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13/05/2005
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ISBN 1-84544-137-0
ISSN 1741-038X
Volume 16 Number 4 2005
Journal of
Manufacturing Technology Management formerly Integrated Manufacturing Systems
EurOMA-POMS Joint International Conference Guest Editors: Raffaella Cagliano, Matteo Kalchschmidt, Pietro Romano and Fabrizio Salvador
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Journal of Manufacturing Technology Management
ISSN 1741-038X Volume 16 Number 4 2005
EurOMA-POMS Joint International Conference Guest Editors Raffaella Cagliano, Matteo Kalchschmidt, Pietro Romano and Fabrizio Salvador
Access this journal online _________________________
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Editorial advisory board __________________________
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Guest editorial ___________________________________
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Managing through measures: a study of impact on performance Mike Bourne, Mike Kennerley and Monica Franco-Santos _____________
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Formalising the ordering process to achieve responsiveness Gera A. Welker and Jan de Vries _________________________________
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Managing product variety in quotation processes Jo Bramham, Bart MacCarthy and Jane Guinery _____________________
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A case study of product modularization on supply chain design and coordination in Hong Kong and China A.K.W. Lau and R.C.M. Yam ____________________________________
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Modelling complexity in the automotive industry supply chain Kevin Turner and Geoff Williams _________________________________
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Conference announcement ________________________
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CONTENTS
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Editorial advisory board
EDITORIAL ADVISORY BOARD
Harry Boer University of Aalborg, Denmark Nourredine Boubekri Northern Illinois University, USA Mike Byrne University of Nottingham, UK Dr Felix T.S. Chan The University of Hong Kong, Hong Kong Afonso Fleury University of Sa˜o Paulo, Brazil Ian Gibson The University of Hong Kong, Hong Kong A. Gunasekaran University of Massachusetts, Dartmouth, USA Abdel-Aziz Hegazy Helwan University, Egypt Bob Hollier Manchester Business School, UK Hiroshi Katayama Waseda University, Japan Tarek Khalil University of Miami, USA Ashok Kochhar University of Aston, UK Doug Love University of Aston, UK
Dr Bart MacCarthy Business School, University of Nottingham, Nottingham, UK Douglas K. Macbeth University of Glasgow, UK Les Mitchell University of Hertfordshire, UK Shunji Mohri University of Hokkaido, Japan Dr Marly Monteiro de Carvalho Universidade de Sa˜o Paulo, Brazil Andy Neely Cranfield University, UK
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Kul Pawar University of Nottingham, UK Roy Snaddon University of Witwatersrand, South Africa Amrik Sohal Monash University, Australia Don Taylor Virginia Tech, USA Wang Xing Ming Renmin University, China Peter Wright Celestica, UK
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Guest editorial EurOMA-POMS Joint International Conference
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Journal of Manufacturing Technology Management Vol. 16 No. 4, 2005 pp. 368-372 q Emerald Group Publishing Limited 1741-038X
About the Guest Editors Raffaella Cagliano graduated in Management, Economics and Industrial Engineering and took a PhD in Management, Economics and Industrial Engineering at Politecnico di Milano. She is Associate Professor in the Area of Management, Economics and Industrial Engineering at Politecnico di Milano and part of the Faculty of MIP-Politecnico di Milano in the area of Organisation and Manufacturing and Logistics Management. She is co-director of the Executive Master in Business Administration of MIP-Poilitecnico di Milano. From 1995 to date Raffaella Cagliano has been performing research activities in the area of Innovation and business processes management. Her research interests include: manufacturing strategy, emerging manufacturing paradigms, operations management within networks of companies, supply and purchasing management. Her research was published in relevant international journals in the field. Raffaella Cagliano has held important roles in the co-ordination and execution of a number of research projects at national and international level. Matteo Kalchschmidt earned a Master and a PhD degree in Management, Economics and Industrial Engineering from Politecnico di Milano, Italy. He is currently working at Universita` di Bergamo as Assistant Professor of Business Administration and Operations Management. His research interests focus on Demand Management and Forecasting, Supply Chain Planning and Organization. On these topics he published several papers in international journals and conferences. He is member of the Global Manufacturing Research Group and contributes to the world data gathering of the association. Pietro Romano is Associate Professor of Supply Chain Management and Business Marketing at University of Udine, Italy. He graduated in management and engineering and completed his PhD in Operations Management at the University of Padova, Italy, with a doctoral dissertation on supply chain management. He works within the Operations Management research group at the Department of Electrical, Managerial and Mechanical Engineering (University of Udine). His principal research interests concern supply chain management and production planning. He is member of EUROMA and his publications have appeared in Sloan Management Review, International Journal of Operations & Production Management, International Journal of Production Research, Journal of Manufacturing Technology Management, International Journal of Logistics: Research and Applications, Journal of Purchasing and Supply Management, International Journal of Manufacturing Technology and Management, Supply Chain Forum: An International Journal and Quality Management Journal. Fabrizio Salvador is Professor of Operations Management at the Instituto de Empresa Business School, Madrid. He has been Faculty Research Associate at Arizona State University in the Departments of Management (2001-2002) and Supply Chain Management (2003). He holds a PhD in Management and Engineering from the Univerista` di Padova. He has been successfully publishing his research on premier academic journals, such as Journal of Operations Management, International Journal of Operations and Production Management, Production Planning and Control, Computers in Industry, International Journal of Production Economics, etc. His research The Guest Editors would like to thank the referees who contributed with their insightful and detailed reviews to the quality of this special issue. In addition, the Guest Editors would like to express their gratitude to Professor David Bennett, the Editor of the Journal of Manufacturing Technology Management, for the opportunity given to publish this special issue in his journal. A final acknowledgement goes to the Como conference Chairmen – Professor Gianluca Spina and Professor Andrea Vinelli – for their key role in the organisation of the conference and all the related events.
is focused on in modularity, product configuration and co-ordinated product, process and supply chain design. He regularly teaches Mass Customization and Operations Management at Instituto de Empresa, both in the International MBA program and in the Executive MBA program, as well as in other institutions within companies. He has been successfully assisting multiple industrial companies in addressing product variety-related operational problems. Dr Salvador is member of the Editorial Team of Decision Sciences Journal, and Associate Editor for the International Journal of Mass Customisation. He is funding member of the International Institute for Mass Customisation and member of the European Operations Management Association, of the Decision Sciences Institute of the Institute for Operations Research and the Management Sciences.
This special issue of the Journal of Manufacturing Technology Management includes a selection of papers from the first E-UROMA-POMS Joint International Conference organized by Politecnico di Milano and Universita` di Padova on 16-18 June 2003. The conference was held in Como, Italy and hosted 447 participants. Among the 319 presented papers, eight have been selected for potential publication on the Journal of Manufacturing Technology Management, based on recommendations made by session chairs at the Conference, and on Guest Editors’ evaluations. The papers that accepted the invitation underwent a double blind review process, after which five papers have been finally accepted for publication in this special issue. The papers cover a quite broad range of different subjects within the operations management field, and represent well the different faces of the discipline. In particular, the areas covered are: performance management systems (Bourne, Kennerley and Franco-Santos), process management (Welker and de Vries), product variety management (Bramham, MacCarthy and Guinery) simultaneous product and supply chain design (Lau and Yam) and distribution channel design (Turner and Williams). The content of the papers is summarised below. The first paper, titled “Managing through measures: a study of impact on performance” by M. Bourne, M. Kennerley and M. Franco-Santos explores the impact of contextual, process and content factors on the effectiveness of performance measurement systems. While the literature agree on the importance of performance measurement for managing companies, there is mixed evidence as to whether these systems can really improve organizational performance. The literature review suggests that the effectiveness of performance measurement systems depends on the way they are designed, implemented and used within the company. Hence, the research investigates what factors positively influence performance measurement systems’ effectiveness and under what circumstances these factors are relevant. The empirical evidence is drawn from a multiple case study concerning different business units within the same organisation. This choice allowed the authors to control for a number of contingent aspects, thus focusing on the differences in implementation and use of performance measurement systems. The results show that high performing business units have more sophisticated and interactive systems, higher alignment with organisational objectives, and wider areas on which to take action after measuring performance. To conclude, the authors try to synthesize the characteristics of effective performance measurement systems with the concept of “interactive control” proposed by Simons (1991), and they suggest an extension of this definition in order to include the aspect emerged from their research. The second paper, titled “Formalising the ordering process to achieve responsiveness” by G. Welker and J. de Vries explores whether formalization within
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the ordering process reduces the trade-off between efficiency and responsiveness or not. They conceptualise formalization within the ordering process across three dimensions: (1) logistic control; (2) information processing; and (3) organizational embeddedness. Formalization takes place in the logistical control domain when managers agree before order inception on structural aspects of ordering process, such as criteria for accepting customer orders, for promising delivery dates, for allocating stocks and for prioritising customer orders. Formalization in the information processing domain refers to the definition of formal strategies to process order-related information, such as rules and programs, vertical information systems, hierarchical referral, lateral relations and self-contained tasks. Finally, formalization in the organizational embedding domain refers to the level of co-operation and interdependence across actors in the ordering process, as well as to the clarity of authority and responsibility assigned to them. The authors address their research question by means of a multiple case study research design involving five companies facing hard-to-predict customer-specific demand patterns. Their findings support the idea that formalization is simultaneously compatible with responsiveness and efficiency, so that it can be interpreted as a trade-off shifter. Furthermore, their empirical analysis suggests that to fully understand the relation among formalization, efficiency and responsiveness, the analysis should be performed at the level of the individual components of formalization, i.e. logistic control, information processing and organizational embeddedness. The third paper, titled “Managing product variety in quotation processes” by J. Bramham, B. MacCarthy and J. Guinery investigates what activities and what mechanisms a firm may adopt to mitigate the negative impact of product customisation on its front end operations. The study builds on past research on the topic, which mostly focused on the process of translating a customer specification into a technical specification (product configuration process) considering also the case of engineering-to-order companies, where the product space can be extended in response to every customer inquiry. To do so, the authors engage into the comparison of two cases, an instrument company and an office seat company. Notwithstanding the thoroughly detailed differences between these two companies, the authors are able to identify some regularities that provide useful insights into the structure of the quotation process in the presence of customisation. Taking a decision-making perspective over the quotation process, they identify four different “decision centres”: (1) customisation request initiation; (2) customisation request classification; (3) resource control; and (4) identification of information for reuse. They argue that such decision-centres perspective can offer important insights on how to improve the quotation process including, for example, balancing process automation and human support and promoting information re-use. Finally, the authors provide
evidence of the mechanisms a company may adopt to better manage for product variety in the front-end. In doing so, following the contingency theory tradition, they propose a number of mechanisms increasing the capacity of the company to deal with product variety in the front-end (what they call variety-absorbing mechanisms) and a number of mechanisms reducing the need to deal with product variety in the front-end (what they call variety-mitigating mechanisms). The fourth paper, titled “A case study of product modularisation on supply chain design and coordination in Hong Kong and China” by A. Lau and R. Yam investigates how product innovativeness and product architecture affect supply chain structure and coordination in new product development. They motivate their research based on the fact that extant literature provides opposite advices as to the criticality of supply chain integration to improve product development performance, when modular product architectures are implemented. They address this issue by means of a case study of a Hong-Kong-based division of a leading global producer of audio consumer electronics. The selected case involved as sub-cases the product development process of integral vs modular and innovative vs conventional products. By comparing what actors got involved in product development by the company and by observing how such involvement took place, the authors identified two main messages, which they synthesize in the form of propositions. First, product modularisation tends to increase the length of the supply chain, by adding a layer of module manufacturers. The presence of such additional layer might negatively affect supply chain performance, so that corrective actions have to be taken to maintain efficiency across the supply chain. Among these actions, the authors suggest better communication and geographical proximity are important. The second message derived from case analysis, instead, suggests that innovative products require closer supply chain coordination than conventional products. The fifth paper, titled “Modelling complexity in the automotive industry supply chain” by K. Turner and G. Williams, analyses how the distribution side of the automotive supply chain can be adapted to handle differentiated goods without compromising service levels and efficiency. They trace this problem to the fact that the demand side of the automotive supply chain did not undergo the same improvements that took place on the supply side. Consequently, distribution chains designed for the stable and standardized environment of the past would lead to high inventory and poor service levels under present environmental turbulence. They essentially consider two possible ways to improve supply chain design: (1) consolidation of dealers’ inventory into a distribution centre; (2) consolidation of dealers’ inventory into a distribution centre and postponement of differentiation activities from the assembly plant to the distribution centre. By means of discrete-event simulation they compare performance of these two distribution channel configurations with a traditional assembler-dealer distribution channel, wherein most of inventories are kept at the dealers’ site and trans-shipments across dealers are allowed. The simulation model includes a wide variety of settings, including individual customer, market demand and production characteristics. The simulator tries to match customer requirements (attributes and maximum waiting time) with pipeline inventory and production schedules, predicting also the lost sale occurring when customer’s lead time requirements cannot be satisfactorily met. The
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result of the simulation, based on real data collected in the UK, indicates that inventory consolidation into a distribution centre can significantly improve service levels and satisfaction of customers’ requirements. At the same time, the simulation reveals that in this case and under the model assumptions the additional improvements that can be achieved by means of form postponement are pretty limited. Despite the differences in the subject of these papers, some common issues can be found as a fil rouge in each of them. In the first place, all of the five studies underscore the importance of process management as a methodological tool to understand how companies create value and as a practical instrument to gain insights into performance improvement opportunities. Overall, we feel this special issue effectively renders the idea that new insights and perspectives can be brought into the academic debate by applying the well-established theoretical apparatus underlying process management and design. Secondly, many of the problems discussed in this special issue revolve around the theme of managing complexity and variety. In this sense, this special issue informs the reader on a number of interesting advances into a contemporary theme in operations management. The transversal message emerging across the five papers is that complexity and variety are key aspects to be considered when designing and implementing operations management practices in different domains, from performance measurement systems in different business units to quotation/product development, up to supply chain or distribution processes for differentiated products. Finally, the special issue is a showcase of the use of case-based methodology for studying complex subjects in OM: four out of five papers are, in fact, based on this methodology. Case-based research is strictly tied to the above-mentioned common themes of the papers in this special issue and, hence we believe that accurate case study research is going to become important for the growth of our discipline. Raffaella Cagliano Politecnico di Milano Matteo Kalchschmidt Universita` di Bergamo Pietro Romano Universita` di Udine Fabrizio Salvador Instituto de Empresa Reference Simons, R. (1991), “Strategic orientation and top management attention to control systems”, Strategic Management Journal, Vol. 12, pp. 49-62.
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Managing through measures: a study of impact on performance
Managing through measures
Mike Bourne, Mike Kennerley and Monica Franco-Santos Centre for Business Performance, Cranfield School of Management, Cranfield, UK Abstract
373 Received February 2004 Revised November 2004 Accepted November 2004
Purpose – This study investigates the use of performance measures and how performance measurement impacts performance. Design/methodology/approach – This study was conducted through multiple case studies in a single organisation. Comparisons are made between performance measurement practices in comparable high and average-performing business units. Findings – The findings suggest that current research into the impact of performance measurement on performance may be too simplistic in its approach as much of the research relies on studying the physical and formal systems used, ignoring the types of factors found to be important in this study. Research limitations/implications – Being based on a single organisation, the wider applicability of the specific findings from this study should be questioned. However, if, as we suggest, the interactive nature of the use of the measurement system is important, future research will need to find ways of observing, measuring and quantifying this interactivity to allow a richer picture of the impact of performance measurement on performance to be developed. Practical implications – The differences observed between the high and average-performing cases was in the way they managed with the measures. Average-performing business units used the performance measurement system as a simple control system, whereas, high performing business units were using the measurement system much more interactively. Originality/value – This paper highlights the importance of using performance measures interactively and suggests further research into Simons’ concept of “interactive control”. Keywords Performance measurement (quality), Performance management, Business performance Paper type Research paper
Introduction With the balanced scorecard (Kaplan and Norton, 1992) being cited by Harvard Business Review in 1997 as one of the most important management tools of the last 75 years, performance measurement has been attracting a great deal of interest (Neely, 1998b). There are now numerous balanced performance measurement frameworks (Keegan et al., 1989; Lynch and Cross, 1991; Fitzgerald et al., 1991; Kaplan and Norton, 1992; Neely et al., 2002a) and multiple processes for the design of performance measurement systems (Bitton, 1990; Dixon et al., 1991; Kaplan and Norton, 1993, 1996; Neely et al., 1996, 2002b; Krause and Mertins, 1999). The problems of implementation have also been studied (Meekings, 1995; Bierbusse and Siesfeld, 1997; Lewy and Du Mee, 1998; Schneiderman, 1999; Bourne et al., 1999, 2000, 2002, 2003), but the whole This paper was produced during the research project “Managing through measures”, which was sponsored by the EPSRC under grant number GR/R56136/01 and “Evaluating the impact of performance measurement systems” under grant number GR/S28846.
Journal of Manufacturing Technology Management Vol. 16 No. 4, 2005 pp. 373-395 q Emerald Group Publishing Limited 1741-038X DOI 17410380510594480
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area of how performance measures are used has attracted less attention until recently. This now appears to be the focus of current research (Lipe and Salterio, 2000, 2002; Kalagnanam, 2001; Vakkuri and Meklin, 2001; Barsky and Marchant, 2001; Malmi, 2001; Malina and Selto, 2002; Epstein, 2002) and the use of performance measures is the subject of this paper. The research described in this paper was designed to address the question “how do the differences in use of performance measurement have different impacts on business performance?” Our working proposition was that the manner in which the data are acquired, analysed, interpreted, communicated and acted upon has an impact on business unit performance. But in undertaking this research, many other factors have to be taken into account. Currently, there is a continuing debate in the performance measurement literature as to whether performance measurement has a positive impact on business performance or not. As the literature review will show, the evidence is mixed. As a consequence, a better question may be “under what circumstances does performance measurement positively impact on organisational performance?” In practice, the organisational context, performance measurement content and process will all impact on the outcome. Our observation from reviewing the literature was that there was little field research focusing on the process[1] of using performance measures and, therefore, we designed this research specifically to investigate the use of measurement and impact on performance. In this study, by examining different business units in the same organisation, many of the contextual, process and content factors were common allowing us to focus on the use of the measures. The case studies examined how performance measures were used in high and average-performing business units. High and average-performing business units were selected, as we wanted to know what differentiated the performance between the best and the average, rather than between the best and the worst. Our research analysed the difference in practices and relates these to differences in performance. The format of this paper is as follows. Firstly, we review the literature, summarising the factors believed to influence performance measurement effectiveness using Pettigrew et al. (1989) framework. Secondly, we outline the research itself and the methodology used to gather case study data and to “control” for common organisational factors. Thirdly, we describe the organisation in which our case studies were conducted and, in particular, the management structure and performance measurement systems in use. Fourthly, we report our findings including the differentiators between high and average-performing business units. These are then discussed and contrasted with Simons’ (1991) concept of interactive control. Finally we conclude and suggest this is an area for further research. The literature Many practitioners embarking upon a redevelopment of their performance measurement system assume that their efforts will have a positive impact on the organisation’s overall performance (Bourne et al., 1999). This is often their basic reason for beginning such a project, but published research suggests that success is not certain. A recent study has analysed 99 published papers on the impact of performance measurement on organisational performance (Franco and Bourne, 2004). Although the
study revealed that the majority of papers found that performance measurement had a positive impact on organisational performance, further analysis suggested that the more rigorous the research method used, the less likely performance measurement would be found to have a positive impact. The conclusion has to be that the research findings are contradictory. Whilst some studies have found that the use of non-financial performance measures has a positive impact on business performance (Ittner and Larcker, 1998, 2003; Banker et al., 2000) others found no relationship (Perera et al., 1997; Neely et al., 2004). Whether performance measurement per se is a “good thing” is certainly of academic interest, but for those engaged in directing and managing organisations, the more immediate question is “under what circumstances does performance measurement positively impact on organisational performance?” To answer this question we will briefly review the literature. As stated previously, the organisational context, performance measurement content and process will all impact on the outcome, so we have adopted Pettigrew et al.’s (1989) framework of context, content and process in our presentation of the literature. Context Pettigrew et al. (1989) defined context as both the organisation’s external environment (such as the competitiveness of the industry, the economic and political situation) and internal context (such as structure, culture, management style and resources). We address these in turn. Our review of the literature found studies of the impact of external context on perceptions of performance measurement effectiveness and one study that made the link to organisational performance. Smith and Goddard (2002) and Waggoner et al. (1999) have suggested market uncertainty, supplier characteristic and the economic situation all impact performance measurement effectiveness, whilst Goold and Quinn (1991) argued that performance measurement effectiveness is contingent on the speed of change and the measurability of performance. Lokman and Clarke (1999) studied the influence of market competitiveness on the use of information, performance measurement and business unit performance and Hussain and Hoque (2002) found that economic constraints and regulatory regimes influenced the use of measurement systems. Published research suggests that external environmental factors do have an impact on perceived performance measurement effectiveness, but so far there is no overarching framework to describe this relationship. The impact of internal context has been more widely researched and there are many aspects cited, from organisation size and structure, culture and management style, management resources and capabilities, to the interface between the measurement system and other processes and the maturity of the system itself. We have summarised these in Table I. Content The content of the measurement system is concerned with what is being measured and how the measures are structured. For example, authors have identified that: . The specific definition of the measures themselves is important (to both the designer of the measures to clarify strategy and to the user of the measures to influence behaviour and direct action, Neely et al., 1997).
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Internal context
Authors
System maturity More mature systems are more effective
Evans (2001) and Martins (2002) Hendricks et al. (1996) and Bourne et al. (2002)
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Organisational structure Importance of aligning structure and measurement Organisational size Measurement is easier in larger organisations and more problematic in smaller ones
Hoque and James (2000) and Hudson et al. (2001a, b)
Organisational culture Alignment between the cultural elements embedded in the measurement system and the users’ cultural preference is beneficial
De Waal (2002), Gates (1999), Johnston et al. (2002), Lingle and Schiemann (1996), Lockamy and Cox (1995), Maisel (2001), Malina and Selto (2002) and Bititci et al. (2004)
Management style Appropriate style is important, appropriate style may be different in different settings and phases of implementation and use
Gelderman (1998), Libby and Luft (1993), Hunton et al. (2000), Simon (1987) and Bititci et al. (2004)
Competitive strategy Measures should be aligned to strategy
Kaplan and Norton (1996, 2001), Lockamy (1998), Mendoza and Saulpic (2002), McAdam and Bailie (2002) and Neely (1998a) Bourne (2004) and Kennerley and Neely (2002)
Resources and capability Companies need resources and capabilities to implement and refresh their measurement systems
Table I. Internal contextual factors impacting performance measurement effectiveness
Information systems infrastructure High data integrity and a low burden of data capture are important
Bititci et al. (2002), Eccles (1991), Lingle and Schiemann (1996) and Manoochehri (1999)
Other management practices and systems There should be alignment between measurement and other systems (e.g. budgeting, compensation)
De Toni and Tonchia (2001), Eccles (1991), Eccles and Pyburn (1992), Kaplan and Norton (1966), Kaplan and Norton (2001), Moon and Fitzgerald (1996) and Otley (1999)
.
.
The different dimensions of the measures used are important to the users of measurement systems as they direct management focus (Kaplan, 1994; Johnson and Kaplan, 1987), be they internal and external or financial and non-financial (Keegan et al., 1989); leading and lagging (Kaplan and Norton, 1996) or balanced (Kaplan and Norton, 1992). The structure (the way the individual measures interrelate) too has been found to be important to the users of measurement systems (Lipe and Salterio, 2000, 2002), be that a pyramid (Lynch and Cross, 1991), matrix of results and determinants (Fitzgerald et al., 1991), strategy map (Kaplan and Norton, 1996) or a success map (Neely et al., 2002a).
Empirical content studies suggest that performance measurement is more effective when the measures are appropriately designed (Neely et al., 1997), include multiple dimensions (Lingle and Schiemann, 1996) and are structured in a way that helps managers understand the interrelationship and reflects strategy (Lipe and Salterio, 2000, 2002).
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Process Four main processes have been identified in performance measurement (Neely et al., 2000; Bourne et al., 2000); these being design, implementation, use and refreshing. As we stated in the introduction, the processes of design and implementation have been studied and both have an impact on the outcome (Bourne et al., 2003) and effectiveness of the measurement system (Neely and Bourne, 2000). Similarly the refreshing, or redesigning, of measures and the measurement system is important. Authors emphasise the need for continuous reviews of the measures themselves, their results, and their impact on goals and strategy with a clear focus on improvement and learning (Ghalayini and Noble, 1996; Johnston et al., 2002; Kaplan and Norton, 2001; Kennerley and Neely, 2002, 2003; Lingle and Schiemann, 1996; Neely et al., 2000) to keep the measures and measurement system relevant for the organisation and its users (Manoochehri, 1999). The argument made is that the measurement system will lose its effectiveness over time if it is not updated in line with the environmental and organisational needs. However, three of Neely et al.’s (2000) processes (the design, implementation and refreshing processes) concern changing the state of the measurement system. From our review of the literature, the status quo (the situation where the performance measures are stable and used in managing performance) is less researched. Empirical studies of the use of measurement systems in the field at the level of detail of the process stages are rare, with Simons’ (1991) work on interactive control being a notable exception. In his research he investigated the “levers of control” used in organisations to measure and manage performance. He concluded by differentiating between simple feedback control, and “interactive control” in which managers interact much more closely with the data and management system. He found the interactive control to be more effective in certain situations. But given the relative lack of field studies, a framework was needed to inform our research. One of the simplest approaches to investigate the use of measures is through the stages in underlying process, being data capture, data analysis, interpretation, communication and decision-making (Neely, 1998a). Our literature review identified that writers focusing on the key processes associated with the use of performance measures have identified seven factors: (1) the linking to strategic objectives (Atkinson, 1998; Otley, 1999); (2) the method of data capture (Lynch and Cross, 1991; Simons, 1991; McGee, 1992; Neely, 1998a); (3) data analysis (Lynch and Cross, 1991; Neely, 1998a); (4) interpretation (Simons, 1991; Neely, 1998a) and evaluation (Ittner et al., 2003; Kerssens-Van Drongelen and Fisscher, 2003);
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(5) the provision of information and communication (Bititci et al., 1997; Forza and Salvador, 2000; Kerssens-Van Drongelen and Fisscher, 2003; Lebas, 1995; Lynch and Cross, 1991; Simons, 1991; McGee, 1992; Neely, 1998a; Otley, 1999); (6) decision-making (Ittner et al., 2003; Neely, 1998a); and (7) taking action (Flamholtz, 1983; Flamholtz et al., 1985; Simons, 1991). Synthesising these five stages and seven factors we have arrived at the process stages presented in Table II. During this research, we adopted the framework from Table II to guide our data collection, case and cross case analysis. However, we did make one adjustment. It was decided that as “decision-making” is often difficult to observe (Ramachandran, 2004), it would be subsumed in this study under the more observable outcome of the decisions – “taking action”. Summarising the literature Our review identifies the contextual, process and content factors found in the literature that is believed to impact the effectiveness of performance measurement. Given the significant number of the contextual, process and content variables identified, studying the impact of performance measurement on performance is difficult. Therefore, an approach that simplified the issues was needed. Hence the approach adopted here, that of researching high and average-performing operations in the same organisation, where many of the contextual, process and content variables are the same. This enabled us to focus on “how the differences in use of performance measurement have different impact on business performance?” within a “controlled” environment. The research methodology In order to progress the research, we needed access to an organisation that had multiple business units operating in a similar manner in the same marketplace. Ideally, the system should have been in place for more than two years, so that it was embedded and not a new system (Bourne et al., 2000; Evans, 2001; Martins, 2002). Further, the system needed to include a range of financial and non-financial measures, ideally that Process stages
Authors
Alignment with strategic objectives Data capture
Atkinson (1998) and Otley (1999) Lynch and Cross (1991), McGee (1992), Simons (1991) and Neely (1998a) Lynch and Cross (1991) and Neely (1998a) Simons (1991), Neely (1998a), Ittner et al. (2003) and Kerssens-Van Drongelen and Fisscher (2003) Bititci et al. (1997), Forza and Salvador (2000), Kerssens-Van Drongelen and Fisscher (2003), Lebas (1995), Lynch and Cross (1991), Simons (1991), McGee (1992), Neely (1998a) and Otley (1999) Ittner et al. (2003) and Neely (1998a) Flamholtz (1983), Flamholtz et al. (1985) and Simons (1991)
Data analysis Interpretation and evaluation Communication and information provision
Table II. Process stages identified in performance measurement
Decision-making Taking action
represented the perspectives of a balanced scorecard or similar recognised framework. The existence of a common data collection and processing system would be beneficial as it would increase the probability of having reliable and comparable performance information. Having the data management and reporting controlled by an IT department independent from those being measured would also reduce the chance of the data and information being distorted by the users. The case study organisation was selected as it provided a network of comparable business units with similar characteristics in which we could study how performance measures were used to manage performance. These practices could then be evaluated against direct information from both financial and non-financial measures, linking practices with comparative levels of performance. The methodology was, therefore, designed to minimise both internal and external contextual differences (Pettigrew et al., 1989; Bourne et al., 1999) allowing the research to focus on process, content and outputs. Table III illustrates how the approach was used and the factors, which were believed to be common (or controlled for) across the cases and those identified as the focus for this research. Ten individual case studies were conducted in total, five in high performing business units and five in average-performing business units. To ensure consistency of data collection across cases and researchers, a case study protocol was established to guide data collection (Yin, 1994) using Table III as the basis for the data collection and being informed by the literature identified above. As many factors were common across the cases being studied, our working proposition was that “the manner in which the data is acquired, analysed, interpreted, communicated and acted upon has an impact on business unit performance”. The study was then conducted over a four-month period in three phases. The first phase comprised interviews with senior management and support staff in head office and observation of the systems and procedures in use (seven days on site in total). During this period, five regions were arbitrarily selected to be studied and financial and non-financial performance data was extracted from the balanced scorecard and profit and loss (P and L) accounts to identify the performance of the business units in these regions. The second phase involved interviewing the five managers responsible for these regions, discussing individual business unit performance and selecting the business units to investigate. The third phase comprised the site visits to the business units across the UK. The data collection itself was conducted through a series of semi-structured interviews with branch managers, branch office personnel and operators. This was supported by direct observation and inspection of data and documentation to increase confidence in the findings by the triangulation of different sources. Between half a day and a day was spent in each of the business units over a two-month period during phase three of the research and between four and six individuals were interviewed per branch. The cases were selected on the basis of two business units per region, with each regional manager proposing a high and average-performing business unit for study. The intention in doing this was to minimise the differences between regions and in the management style of regional manager. However, the regional managers’ recommendations were not accepted without verification. Access to the scorecard performance data and business unit P and L accounts enabled the researchers to form an independent view of comparative business unit performance. As mentioned, during
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Factors External context Industry competitiveness Economy
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Political environment Internal context System maturity Organisational structure Organisational size Organisational culture Management style Competitive strategy Resources and capability Information systems infrastructure Other practices and systems Processes Alignment with objectives Data capture Data analysis Interpretation & evaluation Decision making Communication and information provision Decision making and taking action Content Definition of performance measures Dimensions measured Structure and presentation
Table III. Factors to be researched
“Control mechanism” and research focus All BUsa in the same business. Local differences minimised by comparing BUs in the same region All BUs in the same business. Local differences minimised by comparing BUs in the same region Common In place across the business for 5 years All BUs of a similar size, structure and reporting to similar regional structures All BUs of a similar size inside a medium sized enterprise The same organisation, but local variances to be observed in the BUs studied The same organisation, but local variances to be observed in the BUs studied The same organisation, but local variances to be observed in the BUs studied The same organisation, and staffing levels but local variances to be observed in the BUs studied Common throughout Common throughout Common measures but local usage to be investigated and observed in the BUs studied Formal data capture through a common IT system but to be investigated and observed in the BUs Reports common but additional analysis to be investigated and observed in the BUs studied To be investigated and observed To be investigated and observed Reports common but additional analysis to be investigated and observed To be investigated and observed Common definition and central data processing enabling reliable comparisons on BUs Common balanced scorecard dimensions Common structure with no strategy/success map but comparative data displayed
Note: aBUs¼ Business units
phase one, interviews were conducted with central staff. This included IT staff responsible for performance measurement reporting, representatives from training, senior operations managers, accounting personnel and directors. As a result of the researchers’ own analysis of the performance information, which was confirmed by the
additional interviews, two of the original case studies were rejected. Two further cases were then conducted to replace the lost cases. Following Yin’s (1994) prescriptions, individual cases were compiled before cross case comparisons were made. Cross case comparisons were undertaken in two stages. Initially, pairs of cases in the same region were compared. This was then followed by full cross case comparison. Drawing conclusions from case study research is a difficult process, so the approach adopted was based on Miles and Huberman’s (1994) view of qualitative analysis. This focuses on three phases (1) data reduction; (2) data display; and (3) conclusion drawing and verification. The next section describes the case study organisation in more detail. The case study organisation The case study organisation is a UK-based company providing repair services. These services are sold directly to the consumer but also provided as a service to insurance companies. Service is delivered through an extensive network of branches (local business units of the organisation) across the country. Each business unit has a branch manager and is managed as a profit centre within the service network. Regional managers oversee some 10-15 business units and the regional managers report directly to the operations director. The organisation as a whole has been using a “balanced scorecard” for over five years. At the business level, that includes measures of financial performance, customer and employee satisfaction and operational performance. However, the manner in which the scorecard has been cascaded to the business unit level has resulted in a much greater focus on financial and operational measures. The customer perspective at the business unit level is measured through customer service measures, and the innovation and learning perspective through measures of operator productivity and rework. The branch manager, therefore, receives weekly reports on operational performance including service levels and operator performance. These are generated automatically by the IT system from data capture during transactions, and presented in the form of traffic lights (green for on target, amber for near target and red for off target). The system presents the 12 scorecard measures in summary form, with the last week, month to date and year to date figures appearing on a single screen. The software allows further interrogation and drill down to transaction level. Branch managers also have access to the scorecards of other business units within their own region so that they can compare their own business unit’s performance with that of their colleagues. The branch managers also receive an operators’ scorecard (showing data on attendance, productivity and rework) on a weekly basis and the business unit P and L account each month. There was also an incentive scheme in operation. Branch managers’ annual bonus was paid on the basis of achieving the budgeted profit target for the business unit. However, their performance was assessed on the basis of their scorecard performance, which was used as the basis for their annual salary increase. The operators within the business unit were paid through a productivity bonus system that rewarded high levels of output in a week and penalised non-attendance and poor quality. From our
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observations, the combination of bonus paid on achieving budgeted profit and base pay on scorecard results, balanced the focus between financial and non-financial measures. However, the operator bonus scheme drove different behaviour. This was believed by management to have increased overall productivity across the company, but also caused the kinds of dysfunctional behaviours one would expect in specific situations (Kerr, 1995, “On the folly of rewarding A, while hoping for B”), presenting branch managers with some dilemmas over allocation of jobs. The findings Drawing together the threads from the different cases was a difficult task as much of what was happening in the different business units was similar. Repeating the similarities is of less value, so Table IV summarises the results of our final full cross case analysis emphasising the differences in each of the phases of the use of performance measurement. In this section, we present our findings and observations by phase of the process, again focusing on the differences rather than similarities. We begin with alignment to strategic organisational goals and end with taking action. Alignment to strategic organisational goals The assumption underpinning the use of the balanced scorecard measures at business unit level was that getting a “green scorecard” drove better performance (both financially and non-financially). However, when this proposition was challenged, there was wide spread acceptance by the regional and branch managers that the link between financial performance and the scorecard results had never been fully investigated and ready acceptance that the connection was not perfect. We interpreted this as a strong indication that the scorecard was being used as a means of controlling standards and not for maximising performance. High performing business units were differentiated from the others by their branch managers’ use of simple mental models, which they used to manage the business unit on a day-to-day basis. They described how they used their own indicators (not the formal weekly scorecard measures) to manage, often using unofficial data sources. These had been developed from experience or insight into what the true drivers of business unit performance were. Many revolved around managing volume effectively and efficiently, but others focused on the development of individual skills and team working – aspects absent from the business unit level scorecard. Gathering data The formal balanced scorecard presented data gathered automatically from the service processes. Manual data input purely for measurement purpose was minimal. The practice that differentiated high performing business units from the average was that managers in these business units collected additional data throughout the week from their planning boards, conversations with team members and observations of activity. They did not wait until the end of the week to take appropriate action as they adjusted their activities as the week progressed. As a result, the weekly scorecard results rarely came as a surprise to these managers. This proactive approach to data collection was visibly less apparent in average-performing business units and not mentioned in discussion with branch managers or staff.
Root cause analysis on lost jobs (3, 5, 7) Specialised job costing software developed and used monthly (9) Specialised local consumer usage tracking (7) P&L variance analysis with whole BU team to item line level (5) P&L analysis with office staff (7) Old manual workload display board still used to track jobs in hand (1,9) Rely on automatic Operators’ time sheet reviewed data acquisition weekly (2,4,6,8,10) (2,4,6,8,10) Weekly drill down 10 minute look to interrogate data through results at weekend (4,6,8) (2,4,6,8,10) Detailed weekly drill down to evaluate performance data (2,10)
Green scorecards lead to better performance (2,4,6,8,10) Costs need to be contained (2,8) People are important (2,4,6,10)
Average-performing business units (Business units numbers 2,4,6,8,10)
Own data collection (1,3,5,9) Daily tracking of job progress and results (3,5,7,9) Occasional drill down to interrogate data (1,3,5,7,9) Listening to office conversation and incoming calls (1,7) Challenge cost allocation (3, 5, 9)
Green scorecards lead to better performance (1,3,5,7,9) Personal mental model of assumptions of what drives performance (1,5,7,9) Well trained people drive performance (3,7) Motivated people deliver performance (1,3,5,7,9) Motivation driven by communication (1,3,5,9) Some measures interact together (3,7,9) Costs drive P&L (3,5,9) Volume, lost jobs and rework are key to P&L (1,3,5,9) Rework reflects training, motivation or personal issues (1,3,5) Recruitment key to long term performance (1,3,7)
High-performing business units (Business units numbers 1,3,5,7,9)
Analyse data
Gather data
Alignment to organisational objectives Display weekly results and league tables (1,3,5,7,9) Monthly 2 hour whole team P&L review meetings (5) P&L review monthly with office staff (7) Bi-monthly off site evening meeting with pizza (3) Regular one to one (5,7,9) and at every opportunity with operators (1,3)
Display weekly results and league tables on wall (2,4,6,8,10) Short performance reporting meetings (2,4,6,8,10) Regular one to ones (2, 6,10)
Against company targets (1,3,5,7,9) (if appropriate, 1,3,5) Against own local targets and standards (3,5) Estimate week’s results and use system to confirm evaluation (3,5,7,9) Predict month end P&L – but difficult (3) Against regional BU (1,3,5,7,9) and against whole company (3,5,7,9)
Against company targets (2,4,6,8,10) Against region (2,4,6,8,10) Traffic light colour (2,4,6,8,10) Against budget (2,8,10)
Interpret/evaluate Communicate insight
On variance from budget (2,8,10) On red traffic lights (2,4,6,8,10) On operations director’s monthly focus email (2,4,6,8,10)
On P&L item lines (5,7,9) On trend and not single result (3,5) On morale and/or attitude (1,3,5) On people issues, in and out of work (5,7) After taking conditions into account (1,3,5,7,9) On local BU issues (1,7,9) On own data (1,3,5,9) Early, when appropriate (1,3,5,9)
Take action
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Table IV. The results from the cross case analysis
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Analysing data Basic data analysis and display was performed by the IT system providing traffic light feedback against target and month to date and year to date figures automatically. However, the system did provide opportunities for extensive and time-consuming analysis of the results through data enquiry tools and data drill down to transaction level. Further, these applications and reports allowed comparisons between business units in the same regions, so managers could make direct comparison of their performance measures against their regional colleagues’. The information on average-performing branch managers’ use of data analysis tools was inconsistent, with some managers spending considerable time on analysis and others spending very little time. However, in high performing business units the use of data analysis tools was consistent. In high performing business units, scorecard data analysis using the standard data enquiry tools was very light. We have concluded from this that the managers in these business units were simply using the scorecard data to check their own assumptions and not as a fundamental tool for managing the business. However, in two business units, additional tools had been developed to overcome specific problems. An example was a detailed spreadsheet designed to calculate precisely consumable usage, something that had been a problem for the business unit in the past and that the tool helped overcome. There was also evidence that managers were managing using their own systems rather than relying on the common company performance measurement systems. Evidence for this included the continued use of old planning boards and additional focus on lost jobs. Interpretation and evaluation Interpretation is concerned with extracting meaning from the performance measurement system. This was achieved by providing direct comparisons in the display of the weekly scorecard against targets. Additional comparisons were also available so that a branch manager could compare his performance against other business units or the regional average. All business units were well aware of their performance in comparison to other business units within the region and the managers of the better performing business units could express their performance in terms of company wide league tables. The factor that differentiated high performing business units from the others was the way they ignored inappropriate targets. Many targets were set on a company wide basis, and so were more or less achievable at business unit level depending on local circumstances. High performing branch managers simply expressed the opinion that they ignored inappropriate targets and managed their business unit with reference to their own targets (which on occasions were higher that those set nationally). We have concluded that high performing business units are, therefore, not putting scarce resources into addressing specific inappropriate goals enabling them to maintain a high level of performance overall. Communicating insight At a company level, performance was communicated through the weekly scorecards, operator scorecards and monthly P and L account. Regular management meetings, one on one discussions and the monthly “state of the nation” e-mail from the operations director reinforced these activities.
However, at the business unit level, communication was the biggest differentiator between high and average-performing business units. The intensity of communication in high-performing business units was so much greater, the frequency, the approach, the level of detail in the content as well as the time spent. To provide one illustrative example, some branch managers had difficulty in interpreting their own P and L account, but in one business unit the whole team spent two hours on a Monday morning once a month analysing, item line by item line, the whole of the business unit P and L statement and discussing what actions they could take to improve it. In high-performing business units, regular intense whole team meetings were not uncommon and reinforced the perpetual performance dialogue in the business unit. Taking action Action taking was hard to observe but appeared to differ greatly across the organisation, depending on management style. At the business unit level we observed a real dichotomy. In some instances action was taken immediately on the discovery of a specific problem whilst in other circumstances, action was delayed. We have concluded that when the source of the problem is apparent and could be easily rectified, high-performing managers acted quickly. On the other hand, when either the cause was not apparent or could not be simply fixed, action was delayed. This meant that some natural variation in performance was not acted upon inappropriately. It also meant that considerable latitude was given to individuals whose performance deteriorated if the underlying cause was known (such as a personal problem). In fact this ability to focus on both task and people issues simultaneously was a factor often present in high-performing business units and less apparent elsewhere. Discussion In the above section, the findings from Table IV were presented in terms of their differences between high and low performing business unit by process stage. The studies undertaken did reveal that at a basic level, measurement and management of performance was done in a similar manner across the business units. All used their reports, displayed them in line with company policy, communicated weekly to the staff and fulfilled the requirements of the appraisal system. Some, despite having been trained, were still not fully conversant with the P and L accounts, but at the basic level, this was the only difference observed and was not common across all average-performing business units. However, it is the combination of difference between high-performing business units and average-performing business units, which is most striking. These we highlight in turn. Firstly, simply the richness of the information is apparent. In average-performing business units, managers were describing common but simple control systems based around their use of the performance measures. In contrast, the use of measures in higher performing business units was more sophisticated. Secondly, managers in high-performing business units discussed their model of how the business unit operated. They explained how aspects of operations, people and performance interacted. We have concluded that this was a clear driver of how they managed performance. This is captured in the “alignment to organisational objectives” column in Table IV, but the thinking pervades most of the other process stages.
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Thirdly, their use of performance measurement is interactive. High-performing branch managers have their own data collection and analysis systems. They are listening to conversations in the office. They use multiple sources of data to make comparisons. They intensively communicate and discuss performance both at formal meetings and “at every opportunity”. In Table IV, the evidence of this interaction spreads right across the process stages from “gather data” to “take action”, and this interaction is in stark contrast to the much more simple control response found in average-performing business units. Fourthly, as Table IV shows, managers in high-performing business units take action on a range of issues. In average-performing business units we have observed action being predominantly driven by company targets and directives. In high-performing business units, trends, local conditions and people issues were all factors in taking action. Much of this, we argue, can be traced back to managers’ explicit assumptions of the drivers of performance and their interactive use of both company and locally-developed performance measures and indicators. These findings can be summarized as follows. In average-performing business units, performance was managed using a simple control approach. Data were captured through the standard company systems, simply analysed and compared against company targets. The results were then communicated and acted upon. In comparison, although the same approach was evident in high-performing business units, this was not the main source of control. In high-performing business units, the simple control approach was used to verify performance at the end of the period, but the main drive for performance came from continual interaction with the performance data. Branch managers had their own data collection systems and indicators of performance. They created their own approaches to analysis and used these to project forward future performance. They then intervened using their knowledge of the situation throughout the period, rather than waiting for period end feedback. Table IV also shows that branch managers in high-performing business units are more sophisticated and explicit in their understanding of the drivers of performance, more intense in their communication and more varied in their courses of action. However, the “interactive” nature of how they managed performance is the most prevalent difference observed between high and average-performing business units. We concluded from our earlier review of the literature that, although there are many studies of the impact of performance measurement on performance, very few of these studies focus on how the use of the performance measures impacts on performance. Recently, classroom-based quasi-experimental research studies are being reported, but field studies are rare. Simons (1991) is the one notable exception. Our results suggest that in this organisation the higher levels of performance are being derived from managers being more interactive in their approach. This raises the question, “is what we are observing in high-performing business units just an example of Simons’ (1991) interactive control?” Simons stated: A management control system is categorised as interactive when top managers use it to personally and regularly involve themselves in the decisions of subordinates. When systems are used for this purpose, four conditions are typically present: Information generated by the management control system is an important and recurring agenda addressed by the highest levels of management; the process demands frequent and regular attention from operating managers at all levels of the organisation; data is interpreted and discussed in face-to-face
meetings of superiors, subordinates, and peers; the process relies on the continual challenge and debate of underlying data, assumptions and action plans.
There are similarities. Managers were personally and regularly involving themselves with their subordinates’ performance, performance information was regularly used in face-to-face discussions and meetings and performance was continually debated. However, there are also differences. Taking each of Simons’ (1991) four conditions in turn: (1) “Information generated by the management control system is an important and recurring agenda addressed by the highest levels of management”, but in this study our focus was not on senior managers but on the business unit level. Our research investigated aspects of senior management involvement, but the differences in performance and activity we observed was at the business unit level. (2) “The process demands frequent and regular attention from operating managers at all levels of the organisation” and this difference we did observe at the business unit level. However, the information used in this study was not confined to the formal performance measurement system. The findings suggest that higher performing branch managers were responding to informal indicators during the week, using the formal measurement system to keep the score at the end of the week. (3) “Data is interpreted and discussed in face-to-face meetings of superiors, subordinates, and peers” and this we did observe, particularly in high-performing business units. However, in our research, the consequence of this was that the taking of action was influenced by local knowledge of personal and business circumstances. This finding can be assumed from, but is not explicit stated in, Simons’ (1991) research. (4) “The process relies on the continual challenge and debate of underlying data, assumptions and action plans”. This is at the centre of Simons’ (1991) concept of interactive control but he argues that such an approach is unsustainable across all systems for prolonged periods. In this research, the interactive nature of the use of the measurement system was across the whole business unit’s performance measurement system not one single dimension. One other observation should also be made. The explicit concept of visual mental models (cause and effect relationships, strategy and success maps) described by Eccles and Pyburn (1992) and now widely used in balanced scorecard (Kaplan and Norton, 1996, 2001) and other frameworks (Neely et al., 2002a) appeared after Simons’ (1991) study. It can be argued that some of the differences (1, 2 and 4 above) can be explained by the smaller size of the business units in this study compared to Simons’. However, it might suggest that “interactive control” may be different at different levels of the organisation, may be used differently or may require refinement: (1) Interactive control at a corporate level guides the development of formal responsive and timely reporting on key lead indicators; at the business unit level less formal indicators play this role.
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(2) At business unit level it is possible to take account of knowledge of personal circumstances and local differences in deciding on when and how to act. This is also known to be the case at the corporate level (Mintzberg, 1972) but not explicit in Simons’ (1991) concept of interactive control. (3) If the pervasive interactive control we observed in this study is sustainable at the business unit level, but (according to Simons, 1991) not at the corporate level, can we design strategies to link the two and maximise the benefit from both? (4) Should we revisit Simons’ (1991) “interactive control” in the light of subsequent developments in explicit mental models of cause and effect relationships such as strategy maps (Kaplan and Norton, 1996) and success maps (Neely et al., 2002a)? There is evidence that such frameworks are useful for decision-making (Lipe and Salterio, 2000, 2002) and challenging assumptions is explicitly mentioned in Simons’ (1991) paper, but their role in “interactive control” has not been studied. As with Simons (1991) we identified models and assumptions as being important, but the company had not reached the stage of formally developing a success map.
Conclusions The impact of performance measurement on business performance has been studied and reported in the literature, forming part of the continuing debate as to whether or not performance measurement has a positive impact on performance. The majority of performance measurement researchers are not explicit about the theoretical underpinning of their research (Micheli et al., 2004). Here we have taken the proposition that the manner in which the data are acquired, analysed, interpreted, communicated and acted upon has an impact on business unit performance. Our findings suggest that this is over simplistic. The intensity of engagement and interaction with the performance measurement processes has a greater impact than would be suggested from most of the measurement literature (Simons, 1991 excepted). As stated in the discussions, our findings have similarities to Simons’ concept of “interactive control”, but there are differences. Given the research is based on a single organisation, we cannot claim that Simons’ (1991) concept is incomplete, but this study should suggest that “interactive control” would benefit from further study in organisations using more recently developed performance measurement concepts and at multiple levels of the organisation. This study has tried to contribute to this debate through multiple case studies of the use of performance measurement in a single organisation. This approach has the advantage of controlling many of the contextual, process and content factors identified in the literature as having an impact on performance measurement. In particular, the physical aspects of the performance measurement system used in each of the business units was based on the same measures, using the same data collection and processing systems and producing the same data output and reports. It also has the advantage of allowing comparisons to be made between the performance of the business units being studied as, firstly, performance could be compared using the same performance data and, secondly, the business units studied are in the same industry, in similar locations, with similar customers and all subjected to the same business constraints.
But such an approach does have disadvantages. Being conducted in a single organisation has implications for wider validity. Are the findings identified here relevant in a wider context? Similarly, as the research was based on a common performance measurement system, the uniqueness of this system has to be considered. Although the system was loosely based on the balanced scorecard (all four perspectives were measured), it could not be necessarily considered representative of performance measurement systems generally in use. The IT tool in use supporting the measurement system is one of some 27 currently on the market (Marr and Neely, 2003). The industry itself is a further factor. Multiple branch repair services companies are not uncommon, but are far from widespread. The results need to be interpreted in that light. Further, the business unit comparisons relied on us being able to control for all the contextual, process and content factors as identified in Table III. By taking and comparing pairs of business units in the same region, we believe that we eliminated most of these influences. However, one factor, which is much more localised than the customer demand, is the local job market. In London this was highlighted as an issue and, as we did have a high and average-performing pair of business units in London, this emerged during our initial cross case analysis. Although this was not a factor identified during other cross case comparisons, we cannot fully rule out the possibility that the quality of staff was a factor and this limitation should be more closely controlled in any future research. Being based on a single organisation, the wider applicability of our specific findings from this study should be questioned. However, what is important is the issue that this study raises, that studies that are confined to the physical and formal performance measurement routines are likely not to observe many of the key factors that differentiate between high and average-performance. If, as we suggest, the interactive nature of the use of the measurement system is important, future research will need to find ways of observing, measuring and quantifying this interactivity to allow a richer picture of the impact of performance measurement on performance to be developed. This exploratory study has raised issues for future research. Firstly, we would recommend that further cross business unit research be conducted in multiple organisations to test whether the findings are replicable and applicable to a wider cross section of industry. Secondly, we would recommend longitudinal studies that tracked changes in practice and changes in performance. This would be useful to understand whether the practices found here are sustainable and produce sustainable higher levels of performance. It also would provide insights when managers and practices changed in the same business unit. Thirdly, once the factors have been refined, it would be appropriate to test the findings using survey instruments developed from the case study insights. Ideally these surveys would be conducted across multiple organisations, but with multiple respondents from each of the organisations, rather than single organisational responses, which currently dominate the literature (Lingle and Schiemann, 1996; Gates, 1999). Fourthly, quasi-experimental approaches (similar to those by Lipe and Salterio, 2000, 2002) could be used to validate the importance of the individual elements identified in the case and survey research. This would tease apart factors that may naturally occur together. Finally, it must be remembered that the impact on performance measurement effectiveness of many of the contextual,
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process and content issues in the literature has still not been fully researched and there is still much work to be done in this arena.
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Note 1. Despite significant research into the impact of performance measurement on performance, little of this research focuses on the process of using the measures to manage performance and the consequential impact on performance.
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Simon, H.A. (1987), “Making management decisions: the role of intuition and emotion”, Academy of Management Executive, February, pp. 57-64. Simons, R. (1991), “Strategic orientation and top management attention to control systems”, Strategic Management Journal, Vol. 12, pp. 49-62. Smith, P.C. and Goddard, M. (2002), “Performance management and operational research: a marriage made in heaven?”, Journal of the Operational Research Society, Vol. 53 No. 3, pp. 247-55. Vakkuri, J. and Meklin, P. (2001), “Ambiguity in the using of performance measurement information in organizations: the application of the theoretical framework of measurement risks in a finnish organizational context”, paper presented at the Workshop on Performance Measurement and Management Control, Nice,. Waggoner, D.B., Neely, A.D. and Kennerley, M.P. (1999), “The forces that shape organisational performance measurement systems: an interdisciplinary review”, International Journal of Production Economics, Vol. 60 No. 1, pp. 53-60. Yin, R.K. (1994), Case Study Research, Design and Methods, 2nd ed., Sage, Thousand Oaks, CA. Further reading Coates, J., Davis, T., Emmanuel, C., Longden, S.G. and Stacey, R. (1992), “Multinational companies’ performance measurement systems: international perspectives”, Management Accounting Research, Vol. 3, pp. 133-50. Mooraj, S., Oyon, D. and Hostettler, D. (1999), “The balanced scorecard: a necessary good or an unnecessary evil?”, European Management Journal, Vol. 17 No. 5, pp. 481-91.
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Formalising the ordering process to achieve responsiveness
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Faculty of Management and Organisation, University of Groningen, Groningen, The Netherlands
Received February 2004 Revised October 2004 Accepted November 2004
Abstract
Gera A. Welker and Jan de Vries
Journal of Manufacturing Technology Management Vol. 16 No. 4, 2005 pp. 396-410 q Emerald Group Publishing Limited 1741-038X DOI 10.1108/17410380510594499
Purpose – This paper aims to focus on the question whether formalisation of the ordering process can be helpful in achieving responsiveness, while remaining efficient. Design/methodology/approach – Three dimensions of the ordering process are discussed, namely logistical control, information processing and the organisational setting of the ordering process. Data were gathered from case studies at five different production companies. Findings – It is suggested that a highly formalised logistical control structure is essential in achieving responsiveness and efficiency. From the formalisation strategies applied by the companies it can also be concluded that a formalised organisational setting of the ordering process is necessary for being responsive in case the logistical control is characterised by a low degree of formalisation. Originality/value – The paper presents a detailed operationalisation of the formalisation of three dimensions of the ordering process. This is helpful in formulating guidelines for structuring the ordering process to become more responsive. Keywords Order systems, Response flexibility Paper type Case study
Introduction Production companies are increasingly confronted with higher market demands in terms of flexibility and delivery reliability and are therefore, forced to place a high priority on being customer oriented. Consequently, companies must focus on producing and delivering products that meet the exact requirements of customers as quickly as possible. At the same time, however, companies need to remain efficient as well. As a result, the classical conflict between external objectives like short delivery times and offering a broad product range and internal objectives such as low stock levels and reducing set-up times seems to become increasingly manifest within companies. In the search for a proper balance between these internal and external objectives, the ordering process plays an important role. On one hand, the ordering process must contribute to the responsiveness by acting quickly and flexibly to customer demand. On the other hand, the ordering process should ensure an efficient production process in order to keep abreast of the competition. Production companies in other words, are increasingly challenged to structure the ordering process to become more responsive while remaining competitive at the same time. Both in theory and in practice many different approaches are proposed to deal with this structuring problem. Time-based competition, lean thinking, agile manufacturing, mass customisation and business process re-engineering for instance, are some of the often-mentioned approaches aimed to reduce costs and increase competitiveness at the The authors would like to thank the anonymous referees for their constructive comments and suggestions.
same time. Many studies in these areas acknowledge that higher market demands eventually will result in more diverse customer wishes, ultimately leading to a greater exchange of information between the customer and the departments involved in the processing of orders (Forza and Salvador, 2002; Kritchanchai and MacCarthy, 1999; Lin and Shaw, 1998). Clearly, information technology offers the possibility to cope with this information-processing load and with the necessity to accelerate the information processing. However, using information technology to support the ordering process proceeds from the assumption that this process can be modelled and formalised to some extent. The above-mentioned observations have triggered our interest in a further investigation into the structuring, especially the formalisation, of the ordering process. Although many studies in the field of operations management and information technology have addressed the importance of the ordering process to achieve responsiveness (Waller et al., 1995; Kingsman et al., 1996) only few studies have attempted to examine the ordering process from a formalisation perspective. It is for this reason that we seek to fill this void by examining the role and influence of formalisation in the shaping of the ordering process. Case data collected from five production companies are behind our analysis. In the next section, first, a framework is developed based on the assumption that three dimensions (e.g. logistical control, information processing and organisational setting) are of eminent importance in the process of formalising and structuring the ordering process. These three dimensions are then used to describe and analyse the five ordering situations. The paper concludes with a discussion on the benefits of formalising the three dimensions of the ordering process to achieve responsiveness. We view this study’s contributions from three perspectives. First, this paper aims to advocate the emerging role of formalisation in shaping the ordering process. Clearly, this role is not well explored yet and only few articles in the area of operations management and information technology have addressed the issue of formalisation in the context of order processing. Second, an operationalisation of the formalising aspects of the ordering process is presented. This operationalisation distinguishes three dimensions and aims to contribute to our understanding of finding a balance between responsiveness and efficiency on one hand and formalising the ordering process on the other hand. Third, although our case data come from different organisations, including different types of ordering processes, the case studies indicate a direct link between the way companies formalise the ordering process and the degree of responsiveness. The ordering process In literature, the ordering process is often considered a key business process in which customer orders are translated into production orders to attain feasible order agreements. According to Davenport and Short (1990), a business process entails two main characteristics: it includes both internal and external customers and goes beyond organisational boundaries. Clearly, these characteristics also apply to the ordering process, as the ordering process consists of a number of succeeding activities that need to be performed to achieve pre-defined performance objectives and which relate to different functional disciplines. According to Ould (1995), the ordering process is one of the core business processes because it concentrates on satisfying external customers
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and directly adds value in a noticeable way. In other words, by means of the ordering process the company and customers create a commitment to product specifications, order quantities and the timing of delivery. To describe the ordering process more accurately, this process can be modelled on a rather abstract level as an input-output system (Figure 1). In order to deliver the right products at the right time, demand requirements need to be translated into production orders by means of the ordering process. Hence, to attain feasible order agreements, production and demand needs to be matched. These transactions or interactions involve at least two business functions (Parente, 1998; Rho et al., 1994). In the ordering process sales and production are functionally interdependent, as sales provides the customer with products made by production (Konijnendijk, 1992). Co-ordinating customer demand and production possibilities is in the first place a logistical decision-making process. In attaining feasible order agreements decisions must be taken concerning accepting orders, allocating capacity and materials, promising delivery times and prioritising of orders. In order to make these decisions often a large amount of information needs to be exchanged and processed among the actors involved in the ordering process. Depending among others on the organisational context of the ordering process, these actors are often part of different functional departments. It can, therefore, be concluded that the logistical decision-making structure, the processing of information and the organisational setting are three important dimensions of the ordering process. These dimensions will now be discussed in more detail. Logistical control From a logistical control point of view, the ordering process can be considered as a set of decision functions with the purpose of co-ordinating demand and production. This co-ordination can take place at different levels of control (Bertrand et al., 1990). On an operational level planning decisions mainly relate to making order agreements, including for instance accepting customer orders, promising delivery times, allocating stocks and prioritising customer orders. Clearly, these logistical decisions depend on production possibilities because feasible order agreements can only be made when taking available capacity and materials into account. Therefore, often more structural co-ordination decisions must be made. According to Bertrand et al. (1990), structural co-ordination relates to agreements made between production and sales on delivery conditions or sales per product group. We refer to these managerial agreements among the parties involved in the ordering process as the logistical control concept of the company. In general, operational logistical decisions are embedded in the logistical control concept of the company. Information processing A second dimension of the ordering process relates to the processing of information. Customer orders often contain much information about the demand specified by
Figure 1. Input-output model of the ordering process
customers. These specifications have to be translated into production orders and order agreements. From this point of view, the ordering process is a sequence of information processing activities consisting of preparation, receipt, entry, acceptation, confirmation and scheduling of orders (Waller et al., 1995). The ordering process not only concerns the processing of order information through the company but also includes the matching of information with respect to customer demand and the state of the production system. To make agreements with customers on specifications, volume and timing of the orders, not only information about demand specifications has to be handled within the ordering process but also information about production constraints (Figure 1). In the previous section, this matching process is referred to as operational co-ordination. In order to both efficiently use available production capacity and assign realisable delivery times sales and production frequently need to exchange a great deal of information in a rather early phase of the ordering process (Kingsman et al., 1996). Organisational setting A third key dimension of the ordering process is the organisational setting of this process. We already mentioned that in many cases, different functional disciplines are involved in the ordering process, for instance, sales, planning and production. In many studies on the organisational context of order processing it is implicitly assumed that organisations are functionally organised. Consequently, the focus of these studies is frequently on the issue of co-ordination and on the influence a functional de-coupling of the departments has on the ordering process (Shapiro et al., 1992; Konijnendijk, 1992). In this context, Shapiro et al. (1992) argues that co-operation within the ordering process is hindered by functionally organised organisations, as the various departments often have conflicting interests and know too little of what goes on in the other departments. Therefore, the co-operation, the relationships and degree of interdependence among the actors participating in the ordering process, as well as the clarity in tasks and responsibilities seem to be of significant importance when studying the effects of formalising the ordering process (De Vries, 1999). Distinguishing among the dimensions mentioned above is especially helpful to further explore the complexity and dynamics of the ordering process and to operationalise the possibilities of formalising the ordering process. By addressing the ordering process along the three dimensions we are also able to conceptualise formalisation more precisely. Or stated in another way, the degree of formalisation may differ for the three dimensions distinguished. We will further elaborate on this subject in the following section. Formalising the ordering process In general, the structuring of processes aims at solving co-ordination problems arising from the division of labour. As argued before, the ordering process is a process in which co-ordination plays a central role. By translating customer orders into production orders the ordering process co-ordinates demand and production in a very direct way. However, to achieve this co-ordination it is necessary to co-ordinate not only the actors involved in the ordering process (working relationships) but also the information that needs to be processed (activities) and the logistical decisions that must be made to attain feasible order agreements. It is for this reason that we consider formalisation as the degree to which working relationships, activities and decisions are co-ordinated by formal, explicit rules and procedures.
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In order to study the effects of formalisation on the responsiveness of companies we need to obtain operational measures of formalisation on the three dimensions of the ordering process. Because co-ordination is a central aspect of the ordering process, the co-ordination strategies of Galbraith (1973) are used as an underlying framework for our analysis. In this section, some potential co-ordination strategies that can be applied by companies to each dimension of the ordering process are discussed. In doing so, we also elaborate the way formalisation can be operationalised per co-ordination strategy. In the next sections, these strategies will be used to frame our observations made in five companies regarding the effects formalisation has on responsiveness. When studying the formalisation of logistical control in more detail we need to address the way in which logistical decisions within the ordering process are co-ordinated by formal rules and procedures. In general, logistical decisions will be embedded in the logistical concept of the company. A logistical concept can be defined as a coherent whole of managerial rules encompassing both operational planning algorithms, as well as guidelines on a more tactical and strategic level concerning the overall control structure of the material flow (Bertrand et al., 1990). The logistical concept can be considered as a part of the co-ordination strategies that are referred to by Galbraith as “rules and programs” and “goal setting”. In case of a well-defined logistical concept it can also be seen as a formalised way of co-ordinating production and sales decisions. Additionally, the creation of slack resources is a co-ordination strategy focusing on reducing required performance levels. Slack can be created in the delivery time quoted to the customer, the reservations made for specific customers or for rush orders in the production scheme, or by giving a longer lead-time than necessary to account for uncertainties in the production system. When the application of slack is agreed upon in the organisation it can be considered as a formalised form of co-ordination. The processing of information in the ordering process can be co-ordinated through rules and programs, by means of formal work instructions on how to handle an order and which documents to use, and by a formal sequence of information processing activities. The application of (vertical) information systems is another often-applied co-ordination strategy. The ordering process of companies is frequently automated as part of an enterprise resource planning system (ERP), which integrates distinctive business processes within a company. However, an ERP system not only automates the processing of data but in many cases supports the decision-making processes as well. By means of an ERP-system information about orders, customers, material requirements and resource availability is stored in a central database that can be consulted by the actors involved in the ordering process. Furthermore, the application of computer networks makes it possible to communicate within the company by e-mail. Moreover, using internet also gives the opportunity to communicate more directly with customers. We consider the application of information systems to be formalised when it structures the sequence of information processing activities and the transaction of information, and when standard documents are used. Additionally, planning rules (based on logistical agreements) are considered to be formalised by means of an information system whenever planning algorithms embedded in the information system support the decisions. The formalisation of the organisational setting of the ordering process can be addressed by the way relationships among actors are co-ordinated by formal rules and
procedures. Clearly, a formalised organisational setting of the ordering process closely relates to a formalised organisational structure and formalised jobs, thus specifying the actors, their tasks and responsibilities by formal rules and programs. Directly linked to this co-ordination strategy is hierarchical referral (Galbraith, 1973). Co-ordination often takes place by a formalised hierarchical structure in which the positions of the different actors and their responsibilities are specified. For example, planners may ask their superior whether it is allowed to accept an exceptional delivery date or not. Another co-ordination strategy that is often used in the ordering process is co-ordination by means of lateral relations. The lateral relation itself can be formalised by the type of liaison relation, the tasks or agenda of the parties involved and the responsibilities of the actors. Finally, the creation of self-contained tasks can be considered as a way to deal with co-ordination issues in the ordering process. Self-contained tasks relate to a group of employees having all the resources needed to perform a certain task. Generally, when using self-contained tasks as a co-ordination strategy the degree of formalisation will be rather low because the group is responsible for dealing with normal, as well as deviant situations within their task domain. In this case, the aspect of formalisation is limited to who belongs to the group, the responsibilities of the group members and the definition of the task domain. Table I presents an overview of the formalisation measures for each dimension of the ordering process. So far, we discussed our definition of the ordering process. In doing so, we differentiated among three dimensions of the ordering process that we used to further conceptualise the formalisation of the ordering process. In the next section, this conceptualisation will be applied to explore the effects of formalisation on responsiveness.
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Case study research We conducted a multiple case study research to describe and analyse the effects of formalisation on the responsiveness of the ordering process in a practical setting. Case study research offers the opportunity to present the ordering process in its real-life context (Yin, 1994). In studying multiple cases an important issue is the case selection. Cases should be selected according to clearly specified criteria using replication logic. According to Yin (1994, p. 46), cases must be selected either to predict similar results (literal replication) or to produce contrasting results but for predictable reasons Co-ordination strategy Formalisation measures Logistical control
Rules and programs Goal setting
Slack Information processing Rules and programs Information system
Organisational setting
Rules and programs Hierarchical referral Lateral relations
Use of logistical concept and managerial agreements Use of planning of capacities and materials within the goals of an aggregate schedule Agreed upon use of slack Use of formal work instructions and sequence of information processing activities Use of information system for administrative order processing and/or for planning and production control Use of job descriptions Use of formalised hierarchical structure Use of formalised consultative structure
Table I. An overview of formalisation measures for each dimension of the ordering process
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(theoretical replication). In our study, the main focus was on replication logic. Each case was selected carefully on the basis of variables, which assumedly influence the degree of formalisation. Some of these variables were kept constant to predict similar results with respect to the structuring issues of the ordering process. Some other variables varied (Meredith, 1998) as to produce different results on the degree of formalisation of the ordering process. We selected five companies for our study. This selection was based on a comparison of the input variables of the ordering process, shown in Figure 1 as customer demand and production possibilities. In doing so, we assumed that formalising the ordering process will be especially complex in production companies with a customer-specific demand that is hard to predict. We furthermore assumed that the specific combination of demand and production system influences the complexity of the ordering process and consequently the degree of formalisation of the ordering process. In our empirical study, customer demand was described and analysed through variables like predictability, variety and degree of customisation. The production system on the other hand was described and analysed by means of specific characteristics of both the resources and the products being manufactured by the production system. In doing so, we focused on the complexity of the resources and materials, in literature often addressed as capacity complexity and material complexity (Bertrand et al., 1990). On the basis of the aforementioned input variables, we selected five companies. Table II presents an overview of the characteristics of the selected companies. During our empirical research various research instruments have been used. Relevant documents such as management reports, order books, quality handbooks and work instructions were carefully studied. Furthermore, the ordering process was observed for an extensive period of time by monitoring the employees during the
Company Size Demand
Production
Use ICT
A
Steel-based office furniture
Only administrative order processing
B
C
D
E Table II. Characteristics of the five selected companies
95 95 per cent standard products with customer specific variations Four main product groups 124 60-70 per cent customized 30-40 per cent standards 110 95 per cent customer specific variations 5 per cent new design Seven main systems 125 70 per cent customer specific variations 30 per cent standard Eight product groups 90 80 per cent customized products (nine product groups) 20 per cent standard þ customer specific variations (four product groups)
Four main operations Heat exchangers Integrated, standard, Three main operations but new information system Flue gas terminal and Integrated, customized ventilation systems information system Five main operations All kinds of steel-based Mostly administrative products order processing Three main operations Steel-based manufacturer of products for office and shop design Four main operations
Integrated, standard, but “outdated” information system
processing of orders and by attending pre-arranged and spontaneous meetings. Following these observations, key-persons in the ordering process were interviewed. Within each organisation the employees being interviewed consisted of employees working at both the lower, middle and higher management levels and included sales representatives, planners, production managers and the managing director. During the interviews, the results of the observations were discussed and further information was provided by the employees regarding the ordering process thereby focusing on the assumed influence of the degree of formalisation of the ordering process on the responsiveness of the company. Findings: formalisation of the ordering process In this section, the degree of formalisation of the ordering process on the three distinguished dimensions is described and analysed for the five companies. In the following sections, the relationships between formalisation and responsiveness that emerge from this analysis are discussed. In presenting the findings on formalisation, tables are used to present an overview of the degree of formalisation for each dimension of the ordering process. In order to compare the findings we evaluated the degree of formalisation and scaled the values from high to moderate to low. The scaling must be interpreted as the observed extent of using the relevant co-ordination strategies, a high degree (þ ) for extensive use, a moderate degree (þ /2 ) for some use and a low degree (2 ) for very little or no use at all. The scaling thus shows the difference in the degree of formalisation among the five companies. Logistical control Table III presents an overview of the formalisation of logistical control per company. In four of the five companies a well-defined logistical concept is lacking, but in companies A and E there are some managerial agreements with respect to a fixed delivery time per product group. Company C has a well-defined logistical concept that is characterized by a planning philosophy based on non-finite capacity. Also, none of the companies explicitly applies a master production schedule. Starting from the notion that production ought to produce what sales can sell often no formalised production plan is used in the companies. Companies B and D, for instance, do not construct a detailed sales or production plan and in these two companies all co-ordination between sales and production takes place during the operational handling of individual orders. Companies A and E only use a yearly sales plan to check the availability of the production capacity. Company C, however, uses the sales plan to not only check production capacity but also stock levels. In this company, the average capacity utilisation per operation serves as standardisation of output for the planning schedule. In almost all companies some form of (agreed upon) slack is created to co-ordinate production and sales. In company A, for instance, delivery times and stock levels are increased to compensate for an excessive co-ordination load in the ordering process. Company B also creates slack by keeping safety stocks and built-in margins for delivery time. In company C on the other hand, only little slack is created in delivery time quoting, the planning scheme and stock levels. It can be concluded from the logistical concepts and the slack-strategies applied by the companies that the degree of formalisation on the dimension of logistical control differs for the five companies. Company A applies a logistical concept based on fixed
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Formalisation of logistical control Rules/programs
404 Table III. Formalisation of the logistical control of the ordering process per company
Goal setting
Slack
Use of logistical concept and managerial agreements Use of planning of capacities and materials within the goals of an MPS Agreed upon use of slack
A
B
C
D
E
Observed, þ/2
Observed, 2
Observed, þ
Observed, 2
Observed, þ /2
Observed, þ/2
Observed, 2
Observed, þ
Observed, 2
Observed, þ /2
Observed, þ
Observed, þ /2
Observed, þ /2
Observed, þ
Observed, þ
delivery periods and by creating slack in delivery time, as well as in stock levels. Decisions related to material and capacity allocation however, are not formalised by a logistical concept in company A. In fact, within company A there is no formalised production and stock control. Company E has a comparable degree of formalisation, although in this company the focus of the logistical concept seems to be on formalising capacity and material allocation by planning rules and delivery times. In companies B and D, logistical decisions concerning material and capacity allocation are only partly formalised by means of planning rules and the creation of slack. Decisions on order acceptance or delivery time promising are not formalised by means of a logistical concept in these companies which results in a rather high degree of interdependence between sales and production regarding delivery time promising. The degree of formalisation of companies B and D is comparable and is evaluated here as being lower than of companies A and E. Company C seems to have the most defined logistical concept which is based on producing against infinite capacity (chase demand plan) combined with a fixed delivery time. This logistical concept formalises all logistical decisions except decisions on prioritising orders. As a consequence the degree of formalisation on the dimension of logistical control is assessed here as being higher compared to the other four companies. Information processing Table IV presents an overview of the formalisation of the information processing per company. In all five companies the processing of information appears to be highly formalised. Formal work instructions are often written down and recorded in a quality handbook. Only company B is not yet certified according to ISO. All companies have explicit work instructions and formalised documents to support the ordering process. The actors who have been interviewed address this way of formalisation as being a direct result of applying both ISO and ERP systems. The application of an ERP system apparently often results in a clear and prescribed sequence of information processing activities. In some companies this sequence results in authorisation per department. After finishing an information processing activity the actor involved has to give a new status to an order to authorise the next actor for further handling the order.
Formalisation of information processing Rules/programs Use of formal work instructions and sequence of information processing activities Information Use of information system system for administrative order processing Information Use of information system system for planning and production control
A
B
C
D
E
Formalising the ordering process
Observed, Observed, Observed, Observed, Observed, þ þ /2 þ þ/2 þ /2 Observed, Observed, Observed, Observed, Observed, þ þ þ þ þ Observed, Observed, Observed, Observed, Observed, 2 þ /2 þ /2 þ/2 þ /2
The ERP system is not always applied to support the planning and control of the production process, however. In company A, for instance, the inventory module of the ERP system is not yet implemented. The planning department keeps a handwritten record of stock levels. Although there is a procedure for keeping this record up-to-date, the information is most of the time outdated. Information processing at company A is therefore assessed as being the least formalised. The other companies use the integrated ERP system only partly for supporting production planning and control. Within the companies, customer orders are often translated into production orders by means of the ERP system. Based on product specifications the availability of material is checked and the system signals what materials need to be purchased. It is interesting to note though that within all the companies the check on actual capacity availability is not performed by means of the ERP systems. Moreover, in all companies additional information provided by the production supervisor is necessary to check the actual capacity availability. Only at company C the ERP system used is completely customised to the wishes of the users and therefore supports better the information requirements of the various information-processing activities. Consequently, information-processing activities in the ordering process of company C are to a great extent determined by the ERP system. For this reason, the information-processing dimension of the ordering process is assessed here as being the most formalised in company C. The organisational setting Table V presents an overview of the co-ordination strategies related to the organisational setting of the ordering process for each company. In all five companies job descriptions are used, but only in companies A and D the actors involved in order processing also considered the use of job descriptions as an important formalizing measure for specifying tasks and responsibilities. In the other three companies tasks and responsibilities regarding the processing of orders are not always clear. In companies C and D a well-defined hierarchical structure exists by means of which communication and co-ordination channels regarding the processing of special order requests are formalised. In these companies sales employees, as well as planners frequently ask their supervisors how to handle specific orders. At companies A, B and E the hierarchical structure is less clear in the sense that the actors not always know whom to consult.
405 Table IV. Formalisation of the information processing of the ordering process per company
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In companies A, D and E a wide-ranging consultative structure exists which enables participants in the ordering process to discuss special requests and to monitor the progress of orders through the ordering process. In companies B and C on the other hand, no formalised meetings are arranged between actors participating in the ordering process.
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Responsiveness In this contribution we are interested in the question whether formalisation of the ordering process can be helpful in achieving responsiveness whilst remaining efficient. Responsiveness relates to the ability of companies to respond to change in customer needs or requirements and to fulfilling customer wishes. Responding to changing customer wishes is frequently associated with the capacity to introduce new products, to provide a broad range of products, to change the timing of the delivery or to adapt the level of output of the operation (Chang et al., 2003; Slack et al., 2004). Fulfilling customer wishes is furthermore often associated with speed of delivery and delivery reliability. Therefore, two dimensions seem to be relevant when assessing the influence of formalisation, e.g. the ability to respond to changes in customer requirements and the ability to fulfil customer wishes. In co-ordinating demand and production companies will often try to realise responsiveness (on one or more aspects) without compromising the efficiency of production. Consequently, in studying the effects of formalisation of the ordering process not only the issue of responsiveness but also the efficiency of the production system appears to be of importance. For this reason, indicators of efficiency that can be directly linked to the issue of co-ordinating demand and production are essential in assessing the effects of formalisation. From an efficiency point of view, production will be often strongly focused on a high capacity utilization, for instance by producing in large batches. It is noticed here that large batches may not be desirable from a flexibility point of view. Reacting flexibly to changing customer demands often requires short set-up times and small batch sizes. The utilisation of production capacity therefore, encompasses a complex, multi-dimensional set of trade-off decisions to be made in the ordering process. Another trade-off decision to be made in the ordering process relates to stock levels. On one hand, companies are forced to reduce stock levels for financial reasons. At the same time, however, there seems to be a necessity within many companies to strive for stock levels that are high enough to account for unexpected demand (Konijnendijk, 1992; Bertrand et al., 1998). As a consequence, inventory costs are directly or indirectly affected by decisions made in the ordering process. Following from the above, it can be argued that additionally to Formalisation of organisational setting
Table V. Formalisation of the organisational setting of the ordering process per company
A
Rules/programs Use of job descriptions Observed, þ Hierarchical Use of formalised Observed, referral hierarchical structure þ /2 Lateral relations Use of formalised Observed, consultative structure þ
B
C
D
E
Observed, 2 Observed, þ /2 Observed, 2
Observed, þ/2 Observed, þ Observed, 2
Observed, þ Observed, þ Observed, þ
Observed, þ /2 Observed, þ /2 Observed, þ
responsiveness, both the utilisation of production capacity, as well as inventory levels are important indicators of the adequacy of the coordination achieved in the ordering process. In our study, stock levels are operationalised by relating the perceived stock levels to the company goals for stock levels. Capacity utilisation is operationalised as the proportion of maximum capacity used for production. From the overview of performance-related indicators as presented in Table VI, it can be concluded that company C performs better than the other four companies. Company C is able to react responsively to customer demand while remaining efficient. Company A also seems to be rather responsive, but this company holds relatively high stock levels resulting from slack creating measurements. Companies B and D perform well when taking the variety of products into account. However, both companies are not able to deliver fast. Furthermore, company B shows rather poor delivery reliability while company D is not able to handle rush orders. At the same time, both companies do not perform well on the dimension of efficiency. Company E also shows an inadequate efficiency of production. Additionally, the ordering process of company E is characterized by an inefficient ordering process and an inability to be responsive.
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Discussion: formalisation – responsiveness In this section, we reflect on the results of the case studies thereby focusing on possible patterns between the degree of formalisation of the ordering process and the performance on responsiveness and efficiency. Clearly, company C performs good on responsiveness, as well as on efficiency. The ordering process at company C is characterised by a well-defined logistical concept, which enhances the formalisation of logistical decisions. Furthermore, company C applies an integrated and customised ERP system, which formalises the information processing and advances the decision-making processes regarding the processing of orders. The logistical concept accommodates the delivery of a broad product range against a relatively short time period and also the ability to handle rush orders. Moreover, the logistical concept of company C supports an efficient and flexible production system by adjusting capacity to incoming orders on a daily base. Additionally, information necessary to process the orders and to decide on issues concerning co-ordination of demand and production is provided for by the customised ERP system. This configuration of a formalised logistical control and information processing undoubtedly influences the responsiveness of the company without compromising the efficiency of the production system in a positive way. Clearly, co-ordination of demand and production is accomplished in company C at a structural level and needs hardly any operational co-ordination. Operationalisation Responsiveness
Efficiency production Notes:
p
Broad product range Ability to handle rush orders Speed of delivery Delivery reliability (timing) Low stock levels Efficient capacity utilisation
A
B
C
D
E
p p
p
p
p
p
£ £ £
p p p
£
£
p p
¼ good performance; and £ ¼ bad performance (interpreted in mutual comparison)
£ £
Table VI. Performance related to responsiveness, efficiency production and efficiency ordering process per company
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Company A also succeeds in realising a rather good performance on responsiveness, but performs not so well on efficiency. The ordering process at company A is especially characterised by a lack of production and stock control resulting in a lacking understanding of production opportunities. To compensate for uncertainties in the material and capacity availability, company A applies a formalised and extensive consultative structure. At the same time, both regarding the promising of delivery times, as well as regarding stock levels, extensive forms of slack are created. The consultative structure is mainly used to co-ordinate demand and production on an operational level resulting in an adequate response to customer wishes. However, being responsiveness also seems to result in high co-ordination costs in company A. The creation of different forms of slack helps in realising delivery reliability and delivery flexibility but also causes relatively high stock levels. Regarding the organisational setting of the ordering process it can be concluded from the case data presented in the previous sections that company A applies a highly formalised organisational setting of the ordering process to co-ordinate demand and production in order to additionally realise a high degree of responsiveness. Companies B and D succeed in offering a broad product range but do not perform well on the other dimensions of responsiveness, nor on efficiency. Regarding the logistical decision making in these companies it can be concluded that these companies only apply formalised planning rules to structure the decision-making processes on an operational level. Delivery time promising is completely based on operational co-ordination of demand and production and at company B this operational co-ordination is primarily executed via non-formalised bilateral communication between sales and planning. In company D the operational co-ordination between production and sales is primarily based on a formalised consultative structure. According to the actors involved in the ordering processes at these companies a clear operations strategy is lacking resulting in much ambiguity during the processing of customer orders. In company B, this ambiguity is not adequately dealt with and as a consequence there seems to be little trust between sales and planning. Company D on the other hand, copes with this ambiguity by many formal meetings. In this company, a formalised organisational setting of the ordering process seems to be helpful in structuring the co-ordination of demand and production on an operational level, but clearly does not increase responsiveness as we found in company A. This may be explained by the fact that production is the most powerful party in company D. Conclusions The data discussed in this paper seem to implicate that formalisation of logistical control and formalisation of the information processing activities positively influence the responsiveness of the ordering process without compromising the efficiency. In this context, our case data also suggest that a high degree of consensus between the parties involved in the ordering process and the existence of managerial agreements regarding the trade-offs to be made within the ordering process are of significant importance to achieve responsiveness while remaining efficient. However, when the logistical concept is not well-defined companies apparently compensate a lack of co-ordination on the decision-making dimension by means of a formalised organisational setting of the ordering process. This situation also appears to negatively influence efficiency. When the degree of formalisation is rather low on the logistical control dimension, as well as on the organisational setting of the ordering process, the companies studied show to
perform badly on responsiveness, as well as on efficiency. It can therefore be concluded that not only the formalisation of the three separate dimensions of the ordering process (e.g. logistical control, information processing and organisational setting), but particularly the set of varying degrees of formalisation is of importance in explaining and understanding the contribution of the ordering process to both efficiency and responsiveness objectives. We consider our study to be only “exploratory” in nature and a number of limitations linked to our study should be addressed in future research. Our study for example, was only based on five in-depth case studies. Although a number of important issues were addressed, future research should try to expand the analysis to a larger number of organisations to insure more generalisable results. In addition, the framework presented in this paper needs more refinement. To improve our understanding of the ordering process and the trade offs to be made within this process, we also think that it is of importance that future research should focus more on examining quantitative measures. Hopefully, this will lead to a more integrated body of knowledge regarding the effects of formalising the ordering process, which relies on both the field of operations management and organisation theory.
References Bertrand, J.W.M., Wortmann, J.C. and Wijngaard, J. (1990), Production Control: A Structural and Design Oriented Approach, Elsevier, Amsterdam. Bertrand, J.W.M., Wortman, J.C. and Wijngaard, J. (1998), Productiebeheersing en material management, Stenfert Kroese, Groningen. Chang, S-C., Yang, C-L., Cheng, H-C. and Sheu, C. (2003), “Manufacturing flexibility and business strategy: an empirical study of small and medium sized firms”, International Journal of Production Economics, Vol. 83, pp. 13-26. Davenport, T.H. and Short, J.E. (1990), “The new industrial engineering: information technology and business process redesign”, Sloan Management Review, pp. 11-27, Summer. De Vries, J. (1999), Logistiek Organiseren, Van Denderen B.V., Groningen. Forza, C. and Salvador, F. (2002), “Managing for variety in the order acquisition and fulfillment process: the contribution of product configuration systems”, International Journal of Production Economics, No. 76, pp. 87-98. Galbraith, J.R. (1973), Designing Complex Organizations, Addison-Wesley Publishing Company, Reading, MA. Kingsman, B., Hendry, L., Mercer, A. and De Souza, A. (1996), “Responding to customer enquiries in make-to-order companies: problems and solutions”, International Journal of Production Economics, Nos. 46/47, pp. 219-31. Konijnendijk, P.A. (1992), Coordination of Production and Sales, Maklu, Antwerpen. Kritchanchai, D. and MacCarthy, B.L. (1999), “Responsiveness of the order fulfillment process”, International Journal of Operations & Production Management, Vol. 19 No. 8, pp. 812-33. Lin, F-R. and Shaw, M.J. (1998), “Reengineering the order fulfillment process in supply chain networks”, The International Journal of Flexible Manufacturing Systems, Vol. 10, pp. 197-229. Meredith, J. (1998), “Building operations management theory through case and field research”, Journal of Operations Management, Vol. 16, pp. 441-54.
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Ould, M.A. (1995), Business Processes: Modeling and Analysis for Re-engineering and Improvement, Wiley, New York, NY. Parente, D.H. (1998), “Across the manufacturing-marketing interface: classification of significant research”, International Journal of Operations & Production Management, Vol. 18 No. 12, pp. 1205-22. Rho, B-H., Hahm, Y-S. and Yu, Y-M. (1994), “Improving interface congruence between manufacturing and marketing in industrial-product manufacturers”, International Journal of Production Economics, Vol. 37, pp. 27-40. Shapiro, B.P., Rangan, V.K. and Sviokla, J.J. (1992), “Staple yourself to an order”, Harvard Business Review, July/August, pp. 113-22. Slack, N., Chambers, S. and Johnston, R. (2004), Operations Management, 4th ed., Prentice-Hall, Harlow. Waller, M.A., Woolsey, D. and Seaker, R. (1995), “Reengineering order fulfillment”, The International Journal of Logistics Management, Vol. 6 No. 2, pp. 1-10. Yin, R.K. (1994), Case Study Research. Design and Methods, Applied Social Research Methods Series, Sage Publications, Thousand Oaks, CA. Further reading Miles, M.B. and Huberman, A.M. (1994), Qualitative Data Analysis: An Expanded Sourcebook, Sage Publications, Thousand Oaks, CA.
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Managing product variety in quotation processes
Managing product variety
Jo Bramham, Bart MacCarthy and Jane Guinery Mass Customization Research Centre, Operations Management Division, Nottingham University Business School, Nottingham, UK Abstract
411 Received February 2004 Revised November 2004 Accepted November 2004
Purpose – Manufacturers across many sectors increasingly operate in high variety environments. Research evidence suggests that variety has a negative impact on performance. However, the research literature is limited on the enablers that allow variety to be managed effectively and efficiently at the “front-end” of an organisation and in quotation processes in particular. Design/methodology/approach – This paper presents case analysis of the quotation processes from manufacturers operating in high-variety environments. Qualitative process modelling tools have been developed to allow representation of process complexities and informal process elements. Findings – Findings are presented on generic mechanisms for absorbing and mitigating the impact of variety on quotation processes. A generic quotation process model is presented comprising four key decisions centres: customization request initiation and information gathering on customer needs, classification of requests, resource control, and identification of information for reuse. Practical implications – The implications of the study for the automation of quotation processes in high variety and mass customization environments are discussed and it is speculated that different decision centres will dominate in different environments. Originality/value – The generic model developed by this research offers insight into the functioning of the core process elements of the quotation system. Reviewing an organisation’s structure and the information systems infrastructure supporting these decision centres should lead to the identification of potential system or reorganisation improvements. Keywords Mass customization, Product adaptation, Order systems Paper type Research paper
Introduction Recent trends show that variety is increasing across most sectors (Funke and Ruhwedel, 2000; Holweg and Greenwood, 2000). Indeed most manufacturing enterprises face mounting pressure to offer more variety. The core reasons for this are rooted in societal and economic changes, growth in consumer affluence and aspirations (Toffler, 1970). In business-to-business (B2B) environments, increases in variety may result from . the need to respond to customer requests by continually developing and expanding the product envelope; and . the need to provide complete “solutions” to customers. The work has been funded by an Engineering and Physical Sciences Research Council grant to the Nottingham Innovative Manufacturing Research Centre. We would like to thank the industrialists who have participated in this research. In addition, we should like to express our thanks to the anonymous reviewers for their valuable comments on an earlier draft of the paper.
Journal of Manufacturing Technology Management Vol. 16 No. 4, 2005 pp. 411-431 q Emerald Group Publishing Limited 1741-038X DOI 10.1108/17410380510594507
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Such pressures may be due to the power of some customers in industrial markets to negotiate product specifications that are tailored to meet their requirements. The need to provide solutions is often driven by the level of competition in the market; intense competition means that innovative solutions are required beyond the scope of the current core product or product range, e.g. offering an additional service or integrating the product into a system. The impact of variety has been studied in a number of sectors but has mostly concentrated on the management and control of manufacturing operations, particularly with respect to order fulfilment and quality (Stalk, 1988; Hayes and Wheelwright, 1984; Yeh and Chu, 1991; Anderson, 1995). Less research has been performed into the handling of variety at the front-end of the business particularly where the level of customer interaction may be significant with regard to product specification and quotation. The customer interface is critical to business success (Moos and Milling, 2002). This paper investigates the role of the customer interface in facilitating the quotation process. It focuses on the characteristics and management of quotation processes in high-variety environments. Many businesses operating in industrial markets are adopting product configurator technologies to process customer requests for a broad range of product variants (Forza and Salvador, 2002). Such configurators typically supplement the functionality of ERP and production planning systems by providing an interface for customer choices – a customer variant bill of materials. ERP systems may constrain customer choice because they are based on the production structure required for variants (Kruse and Bramham, 2003). Product configurators are typically set-up with the product options from which the customer can choose (often with sales assistance from sales and/or technical staff) and associated costs and lead times can be generated according to the customer specification. However, fully engineering a product before a customer enquiry is received is not the only way to present variant capability to customers and indeed may not be sufficient to provide variety that is focused on customer needs in particular environments. Spring and Darymple (2000) present evidence of manufacturers in business markets that need to provide continuously evolving product ranges driven by customer demand. Requests may be received from business customers that are outside the product range described by the product configurator. These may be “non-standard” products that warrant a cooperative response. The business must therefore provide some reactive decision making to consider the implications of modifications to existing product variants. This paper investigates how businesses can combine the “configure-to-order” approach with reactive decision making, specification and engineering, triggered by a non-standard request. The research investigates the characteristics exhibited by manufacturers who provide products to business markets. The aim is to identify mechanisms for providing rapid effective quotations and the constraints that may obstruct this. Being able to handle high levels of product variety in a responsive manner, both in terms of the customer interface and in order fulfilment, is a key enabler of mass customisation (MacCarthy et al., 2003). The study reported in the paper is based on an in-depth analysis of case studies undertaken by the authors. The analysis provides insights into the nature of the challenges faced, specifically in customer interaction processes, in two manufacturing enterprises operating in high-variety B2B environments. The paper
analyses the organisational, informational and decision-making elements that support their quotation processes. Managing product variety Demands are being placed on existing operations to adapt to and ideally, to absorb increased levels of variety with minimal negative impact on performance or profit margins. There is some documented evidence to suggest that this is being achieved by a number of companies in some sectors (McDermott et al., 1997; MacDuffie et al., 1996). However, other studies show that businesses are incurring significant overhead costs with respect to management and control, particularly for customer-driven variety (Sievanen et al., 2000). Evidence suggests that a major part of the burden lies in customer interaction processes and in activities such as product specification for manufacture (Erens and Hegge, 1994). The research of Miller and Vollman (1985) quantifies this burden – they estimate that quality related transactions, which include product specification, constitute 25-40 per cent of costs. Some research has highlighted the challenge of managing the additional activities that are required to respond to “new” customer requests (Amaro et al., 1999) and the conflict of managing these whilst avoiding deterioration in responsiveness (McCutcheon et al., 1994). This would indicate that managing high levels of product variety is a key challenge requiring important trade-offs between customer needs and the mitigation of negative impacts. Much of the literature on managing product variety centres on the re-design of product architecture as the lever to manage costs whilst maintaining product choice – modularity has long been argued to be the solution to product complexity problems (Starr, 1965; Pine, 1993). However, Child et al. (1991) in their guidance on managing complexity suggest the deployment of a wide range of measures; including those not confined exclusively to the product development department. Their perspective is focussed on the avoidance of unnecessary variety and does not embrace the need for high levels of customer-driven variety. Miller and Vollman (1985) recommend the integration of information systems and the removal of manual handling of ordering to reduce costs. In the same vein, Swaminathan (2001) recommends standardization of processes. Another approach to managing variety is to separate it into different types and provide processes dedicated to each type (Skinner, 1974). The findings of previous studies prompt many questions about the most effective approaches for the management of product variety in the front-end of the organisation and their relevance to quotation processes. Which management activities are effective in controlling the impact of variety? Can evidence of mechanisms that enable the management of high product variety be found? This research investigates two manufacturers with high product variety to explore these questions. Conceptual framework A “quote” is a document that describes the commitment by a business to the customer in terms of product specification, price and delivery. A quote results from a quotation process that must convert a description of customer needs into organisational capabilities. Resources need to be provided within the front-end of the business to
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Figure 1. Conceptual framework of the quotation process
facilitate the quotation process. Resources will typically include information technology, stores of drawings of previous customer orders, product and process knowledge and individual expertise. The documented sections of the quotation process are the visible part of processing customer needs. However, verbal communications (particularly customer communications) may not be fully documented but may be an important element of the process. This means that the front-end resources consumed may not all be conspicuous from tracing the quotation paper trail. Process mapping tools are required to capture all decisions made and resources used including knowledge and expertise. Figure 1 shows the conceptual framework adopted in the research. It shows the physical aspects of a quotation process that uses a product configurator. The figure highlights the people involved in supporting the quotation process. At the centre of the diagram is the quotation process. The next level comprises the front-end business system which operates within the business organisation characteristics and which is subject to environmental influences. The elements of the systems identified are typical but may not be explicit in all cases. By considering the system in terms of a number of hierarchical levels the influences on the design of the quotation process may be decomposed. This approach is recommended by Pettigrew (1992) for empirical studies in search of “holistic rather than linear explanations of processes”. This holistic approach is important for analysis of the quotation process because the process is strongly interconnected with other systems such as the market and manufacturing systems and may in some cases hold significant stores of tacit organisational knowledge. This paper will conduct a first level of analysis by investigating the quotation process in detail.
Methodology Research approach The research approach adopted in this study is to explore (1) the characteristics exhibited by two businesses; (2) the processes used by these businesses to provide quotations to customers for configured and non-standard products; and (3) the mechanisms and decision making that underpin these processes. A case study approach is a proven method for collecting rich empirical evidence (Voss et al., 2002). It was for this reason that the case study approach to data collection and analysis was used. The case study unit of analysis used in the research was defined as the interface between customers and the company. Interviews were conducted to give the qualitative evidence on the characteristics of quotation processes. Data were recorded using participant summary sheets and “memos” of emergent themes to inform the development of theory (Miles and Huberman, 1994). Company profiles The two businesses selected for the study were chosen because they represented two different approaches to managing product variety. The companies’ identities have been protected to respect confidentiality but other than this, all of their characteristics and processes are presented unaltered. The first business examined in this study, seat selector, manufactures office furniture in the UK to the individual specifications of business customers from around the world. The second business, custom instrument, provides customized instrumentation to aerospace, automotive and equipment manufacturers from a UK and US manufacturing base. This business has more complex quotation processes than seat selector and provides three case studies as the parent company and two of its international subsidiaries have been studied. Each has its own specific customer interface and markets. Both businesses provide “call-off customization” (MacCarthy et al., 2003) where a non-standard product is requested on the basis that it is likely to be reordered. The implication of producing an “inaccurate” quotation is significant as the customer is likely to reorder that product and inaccuracies in lead time estimation or costs may also have long-term effects. The key characteristics of the two businesses are summarised in Table I. The first business, seat selector, utilises a high level of information technology to automate customer interface processes and related activities. In contrast, custom instrument’s processes require a significant effort in manual processing and human decision making. Both companies offer a high level of variety across their product ranges but regularly, expand their product envelope due to requests from customers. Their product ranges also expand because of new product offerings involving new technologies, new designs, or solutions for niche markets. Both companies employ product configurators to piece together the customer’s requirements from existing product elements but use them in different ways. The configurator also checks the feasibility of the product specification based on rules that have been embedded in the software. Once new products are established, these are often incorporated in the configurator or catalogue for the customer to reorder.
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Table I. Profile of seat selector and custom instrument front-end characteristics Sales representative uses product catalogue and configurator to specify the product with the customer for “standard” product. This specification is fed directly into the manufacturing information system
Styles (many options), functionality (many options) and personalisation, e.g. with the customer’s logo
Cost and rapid response 278 product models 5 per cent special requests
Response times vary across sectors and markets Precise data not available on the size of the catalogue – but very large 95 per cent configured product and 5 per cent non-standard customer driven project work Applications-based solutions provided Functional parameters, properties, performance and cost
Specialised instruments High technical complexity. Modular product architecture but strong interdependencies between modules leading to difficulties in making product modifications
Custom instrument
For “standard” product the sales engineer discusses what the customer requires and communicates this to order processing who configure the request. Customer relationship management (CRM) tool used to maintain customer records and to communicate actions. Product configurator software developed in house (rules based) Problem-solving techniques Discussions are based on a list that is maintained of Frequent interaction between sales, applications special components engineers and design Activities as a consequence of the request Effort spent on sourcing of components/materials Effort often focussed on providing customer and agreeing lead times for special requests drawings and prototypes to confirm specification
Front-end process activities Customer interactions
Product attributes chosen by customers
Customer requests Customer service needs Types of customization
Furniture product Relatively simple product structure. Strongly modular with interchangeable subassemblies but very high number of product families, variants and options
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Inherent characteristics Product type Product architecture
Characteristic
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The study and analysis of the quotation processes informed the development of a conceptual model of generic quotation processes. De Bono (1998) recommends comparing a complex system against a simple model for decomposing complexity. The quotation process used by seat selector was well formalised with distinct process stages so it gave a valuable start point for a simple model that could be developed into a generic model.
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417 Data collection The participants in the study from seat selector were some of the important stakeholders’ in the quotation process – the information systems manager and the operations director. They were interviewed on the nature of systems and processes. The second study at custom instrument necessitated the collection of more detailed information in order to understand the complexities of its quotation processes. Over 30 participants were interviewed in total. Their roles spanned all front-end functions – sales, engineering and product management. Meetings between these personnel and the production department for non-standard products were also recorded and analysed. Case studies were conducted for three of the customer interfaces of custom instrument, as follows: (1) the head office sales team, which were co-located with the central design facility based in UK; (2) a small subsidiary providing the customer interface for the French market, which relies on the central design facility because their local technical expertise is limited due to size of the organisation; and (3) a large subsidiary providing the customer interface for the USA market, which not only has a local (decentralised) design facility but also uses the central design facility in the UK. These three customer interfaces were examined and compared to understand the factors influencing the operation of the customer interface in providing a quote. This provided evidence on the impact of organisational structure on quotation decision making and how different communication mechanisms are used for conveying customer needs to the technical decision makers in different units. Development of methods for capturing and analysing quotation process Variety generates complexity (Child et al., 1991) and since the customer interface is exposed to the true variety of the market this is an area of the business that is likely to manifest process complexity. This agrees with the findings of Kingsman and de Souza (1997) who uncovered evidence of practices using over 200 rules to generate the cost information for quotations. A tool kit of modelling techniques was assembled for the investigation including interview tools for process knowledge elicitation. Role activity diagramming was used to represent process stages and the division of activities across functions. Other process modelling tools needed to be developed including methods for describing information systems applications for each organisational function and interaction diagramming to represent different types of communication.
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Aim of the analysis The analysis aimed to provide detailed sources of evidence for quotation processes and how they operate in order to support the development of a generic quotation process model. The analysis also sought evidence of “good practices” for managing variety in the front-end system. The evidence forms a basis to provide insights into process improvement opportunities in quotation processes for managers of front-end systems. The identification of process improvements is the guiding aim for this research. Findings Conceptual model of process flow for quotation activities The research has developed a process model from seat selector showing key activities and communications. This “process flow of quotation activities” represents an important framework for the research study. It provides an initial model of a business offering twin modes of configure-to-order and custom engineering and offers a useful initial model of the quotation process against which other quotation process flows may be compared. Key process stages have been identified from the evidence collected. These form the basis of the conceptual model of the quotation process: (1) discussion of customer needs; (2) consideration and development of quotation: . assess feasibility of configuration to the customer’s specification; . refer to list of available options outside standard configuration; . discuss modifications with internal experts; and . accept or reject customer request. (3) presentation of quotation to the customer: . present quotation; . present quotation with sample/prototype; and . present alternative product specification in a quotation. (4) update of information systems including the product configurator. Figure 2 shows a conceptual model that has been developed based on these key stages to represent generic process steps.
Figure 2. Quotation process stages and decision centres
Analysis of decision processes Capture of the quotation processes of custom instrument was more demanding because the key activities were more complex and less formalised than seat selector. Many of the process activities that varied from customer request to customer request were embedded in the process knowledge of key individuals. The investigation of the processes used by custom instrument provided the opportunity to extend the toolkit of process representation tools. Analysis of these informal processes allowed a model of key quotation decision centres to be developed where decision centres represent core elements of the front-end system. The conceptual model was developed based on the identification of key stages of the process. Figure 3 shows how these decision centres are related to the key process stages in the conceptual model of the quotation process. This represents the quotation process as a cyclical process with information deemed to be valuable for reuse. The conceptual model represents the quotation process as a cycle. This begins with initiation of a customization request, followed by classification of the customization request. The final stage in the loop is the identification of information for reuse in further enquiries. Each stage has a decision centre in which people, with the support of information systems, make decisions on the information received and act to progress the customization request by generating and exchanging information. The first stage of the loop, decision centre (I) refers to diagnosis of customer needs. The second stage decision centre (II) represents diagnosis of customer needs within the organisation; internal constraints and requirements are assessed by the relevant experts. The final stage in the loop is decision centre (IV) that identifies information for reuse. At the centre is the sub-system relating to resource management. This is decision centre (III) and it is this decision centre that is central to all quotation stages. The decision centres are described in more detail in Table II using examples of the activities relating to the aims of each decision centre. Each decision centre is reviewed in turn against the evidence from custom instrument. Decision centre (I) customization request initiation and information gathering on customer needs. The customer interaction process model shown in Figure 4 illustrates the complexity of one of the customer interfaces at custom instrument using an example taken from the UK head-quarter sales staff responding to a UK customer request. The diagram is divided into communications relating to the initial enquiry and then revisions to the customer request when the order is placed. Many different organisational functions are involved in dialogue with the customer in some informal
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Figure 3. Quotation learning process and decision centres
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Decision centre
Aims of the decision centres
(I) Customization request initiation and information gathering on customer needs (II) Classification of requests
Collect information in dialogue with the customer on their requirements Route customer enquiries to the relevant experts in the company Understand the scale of the modifications to meet customer requirements Recognise the closest match product that might be “cannibalised” to meet customer needs or initiate new product development to meet customer needs Assign resources to the consideration of customer requests Assess what information is likely to be useful in the future for further customer orders or quotations Analyse the feedback on the success of quotations and accuracy of estimates associated with customization requests
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(III) Resource control (IV) Identification of information for reuse
Table II. Decision centres of the quotation process
or formal way. Information gathering at custom instrument requires many different organisational functions to interact with the customer. Decision centre (II) classification. At custom instrument the classification of a request is agreed collaboratively by a group of people from different functions, e.g. product managers and engineers. The process flow for processing a customer enquiry through the US customer interface team is shown in Figure 5. This diagram was constructed using role activity diagramming techniques (Ould, 1995). The roles involved in each process step are diagrammed. This illustrates the complex nature of cross-functional collaboration. Both the product range and the application domains for products at custom instrument are very broad. Therefore, the knowledge is segmented into different expert roles associated with product types and applications, e.g. specific kinds of applications or specific sectors such as aerospace. A matrix organisation has evolved where technical expertise is divided across product managers and engineers. A problem for the organisation is that process and organisational knowledge is required to know which expert to access in the network and how to access them. The model of decision centres (Figure 3) allows analysis of the three different organisational structures of the custom instrument case studies. Different challenges occur when there are local decentralised design facilities (such as in the US) or where these must be referred to UK headquarters. There are duplicate classification decision centres in the customer interface local to the US market and in the centralised decision facilities in the UK. This duplication can lead to reversal of decisions, e.g. a rejected enquiry becoming a live request. Decision centre (III) resource control. Organisational complexity is highlighted when decision centres are overlaid on an organisation chart. Key experts within a decision centre are often scattered across a number of functions. This means that management of human resources and expertise is difficult. No overarching manager was found who had visibility across the breadth of the quotation process. The holistic control of the
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Figure 4. An example of a customer interface model at custom instrument
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Figure 5. An example of a role activity diagram for the customer interface
process in these circumstances is difficult due to the fragmentation of managers’ scope of responsibility. Decision centre (IV) identification of information for reuse. In one of the subsidiaries a role has evolved for an individual who is proactive in identifying similar opportunities so that technical expertise that has been generated by previous quotations can be reapplied. This is effectively an offline role as an “applications consultant”. All other technical experts are engaged in the day-to-day processing of requests. In this situation, it has been judged more effective to use an individual’s knowledge rather than using an information system.
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Impact of variety on the quotation process We have identified two types of mechanisms for managing variety in the quotation process. The first we refer to as “absorbing” variety. A mechanism that absorbs variety is set-up to contain the effects of variety. It absorbs product variety with minimal impact on performance due to some intrinsic characteristics of the system, possibly how the system is managed. Table III shows some of the mechanisms that have been observed in the case studies for managing the impact of variety through absorption of variety. The second type of variety represents a different approach – mitigation of the impact of variety. These mechanisms are designed to deflect the impact of variety Mechanisms Absorbing variety Process customer requests in order of importance and allocate resources accordingly Meet a customer’s needs with a product specification of closest appropriate fit
Seat selector
Custom instrument
The processing of customer requests is prioritised by the perceived commercial opportunity at the individual level Mainly autonomous Hierarchical support of sales decision-making by product team by specialists with collaborative operations and technical support from other functions managers Six application specialists One applications specialist within the central organisation supports the sales team of 80 support seven national sales people representatives. There are additional applications specialists within subsidiary companies Product model structure in the Rules embedded within the Update information systems configurator is such that product with efficient component changes need to be configurator replicate use of resources component and costing changes replicated across each product type to the entire range Standard delivery times – not Deliveries estimated by specials Monitor changing delivery adjusted for production loading product team with designer. capabilities However, this estimate needs to according to the product variety be updated when the order is loading placed Targets set for customer response based on customer rating
Table III. Mechanisms for absorbing product variety through management of the quotation process
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through proactive management of variety. Table IV shows the mechanisms identified for mitigating the impact of variety. It is important to note that the quotation process provides an important source of information that allows mitigation of the impact of variety in other areas of the business such as product development and supply chain management. Discussion This section reviews process improvements and good practice for handling variety through quotation processes. First, we use the quotation process to review opportunities for process improvements. We also review the processes used by the two businesses for handling variety. Finally, we turn to the quotation process model – a key finding of this research – to make a critical review of the model. Extensions to the model and further work are discussed. Insights into quotation process improvements A generic model of quotation processes has been developed by this research to give insight into the functioning of the core process elements of the system – the decision centres. Reviewing an organisation’s structure and the information systems infrastructure supporting these decision centres should lead to the identification of potential system or reorganisation improvements. The process modelling tools have allowed a structured review of processes. The process models provide a representation that is useful for cross-functional understanding of the entire process of conversion of Mechanisms Mitigating the impact of variety Minimise the proliferation of variety
Table IV. Mechanisms for mitigating the impact of product variety through management of the quotation process
Seat selector
Custom instrument
Listings of special components are regularly reviewed in order to eliminate obsolete variants
Lists of non-standard parts are available to engineers. Drawing database is searched for similar designs but this has limited success. Reinvention of designs does frequently occur Some steering towards catalogue products through informal processes Rejection is rare
Filter requests by customers for non-standard products
Active steering towards standard products
Rejection of customer requests for infeasible or “inappropriate” products Monitor customer requests in terms of types of enquiries and orders
Rejection of requests for product that do not fit with brand image All products are ordered using the configurator. Information systems allow monitoring of demand to be decomposed to the product module level. This information is used to inform future product development and supply chain management
Very limited – in the process of setting up reports on enquiries through CRM system. The part coding used by the configurator and product structure means that reports do not give senior management the information they would wish for
customer requests into product orders. During model validation it was found that individuals had difficulty in either appreciating or confirming parts of process models and interaction diagrams outside their immediate area. The development of modelling techniques provides new tools for eliciting front-end process knowledge. These are useful for highlighting the contribution of an individual’s knowledge and the informal networks they use. On face value the differences between the two front-end systems could be attributed to the technical complexity of the products – instrumentation is far more complex and technically demanding than furniture. However, both companies respond to the same proportion of customer enquiries for “non-standard” products thus requiring agility in their processes to respond to new customer requirements. The study provides insights into how quotation processes can be “flexed” with customer-driven variety and how quotation systems can be managed in the broader context. At custom instrument there was evidence that the decision centres (I) and (II) were strongly supported by a “can do” attitude. Evidence was found of a strong motivation in all the front-end personnel to solve customers’ problems. This was driven by senior management’s customer orientation. The study of Fisher et al. (1995) into the impact of variety on operations in automotive plants found that having a lean philosophy allowed factories to absorb high levels of variety with minimal impacts to performance. Analysis of the custom instrument case studies shows that the attitudes of people and organisational culture are an important mechanism in the absorption of variety. These attitudes allow problem-solving teams to be quickly assembled to address a customer’s request and respond rapidly and effectively. The problem-solving mechanisms used by custom instrument relied on the formation of “virtual” teams for problem solving for a particular customer request. These teams were assembled using personal networks that allow organisational knowledge to be tapped effectively. The assembly of teams required flexibility of people and cross-functional collaboration. Review of the processes for managing variety reveals that, although product configurator technologies may be powerful in enabling variety to be handled quickly and effectively, there are still human intensive activities in both the companies examined, e.g. support of the sales team by experts and “administering” of the configurator by the sales representative. Businesses should be aware that product configurators do not offer a panacea for automating the responses to customer requests. Management of decision centres – the “triage” concept The decision centres relate mainly to the processing of non-standard customer requests because the expertise of people is required to consider product specifications that are beyond the “hard-wired” product variant envelope contained in the configurator. However, the identification of information for reuse is directly related to the configurator because this decision centre can help to keep the configurator database live. The classification decision centre is one of the most important decision centres for effective and efficient quotation processing. It is an area with a high concentration of organisational knowledge. It requires a customer request to be assessed and categorised. Some requests may be rejected and so the decision centre provides an
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important filter for variety. It is likely that at this stage customer needs will not be fully described in technical specification and therefore, considerable applications knowledge may be required. An analogy is that of a “triage” nurse who assesses and prioritises an injured patient on arrival at a hospital. Considerable experience is required for this role. At custom instrument the “triage” role is provided by a team of people because of the diversity of knowledge required. The case study has highlighted the importance of the management of this decision centre. It needs to be managed carefully because all customer requests come through this “funnel”. Process improvement opportunities Choosing the right level of automation to support variety will depend on how information systems may enable a business to compete and in particular which dimensions it is competing or customizing upon. If custom instrument were to move to the information systems model used by seat selector, more of the implicit knowledge held by individuals within the organisation would need to be externalised and proceduralised. There are few tools available to perform such a transformation of situation specific knowledge, although Neve (2003) among others provides generic knowledge elicitation tools. It is also worth questioning if, once the information is in a recordable format, the effort of updating the information system is worthwhile because the reuse of information is uncertain. Seat selector have plans to use the concepts and software tools they employ for describing products in the product configurator – the rules for describing elements and restrictions as to how these elements can be pieced together – in other areas of the business. In particular, they have the capability to use this “process configuration” to provide further definition and automation of other processes with constraints, e.g. configuration of services. However, a balance needs to be sought between process formalisation and the need for flexibility in responding to customers (Welker and de Vries, 2001). Information reuse and mass customization The front-end of the organisation may be viewed as an information generating system (Reichwald et al., 2001), which creates information and knowledge as a result of customer interactions. Mass customization demands that the front-end processes create a solution to match the customer’s needs for specification, cost, delivery and quality. The challenge is to enable efficient reuse of product, customer, applications and process information for creating customer solutions. Information technology offers the most efficient tools for repeatedly handling information. However, analysis of the custom instrument case study has highlighted the constraints that arise in achieving high levels of front-end automation; reliance on implicit organisational knowledge and a complex product applications architecture. Automation in this context would require the use of decision-making tools that could handle a multitude of interrelated factors with minimal effort required for updating. More sophisticated tools such as “process configuration” tools both for handling process knowledge (Child et al., 1991) and product related knowledge (Wongvasu et al., 2001) are being developed increasingly. This means that gaining the efficiencies demanded by mass customization are potentially more feasible. It can also be argued that dynamic response by a team of human experts may be more effective in some environments than attempting to fully automate a quotation process.
It is worth noting that in neither company did information systems – ERP and configurators – offer a panacea for providing quotations. The human contribution to these processes was critical. Product modularity is emphasised as essential for the implementation of product configuration systems (Forza and Salvador, 2002). A modular product architecture was a precursor to the implementation of the configurator system in both companies. After implementation there was significant resource required for ongoing management of the product assortment described by the configurator. The mechanisms for mitigating the impact of variety describe some of the practices that were used to manage the dynamic envelope whilst aiming to gain the benefits of the product configurator.
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Testing the applicability of the quotation model An important part of theory development is to test generalizations to find out the circumstances in which the theory does not hold. The model has been developed from two case studies and whilst it is based on in-depth analysis of the cases to provide robustness of theory, further confirmation is still required to ensure the applicability of the theory to a wider context. This section reviews the conceptual models of the quotation processes in different contexts. The quotation process model suggests that four decision centres should be present in an organisation. Providing there is some element of customer-driven variety this is likely to be the case because there needs to be mechanisms for conveying the customer’s requirements to the organisation. However, differences may evolve in the decision centres according to their relative importance. Some decision centres are likely to be more important than other decision centres in different contexts. Table V lists some instances where certain decision centres may be less significant. This highlights that for extremes of very low or very high variety some decision centres may be less significant. This may mean that the quotation model of four decision centres is not applicable in these contexts. We would suggest that many companies exist in the mid variety range; that companies with very low or very high levels of rates of change of variety are fewer in number. Also, variety surveys show that there are relatively few companies that have homogeneous markets in today’s dynamic business environments (Funke and Ruhwedel, 2000). Towards the development of a system for classifying quotation processes An extension to the quotation process model is based on the relative importance of the four decision centres in any particular context. It is predicted that a customization business will have a dominant decision centre and that this decision centre is determined by environmental characteristics. This leads to a classification system for Decision centre
Business environment in which decision centre is less significant
DC (I)
All configured through automatic rules. Product envelope static or intelligent system can cater for all scenarios Customer requests are homogeneous Customers are homogeneous Low opportunity to reuse request information – high variability of requests
DC (II) DC (III) DC (IV)
Table V. Evaluation of decision centre concepts
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quotation processes based on the most significant decision centre. This significant decision centre is likely to represent the decision centre consuming the most resource and may offer the focus for future process improvements. Figure 6 shows each of the four categories and the terms that are used to refer to each category of front-end systems; these are value proposition, proposal, competitive bidding and high-frequency quotation. The table also shows how the two case studies investigated in this analysis have been classified; the quotation processes of custom instrument have been classified as “proposal” and seat selector as “high-frequency quotation”. Further, work is required to confirm the prevalence of each category. Further, validation may be achieved by conducting a survey of manufacturers of industrial products to test the fit of the typology. Such a survey should explore the relationships between environmental drivers and specific decision centre dominance. Conclusions The aim of this research study was to capture and analyse quotation processes of two very different businesses in high-variety environments. They have similarities in the fact that both use a product configurator and both respond to customer requests for non-standard products outside the existing variety envelope (which is itself dynamic and continually evolving). Examination of the two businesses has provided evidence of the mechanisms that are beneficial in managing high product variety.
Figure 6. Classification of quotation processes according to decision centre configurations
We offer a model of the quotation process in this context. Existing theory in this area does not acknowledge the desirability and difficulties in reusing knowledge generated by a quotation process. The quotation process model presented in this paper differs from existing theory in this area because it recognises that (1) effectively managing the dynamic variety envelope in high-variety environments is both essential and challenging; (2) businesses are increasingly adopting product configurator and customer relationship technologies and systems; and (3) human expertise and decision making are necessary in many cases to respond to non-standard orders and to manage the process. Analysis of case studies of businesses operating in high-variety environments that provide both configured and responsively engineered products has revealed that the product configurator does offer support for some of the information and decision burdens arising from high variety. However, quotation processes are still strongly people intensive – analysis of quotation processes has highlighted how businesses are reliant on many informal practices. This is important process knowledge, which needs to be understood by an organisation concerned about the effectiveness and responsiveness of its quotation processes. The research has presented models to provide insights into these complex informal practices. It has also highlighted important generic strategies for absorbing and mitigating the impact of high variety in quotation processes. Further research is needed to provide explanatory models that can lay the basis for the design and management of quotation processes in specific contexts.
References Amaro, G., Hendry, L. and Kingsman, B.K. (1999), “Competitive advantage, customisation and a new taxonomy for non make-to-stock companies”, International Journal of Operations & Production Management, Vol. 19, pp. 349-71. Anderson, S. (1995), “Measuring the impact of product mix heterogeneity on manufacturing overhead cost”, The Accounting Review, Vol. 70, pp. 363-87. Child, P., Diederichs, R., Sanders and Wisniowski, S. (1991), “SMR Forum: the management of complexity”, Sloan Management Review, Vol. 33, pp. 73-85. De Bono, E. (1998), Simplicity, Penguin, London. Erens, F.J. and Hegge, H.M.H. (1994), “Manufacturing and sales co-ordination for product variety”, International Journal of Production Economics, Vol. 37, pp. 83-99. Fisher, M., Jain, A. and MacDuffie, J. (1995), “Strategies for product variety: lessons from the auto industry”, in Bowman, E. and Kogut, B. (Eds), Redesigning the Firm, Oxford University Press, New York, NY, pp. 116-54. Forza, C. and Salvador, F. (2002), “Managing for variety in the order acquisition and fulfilment process: the contribution of product configuration systems”, International Journal of Production Economics, Vol. 76, pp. 87-98. Funke, M. and Ruhwedel, R. (2000), “Product variety and economic growth – empirical evidence for the OECD countries”, International Monetary Fund, Vol. 48 No. 2, pp. 225-42. Hayes, R. and Wheelwright, S. (1984), Restoring Our Competitive Edge, Wiley, New York, NY.
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Holweg, M. and Greenwood, A. (2000), “Product variety, life-cycles and rate of innovation”, Proceedings of the 2000 Logistics Research Network Conference, Cardiff. Kingsman, B. and de Souza, A.A. (1997), “A knowledge-based decision support system for cost estimation and pricing decisions in versatile manufacturing companies”, International Journal of Production Economics, Vol. 53, pp. 119-39. Kruse, G. and Bramham, J. (2003), “You choose”, Manufacturing Engineer, August/September pp. 34-7 . McCutcheon, D., Raturi, A. and Meredith, J.R. (1994), “The customization-responsiveness squeeze”, Sloan Management Review, Winter, pp. 89-99. McDermott, C.M., Greis, N.P. and Fischer, W.A. (1997), “The diminishing utility of the product/process matrix: a study of the US power tool industry”, International Journal of Operations & Production Management, Vol. 17, pp. 65-84. MacCarthy, B., Brabazon, P.G. and Bramham, J. (2003), “Fundamental modes of operations for mass customization”, International Journal of Production Economics, Vol. 85, pp. 289-304. MacDuffie, J.P., Sethuraman, K. and Fisher, M.L. (1996), “Product variety and manufacturing performance: evidence from the international automotive plant study”, Management Science, Vol. 42, pp. 350-69. Miles, M.B. and Huberman, M. (1994), Qualitative Data Analysis, Sage, CA. Miller, J.G. and Vollman, T.E. (1985), “The hidden factory”, Harvard Business Review, September-October, pp. 142-52. Moos, C. and Milling, P. (2002), “Customer-orientated manufacturing strategies”, Proceedings of the EUROMA Conference, Copenhagen, Vol. 2, pp. 993-1002. Neve, T. (2003), “Right questions to capture knowledge”, Electronic Journal of Knowledge Management, Vol. 1 No. 1, pp. 47-54, available at: www.ejkm.com/ Ould, M.A. (1995), Business Processes: Modelling and Analysis for Re-engineering and Management, Wiley, Chichester. Pettigrew, A.M. (1992), “The character and significance of strategy process research”, Strategic Management Journal, Vol. 13, pp. 5-16. Pine, B.J. (1993), “Standard modules allow mass customization at Bally Engineering Structures”, Planning Review, Vol. 21 No. 4, pp. 20-2. Reichwald, R., Zanner, S. and Jaeger, S. (2001), “Creating individualized solutions in decentralized, customer-centric production units: investigations from an economic perspective”, Proceedings of the First World Congress on Mass Customization and Personalization, Hong Kong, (CD-ROM). Sievanen, M., Suomala, P. and Paranko, J. (2000), “Cost of customization”, Proceedings of the 16th International Conference of Production Research, Prague. Skinner, W. (1974), “The focussed factory”, Harvard Business Review, May-June, pp. 113-21. Spring, M. and Darymple, J. (2000), “Production customisation and manufacturing strategy”, International Journal of Operations and Production Management, Vol. 20, pp. 441-67. Stalk, G. Jr (1988), “Time – the next source of competitive advantage”, Harvard Business Review, Vol. 66, pp. 41-51. Starr, M. (1965), “Modular production – a new concept”, Harvard Business Review, November-December, pp. 131-42. Swaminathan, J.M. (2001), “Enabling customization using standard operations”, California Management Review, Vol. 43, pp. 125-36. Toffler, A. (1970), Future Shock, Bantam, New York, NY.
Voss, C., Tsikriktsis, N. and Frohlich, M. (2002), “Case research in operations management”, International Journal of Operations & Production Management, Vol. 22, pp. 195-219. Welker, G.A. and de Vries, J. (2001), “Formalisation and flexibility in order management”, Proceedings of the EUROMA Conference, Bath Vol. 2, pp. 1212-25. Wongvasu, N., Karmarthi, S. and Zeid, I. (2001), “Case based reasoning for rapid estimation of mass customized products”, Proceedings of the First World Congress in Mass Customization and Personalization, Hong Kong, (CD-ROM). Yeh, K. and Chu, C. (1991), “Adaptive strategies for coping with product variety decisions”, International Journal of Operations and Production Management, Vol. 11, pp. 35-7. Further reading Chung, P.W.H., Cheung, L., Stader, J., Jaris, P., Moore, J. and Macintosh, A. (2003), “Knowledge-based process management”, Knowledge-Based Systems, Vol. 16, pp. 149-60.
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A case study of product modularization on supply chain design and coordination in Hong Kong and China A.K.W. Lau and R.C.M. Yam The Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong, People’s Republic of China Abstract Purpose – Modular product design is a practice manufacturers frequently adopt to develop new products. Some literature has reported the importance of the modular product design and its effect on supply chain management. However, until now, very few empirical researches have examined the relationship of product modularisation and supply chain design and coordination. Furthermore, the exploration on how manufacturers capitalize upon product modularization with supply chain design and coordination is rarely reported. This paper addresses this gap. Design/methodology/approach – This paper conducted a case study to review the experience of an Audio Consumer Electronics Manufacturer (ACEM) in Hong Kong and China. This company has successfully integrated modular product design with supply chain design and coordination for more than five years. Findings – Results indicate that product modularization affects supply chain design, whereas product innovation influences on supply chain coordination. Originality/value – This study explores new relationships between supply chain and modular product design into three propositions for further studies. The first proposition shows that supply chain for modular product design has one more level than integrated product design in multiple-tier supply chain. The second proposition shows that, regardless of either a modular or integrated product, an innovative product requires closer supply chain coordination than a conventional product in new product development. The final proposition shows that product modularization with close supply chain design and coordination brings down the inventory level, improve the quality of conformance and reduce development lead time. Keywords Product design, Supply chain management, Hong Kong Paper type Case study
Journal of Manufacturing Technology Management Vol. 16 No. 4, 2005 pp. 432-446 q Emerald Group Publishing Limited 1741-038X DOI 10.1108/17410380510594516
Introduction In response to different customer preferences and fast product life cycles, successful manufacturers have developed new products more frequently to keep pace with the dynamic business environment. Product modularization is a strategic product design process used by manufacturers to increase product variety without seriously affecting production costs (Salvador et al., 2002; Starr, 1965; Child et al., 1991; Pine, 1993). Through a modular product approach, product components can be standardized, shared and reused in a range of products so that new products could frequently be launched by combining and interchanging different qualified modules from the existing designs with a short lead time (Ulrich, 1995). A few researches have also identified that this approach affects the decisions to make-or-buy (Robertson and
Ulrich, 1998; Garg, 1999; Ulrich and Ellison, 1999; Krishnan and Ulrich, 2001), and it may also affect supply chain design and coordination (Von Hoek and Weken, 1998). It has been suggested that properly combine decisions on modular product design and supply chain design and coordination not only save overall production cost (Ernst and Kamrad, 2000), but will also improve supply chain performance (Fine, 1998). However, until now, very few empirical researches focus on the integration of product modularization and supply chain design and coordination (Salvador et al., 2002; Krishnan and Ulrich, 2001) to optimize both operational and supply chain performance. Supply chain design and coordination with suppliers are two key issues in product development (Harrison, 2001; Swaminathan and Tayur, 2003; Simatupang et al., 2002). Supply chain design is the process of determining the supply chain infrastructure, e.g. plant facilities, transportation modes and nodes, warehouse and distribution locations and production processes, to satisfy customer demands (Harrison, 2001). The decisions on the suppliers’ geographic locations are important for supply chain design (Fine, 1998). Supply chain coordination is the practices of the supply chain, which involves the management of information, cash and material flows, and the collaboration of supply chain partners in product development (Swaminathan and Tayur, 2003). The primary objective in this paper fills this gap by examining the relationship between product modularization and supply chain design/coordination through an exploratory study of a large-scale Audio Consumer Electronic Manufacturing firm (ACEM) in Hong Kong and China. In this case, the firm has successfully implemented product modularization integrating with the supply chain design and coordination for over five years. The successful experience of this firm helps to identify different ways to optimize operational performance for manufacturers. The paper begins with a literature overview of supply chain design and coordination for product modularization, followed by research methodology, data analysis, findings and discussion. Literature overview Product modularization The concept of product modularization emerged in the 1960s. Simon (1962) showed that product is a complex system, which is made up of many interacting parts. Each part is subordinated to the product system hierarchically. To simplify the complexity of the system, the product should be designed as a set of sub-assemblies (sub-systems) so that their assembly constitutes a new product. Through product modularization, the manufacturer can create many products by assembling different sub-assemblies within a short product development lead time (Simon, 1962). Alexander (1965) identified that, to develop new products, the product sub-systems should perform defined functions, and could be changed independently so that adjustment of one sub-system would not affect the others. When the product sub-systems are significantly independent, the product redesign is limited to the modification of a set of related sub-systems, which could be done independently. The process of product changes can, therefore, be speeded up and the design time is reduced. Starr (1965) defined the sub-systems as interchangeable parts modules, which can be transferred among products. Product modularization is the process “. . . to design, develop and produce those [interchangeable parts] modules parts which can be combined in a maximum number of ways”. In this study, product modularization is defined as a
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product design approach where a complex product is assembled from a set of smaller sub-systems that can be designed independently but function together as a whole (Sanchez and Mahoney, 1996; Baldwin and Clark, 1997). Product modularization and supply chain design and coordination Product modularization implies a product design approach in which a product is assembled from a set of independent modules. The design approach standardizes product modules and interfaces across different modules (Ernst and Kamrad, 2000; Sanchez and Mahoney, 1996). The standardized modules and interfaces are vastly separated, specified and can thereby be outsourced to module suppliers (Schilling, 2000; Novak and Eppinger, 2001) in a “loosely” integrated manner (Sanchez and Mahoney, 1996). Novak and Eppinger (2001) found that product quality can be ensured when manufacturers design simpler products for outsourcing to module suppliers. The simpler products refer to modular product architectures (Ulrich, 1995). Fine (1998) suggested that this set of module suppliers has greater autonomy and lower proximity to improve supply chain performance. As each product module is independent of the others, the supplier is only required to conform to the pre-defined module specifications, but not to consider the modifications of other modules. The well-defined standardized interfaces also allow the suppliers to work on particular modules by themselves and still assure that the modules will interact effectively in the product development process (Schilling, 2000). It may reduce the iterative communication and coordination between suppliers and manufacturers in product development modification (Ulrich and Eppinger, 2000). This, therefore, provides greater autonomy for the module supplier. The lower proximity is also achieved if the iterative communication across modules is reduced. Fine (1998) suggested that modular products can use a modular supply chain, meaning that the module suppliers could stay within a large physical distance, have independent managerial and ownership structures, have diverse cultures and with a low level of electronic connectivity. The suppliers in the modular supply chain could also be interchangeable for major components, for example, in PC industries. In addition, Leseter and Ramdas (2002) stated that when product modules are shared and reused with various products, the supplier involvement is less required in product development. Extensive supply chain coordination may not be necessary so that the supply chain can be designed at any distance to reap other benefits. For example, multinational computer manufacturers have sourced standard modules from distant countries, i.e. China, to cut the purchasing cost. On the contrary, without product modularization, a product is integrated and assembled as a set of specific and tightly coupled components (Schilling, 2000), which requires close integration of the component suppliers (Fine, 1998). Any change of one component may affect the physical, functional and geographical structures of other components. Moreover, extensive coordination with supply chain partners is required to ensure the conformance of different product components following any changes made. Therefore, the supply chain must be designed within as short a time as possible for easy communication and coordination. More recently, Gerwin (2004) theorizes the supply chain coordination with modular product architecture and clearly shows that, in the contractual relationship between buyers and suppliers, coordination requirement (referring to the total intensity of
information processing needed in product development) and the ability of coordination (defined by the number of available coordination methods) in modular product development are lower than that in integrated product development. However, extensive coordination with suppliers has been reported as a competitive advantage in developing successful new products by synergistically co-designing new products (Twigg, 1998; Ragatz et al., 2002). Ragatz et al. (2002) found that supplier integration can reduce material costs and quality, development time and cost, and production cost while improving functionality, features and technology of the product. Twigg (1998) found that the suppliers of critical modules should be integrated in the early development processes. The author found that the Rover Group jointly designed the product specifications with module suppliers (e.g. Drive-shaft exhaust) to enhance the performance of Rover’s engine. More recently, Mikkola (2003) showed that when product modules are outsourced, the degree of supplier and customer interdependence is increased because the supplier takes higher design responsibility of the outsourced modules and is thus critical to the customer in product development. It encourages the supplier and customer to cooperate in solving technical problems during product development. From a knowledge-based perspective, close supply chain design (e.g. close supplier and customer geographical proximity) improves supply chain coordination in terms of tacit knowledge sharing, e.g. physical co-location and face-to-face communication (Diez, 2000). Tacit knowledge is difficult to articulate and is embedded in individuals and organizations, but it improves new product development (Mascitelli, 2000). In fact, for the product development team to gain tacit knowledge to innovate, the team members require frequent face-to-face interaction so that they can share technical and cognitive knowledge. (Mascitelli, 2000; Madhaven and Grover, 1998). Team members are more likely to interact directly, frequently and informally with each other to gain substantial tacit knowledge (Madhaven and Grover, 1998) and this knowledge sharing and accumulation are important for product innovation (Mascitelli, 2000). A close supply chain design with manufacturers and suppliers (e.g. physical collocation) would improve the chances of face-to-face communication and joint product development between them, leading to better tacit knowledge sharing which is vital for product innovation. Where close supply chain coordination is required for product innovation, close supply chain design may be necessary in the product development process. For example, through its close supply chain design, Chrysler has collaborated with the suppliers by having frequent face-to-face meetings, joint problem solving and daily phone calls to share and learn technological knowledge. This helps to solve technical problems in Chrysler Jeep’s windshield wipers controller, leading to increased product variety in the Jeep family, superior product performance and cost saving (Mikkola, 2003). Competing views have been reported in literatures, in which articles on new product development advocate that a close supply chain design and coordination improves the innovation of modular products (Mikkola, 2003; Twigg, 1998; Ragatz et al., 2002). However, articles on product modularization favour a loose supply chain design and coordination to improve supply chain performance (Gerwin, 2004; Fine, 1998). Furthermore, the relationship of product modularization with supply chain design and coordination has only a few empirical studies (Salvador et al., 2002; Krishnan and Ulrich, 2001). The outcome of this study is useful to discuss the competing views and
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optimize both operational and supply chain performance simultaneously. Following are the research questions of the study. How does product modularization relate to supply chain design and coordination? How does product modularization complement supply chain design and coordination in influencing operations and supply chain performances?
436 Research methodology The above research questions are addressed via the case study method. A four-step approach is taken to ensure reliability and validity of the results (Yin, 1994). First, the scope of the case is defined to avoid irrelevant data collection. In this paper, only data in the area of product modularization and its related supply chain design and coordination are collected. Other unrelated or general product development and supply chain management information has been excluded from this study. Second, the unit of analysis is set precisely in framing the research question. Since the case firm uses the modular or integrated product design in its product development projects for different market segments, project is used as the unit of analysis in the case study. Third, a single and embedded case design approach is used to study four product development projects in a single case firm. This approach critically reviews the existing conflicts identified from literature overview and provides significant opportunities for extensive analysis within the single case study (Yin, 1994). In this study, modular products refer to the products for which a product modularization approach had been adopted. Integrated products refer to the products where a product modularization approach had not been employed. Innovative products refer to the products with “new-to-the world” functions or modules. Conventional products refer to the products that do not involve innovative functions and modules. The classification of innovative and conventional products coheres with the classification of the innovative and functional products by Fisher (1997). However, the conventional product is not fully functional and it has incrementally changed the product appearance for marketing purposes. Fourth, four validity tests are used to determine the quality of research works (Table I). Case description It is important to understand the contemporary context of the case background in order to understand the findings (Yin, 1994). This study first describes the current product development and supply chain management systems in the case firm. It is followed by an analysis of the product modularisation, supply chain design and supply chain coordination of the four new product projects in the company by pattern matching (Yin, 1994). This method compares empirically based patterns. If the patterns match, the results can help a case study strengthen its internal validity (Yin, 1994). The ACEM under study is a sub-division of a global electronics company established in the 18th century in Netherlands, with a total workforce of over 200,000 worldwide. ACEM has 11 product families servicing several market segments in over eight countries. Its product variant exceeds 1,700 per year. ACEM has two business
Tests
Definitions
Tactics being used
Construct validity
Establishing correct operational measures for the concepts
Multiple sources of evidence: public information, corporate restricted documents, product demonstration and personal interviews with marketing, supply chain management, purchasing, R&D and project management personnel of the firm, as well as its module and component suppliers Pattern-matching: matching the similar pattern in the product modularization and supply chain design Comparing the empirical data with literature review Replication logics from embedded subunits (four projects) A case study protocol used in each project A case database being developed
Internal validity
Establishing causal relationship, whereby certain conditions are shown to lead to other conditions, as distinguished from spurious relationships
External validity
Establishing the domain to which a study’ findings can be generalized Demonstrating that the operations of a study can be repeated with the same results
Reliability
Source: Yin (1994)
units, i.e. the portable audio and audio system. The headquarters were relocated from Netherlands to Hong Kong ten years ago in order to work closely with the development and manufacturing base in the Far East and especially in China, which has enormous growth potential. Facing the competitive audio consumer market, ACEM’s strategies have been to continuously develop good quality and innovative products.
Product modularization ACEM develops new products in both innovative and conventional manners to satisfy the eight different market segments through a strategic product modularization approach. This approach develops a set of standardized product modules complying with one or more product families for multiple uses. ACEM implements a three-step product modularization development process. The first step is planning. It directs the future product development business with a product development roadmap. This process also defines product architecture with product functions and features, and decides internal technological and human capabilities roadmaps. The next step is to analyze the product family into a set of product modules. It maps and integrates the functions and the interfaces across different modules, as well as the specifications, development and testing within each module so as to create a product platform. The final step is to develop the new product rapidly and efficiently on the basis of a product platform characterised by the early steps. New products are mostly developed
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Table I. Four validity tests used
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through building up a number of standardized product modules purchased or developed jointly with standardized module suppliers on the same product platform. ACEM defines product family as a set of products that shares common technology and addresses a related set of market applications. For example, ACEM has 11 product families including clock radio, cassette walkman, portable CD player, portable CD cassette player, solid state MP3 player, CD recorders, micro Hi-Fi system, mini Hi-Fi system, VCD/DVD player, receiver and amplifier, and internet audio. Product platform is defined as a set of implemented and pre-defined modules, which have functionalities and interfaces defined in the planning process. For example, a product platform of Hi-Fi system has four modules for keyboards, four modules for control, two modules for tuner, two modules for audio signal processing, three modules for power supplier and so on. Product architecture is defined as the way in which the functional elements of a product are arranged into physical units and the way in which these units interact with the product. Case analysis There are three levels of relationship between ACEM and its suppliers. (1) Commodity suppliers. They sell standard catalogue materials and components to ACEM and are disinterested in the ACEM product development processes. ACEM keeps them at a distance and all transactions with them are based on cost. (2) Preferred suppliers. They supply high-quality components to ACEM and play an active role in product development jointly with ACEM throughout the product development processes. ACEM has partnership relationships with its suppliers, sharing expertise knowledge and collaborating with them in engineering works. (3) Long-term partnership suppliers. They are committed to product development projects with ACEM. They attend business meetings, co-develop new products, and share new knowledge, business opportunities and risks with ACEM. ACEM also outsources the entire final assembly process to one of its long-term partnership suppliers with the support of know-how, machines and human resources.
Supply chain design The supply chain design of the four product development projects in ACEM are shown in Figure 1, i.e. final assembly level, modular level and component/material level. In Figure 1, the three kinds of suppliers are highlighted in different shades and classified by the functional components of the products in order to provide a simpler view of the whole supply chain and keep confidentiality of the ACEM. A new product development team is responsible for managing each individual product development project with other departments playing the supporting role. The final assembly supplier and standardized module suppliers are the long-term partnership suppliers. ACEM provides the know-how, human, machine and facility resources while the supplier provides low-cost manufacturing capabilities. The suppliers of PCB assembly and plastics parts are the preferred suppliers who are
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Figure 1. Supply chains of four product development projects
responsible for building the functional product components. The accessory components and packaging materials suppliers are either preferred suppliers or commodity suppliers, depending on the technology, cost and risk of those component supplies.
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As shown in Figure 1, both innovative and conventional modular products have extra standardized module suppliers in the supply chain. The standardized module suppliers deliver to the final assembly supplier the standardized modules, i.e. standardized CD, tape desk, power amplifier, and tuner modules. On the other hand, innovative and conventional integrated products have no standardized module suppliers, and the preferred and commodity suppliers are connected with the final assembly suppliers. Supply chain coordination Supply chain coordination is affected by whether the product is innovative or conventional. If the product is innovative, extensive communication among the suppliers is required. In the innovative modular product, e.g. MP3/CD Hi-Fi system, the MP3/CD module suppliers not only co-design the product with the ACEM product development team, but also assess and modify their manufacturing processes with the ACEM resident engineers through frequent face-to-face meetings. The plastic parts and PCB component suppliers also work closely with the ACEM product development team to ensure that their components are suitable for the innovative MP3/CD function. An engineering manager said that the suppliers, who supply innovative modules or components, usually cooperate with ACEM from the new product idea generation, business/technical assessment, product/process concept development to prototype building and full scale production. A strategic purchasing manager considers the suppliers as members of the new product team and invites them to design workshops and meetings for the transfer of technical know how and knowledge in developing new components (i.e. CD/MP3 module). The knowledge transferred includes production, quality and design know-how. In the innovative integrated product, e.g. MP3/CD portable player, the MP3/CD suppliers also need to co-design the product with the ACEM product development team to improve the quality of the product. Because of the integrated nature of the product, any changes in one component change may affect the other components. The product development team coordinates with all relevant suppliers to assure that all the components fit into the integrated MP3/CD in the physical and functional aspects. Performance improvements Aligning supply chain design and coordination with product modularization, product development time, product quality, and inventory level in the firm have all been improved. The new product development lead-time has been reduced from 54 to 38 weeks, while the ratio of reusing existing product modules has increased from 24 to 35 percent. It is noted that the ACEM provided the same delivery time for the four case studies. ACEM has developed a systematic project development program that exercises strict control over development time for each project. This standardized development time is used for all four cases such that team members can undertake a number of projects at the same time and plan their time for each project. The number of complaints on product has also been reduced by nearly 50 percent, especially with innovative products. It is because the qualified modules are reused in the segments of non-innovative product components and innovative modules are incrementally improved following every product launch, if deemed necessary. The inventory level in
the firm has been lowered in about five years as the in-house product components are reduced. Findings and discussion From the case study, it is found that supply chain design is affected by product modularization. Modular products need an extra-standardized module supplier layer in the supply chain (Figure 1: part I vs part II). This longer supply chain may lengthen the material delivery time and remedial actions have to be taken. Strategic purchasing managers stated that, in the past, the material delivery time of modular products was longer because the component suppliers needed to deliver the components at the component level to the standardized module suppliers at the modular level, and in turn, the module suppliers delivered the completed modules to ACEM. Alternatively, the delivery time of integrated products was shorter because the components were directly delivered from suppliers at component level to ACEM. To tackle this problem, ACEM has adopted the web-based SAP ERP system to communicate with suppliers. Through the ERP system, ACEM shares its actual and forecast order information and inventory level with the module suppliers. This helps the suppliers to prepare material quicker and thereby make up for the longer delivery time. Therefore, the two modular product development projects have a similar delivery lead-time as that of the two integrated product development projects. The longer supply chain may also obstruct communication channels in the supply chain. ACEM managers have fewer chances to communicate and share knowledge directly with the suppliers of plastic parts assembly and PCB assembly for modular product development. Project managers have indicated that the suppliers are less participative than the standardized module suppliers at the product design and production stages. The project manager of the conventional modular product admit that they only share product information (e.g. product architectures, physical components, and product market and technology information) with the standardized module suppliers, but not the other suppliers, because conventional components are highly standardized and less resource is used to work with the other suppliers. In addition, except for a very few cases, ACEM does not handle quality, production and development works with the lower level of suppliers, leaving the standardized module suppliers to deal with them. The very few cases would be the plastic mold of the conventional module product because the mold will greatly affect the product appearance and so is needed to assure the mold’s quality. In fact, because of product modularization, ACEM has passed on the responsibility of communicating with lower tier suppliers to the modular suppliers. Although ACEM can manage a reduced number of suppliers, it still limits the communication with lower level suppliers. To make up for the communication channels and the effects of a longer supply chain, ACEM has intentionally selected its suppliers in close geographical proximity and encouraged its existing suppliers to build up production facilities close to ACEM. Geographically, the long-term partnership suppliers are closer to ACEM than with the preferred suppliers. The distant ones are usually the commodity suppliers. This close proximity enables ACEM to secure quick and reliable supplies. It has also facilitated face-to-face communication with different levels of suppliers during the product development and social activities. This approach has economized the product development activities of ACEM on production and design skills and improved the
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Table II. The matrix of product modularization, supply chain design and coordination
information sharing among the supply chain members. ACEM describes this approach as “one roof”, indicating that the suppliers are recognized in the house of ACEM. This helps to promote close cooperation and communication with supply chain partners. It has been found that product innovation affects supply chain coordination. That is, innovative products demand more intensive supply chain coordination than conventional products (Figure 1: (a) vs (b)). ACEM always expends efforts on supply chain coordination with their suppliers by co-developing the innovative products. Co-development and extensive communication with the suppliers speed up development time and assure product quality. The module suppliers often co-develop new components to conform to ACEM’s innovative products. Frequent product co-development and co-engineering among the engineers of the ACEM and the module suppliers in product engineering has led to better manufacturing quality. However, for the conventional products, fewer efforts are required for supply chain coordination as product components/modules are available in the market. ACEM can reduce the conventional product costs at the expense of its old module/component supplier’s profit. ACEM focuses on cost reduction in standard supplies, e.g. the PCB assembly, plastic parts, packaging materials and accessory components. In fact, ACEM has reduced the cost of its standard supplies by 5 percent annually in the last few years. As the conventional component market is highly competitive and conventional technology is readily available, ACEM can easily find other suppliers to substitute the standard module/component suppliers. The above findings are summarized in Table II. With proper alignment of supply chain design and coordination with product modularization, manufacturers may reduce product development time dramatically by developing standardized modules. The project team can choose existing standardized modules to embark on the project or use the module as a blueprint to work with suppliers to develop an innovative product with improved quality. Product modularization standardizes product components so as to prevent the generation of unneeded variations from different products (Spivak and Brenner, 2001). In addition, the reusable standardized modules would reduce the complexity of component varieties, which, in turn, improves the conformance of quality. Since a few modules are shared and reused for a great many product variants, types of inventory could be reduced. The geographically closed supply chain partners would further reduce the level of safety stock. In fact, ACEM has reduced about 5 percent of total inventory cost per annum in the last five years. Table II summarizes the findings of product modularization against supply chain design and coordination for the four product development projects in ACEM. Emerging from the research of ACEM and its four product development projects, the following propositions are made:
Supply chain design Supply chain coordination
Innovative Conventional Innovative Conventional
Modular
Integrated
One more layer of module supplier One more layer of module supplier Extensive Low
Short Short Extensive Low
P1.
The supply chain for the modular product design has one more level than the integrated product design in the multiple-tier supply chain.
P2.
Regardless of either a modular or integrated product, an innovative product requires closer supply chain coordination than a conventional product in new product development.
P3.
Product modularization with close supply chain design (i.e. electronic and geographical proximities) and coordination (i.e. joint product co-development and information sharing by IT systems) brings down the inventory level, improve the quality of conformance and reduce development lead time. P1 suggests that modular product design leads to one more level of supply chain than integrated product design. The finding empirically supports the literatures that modular product design requires reconfiguration of existing supply chain and a new tier of module supplier is formed (Feitzinger and Lee, 1997; Von Hoek and Weken, 1998). The module supplier integrates forward in the supply chain by taking over the module development and production process from the manufacturer. The manufacturer then has to manage the increased complexity, value and size of the module sourced from their suppliers. The module supplier is, therefore, critical and is invited to build their production facilities near to the manufacturers. ACEM follows this approach by helping its module supplier to develop complex and innovative modules and locating the supplier plants near ACEM. However, this proposition further suggests that whether the product is innovative or not; the modular product design would add one tier in the supply chain. In addition, remedial actions to improve communications and supply lead-time have to be taken in the longer supply chain. For example, it has been suggested that information technology is used to share information with the lower tier of suppliers (Fine, 1998). Locating the module suppliers in close proximity has also been suggested (Von Hoek and Weken, 1998). P2 suggests that product innovation requires a closer supply chain. This finding is inconsistent with Gerwin (2004). Modular product may not independently lower the coordination requirement across the supply chain when product innovation is taken into account. This study suggests that, when modular product design is critical to coordination requirement, product or module innovation should also be considered in the supply chain coordination (Twigg et al., 1998; Mikkola, 2003). P3 suggests that successful product modularization requires the integration of the supply chain (re-)design and coordination. This proposition tries to explore the links between product modularization and supply chain management. When some researches have highlighted the relationship between product modularization and supply chain relationship (Doran, 2003; Mikkola, 2003), this proposition extends their works by implicitly exploring the variables of the relationship so that they could be analyzed. It suggests that if a manufacturer can implement product modularization, supported by close electronic and geographical proximity of supply chain design and joint product development of supply chain coordination, there will be better quality of conformance, shorter delivery time and lower inventory level. In short, product modularization reduces product development time, and improves product quality and inventory level, which are the essence of large product variety
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(Pine, 1993). However, it also makes the supply chain longer (i.e. at least one more tier of supply chain), which would distort communication and postpone delivery time. This study, therefore, suggests that when manufacturers outsource their product modules, they should enhance their communication with their suppliers. A web-based communication system could help to solve this problem. For either modular or integrated innovative products, manufacturers should work closely with the suppliers to assure new product quality. They should also ensure that tacit knowledge is exchanged with their supply chain partners. However, when manufacturers develop conventional products, they should keep the suppliers at arm’s length to gain cost advantage. Limitations This exploratory study has a number of limitations, and further studies should be conducted to validate them. The first limitation is the theoretical generalizability. The single case study approach has explored three propositions, constructing a few relationships between product design and supply chain management, but we cannot generalize from one case study. Further studies via multiple cases or statistical sampling are required to validate our propositions. A second limitation is the single case-embedded approach. Although this single case-embedded approach has provided significant opportunities for extensive analysis in multiple levels of supply networks, its analysis is shifted to the sub-unit level, i.e. the four product developments, rather than the whole case itself (Yin, 1994). Further studies analyzing the product families or product platform levels may end up with more meaningful findings of product modularization and its impact on supply chain design and coordination (Meyer and Lehnerd, 1997). A third limitation is the industry characteristics in the electronic industry. The study focuses on a very typical and large-scaled electronics firm in Hong Kong and China, which may be specific for the electronics industry only. To simplify the theory (Wacker, 1998), the industry characteristics are not included in this study. Cross industry study of a few different industries may help to generalize the findings. Conclusion This exploratory study has contextually tried to explore the competing relationship between product modularization and supply chain design and coordination. It suggests that the supply chain design is greatly affected by product modularization while supply chain coordination is affected by whether the product is innovative or conventional. In fact, manufacturers should design and coordinate their supply chains for different products in order to optimize both operational and supply chain performance. References Alexander, C. (1965), Notes on the Synthesis of Form, Harvard University Press, Cambridge, MA. Baldwin, C.Y. and Clark, K.B. (1997), “Managing in an age of modularity”, Harvard Business Review, September-October, pp. 84-93. Child, P., Diederichs, R., Sanders, F.H. and Wisniowski, S. (1991), “SMR forum: the management of complexity”, Sloan Management Review, pp. 73-80.
Diez, J.R. (2000), “Innovative networks in manufacturing: some empirical evidence from the metropolitan area of Barcelona”, Technovation, pp. 139-50. Doran, D. (2003), “Supply chain implications of modularization”, International Journal of Operations & Production Management, Vol. 23 No. 3, pp. 316-26. Ernst, R. and Kamrad, B. (2000), “Theory and methodology: evaluation of supply chain structures through modularization and postponement”, European Journal of Operational Research, Vol. 124, pp. 495-510. Feitzinger, E. and Lee, H.L. (1997), “Mass customization at Hewlett-Packard: the power of postponement”, Harvard Business Review, January-February, pp. 116-21. Fine, C.H. (1998), Clockspeed-Winning Industry Control in the Age of Temporary Advantage, Perseus Books, Reading, MA. Fisher, M.L. (1997), “What is the right supply chain for your product?”, Harvard Business Review, March-April, pp. 105-16. Garg, A. (1999), “An application of designing products and processes for supply chain management”, IIE Transactions, Vol. 31, pp. 417-29. Gerwin, D. (2004), “Coordinating new product development in strategic alliances”, Academy of Management Review, Vol. 29 No. 2, pp. 241-57. Harrison, T.P. (2001), “Global supply chain design”, Information Systems Frontiers, Vol. 3 No. 4, pp. 413-6. Krishnan, V. and Ulrich, K.T. (2001), “Product development decisions: a review of the literature”, Management Science, Vol. 47 No. 1, pp. 1-21. Leseter, T.M. and Ramdas, K. (2002), “Product types and supplier roles in product development: an exploratory analysis”, IEEE Transactions on Engineering Management, Vol. 29 No. 2, pp. 107-18. Madhaven, R. and Grover, R. (1998), “From embedded knowledge to embodied knowledge: new product development as knowledge management”, Journal of Marketing, Vol. 62, pp. 1-12. Mascitelli, R. (2000), “From experience: harnessing tacit knowledge to achieve breakthrough innovation”, Journal of Product Innovation Management, Vol. 17, pp. 179-93. Meyer, M.H. and Lehnerd, A.P. (1997), The Power of Product Platforms, The Free Press, New York, NY. Mikkola, J.H. (2003), “Modularity, component outsourcing and inter-firm learning”, R&D Management, Vol. 33 No. 4, pp. 439-54. Novak, S. and Eppinger, S.D. (2001), “Sourcing by design: product complexity and the supply chain”, Management Science, Vol. 47 No. 1, pp. 189-204. Pine, J.B. II (1993), Mass Customization: The New Frontier in Business Competition, Harvard Business School Press, Cambridge, MA. Ragatz, G.L., Handfield, R.B. and Petersen, K.J. (2002), “Benefits associated with supplier integration into new product development under conditions of technology uncertainty”, Journal of Business Research, Vol. 55, pp. 389-400. Robertson, D. and Ulrich, K. (1998), “Planning for product platforms”, Sloan Management Review, Summer, pp. 19-31. Salvador, F., Forza, C. and Rungtusanatham, M. (2002), “Modularity, product variety, production volume, and component sourcing: theorizing beyond generic prescriptions”, Journal of Operations Management, Vol. 20, pp. 549-75. Sanchez, R. and Mahoney, J.T. (1996), “Modularity, flexibility, and knowledge management in product and organization design”, Strategic Management Journal, Vol. 17, pp. 63-76.
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Schilling, M.A. (2000), “Toward a general modular systems theory and its application to interfirm product modularity”, Academy of Management Review, Vol. 25 No. 2, pp. 312-34. Simatupang, T.M., Wright, A.C. and Sridharan, R. (2002), “The knowledge of coordination for supply chain integration”, Business Process Management, Vol. 8 No. 3, pp. 289-308. Simon, H.A. (1962), “The architecture of complexity: hierarchic systems”, Proceedings of the American Philosophical Society, December, pp. 467-82. Spivak, S.M. and Brenner, F.C. (2001), Standardization Essentials, Principles and Practice, Marcel Dekker, New York, NY. Starr, M.K. (1965), “Modular production – a new concept”, Harvard Business Review, November-December, pp. 131-42. Swaminathan, J.M. and Tayur, S.R. (2003), “Models for supply chains in e-business”, Management Science, Vol. 49 No. 10, pp. 1387-406. Twigg, D. (1998), “Managing product development within a design chain”, International Journal of Operations & Production Management, Vol. 18 No. 5, pp. 508-24. Ulrich, K.T. (1995), “The role of product architecture in the manufacturing firm”, Research Policy, Vol. 24, pp. 419-40. Ulrich, K.T. and Ellison, D.J. (1999), “Holistic customer requirement and the design-select decision”, Management Science, Vol. 45 No. 5, pp. 641-58. Ulrich, K.T. and Eppinger, S.D. (2000), Product Design and Development, 2nd ed., Irwin/McGraw-Hill, Boston, MA. Von Hoek, R.I. and Weken, H.A.M. (1998), “The impact of modular production on the dynamics of supply chains”, The International Journal of Logistics Management, Vol. 9 No. 2, pp. 35-50. Wacker, J.G. (1998), “A definition of theory: research guidelines for different theory-building research methods in operations management”, Journal of Operations Management, Vol. 16 No. 4, pp. 361-85. Yin, R.K. (1994), Case Study Research: Design and Methods, 2nd ed., Sage Publications, California, CA. Further reading Popper, K.R. (1980), The Logic of Scientific Discovery, Hutchinson, London.
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Modelling complexity in the automotive industry supply chain
Modelling complexity
Kevin Turner Supply Chain Information and Integration Research Group, Brighton Business School, University of Brighton, Brighton, UK, and
Geoff Williams International Car Distribution Programme, Solihull, UK
447 Received February 2004 Revised October 2004 Accepted November 2004
Abstract Purpose – In most countries the distribution system for new cars has remained unchanged for many years, with the main emphasis on supplying customers from stock held at dealers. Despite high stocks, the performance of the supply chain has failed to meet customer expectations in terms of delivering the exact specification desired within an acceptable timescale. This paper investigates changes to the design of the supply chain which potentially offer significant improvements in performance. Design/methodology/approach – A simulation of the supply of new cars, from ordering through the assembly plant and finished vehicle stocking to customer delivery, has been used to develop further understanding of the dynamic performance of the supply chain. The application of the simulation model is demonstrated through scenarios drawn from research into the UK car market. Findings – The findings demonstrate the benefits of central stocking (storing new cars in distribution centres, rather than at dealers) for both manufactures and customers, supported by recent industry data. Research limitations/implications – The results presented are based on a representative vehicle model manufactured and sold in the UK; a range of alternative scenarios and changes to the supply chain design can be investigated using the simulation model. Practical implications – For the automotive industry, the research supports the introduction of central stocking, and demonstrates a methodology for assessing future changes to the supply chain. Originality/value – The research extends the range of applications of simulation to investigating supply chain design, and demonstrates the feasibility of this approach in modelling complex supply chains. Keywords Supply chain management, Automotive industry, Simulation Paper type Research paper
Introduction The automotive industry supply chain has been the subject of extensive research, but this has tended to concentrate on the component supplier-production sections of the chain. The industry has been at the leading edge of innovation in this area, with early adoption of new technologies such as EDI and business-to-business trading exchanges. The authors wish to acknowledge the financial support of the UK National Franchised Dealers Association for the initial development of the simulation, and the International Car Distribution Programme for the subsequent development. Also the employees of ICDP and member companies in the automotive industry who have contributed time and data to support this development.
Journal of Manufacturing Technology Management Vol. 16 No. 4, 2005 pp. 447-458 q Emerald Group Publishing Limited 1741-038X DOI 10.1108/17410380510594525
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In contrast, the production-distribution sections of the chain have been the subject of relatively little academic research, and for many years the structure of the supply chain remained frozen in the pattern established by the middle of the last century. No matter how lean the assembly plants became, with component stocks reduced to a few hours, the distribution system remained “bloated” with typically 60 days of new cars either in transit or held at the dealers (Whiteman et al., 2002; Holweg and Pil, 2004). The new car supply chain presents a number of challenges, both for management and as a subject for research. For example: . the complexity of the product – each individual car has a distinct specification in terms of body, engine, trim, colour, etc.; . the complexity of the supply network – multiple stocking locations from the assembly plant to several hundred dealers in each major market; . consumer behaviour – including willingness to wait for a new car to be built-to-order, and the extent to which customers will compromise on specification; . demand seasonality – varying between markets, and its effect in combination with manufacturers preference for level production schedules; . ageing of stock – resulting in heavy discounting to sell cars which remain unsold after several months. In 1992-1993, the authors were members of a team commissioned by the UK National Franchised Dealers Association (NFDA) to study recent developments in UK car distribution and the potential for improvements. The authors developed a computer simulation model of the new car distribution system, which was used to investigate a range of scenarios discussed in the project report Managing New Vehicle Supply and Distribution in the 1990s (Harbour et al., 1993). The success of this study contributed to the establishment of the International Car Distribution Programme (ICDP), funded by a consortium of companies in the automotive industry to continue research on an international scale. ICDP adopted the original NFDA simulation model and have funded its continuing development for the last ten years. During that time the model has been used for five separate ICDP projects, and has also been used by several individual manufacturers. In addition to most European markets, the model has been successfully used to study new car distribution in the USA, Japan and Brazil. Conceptual background Simulation models of the supply chain The use of computer simulation to study the dynamics of supply chain performance originated with Forrester (1958), who developed the approach now known as system dynamics. Although based on a much-simplified model of a supply chain, this identified the important concept of demand amplification, which has subsequently been confirmed to exist in several real-world supply chains (McCullen and Towill, 2002). One of the major contributions of systems dynamics has been the use of the MIT beer game (Jarmain, 1963) in management education. Discrete-event simulation permits models to be developed with greater detail and fidelity than systems dynamics. Each individual event in the real world, for example, a
customer buying a product, is represented by software in the simulation model. Individual items (such as customer and product) can also be represented as discrete entities with distinctive attributes, rather than simply as a “flow” or “level”. This approach has been very widely applied, particularly for studying complex problems such as factory layout and decision rules. Although the potential application of discrete-event simulation to investigate supply chain performance has been recognised, it is often considered impractical due to the need for high computational time (Simchi-Levi et al., 2000; Armbruster et al., 2002) and the large number of decision variables (Lee et al., 2002). However, examples of real-world applications are now emerging, for example, in telecommunications equipment (Persson and Olhager, 2002) and pharmaceuticals (Hung et al., 2004). A recent survey of UK practitioners by Melao and Pidd (2003) found that applications to supply chain processes were now most common, slightly ahead of production processes. The automotive industry supply chain The traditional downstream supply chain begins with production scheduling, with the objective of keeping production as stable as possible and ensuring that vehicles are financed by dealers as soon as they are produced. This is achieved by maximising the allocation of orders to dealers at the earliest point possible – up to 60 days before assembly. Once the car is assembled, the vehicle is delivered to the dealer as quickly as possible. The dealer’s objective is to sell their available stock, if necessary using aggressive sales techniques to persuade customers to accept a car that is not their first (or even fifth) specification preference. This often involves additional discounts to the customer, encouraged by manufacturer incentives. Fisher et al. (1994) proposed that functional products should be matched with efficient supply chains, and innovative products matched with responsive supply chains. The downstream supply chain for new cars is based on manufacturers past perception of cars as functional products (due to the high volumes of production on a single assembly line). The car industry, therefore, endeavoured to create an efficient supply chain type similar to other mass-produced consumer goods. However, from the customers’ viewpoint each car specification (including factors such as engine, colour, options, and trim level) is unique, even if it is the same model. Moreover, the range of body-styles has increased, with crossovers such as the sports-utility vehicle appearing, as has the speed of introduction of new models. Applying Fisher’s criteria, cars are in the awkward position of combining features of both functional and innovative products, while the supply chain can hardly be described as either efficient or responsive. The lean and agile approaches developed within the context of manufacturing in the early 1990s, and were subsequently applied to supply chain management. They have been defined succinctly by Naylor et al. (1999) as: Agility means using market knowledge and a virtual corporation to exploit profitable opportunities in a volatile market place. Leanness means developing a value stream to eliminate all waste, including time, and to ensure a level schedule.
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Christopher (2000) points to the paradox of the automotive industry adopting lean manufacturing whole-heartedly, yet having a supply chain that can be considered neither agile nor lean. Although originally considered mutually exclusive, Naylor et al. (1999) demonstrated that the two paradigms could be successfully combined into a single “leagile” supply chain, using the concept of the decoupling point to separate the lean (upstream) section of the supply chain from the agile (downstream) section. This concept was quickly recognised as valuable in supply chain design, and in providing a mechanism for moving from the lean to agile models (Christopher and Towill, 2000; Mason-Jones et al., 2001; Christopher and Towill, 2001). Huang et al. (2002) have extended the original matrix of Fisher to incorporate a “hybrid” product type, giving automobiles as an example. The hybrid supply chain that is proposed as a desirable match for this product type is very similar to Naylor’s leagile supply chain. Turning to developments within the industry, the move to increase the responsiveness of the supply chain in the UK began in the 1980s with the introduction of stock-locator information systems (Holweg and Pil, 2004). These gave each dealer information about cars held by other dealers, which enabled them to negotiate – not always successfully – an exchange to meet a customer’s specific requirements. By 1992, transfers between dealers accounted for 45 per cent of UK sales (ICDP, 1995). However, the creation of this “virtual” pool of stock resulted in a significant increase in costs – not only the physical transfer of vehicles but also the time spent on negotiating the transfer. In the early 1990s, national sales companies such as BMW, VW, Rover and GM began to introduce distribution centres, reducing stock held at dealers in some cases to showroom cars only (Harbour et al., 1993). Mathematical analysis of inventory location suggests that this should improve service levels by “pooling” safety and cycle stocks (Maister, 1976; Eppen, 1979). However, the assumptions made in this analysis are not valid in the case of new cars. For example, the cars in stock are not all identical, and the seasonality of monthly demand means that there is some correlation between demand at each of the dealers. There is also the potential loss of sales caused by the longer lead-time for supplying a car from a distribution centre rather than from stock. Product variety is a major source of problem in both production and distribution of new cars. Holweg and Pil (2004) found that the variety of specifications available for 19 models in 1999 ranged from 448 to almost four billion. The variety of VW Golf specifications had increased 20-fold between 1980 and 1999, and was one of several models to have more specifications than UK customers (so every customer could, in theory, have purchased a unique car). The main contributor to this increase in variety is the range of optional equipment, such as sunroof and alloy wheels, particularly where these can be specified individually by the customer. Each option can be either “fitted” or “not fitted”, so three options can be fitted in eight (23) combinations. Many manufacturers try to reduce this variety by offering standard packages of options targetted at customer segments, e.g. “sports” or “comfort”. One strategy to manage this problem, successfully applied in sectors such as consumer electronics, computers and clothing, is postponement (Feitzinger and Lee, 1997). Modification of the specification after the car has been through main assembly is impractical for features such as engine capacity or paint colour, but is feasible for many optional features such as in-car entertainment. Performing this at the dealers
creates problems such as inventory, quality and reverse logistics, but fitting optional equipment at distribution centres reduces these problems. Postponement clearly offers particular advantages to manufacturers with long distribution times, e.g. vehicles supplied to the UK from assembly plants in Japan. A fully responsive supply chain would permit mass customisation (Pine, 1993), with every car exactly matching the specification chosen by the customer. A move to build-to-order is seen as one potential means of achieving this, but Holweg (2001) suggests that this is not a “silver bullet”. Individual manufacturers are, therefore, likely to use a combination of approaches to move towards complete mass customisation.
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The simulation model The key elements of the model are shown in Figure 1. The main inputs are data related to the production of vehicles, their distribution to the dealers in the market, and the nature of customer demand. Within the model the dynamic performance of the supply chain is modelled as a series of events, for example, the arrival of a customer at a dealer which leads to a matching process, attempting to identify a vehicle which matches the customer’s individual requirements. The simulation runs for five years, so the model will typically simulate the production, distribution and sale of 500,000 cars in each run. The outputs of the model are performance indicators including service level, customer satisfaction and stock levels. Each car is represented individually in the model, with attributes including the specification, date of production and current location (which may be a “virtual” location, i.e. a car scheduled for future production, or a real location such as in transit to a dealer). The monthly production scheduling process sets daily production volumes and the specification of each car. For a customer order, the specification of the car matches the customer requirements. In determining the specification of stock orders, manufacturers use a form of pareto decision rule to ensure that specifications with low expected
Figure 1. Simulation model diagram
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Figure 2. Monthly service level results (for one simulation run)
demand are not made for stock. This is implemented in the model by setting a minimum threshold of annual demand for stock vehicles, e.g. to prevent manufacturing specifications with a demand of less than four per year. Similarly there are a variety of decision rules to allocate cars built for stock to specific dealers, or to leave these “open” and, therefore, available to all dealers. After assembly, the cars are transported either directly to dealers or to a distribution centre. For the UK market transport is by road, but frequency, delivery times and batch sizes can be varied to simulate transport to the market by rail or ship. Customers are generated with a frequency sampled from distributions of demand seasonality, for example, in the UK there are more customers in March and September than in other months. Each customer is also considered individually, with the visited dealer, preferred specification, maximum waiting time and willingness to accept an alternative specification each sampled from distributions defined by the data. The simulation then uses a matching process (Figure 1), searching the locations where stock and orders are held to identify a car that meets the customer’s requirements and can be supplied within their waiting time. If all possible sources fail to produce a car acceptable to the customer (within the time they are prepared to wait) this is recorded as a “lost sale” – in practice it would lead the customer to change to another brand or model. The initial conditions for each run are limited to allocating stock to various locations, which means the simulation starts with no customer orders in the system. There is an initial transient period of 12 months, with the results for this “warm-up” period being discarded. The simulation uses a single run for each set of data, with results based on the mean of performance in the 48 following months. This is shown in Figure 2, which shows the monthly service level for a single run of the simulation. The first 12 months (light shading) contain several outlying values for service level; after
this the values are more stable, with a repeating pattern caused by the seasonality of customer demand. Convergence testing of the model has been conducted using the method developed by Robinson (2004), which found convergence of around 1 per cent after the first year and below 0.5 per cent by the end of five years. The model consists of approximately 8,000 lines of Visual Basic code. This is supplemented by 22 Visual Basic forms (used for data entry) and an Excel spreadsheet (used for summarising results from each run of the model). Methodology Research using computer simulation is an experimental process, with each “run” of the simulation model comprising a single experiment. To illustrate the application of the model we have used data that defines a “base scenario” similar to the supply chain in the UK in the 1980s, with the simulation run several times to determine the performance with varying levels of stock. We then repeat the process with two alternative scenarios, enabling experiments to be conducted with a supply chain design nearer to the current situation for a typical manufacturer. Base scenario – dealer stock The base scenario is a car manufactured in the UK (the data is based on a composite of several actual manufacturers). Production is 100,000 cars per year, 50 per cent of which are exported. The model is available with 13 body/engine combinations, 3 trim levels, 15 paint colours and 99 option packages (combinations of optional equipment), to give a total of 57,915 potential variants (although those with low demand are manufactured only to customer order). Production scheduling uses a “build-to-forecast” approach with long lead-times. Cars are allocated to dealers when they are first scheduled for production, and all stock is held by the network of 370 dealers (with varying annual sales and stock). Dealers are able to use a stock locator system to identify stock held at other dealers, although they give priority to selling their own stock and orders in the production schedule (i.e. a stock-push approach). The efficiency of the transfer system is reduced to reflect the fact that some potential transfers will not be feasible due to the distance between dealers, and in other cases dealers may not be prepared to make the exchange. Demand varies by month, week, day-of-week and hour, based on recent data collected by ICDP (Waller, 2002). Customer behaviour data such as the distribution of time customers are prepared to wait and their willingness to accept alternative specifications are also based on ICDP data for the current UK market. Alternative scenario 1 – distribution centre In the first alternative scenario, stock at dealers is restricted and most stock is held at a single distribution centre for the UK market. There are two further important changes. First, cars are no longer allocated to dealers when they are scheduled, but remain available to all dealers until they leave the distribution centre. This removes the problems of negotiating transfers between dealers. Secondly, the sales priority is amended to give greater priority to customer orders and sales from the distribution centre, with cars held in the dealer showroom given lower priority.
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Alternative scenario 2 – postponement This is a development of the first scenario, with the number of option packages reduced to eight (bringing the number of model variants down to 4,680). This could be achieved by fitting four options at the distribution centre to meet the requirements of the customer buying the car, for example, alloy wheels, fog lights, satellite navigation and alarm. The option packages fitted at the assembly plant would be the eight possible combinations of three options, for example, sunroof, in-car entertainment and air conditioning. Results and analysis Each of the three scenarios was tested with the simulation model for a range of stock levels. In these scenarios we assume the dealers have access to information systems enabling them to search the “pipeline” of orders prior to assembly. The results presented here are for the UK market only. Figure 3 shows the variation in service level as stock is reduced from around 53 days to 10 days, and even lower for the alternative scenarios. The shape of the curve is as expected, and the benefits of centralisation are very apparent, with 20 days of stock giving a better service level in the alternative scenarios than 53 days in the base scenario. Postponement gives only a small improvement at high stock levels, but this becomes more substantial as the stock level is reduced. Part of the improvement is due to the distribution centre, which makes all stock available to all dealers without the costly dealer transfer process. However, the fact that cars in the production schedule and in transport are not allocated to a specific dealer also contributes to the improvement. This is evident in Table I, which shows the source of sales for one run from each of the scenarios. Without the distribution centre, over 60 per cent of sales are from dealer stock, most of them involving a transfer. With the distribution centre customer orders remain at a similar level (these are customers who will wait for a car to be built to order). However,
Figure 3. Simulation results for service level
dealer sales are reduced to under 3 per cent, with the major source of sales transferred to the distribution centre and the production schedule. The change in dealer approach, from selling their own stock to meeting customer requirements, combined with improvements in information systems associated with the introduction of distribution centres, leads to much greater use of the ability to source customer orders directly from the production schedule. While postponement reduces lost sales it has little effect on the source of sales. There is a small increase in the proportion of customers satisfied from the production schedule because of the lower variation of specifications produced. Figure 4 shows the results for exact sales, i.e. sales where the customer receives the exact specification they want rather than accepting a car with a different specification, such as a different colour. Even at very low stock levels the alternative scenarios perform better on this measure than the result for the base scenario with a high stock level. However, postponement gives only a marginal improvement compared to the introduction of the distribution centre alone. In these scenarios the assembly plant is in the UK, which means cars can be sourced from production for those customers who are prepared to wait. An alternative scenario in which the assembly plant is, say, in the Far East, would show postponement having a greater impact. The model produces a range of additional performance measures such as the age profile of stock (older vehicles are likely to be sold at a discount, with significant impact on dealer margins) and on-time delivery (which may have an impact on customer satisfaction).
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Dealer stock (per cent) Distribution centre (per cent) Postponement (per cent) Visited dealer Dealer transfer Distribution centre Production schedule Customer order
10.1 50.6 0.0 1.3 38.0
1.2 1.4 33.3 27.2 37.0
1.5 0.8 31.7 30.0 36.1
Table I. Source of sales
Figure 4. Simulation results for exact sales
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Conclusions A benchmark exercise carried out by ICDP in the UK during 2002 showed that the source in the supply chain from which customers sold vehicles has changed very significantly. In 1992, 85 per cent of cars were sourced from dealer stock, either from the visited dealer or from a physical transfer from another dealer. In 2002 only 21 per cent came from this source, with distribution centre sourcing rising from 5 to 36 per cent of sales and build-to-order at the factory from 10 to 43 per cent. At the same time the service level (in the dealer perception) has increased from 80 to 95 per cent and the percentage of exact specification sales has increased from 31 to 78 per cent of purchasers. This has confirmed the original predictions of the simulation (Williams and Turner, 1995) concerning the general effect of the introduction of distribution centres, even to the extent that they would encourage build-to-order. Postponement or late configuration has also expanded for vehicles produced outside Europe and delivered over long distances into the marketplace. With widespread acceptance of the benefits of distribution centres, at least in the UK market, manufacturers are now turning their attention to more detailed aspects of supply chain design. The simulation model has been used to investigate questions such as: . How many distribution centres are needed to serve all European markets? . Where should they be located? . How should vehicles be distributed from assembly plant to distribution centre? . What decision rules should be used to allocate a new car to a customer in the situation where several cars will satisfy their requirements? The problem of high computational time has been reduced by faster computer hardware. The time for each run of the simulation using the scenarios above is around two minutes (on a PC with 2.8 GHz Pentium 4 processor), which is hardly excessive. In fact the time taken to collate and interpret results is now greater than the actual computation, particularly with large-scale projects which may require the simulation to be run several hundred times. The model required 1,686 items of data for these scenarios, approximately half of which are needed to define the product range. Past experience has been that manufacturers are able to obtain most of the data required from their internal information systems. The exception has been consumer behaviour, where ICDP has conducted research to obtain data for most European markets. The volume of data requires a rigorous approach to experimentation, with changes for each run kept to a minimum, in order to maintain a manageable number of decision variables. This paper has demonstrated that discrete-event simulation can be applied successfully to modelling complex supply chains, where the sources of complexity include product variety, demand seasonality and consumer behaviour. This brings the benefits normally associated with simulation: the ability to experiment and the generation of insight into the dynamic performance of complex systems. The development of a model which can be adjusted through changes in the data to represent alternative supply chain designs has been particularly valuable in permitting the model to study the performance of a wide range of scenarios.
References Armbruster, D., Marthaler, D. and Ringhofer, C. (2002), “Efficient simulation of supply chains”, Proceedings of the 2002 Winter Simulation Conference, pp. 1345-8.
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Christopher, M. (2000), “The agile supply chain – competing in volatile markets”, Industrial Marketing Management, Vol. 29, pp. 37-44. Christopher, M. and Towill, D.R. (2000), “Supply chain migration from lean and functional to agile and customised”, Supply Chain Management, Vol. 5 No. 4, pp. 206-13. Christopher, M. and Towill, D.R. (2001), “An integrated model for the design of agile supply chains”, International Journal of Physical Distribution & Logistics, Vol. 31 No. 4, pp. 235-46. Eppen, G.D. (1979), “Effects of centralization on expected costs in a multi-location newsboy problem”, Management Science, Vol. 25 No. 5, pp. 498-501. Feitzinger, E. and Lee, H. (1997), “Mass customisation at Hewlett-Packard: the power of postponement”, Harvard Business Review, Vol. 75 No. 1, pp. 116-21. Fisher, M.L., Hammond, J.H., Obermayer, W.R. and Raman, A. (1994), “Making supply match demand in an uncertain world”, Harvard Business Review, Vol. 71 No. 3, pp. 83-94. Forrester, J.W. (1958), “Industrial dynamics – a major break-through for decision-makers”, Harvard Business Review, Vol. 36 No. 4, pp. 37-66. Harbour, M., Brown, J., Jones, D., Wade, P. and Williams, G. (1993), Managing New Vehicle Supply and Demand in the 1990s, RMI Publications, London. Holweg, M. (2001), “Responsive order fulfilment – quantifying the key variables”, Proceedings of the EurOMA 8th International Conference, pp. 216-29. Holweg, M. and Pil, F.K. (2004), The Second Century – Reconnecting Customer and Value Chain through Build-to-Order, MIT Press, Cambridge, MA. Huang, S.H., Uppal, M. and Shi, J. (2002), “A product driven approach to manufacturing supply chain selection”, Supply Chain Management, Vol. 7 No. 4, pp. 189-99. Hung, W.Y., Kucherenko, S., Samsatli, N.J. and Shah, N. (2004), “A flexible and generic approach to dynamic modelling of supply chains”, Journal of the Operational Research Society, Vol. 55 No. 8, pp. 801-13. ICDP (1995), Supply and Stocking Systems in the UK Car Market, ICDP, Solihull. Jarmain, W.E. (1963), Problems in Industrial Dynamics, MIT Press, Cambridge, MA. Lee, Y.H., Cho, M.K., Kim, S.J. and Kim, Y.B. (2002), “Supply chain simulation with discrete-continuous combined modelling”, Computers & Industrial Engineering, Vol. 43, pp. 375-92. McCullen, P. and Towill, D. (2002), “Diagnosis and reduction of bullwhip in supply chains”, Supply Chain Management, Vol. 7 No. 3, pp. 164-79. Maister, D.H. (1976), “Centralisation of inventories and the Square Root Law”, International Journal of Physical Distribution, Vol. 6 No. 3, pp. 124-34. Mason-Jones, R., Naylor, B. and Towill, D.R. (2000), “Engineering the leagile supply chain”, International Journal of Agile Management Systems, Vol. 2 No. 1, pp. 54-61. Melao, N. and Pidd, M. (2003), “Use of business process simulation: a survey of practitioners”, Journal of the Operational Research Society, Vol. 54 No. 1, pp. 2-10. Naylor, J.B., Naim, M.M. and Berry, D. (1999), “Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain”, International Journal of Production Economics, Vol. 62, pp. 107-18.
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Persson, F. and Olhager, J. (2002), “Performance simulation of supply chain designs”, International Journal of Production Economics, Vol. 77, pp. 231-45. Pine, J.B. (1993), Mass Customisation, Harvard Business School Press, Boston, MA. Robinson, S. (2004), Simulation: The Practice of Model Development and Use, Wiley, Chichester. Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E. (2000), Designing and Managing the Supply Chain, McGraw-Hill, Boston, MA. Waller, B. (2002), “Order and registration volatility”, 3 Day Car Research Report M4, ICDP, Solihull. Whiteman, J., Williams, G. and Tongue, A. (2002), The Car You Want When You Want It, ICDP, Solihull. Williams, G. and Turner, K. (1995), “Simulating car supply system improvements”, ICDP Research Paper 8/95, ICDP, Solihull.
Conference announcement Sixth International CINet Conference: Continuous Innovation – (Ways of) Making Things Happen Brighton, United Kingdom, 4-7 September 2005 Organising committee Professor John Bessant, Cranfield University, UK. Dr David Frances, CENTRIM, University of Brighton, UK.
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Academic advisory board Professor Harry Boer, Aalborg University, Denmark. Professor Ross Chapman, University of Western Sydney, Australia. Dr Mariano Corso, Politecnico di Milano, Italy. Professor Paul Hyland, Central Queensland University, Australia. Professor Mats Magnusson, Chalmers University, Sweden. Professor Howard Rush, Centrim, University of Brighton, UK. The Sixth CINet Conference Continuous innovation is the ongoing process of initiating, developing, operating and improving new and existing configurations of products, market approaches, processes, technologies and competencies, organisation and management systems. As organisations strive to achieve a synergistic balance between short-term oriented, operationally-effective exploitation strategies and longer-term, flexibility-oriented exploration strategies, the rapid growth of the global knowledge economy has placed learning at the centre of this critical balance. Who should attend? Practitioners, academics and consultants involved in managing or studying innovation and change are invited to submit papers relevant to the conference theme and the various tracks, or simply register for conference attendance to gain access to leading research outcomes in this key area. PhD Workshop In addition to the main conference there will be a PhD Workshop designed for doctoral students at all stages of their dissertation. The workshop provides PhD students with a forum for presenting and discussing their research with fellow PhD students and experienced researchers in the field. Abstract submission Before: 1 February 2005 More and regularly updated information can be found on the conference website. Please accept our apologies for any possible cross posting of this announcement. Journal of Manufacturing Technology Management Vol. 16 No. 4, 2005 p. 459 q Emerald Group Publishing Limited 1741-038X
