relating to optimising personal outcomes (e.g. as derived from Huang 2001) are reformulated as “maximizing well-being of employees”.

Fig. 8.2 Fundamental objectives hierarchy, HRM context

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The first two levels of the resulting fundamental hierarchy of HR objectives are presented in Figure 8.2. Figure 8.2 completes Phase 1 of the Implementation Framework. Obviously, most of those objectives that have been identified are not included in the fundamental objectives hierarchy as they are means to achieving fundamental objectives rather than being fundamental objectives themselves. Phase 2 and Phase 4a of the Implementation Framework enable these objectives to be structured into a meansends network. In Phase 2, three steps outlined in Sections 8.2.1.1-8.2.1.3 are used to group means objectives into five broad categories (Figure 8.3): ensuring excellence in compensation, staffing, planning and research, industrial relations, and training and development. These categories represent high level means objectives and provide a starting point for Phases 3 and 4a of the Implementation Framework. The term “excellence” is used as a composite term to describe inherited (from the fundamental objectives) attributes of the high level means objectives including good value for money, high quality, timely, efficient and equitable services and client focus.

Fig. 8.3 High level means-ends network for HRM context (decomposition of means objectives is included in Appendix 1)

In Phase 4a, means objectives are related to one of the five activities identified. Objectives that cut across the five categories such as objectives relating to outsourcing (objective 6, Figure 8.3) and use of technology within HR (objective 7, Figure 8.3) are included at the top level of the means-ends network even though they may represent lower level means for some or all of the high level means objectives (objectives 1-5, Figure 8.3). The means-ends network constructed in Phase 4a (presented in Figure 8.3 and Appendix 1) is incomplete both in terms of structure and contents until each ob-

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jective is linked to the function responsible for its delivery and each functional objective is represented within the network (i.e. Phases 3, 4b and 5 are completed). Nevertheless, the initial exercise of identifying and structuring means objectives, given available information, provides a useful input and cross-check for the process structure that is constructed in Phase 4b and, therefore, is not considered wasteful. Given the large number of means objectives, the following shortcuts are taken at this stage to simplify analysis and presentation of the objectives network in Appendix 1. • The causal relationships between the top-level objectives of the means-ends network reflect dependencies of these objectives on each other and translate to individual objectives at the lower level of the network. However, for clarity of presentation, these links are omitted from the means-ends. For example, objective 3.7 “increase HRM capability” contributes to all other means objectives, nevertheless, it would not be helpful to show each of those links. Instead that link is established through connecting objective 3.7 to the higher level means objective 3 (“ensure excellence in training and development”) which is, in turn, connected to the other high level means objectives 1, 2, 4 and 5. • The complexity of lower levels of means objectives varies considerably between the branches of the network, reflecting different levels of complexity and detail that can arise when identifying objectives within an organization. At this stage means objectives that appear at the same level may represent different levels of decomposition due to the absence of some of the levels. For example, as discussed in the previous chapter, objective 2.4.3 (“ensure excellence in selection/recruitment processes”) is a reasonably high level means objective that can be further decomposed according to the activities within the selection/recruitment processes. On the other hand, objective 3.2.2 (“improve decision making and problem solving skills”) is a very specific objective that is most likely to be a low level means objective corresponding to a particular training activity and may not be required to be further decomposed. • Logical relationships between objectives are omitted at this stage to avoid confusion which may result when process and objectives structures are later reconciled. Instead, it is assumed that logical relationships will be clarified as a result of the reconciliation. The original contribution of this section is in the classification and structuring of the objectives, the wording of the objectives is taken directly from the sources discussed in Chapter 3 with the exception of changes required for reformulation as already discussed. 8.2.2 Discussion

While the application of the VFT framework to the HRM context was not based

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on a real business, it nevertheless highlighted some benefits and shortcomings of applying the VFT framework within a complex business environment with many objectives and stakeholders. As expected (based on the discussion in Chapter 4), one of the key benefits of the VFT framework has been the clear separation between the fundamental and means objectives. This ensures that the strategic objectives and means of achieving them (and correspondingly abstract and causal relationships between objectives) are separately identified. The application of the VFT framework also demonstrated that lower level fundamental objectives are single-attribute, whereas means objectives are able to have multiple attributes that are inherited from each of the fundamental objectives they contribute to. While fundamental objectives and relationships between them can be clearly presented with the aid of the fundamental objectives hierarchy, the presentation of the means-ends network quickly becomes unwieldy due to the “many to many” nature of the relationships between means objectives. This lack of representation of temporal and logical relationships between means objectives in the VFT structure (identified in Chapter 3) is reflected in the HRM objectives structure developed in this section. Overall, the use of VFT principles to organize HRM objectives provided a structured approach towards understanding the role of HRM within the business whilst answering the question (Walker and MacDonald 2001, p. 370, fig. 3) "What must HR deliver to enable the business to achieve its goals?" In the following section, process modelling using e-EPC is used to explore and present processes that are aimed at delivering HR outcomes.

8.3 HRM Processes (Phases 3 and 4b) In Phase 3, top-level HRM processes are identified by asking the question of “what activities are aimed at achieving theses objectives” with respect to the toplevel means objectives identified in Phase 2. For example, planning process activities are aimed at achieving excellence in planning, whereas staffing process activities are aimed at achieving excellence in staffing, etc. Accordingly, five broad level processes were identified in Phase 3: planning, staffing, training, IR and compensation. These five processes represent the hierarchical decomposition of the HRM activities. In the remainder of this section, the makeup of these processes and relationships between them are explored (Phase 4b of the Implementation Framework). The review of HR literature indicates that there is no universal agreement about the optimal structure of HRM processes as the processes are usually adapted to fit in with the organizational structure and culture and/or the philosophy of the HR expert (e.g. Brewster 2004, Cakar et al. 2003, Hendry and Pettigrew 1990). Boxall (1992) and more recently Cakar et al. (2003) comprehensively review existing business process models with Cakar et al. (2003, p. 198) concluding that each

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HRM process “should consist of the following sub-processes which make up the continuous cycle: plan (re-plan) HRM strategy; implement HRM strategy; monitor impact on business results”. These recommendations have been incorporated into the detailed description of the processes provided in this section. However, since Cakar et al. (2003) do not provide sufficient detail for the construction of HRM EPCs, a diagnostic model adopted by Milkovich and Boudreau (1997) was used in order to capture the elements of the process about which there is a general agreement in the HRM literature and to allow flexibility for inclusion of different interpretations of these elements as required. Milkovich and Boudreau’s (1997) diagnostic model is especially relevant for the discussion of HRM in the context of this book since this model discusses the HRM processes and actions in the context of “thinking about HR decisions and what factors affect these decisions – the consequences as well as the causes” (Milkovich and Boudreau 1997, p. 13).While Milkovich and Boudreau are not utilising formal objectives or process models, they enable discussion of integration between the business objectives and process modelling approaches by describing HRM activities in the context of assessment of HR conditions and setting HR objectives and by emphasising the need to provide feedback on the success of activities through evaluation (Milkovich and Boudreau 1997, ch. 1). Similarly to the previous section, this section is not intended to contribute to the development of HRM theory through development of universally applicable or all encompassing HRM process models, but rather to draw on the existing HRM theory and practice to illustrate the application of business process modelling tools (specifically EPC model) in the context of applying the value-focusing process engineering methodology. Development of an EPC model to describe a HRM process imposes a formal structure on the representation of the process (as described in Chapter 5). However, elements of this structure are not explicitly articulated within the informal descriptions of the processes in the HRM literature. The translation of the informal description of HRM processes into an EPC model involved the following actions (these actions reflect the description of business process modelling with ARIS by Davis 2001): • from the informal description identify broad-level activities within the process and their objectives, and, if necessary, change the description of these activities so that they can be used as EPC functions; • determine the trigger(s) and outcome(s) of each function and label them as EPC events treating triggers as events preceding the functions, and outcomes as events succeeding the function; • list functions in the order they are going to be undertaken, identify functions that can be performed in parallel or as alternatives then use logical connectors to link these functions as appropriate; • identify orphan events (i.e. triggers that are not an outcome of a function and vice versa) as start and end events of the process; and

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• ensure compliance with EPC rules and syntax by revising functions, events or logical connectors as required. For example, orphan events that are not a start or end event of the process may indicate that another function or process interface must be added to the process flow, alternatively one or more of the events may be redundant. Given the number of subjective transformations to the original process description, processes described by the EPCs represent an interpretation (rather than replication) of the informal process description. Therefore, it is likely that another person may have a different equally valid or better interpretation of the same process description. 8.3.1 Planning

There are many descriptions of HRM planning in the literature (e.g. Bartholomew et al. (1991), Cakar et al. 2003, Gass 1991, bin Idris and Eldridge 1998, Milkovich and Boudreau 1997, Stone 1998, Walker 1989) varying from narrow concerns of manpower planning modelling (e.g. Gass 1991, Bartholomew et al. 1991) aimed primarily at forecasting future demand for HR to a much broader understanding of HR planning as a process aimed at development of policies and plans that facilitate alignment of HRM with organizational strategic objectives (e.g. Milkovich and Boudreau 1997, Walker 1989). The latter approach adopted by Milkovich and Boudreau (1997) facilitates the description of the planning process at a broad level suitable for the purposes of this section. Having reviewed the description of the planning process by Milkovich and Boudreau (1997) and other authors, three main stages of the planning process were identified. In stage 1, HRM requirements are forecast on the basis of environmental scanning that includes an audit of current HR resources. The aim of this stage is to establish long-term and short-term HR requirements. In stage 2, these requirements are translated into HR objectives and strategies that are then used to guide other HR processes and to develop the evaluation criteria for stage 3. Stage 3 is concerned with evaluating the implementation of the HR strategies to provide recommendations for the next planning cycle. The development of the strategy activity is defined by Tyson (1997, p. 277) as “devising ways of managing people which will assist in the achievement of organizational objectives” resulting in (p. 278) “a pattern of decisions [and actions] which take place over a long period of time and focus resources on expected outcomes…[and] which concern the management of employees at all levels in the business”. The e-EPC describing this process and accompanying objectives structure are provided in Figures 8.4a and 8.4b respectively.

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Compensation HR information updated

Org objs and/or Other mngt strategies processes determined

Staffing Training

IR

V Establish requirements for achieving org objs and/or strategies

Gain knowledge about future requirements in response to org objs

Demand for HR determined

V

Assess external conditions

Achieve compatibility between the HRM system and external forces

Undertake internal HR audit

Environmental scanning completed

HR supply determined

V Forecast HR supply and compare to the HR demand

Ensure quality forecast of capabilities required to implement or strategy

HR requirements are determined

V Establish priorities and formulate HR objs

Ensure quality HR objs corresponding to org objs & strategies

HR objs formulated

Formulate HRM strategies

HRM strategies formulated

Ensure HRM strategies to meet HR objs

HRM processes

Develop evaluation criteria

Ensure quality evaluation criteria

V Key performance indicators selected

HRM strategies implemented

V Evaluate outcomes

Deliver quality of progress against HR objs

Recommendations for the next planning cycle Other mngt processes

Fig. 8.4a Planning process e-EPC

Identify HR strengths and weaknesses

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As an initial means-ends network has already been developed, it can be used as a first point of reference for determining functional objectives within the planning process EPC. For example objective 1.3 in Appendix 1 (“gain knowledge about future requirements in response to organizational objectives”) is an appropriate objective for the “establishing the HRM requirements” function in Figure 8.4a. As would be expected, not all objectives from the original network would find expression in the EPC, as some may need to be modified in order to reflect the aims of the function, whereas for other functions, a suitable objective may not be found within the means-ends network. For example, objectives 1.4 and 1.5 in Appendix 1, have been modified to better reflect the aims of “establish priorities and formulate HR objectives” and “formulate HR strategies” functions. The last two functions in the EPC do not have any corresponding objectives within the original means-ends network. The objectives for these functions were derived on the basis of the description of these functions by Milkovich and Boudreau (1997, part 1) and added to the objectives structure corresponding to the EPC (Figure 8.4b).

Fig. 8.4b Planning process objectives structure

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8.3.2 Staffing

The term staffing is usually used in the general sense of “putting people to work” (e.g. Staffing.org website) or to describe HRM activities that are involved with determining “the composition of an organization’s human resources” Milkovich and Boudreau (1997, p. 16). This can have either a narrow interpretation where staffing refers only to selection and recruitment activities (e.g. Fitz-enz and Davison 2002), or a broader interpretation that includes HRM activities such as employee separations, workforce reduction and retention, career management and planning (e.g. Milkovich and Boudreau 1997, Stone 1998). This term can also be all encompassing to include workforce planning and job design (Staffing.org website). The HRM activities that are generally excluded from staffing are training activities (as opposed to more general career development and planning activities), compensation activities and industrial relations. Consistent with the approach in this chapter of adopting a more general definition of each HRM process, the staffing process is defined here to include all HRM activities associated with “putting people to work” other than activities included in planning, training, compensation or IR processes. Within the broad definition of staffing, two types of activities are identified in HRM literature: development of staffing methods and policies (e.g. Stone 1998, p. 23) and implementation of these methods and policies. For example, the staffing function may include the specification of the performance management system that activates and tracks performance reviews or the development of a diversity policy that ensures compliance with the equal opportunity legislation during recruitment and selection activities. These policies are used to guide staffing activities of performance management and recruitment respectively. Changes to staffing strategies specified during the planning process may result in the corresponding changes to the methods and policies that may in turn impact on the compensation strategies. For example, changes to the performance rating system may need to flow through to the payroll system which is part of the compensation process (discussed later in this chapter). If changes to staffing and policies and methods are required, then the corresponding evaluation criteria, communication and training strategies need to be developed in order to operationalise these changes. The high level EPC (and accompanying objective structure) illustrated in Figures 8.5a (and 8.5b) specify each of these activities as a separate high level function. In some businesses these functions may be implicit, whereas in others these functions may themselves involve a range of activities.

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IR

Staff strategies formulated

Integrated, efficient & equitable policies and systems that meet staffing objs

Planning

Identify changes to staffing methods & policies

V Changes to methods & policies approved

Compensation strategies specified Compensation

V Develop evaluation criteria

Enable evaluation against objs & business outcomes

Formulate communication strategy

Key performance indicators selected

Staff understand their rights and responsibilities

Communication strategy formulated

Develop evaluation criteria

Formulate training strategy

Training strategy formulated

Increase delegation of authority & responsibility

Training

V V HRM & other mngt processes

Staffing action identified

HR methods and policies operational

V Process staffing information

Quality information about staff

HR information updated Planning

Staffing action completed

V Provide quality evaluation of staffing against objs

Undertake staffing activity

Evaluate staffing outcomes

Other Evaluations mngt results processes Planning available IR

Fig. 8.5a Staffing process e-EPC

Quality client service

Line management staff have the skills to implement staffing guidelines

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While the development of new policies is usually only triggered by IR or planning processes, the staffing activities such as recruitment and selection, performance management, redundancies etc, can be triggered by any management process within the organization. For example, a strategic review may identify the need to improve productivity by implementing performance management or increasing graduate intake. As illustrated in the e-EPC, these actions should be implemented according to the operational HRM methods and policies and once completed should be evaluated to ensure accountability and allow future improvement (e.g. Milkovich and Boudreau 1997 discuss the need for evaluation).

Fig. 8.5b Staffing process objectives structure

As many different approaches to each of the staffing activities have been adopted in theory and in practice (e.g. Cakar et al. 2003), rather than committing to a particular approach, a generic function “undertake staffing activity” has been used in the EPC (Figure 8.5a). This structure accommodates different organizational approaches to HRM (e.g. staffing activities may be the sole responsibility of an HRM department or the responsibility may be delegated to line management areas, with the HRM department having the role of facilitating best practices and providing expert assistance when required). The means-ends network for the staffing activity provides an indication of the type of lower level activities that are in the scope of the staffing process, e.g. recruitment, performance management, succession and career plan-

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ning, etc. and possible objectives for these activities. However, unlike planning process objectives, staffing objectives available within the means-ends network are not directly relevant at the high level of the staffing EPC. Therefore, functional objectives for the staffing EPC had to be derived from general statements about aims of the staffing process by using the questions provided in the implementation framework (Figure 8.1). In the previous chapter, the recruitment process was an example of how staffing processes can be implemented within specific circumstances. In Figure 8.6, the parts of the staffing e-EPC corresponding to the recruitment process are highlighted in grey, with the instances of EPC objects applicable to the recruitment process described in the brackets of the trigger and outcome events, function and corresponding objective. The EPC symbol next to the function indicates that this is a hierarchically decomposed function that is linked to a recruitment process eEPC (e.g. similar to the one described in the previous chapter). An alternative method would be to represent all possible instances as separate events/functions within the EPC, however as these are very context specific, such treatment would detract from the generic nature of the process description without adding to the discussion. ...

Staffing action identified / Recruitment request received

HR methods & policies operational

V

HR & other mngt processes

...

Quality information about staff

Process staffing information

HR methods & policies operational

Undertake staffing activity

Staffing action completed/ Offer made Planning

V ... Fig. 8.6 Hierarchical decomposition of the staffing process

Max efficiency and effectiveness of recruitment process

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8.3.3 Training

Milkovich and Boudreau (1997, p. 408) define training as “a systematic process to foster the acquisition of skills, rules, concepts, or attitudes that result in an improved match between employee characteristics and employment requirements”. The flow of the training activities is illustrated in Figure 8.7a.

Fig. 8.7a Training process e-EPC

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The functional objectives structure illustrated in Figure 8.7b was mainly derived from process description with some input from the means-ends network. Identification of the gap between the characteristics and employment requirements can occur at different levels of the organizational process. For example, the planning process may identify high level training objectives and strategies such as supervisory and management or new technology training (Stone 1998, p. 315) whilst the lower level staffing activity such as performance appraisal may identify a gap in an individual’s skills and characteristics that may be addressed through formal or informal training (Milkovich and Boudreau 1997, ch. 10, Keeney 1994).

Fig. 8.7b Training process objectives structure

Irrespective of which level the training strategy or objectives are determined, to be consistent with the diagnostic model adopted, the first step in the training process is an assessment of training needs and a choice of the most appropriate method to meet these needs. Training methods discussed in the literature (e.g. Milkovich

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and Boudreau 1997, ch. 10, Stone 1998, ch. 9) include on-the-job, internal (inhouse or outsourced), external, formal and informal training. To engage staff in the training process, training objectives and targets must be communicated to staff and agreed to by management. This will increase managers and staff awareness of the organizational training capabilities and facilitate supportive conditions that are necessary for effective training (Milkovich and Boudreau 1997, ch. 10). The activities involved in the delivery of training would depend on the mode of training chosen, the objectives and participants. Milkovich and Boudreau (1997, p. 427) provide an illustration of the on-the-job training procedures. Ultimately, the success of training must be evaluated in terms of training outcomes. Importantly, Milkovich and Boudreau (1997, p. 436) state that “evaluation [of training] on only one, or a small subset, of criteria can lead to biased conclusions” and recommend (pp. 436-438) at least four levels of evaluation criteria: trainee reactions, learning, behaviour changes, and results. Note that the relationship between recruitment and training processes, as identified in Figure 7.5 of Chapter 7, is reflected in the training process e-EPC as the interface to Line Management and HRM processes from the “demand for training” event that precedes delivery of training. This interface includes activities relating to the “retain” part of the “retain and develop” process described in an extremely condensed and simplified form in Chapter 7. 8.3.4 Industrial Relations

Industrial Relations (IR) (also referred to as Employee or Labour Relations) are defined by Milkovich and Boudreau (1997, p. 568) as activities “which seek greater organizational effectiveness by removing the barriers that inhibit full employee participation and compliance with organization policies”. Usually IR activities are characterised by (often competing) demands of the organization (as represented by its management) and its employees (as represented by an employee body, e.g. Trade Unions). The process of reaching a mutually satisfactory agreement on both organizational policies and the pay and conditions of the employees is at the core of the IR process. Such agreement can impact on every other HR process through changes to policies and methods in staffing and training, impact on the compensation (through pay rise or changed entitlements) and the changed future requirements that must be incorporated into the HR planning process. Even more than other HR processes, specific IR practices vary greatly between countries, public and private sector and individual organizations due to differences in legislative requirements and cultural practices (e.g. Begin 1997, Kane et al. 1999). The high level EPC of the IR process and corresponding objectives structure (Figures 8.8a and 8.8b) is aimed at presenting the flow of IR activities and corresponding objectives without being limited by these differences while still being guided by the diagnostic model.

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Fig. 8.8a Industrial Relations process e-EPC

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IR process objs

V Facilitate effective agreement making

Implementation objs

Address communication, protection, assistance & cooperation issues

Facilitate enabling environment and employee loyalty

Provide quality evaluation of efficiency and equity outcomes

V Enable evaluation against objs & business outomes

Fig. 8.8b IR process objectives structure

8.3.5 Compensation

Milkovich and Boudreau define compensation (1997, p. 460) as the “financial returns and tangible services and benefits employees receive as part of an employment relationship”. The process of compensation can, therefore, be defined as a delivery of these returns, services and benefits in a way that is congruent with organizational values. This process has been the focus of the HR information systems architecture and associated tools with detailed delivery of pay and benefits workflow models implemented in a number of enterprise systems (e.g. OraclePeopleSoft 2005). The e-EPC model and corresponding objective structure of the compensation process presented in Figures 8.9a and 8.9b (respectively) were developed by combining the existing EPC reference model for the compensation process (from the R/3 (SAP) reference model library) with the requirements of the diagnostic model (Milkovich and Boudreau 1997). Consistent with the approach adopted in this chapter, the focus of the compensation process model is on the high level activities that ensure that the compensation delivery system is functioning according to the organizational requirements rather than examining the details of the delivery mechanisms which vary from system to system and from business to business. The regular delivery of compensation is cyclical and triggered by the payroll cut-off. Other changes can be specified at the individual level (e.g. promotion or recruitment of new employee) or system level (e.g. as a result of industrial agreement to implement across the board pay rises). The administration of compensa-

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tion is undertaken in parallel with other activities such as formulating the compensation strategy and developing performance indicators that allow ongoing monitoring and evaluation of the compensation system. Compensation disputes that may arise at the individual level as a result of errors (or perceived errors) in the administration of the compensation may trigger individual changes to the compensation system that would have to be actioned in the next compensation cycle.

Fig. 8.9a Compensation process e-EPC

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The compensation e-EPC illustrates the flow of compensation activities, however to demonstrate temporal dependencies between activities a workflow model should be used. Becker et al. (1999) provide the conceptual bridge between the workflow and business process models that enables both aspects to be represented.

Fig. 8.9b Compensation process objectives structure

As in Section 8.3, the original contribution of this section is in the structuring and linking of functions and objectives within the e-EPC context with the description of functions and objectives taken directly from the quoted sources modified as required to satisfy requirements of EPC methodology.

8.4 Reconciling EPC and Objectives Structures (Phase 5) As a result of constructing e-EPC models for each of the HRM processes identified by the means-ends network (Phase 4b), a set of functional objectives has been created. The aim of this section is to illustrate Phase 5 of the Implementation

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Framework by reconciling the means-ends network constructed in Phase 4a (Section 8.2) with the set of means objectives from Phase 4b (Section 8.3) and by ensuring that the objectives and process structures are able to be synchronized. Since the process structure was not fully decomposed, it cannot be expected to fully reconcile with the means-ends network that has been decomposed to a much greater level of detail for some processes. Therefore, in this section, reconciliation is illustrated across the breadth of HRM processes rather than the depth of a process (c.f. illustration of a recruitment process in the previous chapter). Recall, that the five key processes were derived from the top level of the means-ends network. Does this guarantee that the top level of the means-ends network is synchronized with these processes’ objectives? If so, the top level means objectives worded as “ensure excellence in <process>” must be sufficient to describe the aims of each process. However, the discussion of each process in Section 8.3 highlighted more concrete objectives for each process (reformulated according to the principles discussed in Section 8.2): • the planning process is aimed at minimizing the gap between organizational and HRM objectives; • the staffing process is aimed at minimizing the gap between demand for and supply of HR; • the training process is aimed at minimizing the gap between the desired and existing competencies, behaviours and values; • the IR process is aimed at minimizing barriers that inhibit full employee participation and compliance with organizational policies; and • the compensation process is aimed at minimizing the gap between the practice of compensation and organizational values. Principles of component decomposition discussed in the previous chapter can be used to reconcile these specific objectives with the generic statement of excellence in the means-ends network. The statement of excellence describes the attributes of the fundamental objectives that are inherited by the lower level means objectives. Some of these attributes may be converted to the objectives in their own right whilst others will remain as attributes that each means objective must satisfy, in order for the fundamental objectives to be achieved. For example, one of the components of excellence is efficient and effective use of resources by each process. This component can be considered as an objective in its own right for each of the processes in addition to the process objectives already listed. On the other hand, quality and timeliness can remain as attributes of the means objectives that are made explicit only when components of process objectives are fully articulated, as was shown for the recruitment process in the previous chapter. Additional information about process objectives can be gained by examining the lower levels of the original means-ends network. For example, by examining staffing objectives in the means-ends network (Figure 8.3 and Appendix 1), it becomes apparent that, in addition to minimizing the gap between demand for and

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supply of HR, staffing activities are also aimed at containing labour costs (e.g. by reducing unplanned turnover and absenteeism and encouraging improvements in performance) and managing expectations of staff (e.g. by designing and articulating job descriptions, regular performance feedback and succession and career planning). In other words, it is vital that the overall process objectives reflect the main themes within that process. The interdependent nature of HRM processes is illustrated in Figure 8.10a:

Fig. 8.10a HRM process objectives (relationships across processes)

The structure in Figure 8.10a is consistent with the presentations of high level human resource activities as a cycle (e.g. Cascio and Awad 1981, p. 186, Cakar et al. 2003, p. 193). The network structure of each process’ multiple objectives is presented in Figure 8.10b (note that for clarity of presentation common objectives have been uniquely identified for each process). The next step in the reconciliation of the two structures is ensuring that each of the relevant means objectives is either directly or indirectly reflected in the functional objectives. The planning process is an example of all objectives in the means-ends network being reflected in the e-EPC (although some have been reformulated to better suit the function) and, additional objectives having to be introduced to the means-ends network in order to satisfy the condition that all functional objectives must be included in the means-ends network. This may not always be the case, as illustrated by the other processes. For example, in case of the staffing process, objectives identified in the means-ends network are at a lower level of decomposition than the staffing e-EPC and therefore would not be reflected in the objectives network as decomposed to the level presented in this chapter. The final formulation of functional objectives in the process e-EPCs and objectives structures is the result of an iterative process of reconciliation discussed in the previous chapter.

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HRM process objs (means-ends decomposition)

V Planning process objs

Stuffing process objs

Training process objs

IR process objs

Compensation process objs

V

V

V

V

V

Min gap between org & HRM objs

Min gap between demand & supply of HR

Min gap between desired & existing competences, behaviours and values

Efficiently & effectively use resources within the planning procesess

Efficiently & effectively use resources within the staffing process

Efficiently & effectively use resources within the training process

Contain labour costs

Min barriers that inhibit full employee participation compliance with org policies

Min gap between the practice of compensation and org values

Efficiently & effectively use resources within the IR process

Efficiently & effectively use resources within the IR process

Encourage cooperative rather than adversarial relationship

Reduce labour costs

Manage expectations

Fig. 8.10b HRM process objectives (means-ends decomposition)

Having established the objectives which are relevant for this level of decomposition, the flow-decomposition illustrated for each process is now used to complete the value-focused process model by linking each function to the strategic objectives and organizational values. This structure is presented in Figure 8.11 in its composite form. Decomposition of fundamental objective is presented in Figure 8.2, decomposition of process objectives is presented in Figure 8.10, and decompositions of functional are objectives presented in Figures 8.4b, 8.5b, 8.7b, 8.8b and 8.9b.

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Fig. 8.11 Complete Value-Focused Process Engineering structure

8.5 Summary Based on the reviewed HRM literature there is no question that HRM activities should be aligned with organizational objectives. While informal descriptions of HRM processes identify links to HRM objectives and vice versa, the formal modelling mechanisms currently available for processes and objectives do not facilitate such integration. In other words questions such as: which functions contribute to which objectives in the objectives hierarchy, are all objectives being met, how to (re-)design business processes to facilitate achievement of these objectives remain unanswered at the end of the modelling processes. Given that formal models are used to specify business process systems and to assist with the decision making, the lack of integration between these models could obstruct the integrated view of the business. With the help of the value-focused process engineering methodology proposed in Chapter 7, the generic HRM models of objectives and processes, constructed in

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this chapter, address these questions and illustrate a way forward towards an integrated view of the HRM and more generally.

9 Decision-Enabled e-EPC 9.1 Introduction The focus of the previous chapters was about establishing a link between objectives modelling (originating in the Decision Sciences) and process modelling (originating in the Information Systems discipline) and demonstrating how this link can be used for value-focused process engineering. The implications of establishing this link between these disciplines goes beyond goal-oriented business process modelling, as defined by those in the business process modelling community (e.g. Bider and Johannesson 2002). Analysis of the EPC methodology in Chapter 6, highlighted that the integration of EPC and VFT methodology can be used to address certain deficiencies of the EPC methodology and accompanying tools in the area of business process analysis and complex decision making. A conceptual model of a decision-enabled EPC (de-EPC) that uses this link between the EPC and the VFT methodology to enable decision support, problem solving and diagnosis capabilities of an EPC is developed in this chapter. The terms decision and decision support have different conations within different contexts. These differences are explored in Section 2 of this chapter in order to explain the motivation for development of the de-EPC. Having explored the differences, the complementary nature of decision and business process modelling tools is discussed in Section 3, leading to the proposal of an integrated model in Section 4. The benefits of the integrated model are discussed in Section 5, followed by the introduction of an evaluation framework for the identification of decision enabling potential of a business process in Section 6.

9.2 Decision vs Decision To illustrate differences in the terminology, consider the “advertise” function from the recruitment process, as discussed in Chapter 7. In Figure 9.1, this function is hierarchically decomposed into an EPC that includes the “receive job applications” function. As a result of further decomposition, the EPC model provides a comprehensive description of the steps involved in the process and, as such, it is claimed to provide some decision support in so far as allowing the decision maker to identify the sequence of events and functions within a process, the functional inputs and outputs, and the stakeholders involved with the decision making process (Davis 2001, Loos and Allweyer 1998, Scheer 1999, 2000). This may be sufficient for supporting straightforward decisions. For example, Figure 9.1 illustrates decomposition of the “receive job applications” functions and provides a step-by-step process to decide whether to return a late application.

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Hierarchical decomposition of “Ad” function

External ad medium selected

Hierarchical decomposition of “Receive job application” function

Develop selection criteria & ad strategy

External ad medium selected

Job application arrived

Check closing date

Develop selection & ad strategies

XOR Ad X

Write & place ads

XOR

Applicants shortlisted for interview

Vacancies advertised

Applications closed

Applications open

Check extension granted

File application XOR

V Decide on shortlisting methodology

Extension not granted

Extension granted

Return application

File application

Application returned

Application filed

Receive job applications X

Shortlisting methodology approved

XOR

Application filed

V e-EPC for “Shortlist applicants” function

Meet shortlisting criteria

Personnel

Shortlist applicants

Application file

Application filed

Shortlisting methodology approved

Shortlist applicants

V

Applicants shortlisted for interview

Selection panel PC

Excel spreadsheet

Applicants shortlisted for interview

Shortlist

Fig. 9.1 Illustration of different decisions

Within process engineering texts, the discussion of decisions is limited to these types of decisions (e.g. Davis 2001, pp. 120-127). For example, as discussed in Chapter 7, the OR-connector is referred to as “multi-choice” (Aalst et al. 2003a) in other words at the time of process execution a choice must be made as to which process flow path should be followed next. These decisions are referred to as “structured” within the DSS literature (e.g. Sage 1991) as they can be easily decomposed into a sequence of steps. Many business decisions do not fall into this category, as they require more sophisticated structures and solution algorithms than are available within the OR/MS disciplines. For example, consider the “shortlist applicants” function in Figure 9.1. As can be seen in Figure 9.1, within ARIS environment, the description of this

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function can be extended to include all relevant business objects and flows. For example, the Personnel section is nominated as the organizational unit responsible for the execution of the “shortlist applicants” function, the output of the function is the “applications assessment shortlist” and the choice of the selection panel for the interview (human output), the software used to perform the function is a spreadsheet package run on a computer hardware – PC and using environmental data contained in the Applications file. While this information clearly helps with the making the decision, it does not explain how to go about selecting the best n out of N available applicants according to a specified set of selection criteria in compliance with a multi-criteria methodology. And, unlike the case of a highly structured decision it is neither practical nor beneficial to describe the multi-criteria model as a process decomposition within the ARIS environment especially since there are specialised tools available within the OR/MS discipline that have been developed to deal with this particular decision problem. To differentiate between these very different types of decisions, Gorry and Scott Morton in 1971 (in Gorry and Scott Morton 1989) defined the continuum of unstructured, semi-structured and structured decisions. The exact position of a shortlisting decision on an unstructured-structured continuum is debatable. At the initial stages, this decision is likely to be a highly unstructured decision, however as various elements of the problem become resolved, the decision becomes more and more structured until finally it is solved and, therefore, can, in principle, be described as a sequence of steps leading to the solution. Therefore, in order to structure the decision, an underlying decision model has to be defined. A generic decision model is described in Table 9.1. As can be seen from Table 9.1, a generic model consists of a set of elements including alternatives, constraints, states of the world, consequences or outcomes, optimality criteria and a choice of modelling routine (Clemen and Reilly 2001, Mallach 2000, Winston 1994). Whilst not every element is required for each decision model (e.g. optimality criteria or states of the world may not be applicable for some decisions), the decision-enabled process modelling tool must have the capability to support all elements if required. Given the dynamic nature of decision-making, the classification of a decision is time dependent as the process of identifying (applicable) decision model elements moves decisions along the unstructured-structured continuum – the more we know about a decision the closer it is to a structured decision. At a point in time, the term “structured decision” is used in this paper to describe decisions for which the relevant decision model components described in Table 9.1 are easily identified by a decision maker. The term “unstructured decision” refers to decisions for which none of the decision model components are readily apparent. With the term “semistructured decision” being used for decisions with some (but not all) components clearly defined.

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Table 9.1 Decision situation components Component

Description

Alternatives

Generally speaking, a set of possible actions or choices defines the decision variable space. To construct a decision model, decision variables should be selected to adequately quantify a set of possible actions. The decision variables could be discrete or continuous, and could take on positive, negative or integer values depending on a specific decision situation.

Constraints

Functional constraints on the decision variables define a feasible set of possible actions. Constraint functions could be quite complex including linear, non-linear and probabilistic functions.

States of the world

Depending on the decision situation, there may be one or more states of the world describing circumstances that affect the consequences of the decision and are completely outside of the decision maker’s control. Some decision making situations, such as a team assignment problem, require the decision maker to choose an optimal combination of staff to achieve a pre-specified mix of skills and levels within a team. Such situations are fully deterministic and, therefore, do not explicitly specify the state of the world. In a decision model, states of the world are usually described by a set of environmental variables.

Consequences or One of the essential elements of a decision situation is the consequence or outcomes outcome of the decision. In a decision made under uncertainty, the outcome would depend on the states of the world as well as the action chosen. In some cases, uncertainty could be associated with outcomes as well as states of the world. In a decision model, utilities are used to quantitatively describe the outcome of the action via utility functions that model the objectives of the decision. Optimality crite- Most decision-making situations and models include optimality criteria that ria specify utility preference such as maximum profit or minimum costs. However, there are some models, such as feasibility or constraint satisfaction models that do not require optimality criteria to be specified.

This classification deviates from some definitions of decision structures (e.g. Eom 2000, p. 124) in that it allows problems with conflicting objectives, uncertainty and complex variable structure to be classified as structured, provided a well defined decision routine exists that can provide a solution to the problem. This is consistent with the decision classification based on “whether the decision making process can be explicitly described prior to the time when it is necessary to make a decision” Sage (1991, p. 2) as the availability of a well-defined decision routine (along with other decision model elements) guarantees that the decision process can be explicitly described (although it would not necessarily be the most effective way of making a decision in the business context). 9.2.1 Decision Support Systems

Having clarified what is meant by the term decision, it is now possible to explain different types of decision support systems.

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Similarly to business models, business information systems are also developed to assist businesses “to do the right thing” (effectiveness) and/or “to do things right” (efficiency) (Daellenbach 1994, p. 15). Systems that are primarily aimed at supporting business effectiveness are developed in order to: 1) facilitate identification, communication and structuring of business objectives; 2) gather and report information to enable measurement of how well objectives are achieved; and/or 3) facilitate decision making that leads to the achievement of objectives. A system may have a narrow scope to enable it to fully support just one of these goals (or even a subset of a goal). For example, certain decision modelling programs find solutions to well defined business problems such as selection or product mix problems, or may be more generic, providing support for multiple goals, for example a database can be used to both gather and process information and to provide measurements (e.g. management information reports) that facilitate decisions with respect to the achievement of certain business objectives. Mallach (2000, ch. 1) refers to systems that facilitate decision making within an organization as having decision support capabilities. Systems that are developed for the reason of improving decision-making effectiveness are referred to as Decision Support Systems (Mallach 2000, p. 8, fig. 1-3). According to Mallach (2000, p. 8, fig. 1-3) these systems generally have a “low emphasis on processing efficiency”. The OR/MS discipline provides the tools for decision making within this context. Within this paradigm, information is considered primarily as an input of or an output from the model rather than as part of the information flow interacting with other functions of the business. As a result, it has been long recognised (e.g. Rosenhead 1989, p. 10) that systems within this category often fail to model the interactions between the decisions and other business processes required for a holistic solution. Systems that are aimed at assisting a business to “do things right” are developed in order to: 1) facilitate identification and structuring of business processes; 2) automate business transactions; and 3) facilitate execution of business processes to minimize costs while maximizing returns. Similarly to systems that are aimed at “doing the right thing”, systems that aimed at “doing things right” can be specialised or generic. For example, an Optical Character Recognition system is aimed solely at automating data entry while a University Enrolment System will have components of automation (e.g. generating acceptance letters) and workflow control (e.g. the system will not allocate a student number until the enrolment fees are paid). These systems are limited in that there is no guarantee that the process execution is in accordance with business objectives and constraints even when they aim at improving decision processes and outcomes and, therefore, provide some level of decision support (Briggs and Arnott 2002). For example, we do not know whether the process modelled in Figure 9.1 will result in the selection of the best applicant and will be conducted within legislative constraints such as equal opportunity employment. While it can be argued that most systems include elements that cater towards increasing efficiency and effectiveness the, degree of decision support usually de-

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creases as the focus on efficiency increases and vice versa (Mallach 2000, p. 8, fig. 1-3). For example, most transaction systems include some sort of management information that can be used for decision-making and decision support systems automate certain tasks such as calculation, This trend is apparent within the HRM context as the focus of the HR information systems architecture and associated tools is on operational processes and functions such as administration of payroll and benefits, recruitment, and personnel management (e.g. Oracle-PeopleSoft 2005). On the other hand, decision support tools focus mainly on decisions narrowly defined to fit within the specific techniques such as Markov chains (manpower planning), Linear and Integer programming (scheduling), Multiple Criteria Decision Analysis (selection), etc. (e.g. Winston 1994). The implementation paths of the two methodologies rarely cross due to the differences in paradigms and terminology used by the respective disciplines. An integrated decision and process-modelling framework overcomes this limitation. Within this framework, the overall business context can be integrated with the decision model that assists with structuring a decision problem and/or delivery of the solution for a decision model that has already been structured. Furthermore, decisions become an integral part of the business process reducing the risk of conflicting or inappropriate (from the overall business perspective) decisions.

9.3 Relationship between Business Decision and Business Process Modelling Tools The complementary nature of the decision and business process modelling tools is highlighted when one considers that they are both aimed at achieving an efficient business outcome and are often concerned with the same business functions (e.g. Aalst et al. 2003a, Clemen and Reilly 2001, French 1989, Muehlen 2004b, Santos et al. 2001, Sterman 2000, Winston 1994). Furthermore, decision models require enterprise wide information available within integrated business information systems while process models of an Information Systems nature require decision making capabilities of the OR/MS type for efficient information management purposes. The “shortlist applicants function” is used to illustrate the relationship between the tools supporting process- and decision- modelling. As business objectives modelling is fundamental to both approaches, the relationship between objectives is discussed first. 9.3.1 Objectives

It is interesting to note that business objectives at the strategic level are essentially the same irrespective of whether a process or decision modelling approach is used. In the context of the HRM fundamental objectives (Figure 8.2, Chapter 8), the

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“maximize value of HR services” and “maximize well-being of employees” objectives are fundamental to both decision- and process-modelling methods and it is not possible to determine which method has been used to model the functions by simply examining these objectives. As the objectives hierarchies are built to separate various levels of objectives, the differences between methods begin to emerge as was demonstrated in Chapter 7. To illustrate this point, consider the “shortlist applicants” function in the context of the recruitment process. At the higher level of the recruitment process, strategic objectives may include “max efficiency and effectiveness of the recruitment process” (Figure 7.8, Chapter 7), whilst at the lower level of the “shortlist applicants” function, the specific functional goal is “meet shortlisting criteria” (Figure 9.1). This functional goal reflects the process approach as it focuses on the process outcomes and outputs. A decision objective for the same function would be expressed as criteria (e.g. “relevant employment experience”, “relevant academic qualifications”) subject to the constraints (e.g. equal opportunity legislation, affirmative action practices, etc) or as an optimisation function (e.g. “choose the candidate with the highest relevance value”) reflecting differences in the process and decision modelling methodologies. Superficially, it appears that the two methods are addressing separate and independent goals and objectives. However, on a closer examination, it becomes clear that the decision objectives are a subset of functional goals with decision objectives “relevant employment experience” and “relevant academic qualifications” explaining what is meant by the “criteria” in the functional goal of “meet shortlisting criteria”. This type of relationship is common for functions that include decisions (e.g. Agrell and Steuer 2000). The recruitment example (Chapter 7) also illustrates the relationship between process and decision objectives. In this case, process objectives are used as constraints for decision objectives, for example the process objective “min response time” would limit the amount of time that can be spend on arriving at a choice of applicants and, therefore, affecting the choice of a decision-modelling method to be used. A similar situation is illustrated by Gardiner and Armstrong-Wright (2000). Linking the functional goals, decision objectives and strategic objectives enriches one’s understanding of both the process and the decision and ensures that the strategic objectives are met. Furthermore, identifying the dependencies between objectives allows for dynamic and efficient updating of both models to reflect changes in circumstances. For example, by identifying the dependency between the decision objectives of the “shortlist applicants” function and the objectives of the recruitment process, it can be ensured that changes in the recruitment requirements are immediately reflected in the shortlisting decision objectives thus avoiding time delays and misalignment between the two sets of objectives. This approach facilitates the development of a more holistic business model that can be dynamically updated to remain relevant and contemporary.

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Once the strategic objectives are defined, both modelling methods focus on the functions aimed at achieving these objectives. The relationships between the decision and process views of the function will be considered next. 9.3.2 Functions

Some functions include only a trivial decision component (such as “file application”) and whilst they are included in the process model, they are of no further interest for the purposes of this discussion. Functions that do have a non-trivial decision component (such as “shortlist applicants”) have two dimensions – the process and the decision dimension. A process model is concerned primarily with the “how” component of the business operations, in other words, the order of functions required to achieve specific process objectives. The process view is a “bird's eye” view of the function and functional flows. This view provides a description of the function, its inputs, outputs and resources in the context of the rest of the process. In a process model, the “shortlisting applicants” function will be one of a number of other functions linked together to form an event-driven process chain describing the sequence of steps in the recruitment process. As discussed in the previous section, there is no shortage of HRM process models. In Figure 9.1, a process model using an EPC (Scheer 1999) is provided for the recruitment process. When complemented by the data, organization and output views (as illustrated for the “shortlisting applicants” function) this model can be expanded into an e-EPC to provide an integrated business process model (Sheer 1999). A decision model, on the other hand, is concerned with the “what” component of business operations, in other words, what choice to make among available alternatives in order to achieve the desired objective. This view of the function provides an internal or “x-ray” view of the function. A decision model for the “shortlisting applicants” function would be a prescriptive model, such as, for example, a multi criteria decision analysis model aimed at supporting the specific decision of shortlisting applicants by defining selection criteria, decision constraints, and the mathematical technique to be used to satisfy these criteria subject to the constraints (Bouyssou et al. 2000, Gardiner and Armstrong-Wright 2000, Moshkovich et al. 1998, Olson 1996). Examples of other decision modelling techniques within the HRM context include, in particular, multi-knapsack and network flow methods used for team composition and assignment, multi criteria decision analysis used for staff selection, Markov chains and Dynamic programming used for HR planning (e.g. Bartholomew et al.. 1991, Gardiner and Armstrong-Wright 2000, Gass 1991, Khoong 1996, Winston 1994, Zeffane and Mayo 1994). By looking at the external and internal views in isolation (as is normally the case due to disciplinary and conceptual boundaries between the process- and decision-modelling methodologies) the fact that both views support the same objec-

215

tives in a different but complementary way is easily overlooked, resulting in an incomplete model of the business. Ironically, information that provides the link between the two modelling methods is essential for the effective operation of the business. 9.3.3 Information

Differences in modelling methodologies lead to differences in the role information plays in the corresponding models. Transparency of information flows is one of the objectives of an extended process model of an e-EPC type, however, the links between information inputs and outputs are not apparent from process models. A decision model, on the other hand, is primarily concerned with the transformation of existing information into new information with little reference as to where the existing information is coming from or how the new information is going to be used. The information requirements of decision models are usually well defined and specific, however, there is no guarantee that this information is available as required unless these requirements are incorporated into the process model. Similarly, the decision model needs to be an integrated part of the process, in order to generate the information required by the process to fulfil its objectives. Gaps in the information or extraneous information resulting from the lack of communication between the models may cause process delays and costs to the business.

9.4 Integration Model The conceptual model for integration presented in Figure 9.2 retains the ability of the process model to deliver a holistic business model through providing an external view of the business function. At the same time, the model is decision-enabled as it includes an internal view of the function with the focus on the decision objective. Information is included in both external and internal views of the model as it is essential for successful integration (Ackermann et al. 1999). The contribution of the model towards further integration of the two methods is in the links between the two views of the function. By establishing the links, the dependencies between modelling approaches have been made transparent thus enabling an optimal outcome. Existing methods (discussed in the previous sections) that can be used to model dependencies and interactions between the elements are shown on each of the links. Each structural element of the model is numbered for easier reference. The structural links are referred to by the start and end elements of the link, for instance, the link between elements 1 and 2 is referred to as link 1-2. The “shortlist applicants” function in the context of the recruitment process is used to illustrate the conceptual model presented in Figure 9.2.

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9 Decision-Enabled e-EPC

Obje ctive s Hi

hy arc

Process (external) View

e-E ve nt-

dri ve

nP roc ess

Evaluation Scales & Key Performance Indicators Business Dynamic Modelling

ata ng etad delli ion m mic Mo a rmat Info ess Dyn Busin

sis aly An n io cis De

Decision (internal) View

Hi era rch y

1. Function

er Hi es tiv c je Ob 4. Process Objectives

3. Decision Info

Mathematical Modelling Ob jec tiv es

erarc hy

2. Decision Objectives

Ch ain 5. Process Info

Fig. 9.2 Decision-enabled e-EPC conceptual model

9.4.1 Process view

The “shortlist applicants” function (element 1) is one of the functions in the recruitment process (Figure 9.1). The goals of this function are contained (link 1-4) within the overall recruitment process objectives (element 4). Recruitment process objectives interact with each other and can be modelled with business dynamic tools (Sterman 2000) once they are quantified (link 4-5) using the available information (element 5). The flow of information between process functions (link 1-5) is modelled by the e-EPC (Scheer 1999). 9.4.2 Decision view

In this view, the functional goals are sub-divided (link 1-2) into the specific decision objectives (element 2) such as “select applicants with relevant employment experience and relevant educational qualifications”. Decision variables (e.g. number of years in relevant employment, educational relevance scale, etc.) are populated by the decision information (element 3) and are used by mathematical models (link 2-3) to provide solutions to decision objectives (Williams 1993, Winston 1994). Decision analysis tools (link 1-3) such as influence diagrams (Clemen and Reilly 2001) can be used to identify the inputs and outputs of the decision. 9.4.3 Links between Process and Decision Views: Objectives (Link 2-4)

This relationship between process and objectives modelling, established in the previous chapters enables the link between decision and process objectives to be

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made via the objective hierarchy (Clemen and Reilly 2001). For example, the process objective of equitable recruitment could be expressed as a decision objective for the shortlisting applicants function. A Decision-Enabled e-EPC (de-EPC) – is proposed as a tool that facilitates integration of existing business modelling tools by using quantitative decision models to complement the descriptive power of the e-EPC. The de-EPC is formed by identifying decision objectives as a subset of functional goals and adding a decision dimension or decision view (as illustrated in Figure 9.3) to the e-EPC. As a result, the de-EPC enables appropriate decision modelling techniques to be applied to provide the decision maker with an optimal decision for a particular function within a wider business context.

ss ) ed

(is

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Fig. 9.3 Decision view of a de-EPC adapted from Scheer (1999, pp. 34-35)

The formalism used to describe the combined model in Chapter 6 is extended here to include a decision view into the EPC environment. To do that, a decision model is expressed as a generic 7-tuple g tId = Idt ,ν t ,κ t ,τ t ,τ tκ ,α t ,α tκ

introduced

in Chapter 7 (Section 7.2.3) to define the necessary elements of a decision model

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such as alternatives, constraints, states of the world, consequences or outcomes, optimality criteria and decision modelling routine as follows. The t-subscript takes the values of d (for a decision model) and τ ,τ κ representations are defined as follows: ⎧alternative, decision module, state of the world, ⎫ ⎬ ⎩constraint, consequence, objective ⎭

τ d :ν d → ⎨

(9.1)

τ dκ : κ d → {assignment link} Definitions in (9.1) reflect a generic decision-making situation that can be typically characterised by a set of actions, constraints, states of the world, outcomes, optimality criteria and objectives. Depending on a particular situation, some of these elements may be absent, however, sets of actions, constraints, outcomes and objectives must be non-empty. A rational model typically used for decision support is aimed at modelling a choice from possible actions or alternatives to satisfy one or several decision objectives within the context of a decision situation (Clemen and Reilly 2001, Winston 1994). Mathematical techniques and programming routines that are used to solve decision models constitute the subject of extensive operations research literature. For the purpose of this chapter, it is assumed that once the decision model is formulated, it can be solved using one of the existing mathematical and/or programming routines. Due to the complex technical nature of these models, they are often prescriptive, addressing simplified decision problems with narrow decision objectives. More user-friendly decision models dealing with the structure of and interactions between the decisions (e.g. decision analysis and system dynamics tools) provide a more holistic view of the decision situation at the expense of their ability to support specific decisions (Clemen and Reilly 2001; French 1989; Sterman 2000). Assignment links connect decision objectives with other elements of the decision model. As decision objectives are themselves connected to functional objectives in the objectives structure via objectives decomposition links (Chapter 7), the rest of the elements of the decision module are now linked to the individual functions responsible for the achievement of these objectives. Furthermore, information objects and flows that are used by the function also become accessible to the decision model. Having designed the process in accordance with business goals, the goaloriented business pattern can be used to link the KPIs to business activities, enabling ongoing evaluation of business processes thus facilitating the steering of business process instances towards their goals. Components decomposition of the higher level objectives determines the relevant KPIs for each function. For example, the overall objective of the recruitment process was described in Chapter 7 as “efficient and effective recruitment process” and was decomposed into five com-

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ponents in accordance with Fitz-enz and Davison (2002): max quality of hire, min cost per hire, min response time, min time to fill, and max hit rate. Table 7.5 in Chapter 7 documents which functions contribute to each of these components. In order to evaluate performance of the recruitment process, Table 7.5 in Chapter 7 is populated by KPIs for each function-component combination as illustrated in Table 9.2. Table 9.2 Recruitment process KPIs based on Fitz-enz and Davison (2002) Recruitment KPIs corresponding to components of the “efficient and effective recruitactivities ment process” Max quality of Min cost per hire hire Decide on advertising method

-

Advertise

-

Contact recruitment agencies

-

Schedule interviews -

Interview

Refer

Mins response Min time to time fill

Source costs Response time e.g. managers e.g. number of costs days from request Source costs e.g. managers costs + ad fees + misc costs

Response time e.g. number of days from when the decision regarding the advertising method is made

Source costs e.g. agency costs

Response time e.g. number of days from request to shortlist of applicants

Interview costs Response time e.g. travel e.g. number of +misc. days to complete the schedule

Quality of hire Cost per hire e.g. percentage of e.g. panel costs recommendations + admin costs promoted within the first 12 months Quality of hire Cost per hire e.g. percentage of e.g. admin. and hired applicants management promoted within costs the first 12 months

-

-

-

Max hit rate

Hit rate e.g. 40% referrals hired

-

-

Time to fill e.g. elapsed time scheduled for interviews

Hit rate e.g. 75% referrals hired

-

Time to fill e.g. Hit rate e.g. number of days 95% of recfrom the last ommended apinterview to fi- plicants hired nal decision Time to fill e.g. number of days before clients receive documentation

-

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Make offer

Notify rejections

-

Cost per hire e.g. relocation cost + agency fees + misc. costs

-

-

-

Cost per hire e.g. admin costs

-

-

Hit rate e.g. number of rejections

-

Note that some KPIs are cumulative e.g. costs of each function and time taken by each function contribute to the overall costs and duration of the process, while the relationship of other functional KPIs to the corresponding process KPI may be more complex. For example, the hit rate KPI of the recruitment process (i.e. the proportion hired) may be calculated as the total number of applicants to the total number hired, or the total number interviewed to the total number hired or as a function of both. Irrespective of the method, by defining KPIs at the function level in accordance with the components of the process objectives, the information required to assemble the process KPIs is made available to the process. 9.4.4 Links between Process and Decision Views: Information (Link 3-5)

As discussed in the previous section, information inputs and outputs of a specific function are dependent on the information flows in the rest of the process and vice versa. In some cases a simple list of decision variables side by side with functional inputs and outputs sourced from the e-EPC will be sufficient to identify information gaps and unnecessary information. For more complex interactions, a system dynamics model (such as a causal loop or stock and flow diagram) can be used to identify information dependencies (Sterman 2000). A combination of these tools will allow interactions between decision and process information to be identified and taken into account by the modeller. For example, consider a causal loop diagram for the recruitment decision illustrated in Figure 9.4. The causal diagram shows causal links between variables that are relevant to the recruitment decision irrespective of the process. For example, a positive arrow from work program to budget indicates that as the work program increases so does the budget. Even though, these quantities are outside of the recruitment process, they have a direct influence on the requirements and outcomes of the recruitment process as these increases will result in the increased number of vacant positions and resources available for recruitment as well as the costs and time constraints of the recruitment process. As illustrated in the diagram, the greater the number of vacancies the larger the recruitment effort which in turn would cause high costs of recruitment and lower budget.

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work program

cost of recruitm ent

-

+ +

budget

+ + +

vacant positions

-

unem ploy ment rate size of the labour force

recruitment effort

+

+

recruitm ent tim e lag

applications

+

+ +

+

-

num ber recruited

+

suitable applicants

+

com plexity of selection criteria

+

+

staff turnover

em ploy m ent rate job vacancies

legal requirem ents

workprogram requirements

Fig. 9.4 Recruitment causal loop

On the other hand, the larger the recruitment effort, the more applications are likely to be received. The number of applications would also be larger as the labour force and/or unemployment rate increases although the labour force would have to increase substantially for it to be reflected in the number of applications. More applications is likely to result in more suitable applicants, however the number of applicants would reduce as the complexity of the selection requirements increases and the recruitment time lag increases (as good applicants accept other job offers). This relationship highlights the need for the timeliness in the recruitment process, a factor that has not been made evident elsewhere. The information link is enriched by this diagram, as causal links in the diagram demonstrate that the selection criteria are dictated by work program and legal requirements. Therefore, these data must flow into the advertise function to ensure that job advertisements meet the requirements of the client areas. The integration model presented in this section provides the framework for the integration of the process and decision modelling approaches with the resulting modelling tools having the capability to: • include functions and their descriptions; • provide a static view of the functions including functional goals, resources that are used by the function to achieve these goals, and functional output; • provide a dynamic view of the functions presenting a coherent process that brings the functions together and ensures transparency across functional and information flows;

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9 Decision-Enabled e-EPC

• include decision objects such as decision objectives, mathematical models used to analyse the information and decision variables including decision constraints; and ideally • include reinforcement mechanisms for all the links. To illustrate, let’s assume that the functional goal of “shortlist applicants” is to “select 10 best applicants for the interview”. In order to satisfy the strategic objectives of the recruitment process, however, the functional goal should include decision objectives that can be expressed as “select 10 applicants according to a set of criteria (relevant employment, relevant education, etc) subject to a set of constraints (time, equity, etc)”. This decision objective is specific to the decision module in charge of its realisation (typically, one or more suitable OR/MS models with corresponding objective(s)) and is formulated by utilising information about functional goals and process objectives. The specific decision problem with respect to the shortlisting of applicants can be resolved using Multi Criteria Decision Analysis tools. The variables required for this decision are already available as part of the environmental data of the function (Figure 9.3 and Figure 9.4). By introducing a Decision Module/Object (Figure 9.5), that includes the decision model and the decision objective, it is possible to link the mathematical programming based model to the function creating a de-EPC. The functional goals in the de-EPC include decision objectives. These decision objectives together with the decision variables, that form part of the de-EPC information flows, provide inputs into a decision model. The output of the decision model provides the decision maker with an optimal path to satisfy the decision objective and, if required, contributes to the functional outputs which become available to the rest of the process.

Fig. 9.5 de-EPC of the “shortlist applicants” function

In general, the power and flexibility of this integrated modelling tool is that it allows us to utilise the abundance of existing generic quantitative OR/MS models

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as objects within the comprehensive process-modelling framework. According to the object-oriented methodology (Loos and Allweyer 1998, Scheer 1999) this means that we are not confined to dealing with technical aspects of solving the quantitative models, but rather can treat them as “black boxes” with known sets of properties. This approach enhances the decision capabilities of process modelling by linking the “library” of OR/MS models to the process-oriented view of the enterprise, hence creating a more comprehensive and flexible model of a business enterprise.

9.5 Benefits of Decision-Enabling Since the benefits of process modelling and decision modelling and support have been well documented within the relevant disciplines (Davis 2001, Keen 1981; Mallach 2000, Sterman 1991), the purpose of this section is to explore how the combination of the two approaches could benefit a business. This is illustrated in Figure 9.6. Benefit Categories1 for Process Modelling2

⇒

Decision-Enabled Process

⇐

Decision Modelling and Support3

Efficient and effective use of resources to meet organizational objectives integration and rapid process engineering

⇒

efficient processes focused on effective solutions to organizational problems

⇐

personal efficiency in decision making, solving problems faster or better

Communication single and consistent record, multiple view points

⇒

transparency of decision making mechanisms as well as processes

⇐

group decisions, explicit assumptions and decision model

evaluation of flow on effect from process changes to decision alternatives, feedback mechanisms

⇐

expert systems, simulation models, feedback models

common standards for process and decision making activities

⇐

management information, standard modelling tools

Learning and Training validation, walk-through, testing, evaluation of scenarios

⇒

Organizational Control Rigour, method

⇒

Fig. 9.6 Benefits of decision-enabled processes (1Mallach (2000, p. 22), Daellenbach (1994, p. 13); 2Davis (2001, p. 4), Scheer (2000, p. 7); 3Mallach (2000, p. 22), Sterman (1991))

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9 Decision-Enabled e-EPC

Process modelling allows “the documentation, analysis and design of the structure of business processes, their relationships with the resources needed to implement them and the environment in which they will be used” (Davis 2001, p. 2). This has many advantages for a business, including improved documentation and rigour, integration of processes, systems and information, and increased capability for validation and testing (Davis 2001, p. 4). While there is no universally accepted method for summarising the benefits of these complex and varied modelling paradigms, the framework provided by Mallach (2000, p. 22) is concise, complete and can be applied across the disciplines. This framework is used in Figure 9.6 to summarise the benefits of process and decision modelling and support, and benefits resulting from their integration into a decision-enabled process model. As noted by Mallach (2000, p. 23), the categories in Figure 9.6 are not independent, as changes in one necessarily affect the others. A perfect process model would meet resource, process and market efficiency demands (Scheer 2000, p. 7) but, as mentioned earlier, it does not guarantee that the demand for rational or effective decision making, as required for business goals, is going to be met. For example, a selection process model would describe the steps used in the selection process but would not guarantee that the choice of applicants was optimal given the objectives of the selection process. This latter demand can only be met through the use of a process model for decisions where there are a few well-defined and easily eliminated alternatives or trivial decisions that can be evaluated explicitly at the level of the human decision maker without the assistance of decision modelling aids. Other types of decisions require the use of models to ensure that “logical consequences of the modeller’s assumptions” (Sterman 1991, p. 4) are computed. With the use of decision modelling and support tools the efficiency and quality of rational decision-making within business processes is improved (Mallach 2000, pp. 18-23). As example of this, Multiple Criteria Decision analysis and support have been demonstrated to improve selection processes (e.g. Gardiner and Armstrong-Wright 2000); the use of Markovian models and supporting software is often necessary to solve planning problems; data envelopment analysis enables better assessment of performance management (Tsai and Mar Molinero 2002); and efficiency of shift assignment and scheduling is substantially improved with the use of optimisation techniques (e.g. Winston 1994), etc. Both process modelling and decision support tools improve communication by providing a common basis for business processes (Davis 2001, p. 4) and decision making (Mallach 2000, p. 21) respectively. By linking process and decision stakeholders and requirements, a decision-enabled process will facilitate more effective communication by articulating what problems need to be solved, when, and what information and methods are available to solve these problems in order to achieve overall organizational objectives. The promotion of learning and training is a benefit of some decision support systems (Mallach 2000, p. 22) and is an accepted advantage of both analytical modelling (Savage 1998, p. 3) and process modelling (Davis 2001, p. 4). For ex-

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ample, evaluating “what-if” scenarios within a business process model facilitates learning about critical time lines, resources, information and data requirements. Learning from such evaluation is substantially enhanced if the impact of these changes on decisions such as shift assignments and future forecasts is simultaneously evaluated with simulation models and fed back using System Dynamics models into the appropriate processes such as budget and resource allocation (Sterman 1991). Another important benefit of process modelling (Davis 2001, p. 4), decision modelling (Sterman 1991, p. 4) and decision support (Mallach 2000, p. 22) is increased organizational control through enforcement of common standards resulting in consistency. However, as businesses are not separated along decision and process lines, the organizational control requires common standards to be applied across process and decision making activities (as well as within them). The use of an integrated modelling tool will minimize occurrences of disparate requirements, incompatibilities and contradictory instructions. Process modelling tools have the potential to incorporate functionality from many different systems such as workflow, decision modelling, artificial intelligence, and others. Becker et al. (1999) developed a framework for the evaluation of the workflow modelling potential of a business process. This framework is adapted to the decision modelling context to facilitate the identification of the decision-enabling potential of a business process. Consistent, with Becker et al. (1999), the decision enabling potential of a business process is measured by the benefit the organization is expected to derive from the decision support provided by a decision-enabled process. While not all benefits discussed in this section would be realised for each decision-enabled process and there may be other benefits that have not be included, Figure 9.6 provides the basis for an initial set of operational criteria for evaluation of the decision enabling potential of a process (Sandoe et al. 2001, ch. 3). The specific operational criteria would vary from business to business reflecting individual business requirements, resources and time constraints. It is expected that some criteria could be easily measured (e.g. cost savings from an improved staff roster) and, therefore, be used in a cost-benefit analysis, while others would be more intangible (e.g. transparency of decisions in a selection process) and would require analysis of value rather than cost (e.g. Keen 1981). The capability of the process to support a decision model from a technical perspective is discussed in Chapter 4. The availability of resources, cost and timing associated with the implementation of software functionality necessary for the integration of two methodologies and corresponding systems are the key factors in assessing the decision-enabling potential of the business process. The “people issues” are one of the significant obstacles towards successful implementation of integrated systems (Sandoe et al. 2001, ch. 3). The criteria relating to people issues presented by Becker et al. (1999) within the workflow context as organizational criteria are transferable to the decision-modelling context. A conceptual framework presented in Figure 9.7 as an Entity Relationship Diagram

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9 Decision-Enabled e-EPC

combines operational, technical and organizational criteria to determine the decision enabling potential of a process.

Decision Capability

Operational Capability

Information Capability

Business Process

Evaluation procedure

Provides

Process Capability

Data

Process goals

Support

Organisational objectives

Decision Enabling Potential

Used in

Decision Module

Supports

Entity T ype

Le ge nd

Provides decision support

Relationship T ype

Decision model

Criteria

Fig. 9.7 Framework for identification of decision-enabling potential of a business process

Implies

used in

Weight

associated with

Generalisation/ Specialisation

Reinterpreted Relationship T ype

Choice of the decision solving routine

Identification of alternatives

Organisational

T echnical

Operational

227

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9 Decision-Enabled e-EPC

The framework is based on the workflow potential of the business process framework proposed by Becker et al. (1999) in order to • enable emphasis of the discussion to be on the decision-enabling context rather than technical aspects of the framework; and • facilitate cost-benefit comparison of add-on functionalities for process modelling tools by ensuring consistency between conceptual frameworks. The framework includes the following elements: The decision module using the data, provided by the business process enables decision support (through a decision model) for the business process goals that in turn support organizational objectives. The degree of support is dependent on the decision enabling potential of the process. The decision enabling potential of the process is the result of the match between the business process and a given set of criteria that can be weighted to enable evaluation of the overall decision enabling potential of the process. The weights may vary, depending on the process goals associated with the business process being modelled. The criteria relate to the decision capability of the business process supported by the decision model. The decision model also supports information and operational capability of the business process through quantitative output and operational directives respectively. The decision-enabled process model combines the descriptive power of integrated enterprise architecture tools with the quantitative power of decision modelling tools by linking external (process) and internal (decision) views of business activities. Within this framework the library of OR/MS models is linked to functions within business processes to ensure that specific decision objectives can be met effectively and efficiently within broader organizational constraints and that the information requirements of both models are met. The evaluation framework introduced in this section is crucial to understand why integration of process and decision modelling is beneficial to an organization and when these benefits are likely to outweigh the complexities and costs associated with the integration of information systems. As the benefits and costs vary between organizations, it is not possible to provide a universal answer to these questions. Rather, the framework described in this section provides the tools to assist organizations to recognise benefits of integrating process and decision modelling and to evaluate the decision-enabling potential of a business process as a guide towards understanding the trade-offs between the benefits and costs of implementing a decision-enabled process model.

9.6 Summary The need for integration of process and decision modelling approaches has been well recognised in the respective research communities (Ackermann et al. 1999, Brans et al. 1998, Khoong 1996, Mehrotra 1999, Nilsson et al. 1999, Parker and Caine 1996, Rosemann 2003, Sandoe et al. 2001, Zeffane and Mayo 1994), but

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due to the differences in basic concepts, terminology, development history, and methodologies of these areas, integration has been limited to date. The contribution of this chapter has been two-fold: • to reflect upon the issues of similarities and differences between business process- and decision-modelling methodologies and potential benefits of their integration; and • to suggest practical and formal ways for such integration by introducing a notion of a decision-enabled e-EPC (de-EPC) that extends the decision-modelling power of a standard e-EPC based process-modelling approach. The discussion of the relationship between the two types of business modelling tools has highlighted the duality currently existing in the field of business modelling. This duality can be formulated as follows: the more descriptive and contextual the business model, the less decision-enabled it is. Integration of the two paradigms results in a more complete picture of the whole business and a more powerful business model. This allows logical progression from the representation of the mental picture of the business to precise and quantifiable knowledge, enabling the best local decisions to be made in the context of the strategic objectives of the business. Although considerable future research effort (especially in the areas of reinforcement of links and application of the methodology to real life processes) is required to provide full integration of process and decision oriented modelling paradigms and corresponding modelling tools, it is believed that the concept of the de-EPC, introduced in this chapter, provides the solid basis for this effort.

10 Conclusions and Future Directions 10.1 Introduction Organizational change, whether it involves the development of a computerized system or the re-engineering of business processes, is a purposive activity driven by the goals of the involved stakeholders. Its effectiveness depends on being able to make good decisions about what goals to pursue and on selecting the appropriate strategies for achieving the desired goals

Kavakli (2004, p. 1339) The objective of this research was to develop a methodology that facilitates purposeful process engineering, i.e. process engineering that leads to processes that are congruent with organizational values and objectives. By adopting a systems approach for the development of a value-focused process engineering model, the scope of the research was directed towards integration of existing models of business processes and objectives into a single coherent framework that meets the needs of a value-focused process engineering model. In this chapter, the outcomes of this research, its contribution to knowledge, limitations and future directions are discussed. This chapter is structured in accordance with the research questions posed at the beginning of this book. These questions were: • Question 1. What are the desirable properties of business process and business objectives modelling? • Question 2. What properties must a value-focused process engineering model have? • Question 3. How to link existing models to satisfy the requirements of a valuefocused process engineering model? The context of HRM was proposed as the appropriate application for the illustration of research. Accordingly, following the discussion of the research questions, the limitations of and contributions to the area of HRM in the context of this research are discussed. The chapter is concluded with a summary of directions for future research and conclusions.

10.2 Desirable Properties of Business Objectives and Process Modelling Different approaches were adopted for the review of existing business process and business objectives models, reflecting differences in the origins and current standing of these methodologies.

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10 Conclusions and Future Directions

10.2.1 Objectives Modelling

Objectives models play an important role in supporting decision making and formulation of information systems requirements. Because of disciplinary boundaries, the objectives models within the decision analysis and modelling and information systems contexts were developed largely independently from each other. Objectives models that were developed for the purposes of supporting decision making are usually motivated by the need to translate qualitative objective statements into quantitative statements that can be used to evaluate and/or simulate the impact of business operations on its objectives. Objectives models that were developed in the information systems context, on the other hand, are motivated primarily by the need to ascertain structure and communicate the objectives of the business to ensure that the information systems are developed in accordance with business requirements. The value-focused thinking framework developed by Keeney (1992) comes closest to bridging the gap between the different types of objectives modelling by facilitating an explicit and systematic articulation of business objectives (i.e. providing qualitative structure for the business objectives) as well as introducing a mapping from qualitative statements to quantitative methods that enable these statements to be evaluated (i.e. providing methodology for quantitative evaluation of business objectives). However, since the framework is built solely within the boundaries of the decision analysis discipline, it does not take advantage of some of the qualitative tools that are available within other fields of research, such as requirements engineering or business process modelling. These disciplinary boundaries have dictated (to a large extent) the scope of evaluation frameworks for and comparative reviews of objectives models. Nishit (2002) and Hurri (2000) are notable exception to this as Hurri (2000) compares VFT objectives models with the requirements engineering objectives models, while Nishit (2002) provides an evaluation framework of desirable properties that includes both qualitative and quantitative criteria. Nevertheless, neither author provides a comprehensive review and comparison of the two types of objectives models. Such review is provided in Chapter 4 (Business Objectives Modelling). The review of quantitative objectives models includes an in-depth overview of the VFT framework and its links to the MAUT (Keeney 1992), BSC (Kaplan and Norton 1996) and SD (Sterman 2000). By articulating the relationship between these models, the desirable properties of an objectives modelling approach that includes all of these models, were able to be identified thus complementing the list of properties identified by Nishit (2002). Building on the existing reviews available within the requirements engineering and business process management disciplines, the review of the qualitative objectives models provided a concise description of the properties of objectives models considered desirable within these disciplines. The main contribution of Chapter 4 was the compilation of a comprehensive list of desirable properties of objectives modelling that is not limited by discipli-

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nary boundaries. Although comprehensive, the main limitation of this list is that it has been derived from existing models rather than from a model independent list of requirements. This limitation is considered appropriate in the scope of the methodology adopted for this research which dictates that existing models should be used as a base for the new methodology. Nevertheless, future extensions to this research should include an independent assessment of the sufficiency of desirable properties for various purposes that objectives modelling may be required for. 10.2.2 Process Modelling

The status of the process modelling discipline is substantially different from that of objectives modelling. Unlike objectives modelling, process modelling has developed into a discipline in its own right with many authors undertaking reviews and evaluations of existing approaches. Therefore, the challenge was not to identify the desirable properties from a few modelling techniques but to reconcile and integrate the many different approaches and opinions about what properties a business process model might be expected to have. The resulting list of desirable properties presented in Chapter 5 should not be treated as the “universally acceptable” list, rather it should serve as a concise and reasonably comprehensive (at this point in time) guide to the types of capabilities that a process model can be expected to have (to a greater or lesser extent). For example, the list of the desirable properties of business process models includes the “purpose” category that describes the various circumstances in which a business process model should be used. Some business process models are purposebuilt to satisfy just one of these purposes (for example, the main concern of a workflow-based process model is to ensure efficient execution), while other models aim to be more generic through integration of various modules. Both approaches have their strengths and weaknesses with the “specialise” vs. “generalise” debate not being unique to process modelling field or likely to be resolved to everybody’s satisfaction. Ultimately, the organizational or research requirements and constraints will dictate which of the desirable properties are more critical and accordingly what type of process model is best suited to meet the needs of an organization or researcher. In the case of the research documented in this book, the EPC modelling environment that includes ARIS (Scheer 1999, 2000) and open source (Cuntz and Kindler 2004, Mendling and Nuttgens 2004) implementations was chosen. The were a number of reasons for this choice, with the main ones being wide acceptance of this modelling methodology in practice, ongoing development and investigation into the properties of the methodology by various research groups, an implementation environment that facilitates integration with a range of existing modelling tools, and extensive documentation of the formalism of the model. Since the EPC modelling environment falls into the category of a generic methodology, it should ideally have all of the desirable properties of business

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process models identified in Chapter 5. In addition to identifying these properties, the evaluation of the EPC modelling environment against these properties represents the contribution of Chapter 5 to the knowledge of process modelling. By clearly identifying the gaps in the EPC modelling environment, the research agenda is set both for the authors and others interested in further development of this methodology. The main limitation of the research documented in Chapter 5 is its static nature with the danger that the research can become outdated almost as soon as it is published as new modelling methodologies and tools are being constantly developed and different perspectives are used to analyse and review them, making it necessary to engage in ongoing updating and review of the relevant literature. Ultimately, if this research has provoked thought about the qualities of business process modelling and lead to further analysis and discussion it has served its purpose.

10.3 Properties of Value-Focused Process Engineering By asking the question of what are the desirable properties of value-focused process engineering, attention is drawn to the fact that “a whole is more than the some of its parts” paradigm adopted for this research. This paradigm originates in the systems view of the world and dictates that a whole has two sets of properties: hereditary properties (that are directly inherited from the components that combine to make that whole), and the emergent properties (that are not present in any of the individual components but arise as a result of the integration of component integration). By considering value-focused process engineering as the whole that arises out of the integration of objectives modelling and process modelling, the desirable properties identified for objectives modelling and process modelling become the hereditary properties of the whole. The emergent properties are derived in Chapter 6 from a review of goal-oriented business process modelling approaches that have been developed with the aim of ensuring that business processes achieve business goals. To combine individual models into a coherent whole, it is necessary to determine areas of overlap, redundancy and deficit with respect to the desirable hereditary properties of the candidate models. A systematic approach towards comparing candidate models with a view of forming a whole was proposed in Chapter 6 using the theory of set mapping. The resulting framework referred to as the comparative assessment framework was applied to the candidate objective models and the EPC model to determine firstly, which parts of the candidate models should be linked to provide the most complete and clear description of value-focused process engineering and, secondly, what needs to be done to address the gaps within the EPC environment. The application of the comparative assessment framework was limited to the models considered thus dictating the outcomes of the assessment. However, the framework itself was developed in a way that it can be applied to assess any set of

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models that are being considered for integration. To assess the strengths and weaknesses of the comparative assessment framework, it should, in the future, be applied in different contexts.

10.4 Linking Existing Models to Satisfy Requirements of Value-Focused Process Engineering In Chapters 7, 8 and 9, recommendations from Chapter 6 were implemented resulting in the following: • a modified VFT that is able to represent logical relationships between objectives; • formal and graphical links between the EPC environment and VFT framework; • a mapping from workflow patterns to goal-oriented patterns using modified VFT structures that enables synchronized decomposition of process and objectives structures; • a step-by-step guide to creating a value-focused process; • a decision-enabled e-EPC that enables integration of the EPC environment with a suite of decision modelling tools to facilitate simultaneous achievement of efficiency and effectiveness requirements of the business; and • a framework for assessment of the decision enabling potential of an EPC that allows the requirements of an individual process for decision modelling methodology to be assessed from the organizational cost/benefit point of view. These contributions provide a solid theoretical framework for value-focused process engineering with the EPC as well as address the gaps in the EPC environment (identified in Chapter 5) and meet systems requirements (identified in Chapter 6). The gaps in the EPC environment were addressed as a result of the integration between VFT framework, RE goal models and the EPC environment. While establishment of both formal and informal links between models (in Chapter 7), enabling sharing of history through synchronized decomposition (in Chapter 7), and demonstration of emergent properties of the combined model (in Chapter 8) ensure that system requirements are met. While the theoretical framework provides a necessary first step towards integration of modelling methodologies across disciplines, the framework needs to be implemented within an information system so that its usefulness in practice can be fully tested. It is believed, that as a result of adopting the EPC environment as the base model for the integrated framework and developing the formalism for the combined model, the necessary conditions for the implementation have been made. The next step should involve development of open source code (within the EPML environment (Mendling and Nuttgens 2004, Mendling and Nuttgens 2005)) and/or cooperation with the ARIS enterprise (IDS Scheer AG 2006) to include the VFT module into the suite of models linked to the EPC. Practical application of the combined model is also likely to lead to questions arising out of the need for flexible objectives structures to be available for real-time execution of business

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processes. For example, the need to accommodate multiple instance objectives to correspond to multiple instance workflow paths will need to be investigated.

10.5 Application within the HRM Context While the theoretical framework developed in this book is not claimed to be fully tested in practice, its application has been illustrated within the context of Human Resource Management (HRM). The choice of the HRM context as the application area for this research was motivated by the identified need (by researchers within the HRM field) for alignment of HRM activities with organizational strategies and objectives. In Chapter 3, selected literature sources were reviewed to provide a sense of the type of activities and objectives that are included within the scope of HRM. These examples were used in the remainder of the book to illustrate the concepts being discussed. In Chapter 4, the values and objectives identified by the DialAmerica Marketing Inc. (2004) were used to illustrate how objectives models represent business objectives and relationships between them. In Chapter 5, different aspects of a generic recruitment process were used to illustrate the EPC process modelling methodology. In Chapters 7, 8 and 9 extensive use of the HRM context was made to demonstrate the application of the value-focused process engineering model. In Chapter 7, the focus was on illustrating each of the properties of the combined model, with each decomposition pattern accompanied by an HRM example. Similarly, each step in the implementation of the value-focused process engineering model was illustrated using recruitment and/or retain and develop HRM processes. Different types of objectives decomposition (flow and components) were illustrated with the recruitment process, while alignment of organizational objectives and process structures was demonstrated with the retain and develop process. In Chapter 8, the focus changed from illustrating each of the properties of the combined model, to illustrating how the combined model can be used to align business processes and objectives within a complex context. As a result of this application, a generic VFT framework was constructed for the HRM context with key HRM processes identified and described and HRM objectives identified for each function within the high level processes and aligned with the high level objectives structure. This resulted in a complete high level structure of HRM process and objectives. In Chapter 9, the recruitment process was again utilised to demonstrate the concepts of decision modelling and decision enabling. The breadth and depth of the HRM area have enabled the illustration of a variety of concepts and provided insight into some of the difficulties that may be faced by those who will apply the combined model in practice. It is hoped, that as a result of using the HRM context to apply the model, these issues have been addressed. At the same time, it must be acknowledged that due to the breadth and

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depth of the HRM area, it has been impossible to do justice to the field of HRM. It is suggested that close collaboration with HRM experts is considered in the future to further progress the application of the combined model to the field of HRM. To summarise, the research questions posed in the beginning of the book have been answered, but as evident from the discussion in this chapter, many more questions have arisen as a result. Designing the bridge between efficiency and effectiveness concerns in business modelling is considered to be the main contribution of the research. Using this design to build the bridge in practice is the challenge yet to be undertaken. In the next section, the possible ways to meet this challenge are discussed.

10.6 Future Directions and Conclusion The research reported in this book opens the door to many interesting research questions yet to be addressed. These can be grouped into two categories: further developments and practical application of the concepts, models, frameworks and tools. These categories are not independent as future development will lead to the need for further practical application, while practical application is likely to in turn, identify further development opportunities. Nevertheless, by taking a snapshot of the research at this point in time, it is possible to discuss the two categories independently of each other. 10.6.1 Further Developments

The discussion of objectives modelling and process modelling background in Chapter 4 and 5 (respectively) identified future research directions including: • model-independent assessment of desirable properties of objectives models; • development of a quantitative assessment framework (with the help of multiple-criteria decision analysis methodology) based on the lists of desirable properties in order to rate existing models, according to a set of requirements, with the aim of developing an easy to use tool for practitioners looking to choose or assess available modelling techniques; and • application of the evaluation framework, developed in Chapter 5, for the assessment of potential of other (than the EPC) methods for the purpose of Valued-Focused Process Engineering. In Chapter 7, the VFT framework was modified to include logical connectors. The complex relationship between objectives (such as partial satisfaction, conflict and trade-off) can be expressed through the MAUT part of the VFT framework but they are not evident on the objectives network. In some cases, it may be beneficial to include these relationships on the VFT network in a similar way to the RE representation of goal models. Consideration must also be given to including a more

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10 Conclusions and Future Directions

detailed classification of means objectives to differentiate between objective types within the means-ends network (e.g. decision and functional objectives). Implementing the combined model within an information system is considered to be the most promising enhancement to the research in terms of increasing the usability and facilitating acceptance and application of the combined model in practice. Chapter 9 demonstrated how the link between objectives and process models can be used to expand the scope and capability of existing business process models, however, there are many more opportunities for extending this further. For example, • constraint programming methodology can be used to reinforce process constraints identified at the process level throughout the whole sequence of functions thus adding transparency at the constraint as well as objectives and information flow levels (Lustig and Puget 2001); • system dynamics models can be used to describe interactions between functions within the context of a decision (Sterman 2000); • links between process execution and achievement of business objectives can be strengthened by using a dynamic objectives structure to guide process execution and accommodate configurable business process modelling languages that support multiple-instantiation (e.g. Rosemann and Aalst 2003, Mendling et al. 2005); and • potential for using an integrated model to assist with evaluation of the level of maturity and/or competence of a business process (e.g. Harmon 2004, Spanyi 2004) should be investigated. Similarly to other assessment frameworks discussed, the framework proposed in Chapter 9 for the evaluation of the decision-enabling potential can also be extended to incorporate the multi-criteria decision analysis methodology in order to assist users with the objective evaluation of the decision-enabled potential of individual processes in accordance with organizational criteria. 10.6.2 Practical Application

As discussed earlier in this chapter, the theoretical framework developed in this book would benefit from further application to real-life organizations. For example, to facilitate evaluation of the comparative assessment framework introduced in Chapter 6, it should be applied to a wide range of models and integration requirements. Qualitative assessment of the usability of the framework should be included as part of these evaluations. Action or case-study research would provide useful feedback into the strengths and weaknesses of the framework as well as opportunities for further development. Given that the HRM context has been used to illustrate the application of the research, application to HRM processes within a real-life organization would

239

be the next logical step. However, application of the framework to other contexts should also be considered as it may be equally or even more beneficial for evaluation and further development of the theoretical framework. 10.6.3 Conclusion

In 1990 Hammer (Hammer 1990, p. 108) noted that Conventional process structures are fragmented and piecemeal, and they lack the integration necessary to maintain quality and service. They are breeding grounds for tunnel vision, as people tend to substitute the narrow goals of their particular department for the larger goals of the process as a whole.

While there have been significant advances in the area of business process engineering since 1990, Hammer’s recommendation (Hammer 1990, p. 108) to design a “person’s job around an objective or outcome instead of a single task” is as topical today as it was then. The holistic approach to integration of business process engineering and objectives modelling developed and illustrated in this book contributes towards the stock of knowledge in this area.

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Appendix 1. Decomposition of the means network 1. Ensure excellence in HR planning and research

1.1 Achieve compatibility between the HRM system and external forces (environmental scanning)

1.3 Gain knowledge about future requirements in response to org’s objs (demand forecast)

1.2 Identify HR strength and weaknesses (internal HR audit)

1.4 Define capabilities required to implement the org strategy focusing on pursuit of sustainable success (interpretation of strategic objs)

1.5 Define org’s desired HR position and programs (formulate HR objs & strategies)

Fig. A.1.1 Decomposition of the means objective “Ensure excellence in HR planning and research”

Fig. A.1.2 Decomposition of the means objective “Ensure excellence in staffing” 3. Ensure excellence in training & development

3.1 Modify employees’ values and attitudes

3.2.1 Build strategic competencies

3.2 Ensure that employees have the competencies to keep the org viable in the short & long term

3.2.2 Develop org change skills

3.2.3 Improve decision making and problem solving skills

3.3 Improve ROI &/or return in assets by improving effectiveness and efficiency of employees

3.2.4 Develop leadership competencies & invest in leadership growth

3.4 Increase employees awareness of their strengths and weaknesses

3.3.1 Increase commitment of employees to org goals

3.3.2 Maintain/ improve quality of employees

3.5 Facilitate org renewal

3.3.3 Increase innovation

3.6 Facilitate team building, lateral relationships, shared vision and values

3.3.4 Max flexibility of employees

3.7 Increase HRM capability

3.7.1 Ensure that appropriate HR tools and techniques are used by managers and staff

3.7.2 Ensure that HR expertise is available to managers

254

Appendix 1. Decomposition of the means network

Fig. A.1.3 Decomposition of the means objective “Ensure excellence in training and development”

4. Ensure excellence in IR

4.2 Facilitate enabling environment by looking out for employee needs

4.1 Control and/or cooperate with the Trade Union

4.2.1 Encourage staff involvement in the decision making processes (ID)

4.2.2 Influence employee perception of relative security from termination due to factors beyond

4.2.3 Influence employee perception of opportunity for advancement & development consistent with their needs

4.2.4 Evaluate and respond to org’s and employee’s and employers’ reactions

4.2.5 Ensure individual rights are respected and the diverse nature of the work force is acknowledged

4.2.5.1 Ensure fairness of the decisions

4.2.6 Ensure compliance with labour laws

4.2.7 Evaluate safety of the work place environment & ensure preventative and corrective actions are in place

4.2.5.2 Ensure that employees perceive HR procedures & practices as fair

Fig. A.1.4 Decomposition of the means objective “Ensure excellence in IR”

Fig. A.1.5 Decomposition of the means objective “Ensure excellence in compensation”

255

Fig. A.1.6 Decomposition of the means objective “Ensure excellence in integration of technology infrastructure relevant to HR function”

Index ARIS Framework, 80, 104 Views, 92 Axiological Assumptions, 18 Balanced Scorecard, 63 Business Modelling, 1, 92, 237 Business Objectives Modelling, 7, 21 Business Performance Measurement, 63 Business Process Coordination ~ Model, 24 Definition, 79 Deterministic ~ Model, 24 Dynamic ~ Model, 24 Engineering, 239 Hard ~ Model, 24 Improvement, 1, 78 Interacting ~ Model, 24 Lifecycle, 9 Modelling, 2, 79 Re-Engineering, 79, 83 Soft ~ Model, 23 Business Process Modelling, 2, 79 Goal-Oriented ~, 69, 110 Hard ~, 20 Integrated ~, 20 Soft ~, 20 Taxonomy, 25 Business System, 18 Real-Life ~, 64 Compensation Process ~ e-EPC, 200 ~ Objectives Structure, 201 Conceptual Modelling Context, 28 Grammar, 26 Methodology, 27 Script, 28 Decision Alternative, 54 Attribute, 59 Consequence, 59 Constraint, 60 Criterion, 59 Objective, 21, 53 Trade-off, 61 Value, 52 Decision Support System, 210 Decision-Enabled e-EPC, 207 Conceptual Model, 216 Extended Formalism, 217 Process Benefits, 223

Decomposition Components, 168 Flow, 166 Design Science, 17 Desirable Properties, 18 Business Process Model's ~, 96, 103 Goal-Oriented Business Process Model's, 130 Objectives Model's ~, 74 Effectiveness Requirements, 2, 5 Efficiency Requirements, 2, 5 Elementary Function, 93 Objective, 147 Emergent Properties, 7, 109 Requirements, 128 Event-Driven Process Chain Decision-Enabled ~, 207 Extended Formalism, 93 Formalism, 85, 86, 90, 91 Hierarchical Decomposition, 88, 89 Horizontal Decomposition, 86, 88 Methodology Evaluation, 107 Extended Event-Driven Process Chain, 80, 92 Decision-Enabled, 217 Function Elementary ~, 93 Hierarchically Ranked ~, 86 Objective, 62 Utility, 62 Fundamental Objectives Hierarchy, 55, 56, 57, 60 ~ for HRM context, 183 Goal Driven Metamodel, 111 Goal-Exception-Dependency Framework, 23 Goal-Oriented Business Process Engineering, 2 Business Process Modelling, 139 Patterns, 160, 161 Process Models, 2 Hereditary Properties, 109 Hierarchical Decomposition, 80, 87, 88 Staffing Process ~, 194 HR Information System, 199, 212 HRM Planning Process ~ Objectives Structure, 190 Human Resource Management ~ Process Objectives, 203 Activities, 47

258

Index

Effectiveness, 33 Evaluation, 43 Objectives, 34, 36, 47 Planning, 188 Practices, 31 Industrial Relations Process ~ e-EPC, 198 ~ Objectives Structure, 199 Information Systems Research, 16 Key Performance Indicators, 21, 65, 218 Logical Connector AND, 68, 72, 83 Exclusive-OR, 68, 72, 83 OR, 68, 72, 83 Mapping Categories, 113 Domain, 113 Range, 113 Means-Ends Objectives Network, 55, 57 ~ for HRM Context, 184 Methodology Definition, 15, 95 Model Conceptual ~, 26 Coordination Business Process ~, 24 Definition, 15 Deterministic Business Process ~, 24 Dynamic Business Process ~, 24 EKD framework, 71, 112 Event-Driven Process Chain ~, 83 Goal-Oriented ~, 111 Hard Business Process ~, 24 Integrated Definition (IDEF) ~, 24 Interacting Business Process ~, 24 Objective ~, 50 Object-Oriented ~, 24 Outranking ~, 63 Process-Oriented Coordination Model ~, 24 Reference Level ~, 63 Requirements Engineering ~, 24, 68, 110 Role Activity Diagram(RAD) ~, 24 Soft Business Process ~, 23 State-Flow ~, 110, 111 System Dynamics ~, 220, 225 Traditional Business Process ~, 24 Value ~, 61 Multi-Attribute Utility Theory, 21 Objective Definition, 53 Elementary ~, 147 Functional ~, 27

Fundamental ~, 53 Means ~, 54 Objective Function, 62 Objective Model, 50 Objectives Modelling Qualitative, 66 Quantitative, 50 Objectives Relationships Causal ~, 72 Influencing ~, 72 Logical ~, 72 Temporal ~, 73 Ontological Modelling, 113 Ontological Position, 19 Pattern Analysis, 147 Description, 147 Single Function Pattern, 141 Treatment, 147 Workflow, 147 Philosophical Assumptions, 17 Planning Process

~ e-EPC, 189 Process Definition, 79 Modelling Environment, 95 Objective, 127 View, 78 Process Modelling Environment, 9 Property Deficit, 113 Emergent, 109 Hereditary, 109 Overload, 114 Redundancy, 115 Requirements Effectiveness, 2, 5 Efficiency, 2, 5 Requirements Engineering, 66, 111 Single Function Pattern, 141 Staffing Process ~ e-EPC, 192 ~ Objectives Structure, 193 Synchronization Component ~, 128, 162 Flow ~, 128, 162 Synchronized Decomposition, 141 System Business ~, 18 Definition, 6 Dynamics, 51, 64, 65 Thinking, 4

259 Trade-off Factual ~, 61 Value ~, 61 Training Process ~ e-EPC, 195 ~ Objectives Structure, 196 Unified Modelling Language, 24 Utility Function, 62 Value-Focused Process Engineering, 20, 118 Complete Structure, 205 Formalism, 139, 140 Framework, 172 Framework in HRM Context, 178 Value-Focused Thinking, 4, 51 Formalism, 135, 136, 137 Workflow Pattern, 147, 159, 161 Arbitrary Cycles, 156 Cancellation Patterns, 158

Deferred Choice, 158 Discriminator, 155 Exclusive Choice, 151 Implicit Termination, 157 Interleaved Parallel Routing, 158 Milestone, 158 Multi-Choice, 154 Multi-Merge, 155 Multiple Instances without Synchronization, 157 Orphaned Merge, 154 Parallel Split, 150 Sequence, 149 Simple Merge, 154 Synchronising Merge, 155 Synchronization, 151 Synchronization of Concurrent Threads, 158