Engineering for Business Theory and Cases

Engineering for Business Theory and Cases

Colin O. Benjamin

U N I V E R S I T Y P R E S S O F A M E R I C A ®, I N C .

Lanham • Boulder • New York • Toronto • Plymouth, UK

Copyright © 2009 by University Press of America,® Inc. 4501 Forbes Boulevard Suite 200 Lanham, Maryland 20706 UPA Acquisitions Department (301) 459-3366 Estover Road Plymouth PL6 7PY United Kingdom All rights reserved Printed in the United States of America British Library Cataloging in Publication Information Available Library of Congress Control Number: 2006933987 ISBN-13: 978-0-7618-3552-3 (paperback : alk. paper) ISBN-10: 0-7618-3552-0 (paperback : alk. paper) eISBN-13: 978-0-7618-4189-0 eISBN-10: 0-7618-4189-X

⬁ ™ The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials, ANSI Z39.48—1992

Contents

Illustrations

vii

Preface

xv

Acknowledgements

xvii

Case Study Notes

xix

1 Engineering for Business—An Introduction 1.1 Introduction 1.2 Engineering/Business Interfaces 1.3 Engineering/Business Integration in Academia 1.4 Case Study—School of Business and Industry, FAMU 1.5 Discussion and Conclusion 1.6 References 2

Financial and Economic Evaluation 2.1 Overview 2.2 Evaluation Techniques 2.3 A Project Appraisal Methodology 2.4 Financial Engineering 2.5 Conclusion 2.6 References 2.7 Problems/Cases 2.8 Case Study: Soap Products Limited

3 Multi-Criteria Decision Making 3.1 Introduction 3.2 Multi-Criteria Decision Making iii

1

20

53

iv

Contents

3.3 Multi-Criteria Decision Models 3.4 Other MCDM Techniques 3.5 Conclusion 3.6 References 3.7 Case Study: Planning Engineering Services at the Houston Chronicle 4 Project Management 4.1 The Evolution of Project Management 4.2 Project Planning, Scheduling and Control Techniques 4.3 Network Analysis 4.4 Project Management Software 4.5 Applications in Business 4.6 References 4.7 Worked Example: Pump Installation Project 4.8 Assignment – The CIM Laboratory Facility 4.9 Case Study: Developing an AI-Based Membership Retention System at ALA

82

5 Applications Software for Business 5.1 Introduction 5.2 Types of Application Software 5.3 A Methodology for Selecting Applications Software 5.4 Applications in Business 5.5 References 5.6 Assignment—Selection Applications Software 5.7 Case Study: Selecting a GIS System at the Bureau of Mine Reclamation

125

6

Technology Commercialization 6.1 Introduction 6.2 Proposed Evaluation Framework 6.3 Applications in Business 6.4 Software Support for Business Planning 6.5 Discussion and Conclusion 6.6 References 6.7 Assignment-Commercializing New Technologies 6.8 Case Study-Evaluating a NASA Technology 6.9 References

144

7

Management Science Techniques 7.1 Introduction 7.2 Mathematical Programming

172

Contents

v

7.3 Simulation Modeling 7.4 Other Techniques 7.5 Applications in Business 7.6 References 7.7 Case Study—Warehouse Consolidation at the Coca Cola Company 8

Supply Chain Management 8.1 Introduction 8.2 SCM Academic Programs 8.3 Professional SCM Organizations 8.4 Challenges for SCM Professionals 8.5 Discussion and Conclusion 8.6 References 8.7 Case Study-Global Sourcing at Otis Elevators

9 Facilities Planning 9.1 Introduction 9.2 Systematic Methods 9.3 Facilities Planning Software 9.4 Facility Location Models 9.5 Applications in Business 9.6 References 9.7 Assignments 9.8 Case Study—Mid-West Furniture Company 10

Contemporary Techniques 10.1 Introduction 10.2 Techniques 10.3 Applications in Business 10.4 Discussion and Conclusion 10.5 References 10.6 Case Study—Developing a Business Plan for BioMed Inc.

Index

195

217

253

267

Illustrations

TABLES 1.1. 1.2. 1.3. 2.1. 2.2.

2.3. 2.4. 2.5. 2.6. 2.7. 2.8. 3.1. 3.2. 3.3. 3.4. 3.5.

Typical Contribution of Traditional Engineering Disciplines Engineering/Business Key Internal Interfaces Engineering Tools and Techniques Identified for Incorporation into a Business Curriculum Summary of Cash Flows for New Conveyor Project Variation of Net Present Value at 10% per annum with Changes in Acquisition Costs, Operations and Maintenance Costs, and Labor Savings Variation of Internal Rate of Return (IRR) with Changes in Net Cash Flows Results of Scenario Analysis for New Conveyor Project Results of Simulation Modeling Analysis for New Conveyor Project Online Educational Resources for Financial Engineering Sales Forecast (Years 1 – 10) Proposed Production Program Application of a Scoring Model to Evaluate Candidates for Third Party Logistics Application of a Scoring Model to Evaluate Engineering Design Alternatives Comparison of Multi-Criteria Group Decision-Making Techniques Product Structure – Houston Chronicle Professional Staff – Engineering Services Department vii

2 4 17 23

29 30 31 32 37 43 44 58 59 67 71 73

viii

3.6. 4.1. 4.2. 4.3. 4.4. 4.5. 4.6. 4.7. 4.8. 4.9. 5.1. 5.2. 5.3. 6.1. 6.2. 6.3. 6.4. 6.5. 6.6. 7.1. 7.2. 7.3. 8.1. 8.2. 8.3. 8.4. 8.5. 8.6. 8.7. 8.8. 8.9. 8.10.

Illustrations

ABC Classification of Engineering Tools and Techniques Comparison of Project Management Bodies of Knowledge Techniques for Planning, Scheduling and Controlling Projects Job List for Pump Installation Project Early and Late Activity Start and Finish Times for Pump Installation Project Typical Project Management Applications in Business Planning Data for Pump Installation Project Job List for CIM Laboratory Project Job List for Pilot Project Resource Requirements for Pilot Project Sample of Commercially Available Applications Software Multi-Criteria Decision Model for GIS Software Evaluation Software Functionality Matrix Decision Tools & Techniques used in Technology Evaluation Framework List of Business Planning Software and Vendors Summary of High Potential Applications of Ice Gauge Technology Scoring Model for Comparing Competing Technologies Parameters for Modeling Alternative Licensing Agreements Summary of Cash Flows for Commercializing New Technology Summary of Results Obtained in Alternative Planning Scenarios Performance Indices under Various Planning Scenarios Variation of Annual Revenues with FTE (Credit Hours per Semester) Availability of SCM Programs and Institutions in the USA Regional Distribution of SCM Programs in the USA Distribution by State of Institutions Offering SCM Programs Sample of International SCM Programs Certification and Core Competencies Required by Professional P&SM Organizations Core Competencies Required by P&SM Professionals Definition of Key Knowledge Areas for P&SM Professionals Profiles of Partners in Industry/Academia Collaboration in CBE Simulation Planning Information for the Global Sourcing Project with an Online Auction Planning Information used for Conducting the CBE Simulation

77 84 85 88 89 97 100 113 118 119 127 141 141 148 150 158 166 167 170 177 178 178 197 198 199 200 201 202 204 208 209 211

Illustrations

8.11. 9.1. 10.1. 10.2. 10.3. 10.4.

Results of the CBE Simulation Model Sections Sample of Commercially Available Data Mining Software and EXCEL “add-ins” Statistical Software with Data Mining Capabilities Model Parameters Confusion Matrix Summarizing Results of the Predictive Model

ix

211 241 255 255 263 264

FIGURES 1.1. 1.2. 1.3. 1.4. 1.5. 1.6. 2.1. 2.2. 2.3.

2.4. 2.5. 2.6. 2.7. 2.8. 2.9. 2.10. 2.11. 2.12. 2.13. 2.14. 2.15.

Interfaces Between Engineering and Other Business Interfaces The Integrated 3-Phase QFD Curriculum Planning Framework QFD Chart—Course Planning Phase QFD Chart—Course Design Phase QFD Chart—Course Implementation Phase Results of Sensitivity Analysis Graphical Illustration of Payback Period and Discounted Payback Period Graphical Estimation of the Internal Rate of Return (IRR) Spider Plot Showing Variation of Net Present Value (NPV) at 10 % per annum with Changes in Acquisition Costs, Operations and Maintenance Costs, and Labor Savings Distribution of Net Present Value (NPV) at 10 % per annum for New Conveyor Project Distribution of International Rate of Return (IRR) for New Conveyor Project Typical Life Cycle Costing Model Flowchart for Capital Equipment Selection Methodology Schematic of Proposed Bar-Coding System Schematic of Sucrose Surfactant Process Proposed Floor Plan for Soap and Detergent Plant Proposed Organization Chart for Soap and Detergent Factory Proposed Implementation Schedule for Soap and Detergent Factory Total Initial Investment Costs Proposed Project Financing Annual Production Costs

3 8 10 11 13 16 24 26

29 32 33 34 35 40 46 47 48 49 50 51 52

x

3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 3.7. 3.8. 4.1. 4.2. 4.3. 4.4. 4.5. 4.6. 4.7. 4.8. 4.9. 4.10. 4.11. 4.12. 4.13. 4.14. 4.15. 4.16. 4.17. 4.18. 4.19. 4.20. 4.21. 4.22.

Illustrations

Decision Hierarchy for Accounting Software Selection Courses for Proposed SCM Undergraduate Program Courses for Proposed SCM Graduate Program House of Quality for Planning SCM Program Development— Undergraduate Program House of Quality for Planning SCM Program Development— Graduate Program QFD Chart for Planning Engineering Services-Phase #1 Baseline Model Results of Sensitivity Analysis Partially Completed QFD Chart for Phase #2 Evolution of Project Management Activity on the Node (AON) Network Diagram for Pump Installation Project Activity on the Arrow (AOA) Network Diagram for Pump Installation Project Gantt Chart for Pump Installation Project Using Visio Typical Resource Loading Chart Resource Leveling with a Resource Constraint of Two Analysts Time/Cost Trade-off – Activity Level Time/Cost Trade-off – Project Level LP Formulation of Time/Cost Trade-off Price/Functionality Comparison of Project Management Software Alternatives Network Diagram for Pump Installation Project Using Visio Gantt Chart for Pump Installation Project Resource Aggregation—Skilled Labor Resource Aggregation—Unskilled Labor Resource Aggregation—Skilled and Unskilled workers Budget for Pump Installation Project Set Project Start Date Resource Sheet Input Task Information Task Information Network Diagram for Pump Installation Project Using Microsoft Project Gantt Chart for Pump Installation Project Using Microsoft Project

60 63 63 68 69 78 79 80 83 88 89 91 92 92 93 94 95 96 100 101 102 103 104 105 106 106 107 109 109 110

Illustrations

4.23. 4.24. 4.25. 4.26. 4.27. 4.28. 4.29. 4.30. 4.31. 4.32. 5.1. 5.2. 5.3. 5.4. 5.5. 5.6. 5.7. 5.8. 6.1. 6.2. 6.3. 6.4. 6.5. 6.6. 6.7. 6.8. 6.9. 6.10. 6.11. 6.12. 6.13.

Budget for Pump Installation Project Using Microsoft Project Resource Histogram for Pump Installation Project Using Microsoft Project Project Budgets Under Three Different Scenarios Gantt Chart After Leveling for Pump Installation Project Using Microsoft Project Summary of Alternative Proposals Conceptual Flow Chart showing Artificial Neural Network (ANN)/ES Integration Diagram of Selected Artificial Neural Network (ANN) Architecture Overall Structure of Knowledge Base for Expert System Module Schematic of Data Analysis Procedure for Prototyping STG/AAA System Spiral Modeling Approach to System Development Software Selection Methodology Mission Statement – Bureau of Mine Reclamation Economic Model for Evaluating GIS Investment Baseline Economic Model for GIS Acquisition Project Network for GIS Study Gantt Chart for GIS Study Data Flow Diagram Price/Performance Comparison for GIS Software Information Flow in Technology Commercialization Process Proposed Methodology for a Technology Commercialization Study Network Diagram for NASA/SBI Technology Commercialization Project Gantt Chart for NASA/SBI Technology Commercialization Study Methodology for Market Analysis Methodology for Technology Assessment Technology Profile – U.S. Patent #4,766,369 Technology Profile – U.S. Patent #5,095,754 Technology Profile – U.S. Patent #5,354,015 Technology Profile – U.S. Patent #5,551,288 Technology Profile – U.S. Patent #5,821,826 Manufacturing and Technology Development Costs Financial Models for Evaluating the Feasibility of Commercializing New Technology

xi

110 111 111 112 116 121 122 122 123 124 129 132 133 134 135 136 138 142 145 147 155 156 157 160 161 162 163 164 165 168 169

xii

7.1. 7.2. 7.3. 7.4. 7.5. 7.6. 7.7. 7.8. 7.9. 7.10. 7.11. 7.12. 7.13. 7.14. 8.1. 8.2. 8.3. 8.4. 8.5. 8.6. 8.7. 8.8. 9.1. 9.2. 9.3. 9.4. 9.5. 9.6. 9.7. 9.8. 9.9. 9.10. 9.11.

Illustrations

Types of Mathematical Programming Models Variation of Prices for LP Software Operating Systems Available for LP Software Continuum of Simulation Modeling Techniques Current Process Flow Map of Service Agent and Supplier Locations Supply Chain for the iFountain System Proposed Supply Chain for Legacy Equipment Proposed Integrated Supply Chain Service Agent and Supplier Locations and Volume Service Agent and Supplier Locations Volume (Actual and Projected) Equipment Group Budget Overview Transportation Averages 2000 Geographic Distribution of SCM Programs in the USA Industry/Academia Collaboration in Developing the CBE Simulation Network Diagram for the Global Sourcing Project Using an Online Auction Gantt Chart for the Global Sourcing Project Using an Online Auction Budget for the Global Sourcing Project Using an Online Auction Resource Histogram for Analysts for Global Sourcing Project Using an Online Auction Gantt Chart for Implementing CBE Simulation Buyer/Supplier Network for CBE Simulation Sample of Facility Planning Software Classification of Facility Location Factors Chart of Country Ranks and Grades Chart of Country Scores Phases of Integrated Facilities Planning Initial Plant Layout SLP Pattern of Procedures Improved Plant Layout List of Machines and Work Areas Typical Daily Production Routing Charts for Sample Products (Figures 9.11, 12, 13, and 14): Operations Process Chart for Radius Hardwood Table Top (R-2600) and Operations Process Chart for Cube Table (C-1000) 1 of 2

174 175 175 180 186 187 189 190 191 192 193 193 194 194 198 207 213 213 214 214 215 215 221 223 229 230 235 237 238 239 243 244

245

Illustrations

9.12. 9.13. 9.14. 9.15. 10.1.

Operations Process Chart for Cube Table (C-1000) 1 of 2 Departmental Areas Relationship Chart Relationship Diagram Cumulative Lift Plot

xiii

249 250 251 252 264

Preface

This book is being developed to fill a gap in the educational material currently available to introduce industrial engineering and engineering management concepts into business curricula. It contains course notes and case studies developed and tested on senior undergraduate courses in engineering and business and graduate-level classes in Engineering Management, Industrial Engineering & Management, and Technology Management. The material has been used with great success in the FAMU School of Business and Industry and the FAMU/FSU College of Engineering. The book provides a survey of the more relevant quantitative tools and techniques used to facilitate decision-making in business and uses case studies to illustrate their application. Where appropriate, the readers are provided with frameworks to enable application of the techniques covered and directed to commercially available software developed to facilitate the deployment of these tools and techniques. The book focuses on the more robust techniques which can be applied in several functional areas in organizations. Traditional industrial engineering and engineering management techniques related to Engineering Economy, Multi-Criteria Decision Making, Project Management, Management Science, and Facilities Planning are covered. These are complemented by a review of more topical areas such as Applications Software for Business, Technology Commercialization, and Supply Chain Management. In all areas, the emphasis is on integrating theory and practice through the use of case studies based on projects conducted in a wide range of industry settings. This book’s primary contribution to scholarship is the framework it provides for the explicit integration of engineering tools and techniques into a business curriculum. In the first chapter, the authors describe a modified xv

xvi

Preface

Quality Function Deployment process for curriculum planning and use this framework to develop an integrated suite of Engineering for Business courses for a business curriculum. The case studies are rich in data and provide great opportunities for students to apply the techniques covered and to propose innovative solutions to open-ended project assignments. Colin O. Benjamin, CEng, MBA, PhD Eminent Scholar Anheuser-Busch Professor of Engineering Management School of Business & Industry, Florida A&M University

Acknowledgments

This book, which draws on the author’s teaching experiences at the University of the West Indies, the University of Missouri-Rolla, and the Florida A&M University, School of Business & Industry, was developed to fill a gap in the educational material currently available to introduce industrial engineering and engineering management concepts into business curricula. Special thanks to: • Dr. Sybil Mobley—Dean Emerita, SBI, who pioneered the concept of integrating engineering and business and directed its implementation in the SBI MBA curriculum. • Claire Benjamin—Vice President and Chief Financial Officer, Boys & Girls Clubs of the Big Bend, who reviewed the manuscript and provided invaluable feedback and editorial services. • Professional colleagues in industry and academia who provided background data for several case studies. • Students at UWI, UM-Rolla, FAMU School of Business and Industry, FAMU/FSU College of Engineering who participated in the development and testing of the material. Finally, I must acknowledge the efforts of several talented SBI students who helped in compiling the manuscript. Despite the considerable demands on their time, they displayed an impressive commitment to the goal of completing a quality manuscript in a timely manner. Colin O. Benjamin, CEng, MBA, PhD Eminent Scholar Anheuser-Busch Professor of Engineering Management School of Business & Industry, Florida A&M University xvii

Case Study Notes

The following case studies are based on field study and are intended to provide a basis for class discussion rather than to illustrate either effective or ineffective handling of an administrative situation. 2.8 Case Study: Soap Products Limited Prepared by Dr. Colin O. Benjamin, Professor of Engineering Management, School of Business and Industry, Florida A&M University 3.1 Case Study: Planning Engineering Services at the Houston Chronicle Prepared by Dr. Colin O. Benjamin in collaboration with William C. Hawn, Engineering Services, Houston Chronicle 4.9 Case Study – Developing an AI-Based Membership Retention System at ALA Prepared by Dr. Colin O. Benjamin and Vivian Rosandich, Graduate Student, Department of Engineering Management, University of Missouri-Rolla. 5.7 Case Study: Selecting a GIS System at the Bureau of Mine Reclamation Prepared by Dr. Colin O. Benjamin 6.8 Case Study-Evaluating a NASA Technology Prepared by Dr. Colin O. Benjamin and MBA students Brian Price and Albert Thompson, School of Business and Industry, Florida A&M University. 7.7 Case Study—Warehouse Consolidation at Coca-Cola Company Prepared by Dorian Lee, Analyst, Coca Cola and Dr. Colin O. Benjamin 8.7 Case Study-Global Sourcing at Otis Elevators Prepared by Dr. Colin O. Benjamin in collaboration with Brendan McLaughlin, Account Manager, Ariba, and Mike Charles, Global Procurement Manager, Otis Elevators. xix

xx

Case Study Notes

9.8 Case Study-The Mid-West Furniture Company Prepared by Dr. Colin O. Benjamin and Debra Hunke, undergraduate senior in the Department of Engineering Management, University of Missouri-Rolla 10.6 Case Study – Developing a Business Plan for BioMed Inc. Prepared by Dr. Colin O. Benjamin and Zaheer Benjamin, MBA student, MIT Sloan School of Business.

Chapter One

Engineering for Business—An Introduction

1.1 1.2 1.3 1.4

Introduction Engineering/Business Interfaces Engineering/Business Integration in Academia Case Study—School of Business and Industry, FAMU 1.4.1 The Integrated Planning Process 1.4.2 The Proposed QFD Framework 1.4.3 Phase #1-Course Planning 1.4.4 Phase #2-Course Design 1.4.5 Phase #3-Course Implementation 1.4.6 Analysis of Results 1.4.6.1 Phase #1: Course Planning 1.4.6.2 Phase #2: Course Design 1.4.6.3 Phase #3: Course Implementation 1.4.6.4 Sensitivity Analysis 1.5 Discussion and Conclusion 1.6 References

1.1 INTRODUCTION Throughout history, engineering has been the basis for sustained wealth creation in all countries by ensuring the provision of critical infrastructure such as roads required by communities and fostering the development of a steady stream of innovative consumer products and services. Table 1.1 below lists the typical contributions of engineers in the more traditional disciplines. However, newer fields such as computer engineering, biomedical engineering 1

2 Table 1.1.

Chapter One Typical Contribution of Traditional Engineering Disciplines

and industrial engineering offer exciting potential for addressing the many quality of life issues deemed important by today’s society. While engineering has been characterized by structured approaches to problem solving, rapid adoption of analytical models, and eagerness to embrace new technologies, successful innovation and development of products and services have only been achieved through collaboration. To achieve this, several universities have launched initiatives to infuse engineering concepts into other disciplines (Panitz 1996). These have ranged from developing engineering electives, offering a minor or certificate in engineering, or introducing an undergraduate degree that promotes cross-fertilization.

1.2 ENGINEERING/BUSINESS INTERFACES Figure 1.1 provides a graphical illustration of the critical interfaces that exist between the Engineering function and the other functions in a company. Table 1.2 provides a brief description of the nature of the interaction between the professionals at the various interfaces. As illustrated in the Table, the Engineering function plays a key role in all aspects of a company’s operations. This role includes participating in new product development and testing, identifying cost savings opportunities in company operations, design and installation of production facilities, recruitment, training, and retention of em-

Engineering for Business—An Introduction

Figure 1.1.

3

Interfaces Between Engineering and Other Business Interfaces

ployees in technical disciplines, and participating in quality and safety programs. The examples below illustrate the collaboration required between Engineering and other functions to achieve business success. Case #1: Developing a new Production Facility (Benjamin 1989) A chemical company in the Caribbean is seeking to develop a range of soap and detergents utilizing the region’s indigenous raw materials. The new plant proposed would be designed to satisfy the needs of the regional markets in the Caribbean and would generate employment opportunities. In developing the industrial feasibility study required to assess this investment opportunity and secure resources to launch this project, the engineering function worked closely with the other business functions. Areas of collaboration included: Marketing—to forecast market potential and market share, develop market forecasts and prepare a supporting marketing plan. Architecture—to select a location for the new plant and design new production facilities. Human Resources—to recruit, train, and retain personnel required in all phases of the establishment and operation of the new venture.

4 Table 1.2.

Chapter One Engineering/Business Key Internal Interfaces

Operations—to select the production technology, develop production processes and schedules to meet forecast market requirements. Research and Development—to develop and test alternative formulations for the proposed product mix. Accounting—to conduct cost analyses to compute the costs of manufacturing and distributing products. Legal—to negotiate contracts for licensing the production technologies identified for use in the new venture. Finance—to secure financing for initial investment and working capital.

Engineering for Business—An Introduction

5

Case #2: Implementing a new ERP system (Austin et al 2002) In June 1994, Cisco Systems decided to implement a new Enterprise Resource Planning (ERP) Project using Oracle as its software partner and Andersen Consulting as its systems consultant. The company opted for the “big bang” approach which used an aggressive implementation plan of the $10 million project over a 9-month period. Successful implementation of this project required ongoing collaboration between the computer engineers and the other business functions. Examples of this collaboration were: Marketing—to develop ERP systems requirements to meet the needs of the customer. Human Resources—to recruit, train, and retain personnel required in all phases of the design, testing, operation and maintenance of the new ERP system. Operations—to conduct acceptance testing for the software modules being implemented and operate the new ERP system to realize forecast operating efficiencies. Information Technology—to develop strategies for designing, testing and implementing the modules in the new ERP system. Legal—to negotiate contracts with the consultants and software vendors for the design and satisfactory implementation of the new ERP system. Finance—to secure Board approval for the financial resources required to implement the ERP system. Although problems were inevitably encountered during the implementation of this large, complex software project, the close collaboration enabled Cisco to implement the new ERP system successfully and enable its use to support Cisco’s phenomenal growth in the late 1990s. Case #3: Developing an Intelligent Baby Monitor (Benjamin et al 2004) A small biomedical engineering start-up company was engaged in the development and marketing of a home and self-care health system which incorporated an ultra intelligent, vital signs-real time monitor to operate as a wireless baby monitor. This system would enable the user to interpret readings of an infant’s vital signs and heart rate when the monitor was in use and, as a preventive measure, track and notify guardians in the event of an emergency. One application of the technology was to reduce the incidence of Sudden Infant Death Syndrome (SIDS), commonly called crib death. Developing a business plan to provide a roadmap to launch the business required close collaboration between the Engineering and other business functions as listed below. Legal-to file provisional and non-provisional patent applications to protect the intellectual property associated with the new product. Marketing-to assess the market potential of the home and self-care health system developed, by convening an expert panel of medical professionals with

6

Chapter One

extensive practical experience in medicine and/or in medical education and conducting complementary consumer market research studies to identify the profile of likely purchasers of the system. Information Technology—to provide an objective comparison of the proposed technology with competing patents by commissioning a Technology Panel of engineers with expertise in Software Applications Engineering, Information Technology, and Integrated Product Development. Operations—to design the supply chain to ensure timely delivery of the product to consumers in a cost-effective manner. Accounting—to conduct cost analysis and determine the target cost and the break-even point of the production system to be established to manufacture the new product. Finance—to estimate periodic cash flows and calculate investment indices to measure the profitability of the new venture. The results suggested that the proposed system may have widespread application as a monitoring device for those infants who are “at-risk” for sudden death (including SIDS), which may follow breathing pattern irregularities and heart rate fluctuations during sleep. These findings provided important feedback to the management team in their efforts to launch a successful business venture. These examples highlight the vital role played by Engineering in all phases of the product and project life cycles. Progressive business professionals aspiring to be tomorrow’s industry leaders need to be informed of these engineering tools and techniques and should enthusiastically examine their application in enhancing decision-making in all functional areas of business.

1.3 ENGINEERING/BUSINESS INTEGRATION IN ACADEMIA One particular area of collaboration has been between Schools of Engineering and Business. One mechanism has been Business Plan competitions (Seymour 2002) which provide significant financial incentives for students in Engineering and Business to collaborate in developing technology-based business ventures. A good example is the MOOT Corp global business plan competition (Cadenhead 2002) hosted by the University of Texas-Austin which has grown exponentially over the past twenty five years and now provides an excellent forum for student teams from all continents to develop and present business plans to prospective investors and compete for significant cash prizes. Another approach has been to develop an integrated suite of Engineering for Business courses and incorporate it into the core business curriculum. This approach, adopted by the School of Business and Industry at Florida A&M University, will be described in the following section.

Engineering for Business—An Introduction

7

1.4 CASE STUDY—SCHOOL OF BUSINESS AND INDUSTRY, FAMU 1.4.1 The Integrated Planning Process The School of Business and Industry (SBI) at Florida A&M University (FAMU) was faced with the challenge of developing a suite of Engineering for Business courses for integration into its business curriculum. Among the benefits envisaged to be reaped by the students were an increased awareness of engineering and technology fundamentals, improved teamwork skills, and enhanced analytical and logical thinking. To realize these benefits, careful attention must be given to curriculum planning to maintain the quality and effectiveness of this very innovative program. Quality Function Deployment (QFD) has been used to provide a structured approach for planning in academia in areas such as revising mechanical engineering curriculum (Ermer 1995), research planning (Chen and Bullington, 1993), course design (Burger 1994), planning enhancements to computer laboratories (Benjamin et al, 1997), improving the quality of teaching (Lam and Zhao, 1998), and reviewing academic programs (Pitman 1995). QFD has also been widely used for curriculum planning in international educational environments. In the United Kingdom, QFD was utilized to build a degree program in the Department of Vision Sciences at Aston University (Clayton 1995), and designing an MSc degree in Quality Management at the University of Portsmouth (Seow and Moody, 1996). In Sweden, a QFD process was used to develop a Mechanical Engineering Program which was more responsive to changes in industry (Nilsson et al, 1995). In the case described in the following section, a three-phase modified QFD process was used to provide a structured, integrated approach to curriculum planning.

1.4.2 The Proposed QFD Framework The three-phase modified QFD framework used to provide a structured, integrated approach to curriculum planning proceeded in the following phases: • Phase #1: Course Planning-which prioritized the teaching methodologies best suited to deliver critical competencies to students; • Phase #2: Course Design-which identified and prioritized the engineering tools and techniques to be incorporated into the curriculum; • Phase #3: Course Implementation-which assigned the preferred engineering tools and techniques to specific Engineering for Business courses.

8

Chapter One

Figure 1.2 summarizes the integrated curriculum planning framework adopted using QFD. In all phases, the following seven-step procedure was adopted:Step #1-Define the customer-the students, faculty and industry stakeholders in the process Step #2-Identify the customer wants-the WHATs, and establish their importance Step #3-Identify the technical characteristics of the program—the HOWs Step #4-Map the HOWs into the WHATs using a rating scale of 1-3-9 (1— weak; 3—medium; 9—strong) to indicate the relationship between each HOW and WHAT Step #5-Develop a House of Quality (HOQ)-a spreadsheet-based model of the HOQ to facilitate computation of the row and column totals and the ranking of the HOWs under consideration. Step #6—Conduct sensitivity analysis to assess the robustness of the model. Step #7—Interpret results and provide recommendations 1.4.3 Phase #1-Course Planning The steps adopted in the first phase of this QFD process for planning course development were as follows: Step #1-Define the customer: In this case, the customers were the students enrolled in SBI’s innovative five-year professional MBA program. Step #2-Identify the critical competencies to be delivered to the students and establish the importance of each critical competency-the WHATs: A survey of

Figure 1.2.

The Integrated 3-Phase QFD Curriculum Planning Framework

Engineering for Business—An Introduction

9

SBI faculty identified ten critical competencies. Responses from the faculty survey were also used to gauge the relative importance of each WHAT. Step #3-Identify possible teaching methodologies for the program—the HOWs. After a critical review of teaching methodologies (Joyce and Weil, 1986), the team identified twelve methodologies most applicable to SBI’s program. These were divided into four categories based on the Effort (individual/group) and Participation (low/high) required from students. Step #4-Map the HOWs into the WHATs: Using a faculty member as a group facilitator, the student team mapped the HOWs into the WHATs by assigning ratings on a 1-3-9 scale (1—weak; 3—medium; 9—strong) to indicate the relationship between each HOW and WHAT. Step #5-Develop a House of Quality. The SBI student team constructed a spreadsheet-based model of the HOQ. This facilitated computation of the row and column totals and the ranking of the teaching methodologies under consideration. The following formulas were used: For column j, n

Absolute Score = ∑ (wi cij)

(1

i=1

where i = row number n = total number of rows/WHATs wi = weight assigned to the WHAT in Row i cij = rating assigned when mapping Row i and Column j Relative Score (%) = [Absolute Score/Total Score] x 100 (2 Step #6—Conduct sensitivity analysis to assess the robustness of the model. Step #7—Interpret results and provide recommendations The QFD Chart shown in Figure 1.3 summarizes the first phase of this integrated planning process. 1.4.4 Phase #2-Course Design The steps adopted in the Course Design phase of this planning methodology were as follows: Step #1-Define the customer: In this case, the customers were the students enrolled in SBI’s innovative five-year professional MBA program. Step #2-Identify the critical teaching methodologies to be employed and establish the relative importance of each teaching methodology-the WHATs: The output from the previous phase of the QFD process, the Course Planning phase, provided this information. Step #3-Identify relevant engineering tools and techniques for incorporation in the program—the HOWs. Using the nominal group technique, the SBI

10

Figure 1.3.

Chapter One

QFD Chart – Course Planning Phase

faculty who were recruited to develop the Engineering for Business curriculum identified seventeen engineering tools and techniques applicable to our MBA program. As shown in Table 1.3, these were divided into four categories viz. Industrial Engineering, Artificial Intelligence, Management Science, and Others. Step #4-Map the HOWs into the WHATs With the aid of a student facilitator, the SBI engineering faculty who participated in Step #3 mapped the HOWs into the WHATs by assigning ratings on a 1-3-9 scale (1—weak; 3—medium; 9—strong) to indicate the relationship between each HOW and WHAT.

Engineering for Business—An Introduction

11

Step #5-Develop a House of Quality. Our team constructed a spreadsheet-based model to facilitate computation of the row and column totals and the ranking of the teaching methodologies under consideration. Step #6—Conduct sensitivity analysis to assess the robustness of the model Step #7—Interpret results and provide recommendations The QFD Course Design matrix shown in Figure 1.4 summarizes the second phase of this integrated three-phase QFD curriculum planning methodology. 1.4.5 Phase #3-Course Implementation The steps adopted in Phase #3: Course Implementation of the QFD process for curriculum planning were as follows: Step #1-Define the customer: In this case, the customers were the students enrolled in SBI’s innovative five-year professional MBA program.

Figure 1.4.

QFD Chart – Course Design Phase

12

Chapter One

Step #2-Identify the relevant engineering tools and techniques for incorporation in the program and establish the relative importance of these tools and techniques-the WHATs: The output from the previous phase of the QFD process, the Course Design phase, provided this information. Step #3-Identify Engineering for Business courses that are candidates for implementation—the HOWs. Following a brainstorming session among the engineering faculty in SBI, the following four new courses were proposed: • • • •

Fundamental Engineering Concepts Management Engineering I Management Engineering II Management of Technology

Step #4-Map the HOWs into the WHATs The engineering faculty team mapped the HOWs into the WHATs by assigning ratings on a 1-3-9 scale (1—weak; 3—medium; 9—strong) to indicate the relationship between each HOW and WHAT. Several heuristics were employed to facilitate aggregation of the mappings of the individual engineering faculty team members. Step #5-Develop a House of Quality Our team constructed a spreadsheet-based model to facilitate computation of the row and column totals and the ranking of the courses under consideration. The QFD chart shown as Figure 1.5 summarizes the results of this final phase of the planning process. Step #6—Conduct sensitivity analysis to assess the robustness of the model Step #7—Interpret results and provide recommendation 1.4.6 Analysis of Results 1.4.6.1 Phase #1: Course Planning Examination of the results summarized in the QFD chart in Figure 1.3 revealed the following: Critical Competencies Although all ten competencies were important, faculty assigned the greatest importance to analytical ability, managerial skills, and leadership. On a five-point weighting scale, these competencies received weights ranging from 4.5 to 5.0. Written communication, responsibility, dependability, and accountability were also quite important. These received weights of 3.6 to 4.0. The lowest importance was accorded teamwork, critical thinking, and oral communication. These received weights ranging from 3.0 to 3.4.

Engineering for Business—An Introduction

Figure 1.5.

13

QFD Chart – Course Implementation Phase

These results suggest that the SBI faculty surveyed demonstrated a greater preference for those competencies that were more readily correlated with student performance in the short term in the traditional academic setting. Competencies that would contribute to a student’s long-term success in the work environment but would have limited impact on immediate academic performance (e.g. oral communication, teamwork) were regarded as being less important. Teaching Methodologies The scores obtained by the twelve teaching methodologies examined ranged from a low of 1.32% to a high of 16.17%. The highest scores (13.77% to 16.17%) were obtained by those teaching methodologies requiring teamwork and high levels of participation e.g. group projects, group presentations, computer simulation, and role-playing. On the other hand, the teaching methodologies that provided limited opportunities for active student involvement (e.g. demonstrations, lectures, guest speakers, and computer laboratories) received much lower scores. These scores ranged from 1.36% to 3.04%.

14

Chapter One

The teaching methodologies, which emerged as being moderately important, were case studies, individual projects, individual presentations, and class discussions. Their scores ranged from 6.11% to 8.21%. 1.4.6.2 Phase #2: Course Design Examination of the results summarized in the QFD chart in Figure 1.4 would reveal the following: Teaching Methodologies The results from the Course Design phase indicated that the greatest importance should be assigned to those teaching methodologies requiring teamwork and high levels of participation e.g. group projects, group presentations, computer simulation, and role playing. On a five-point weighting scale, these teaching methodologies received weights of 4 or 5. The teaching methodologies that emerged as being moderately important were case studies, individual projects, individual presentations, and class discussions. These received weights of 2 or 3. The lowest importance was accorded the teaching methodologies that provided limited opportunities for active student involvement (e.g. demonstrations, lectures, guest speakers, and computer laboratories). These received a weight of 1. Engineering Tools and Techniques The scores obtained by the seventeen engineering tools and techniques examined ranged from a low of 1.37% to a high of 10.62 %. Scores in the top quartile (10.62%, 10.27%, 9.25%, and 8.90%) were obtained by Project Management, Quality Function Deployment, Artificial Neural Networks, and Ergonomics. On the other hand, the engineering tools and techniques that received scores in the bottom quartile (2.97%, 2.05%, 2.05%, and 1.37%) were Fuzzy Logic, Risk Analysis, Value Engineering, and Mathematical Programming. The nine remaining tools and techniques, which emerged as being moderately important, received scores ranging from 8.56% to 3.65%. 1.4.6.3 Phase #3: Course Implementation Examination of the results summarized in the QFD chart in Figure 1.5 would reveal the following: Engineering Tools and Techniques The results indicated that the greatest importance should be assigned to those engineering tools and techniques that have a strong team orientation. e.g. Project Management and Quality Function Deployment. These received a weight of 5 on a five-point weighting scale. Five of the seventeen (29.4%) engineering tools and techniques received the median weight of 3 on a fivepoint weighting scale. The least importance was accorded those tools and

Engineering for Business—An Introduction

15

techniques that required considerable mathematical manipulation (e.g. Risk Analysis, Value Engineering, and Mathematical Programming). These received a weight of 1. Engineering for Business Courses Scores obtained by the four courses examined ranged from a low of 22.75% to a high of 25.9%. This narrow range suggests that all four courses were of approximately equal significance in satisfying the School’s curriculum objectives. These results can in part be attributed to the consensus-building heuristics used for aggregating individual preferences during the mapping process. Equal contributions could therefore be expected from faculty charged with the responsibility for delivering these courses. This would afford curriculum planners considerable flexibility in developing an integrated Engineering for Business curriculum. 1.4.6.4 Sensitivity Analysis In all three phases, sensitivity tests were conducted to ascertain the impact of variations in the weights assigned to the student needs (the WHATs) and the rating scale used to map the HOWs into the WHATs. Four scenarios were investigated. Scenario 1 used the weights obtained from the original survey data collected by the student team and a rating scale of 1-3-9 to map the HOWs into the WHATs. In Scenario 2 all WHATs were assumed to be of equal importance and were assigned a weight of three (average importance) on a fivepoint scale. In Scenario 3, all weights adopted in Scenario 1 were reduced by 30%. In the final case, Scenario 4, the weights of the WHATs were similar to those obtained in the original survey. In this case, however, a 1-3-5 rating scale was used (1-weak; 3-medium; 5-strong) to map the HOWs into the WHATs. The results of the sensitivity tests are summarized in Figure 1.6. These confirmed that the proposed planning framework was very robust. Although the four scenarios investigated incorporated significant changes in the input planning data, there was little impact on the output - the ranking of the HOWs. In this first phase, the HOWs that occupied the top four positions were the same, viz. Group projects, group presentations, computer simulations, and roleplaying. The teaching methodologies in the bottom positions also remained constant while there was slight shifting of the positions of the HOWs placed in the middle ranks. In three of the four scenarios examined in Phase 2, the HOWs that occupied the top four positions were the same, viz Project Management, Quality Function Deployment, Artificial Neural Networks, and Ergonomics. The Engineering Tools and Techniques in the bottom four positions also remained constant while there was slight shifting of the positions of the HOWs placed in the middle ranks. However, Scenario #2, which

16

Figure 1.6.

Chapter One

Results of Sensitivity Analysis

Engineering for Business—An Introduction

17

assigned equal weights to the WHATs, produced significant changes in the ranking of the HOWs. In Phase #3, a similar pattern is observed. Significant changes in the input planning data have little impact on the relative scores of the HOWs. However, there is some minor shifting in the ranking of the HOWs. Scenario #2 which assigns equal weights to the WHATs and Scenario #4 which uses a 1-3-5 rating scale both produce significant changes in the ranking of the HOWs. Table 1.3 Engineering Tools and Techniques Identified for Incorporation into Business Curriculum

18 Table 1.3

Chapter One Continued.

1.5 DISCUSSION AND CONCLUSION QFD has proven to be an effective tool in managing product/service development in manufacturing industry, in software development, and in service industries. The case study described in this chapter illustrates the application of QFD in academia to provide a powerful framework for enhancing effective communication, defining clear and accurate tasks, and achieving effective resource utilization. This makes the technique attractive for adoption as a planning tool to enhance any multi-criteria group decision-making process. Among the findings reported in this case study is the wisdom of adopting teaching methodologies, which encourage active student participation. This is consistent with recommendations in the literature (Lam and Zhao, 1998; Joyce 1986). However, the case study’s primary contribution is in illustrating the flexibility of the QFD process in providing a methodology for planning course developments in academia. In this application in academia, QFD provided a flexible framework to support an integrated, robust curriculum planning process. It has served as a good vehicle for facilitating the incorporation of new engineering for business courses into the core curriculum of the professional five-year MBA program at the School of Business and Industry at Florida A & M University. Its effectiveness can be enhanced through the use of groupware to encourage wide stakeholder participation, to facilitate consensus building and ensure timely decision-making. The QFD process can also be expanded to provide a structured approach for assessing the outcomes of these curriculum changes.

Engineering for Business—An Introduction

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1.6 REFERENCES Austin, R.D., Richard L. Nolan, and Mark J. Cotteleer, “Cisco Systems, Inc.: Implementing ERP”, Harvard Business School Case Study # 9-699-022, May 2002, HBS Publishing, Boston MA. Benjamin, C. O. “Soap Products Limited”, Case Study #689-008-1, Case Clearing House of Great Britain and Ireland, Cranfield Institute of Technology, England, February 1989. Benjamin, C.O., E. Archibold and T. Suarez, “Using Expert Panels to Evaluate New Technologies”, Journal of Academy of Business and Economics, Vol. 4, No. 1, October 2004. 38-49. Benjamin, C. O., R. Lynch and A. Mitchell, “A Methodology for Planning Enhancements to Computer Laboratories in Academia”, Proceedings, ASEE Southeastern Conference, Marietta, Georgia, April 1997, 211-218. Joyce, Bruce and Marsha Weil, Models of Teaching, 3rd edition, Prentice Hall, Englewoods Cliffs, NJ 1986. Burgar, P., “Applying QFD to Course Design in Higher Education”, Annual Quality Transactions, 1994. Cadenhead, Gary M., No Longer MOOT, Gary Cadenhead, 2002. Chen, C. L, and S. F Bullington, “Development of a strategic research plan for an academic Department through the use of Quality Function Deployment”, Computers and Industrial Engineering, Vol. 25, Nos. 1-4, 1993, 49-52. Clayton, Marlene, “QFD—Building Quality into English Universities”, Transactions from the Seventh Symposium on Quality Function Deployment, 1995, Novi, Michigan, Ann Arbor, MI:QFD Institute, 171-178. Ermer, D. S., “Using QFD Becomes an Educational Experience for Students and Faculty”, Quality Progress, May 1995, 131-136. Lam, K. K, and X Zhao, “An Application of Quality Function Deployment to Improve the Quality of Teaching”, International Journal of Quality and Reliability Management, Vol. 15, No. 4-5, April-May 1998, 389. Nilsson, Per, Bengt Lofgren, and Gunnar Erixon, “QFD in the Development of Engineering Studies”, Transactions from the Seventh Symposium on Quality Function Deployment, Novi, Michigan, Ann Arbor, MI:QFD Institute, 1995, 519-529. Panitz, B., “Evolving Paths”, ASEE Prism, October 1996, 22-28. Pitman, G., “QFD Applications in an Educational Setting: A Pilot Field Study”, International Journal of Quality and Reliability Management, Vol. 12, No. 6, June 1995), 63, Seow, Christopher, and Tony Moody, “QFD as a Tool for Better Curriculum Design”, Proceedings of ASQC’s 50th Annual Quality Congress, 1996, 21-28. Seymour, Nicole, “Business Plan Competitions: An Overview”, (Kaufmann Center for Entrepreneurial Leadership Clearinghouse on Entrepreneurship Education, May 2002 Digest, No. 02-01,), CELCEE website http://www.celcee.edu (Accessed on July 30, 2004).

Chapter Two

Financial and Economic Evaluation

2.1 Overview 2.2 Evaluation Techniques 2.2.1 Simple Techniques 2.2.2 Discounted Cash Flow Techniques 2.2.3 Risk and Uncertainty 2.2.4 Life Cycle Costing Models 2.3 A Project Appraisal Methodology 2.4 Financial Engineering 2.5 Conclusion 2.6 References 2.7 Problems/Cases 2.7.1 Bar Coding System 2.7.2 Forklift Truck Acquisition 2.7.3 CNC Machining Center Aquisition 2.8 Case Study: Soap Products Limited

2.1 OVERVIEW Engineering economy techniques are used to evaluate the feasibility of making an addition to the capital asset base of an organization. These capital projects typically require not only significant initial investment outlays but also ongoing periodic disbursements for operation and maintenance over the life of the project. The benefits envisaged from implementing these projects might include productivity improvements that result in either increased revenue generation from additional production or cost savings from more effi20

Financial and Economic Evaluation

21

cient production. Thus, acquiring these assets requires allocation of a portion of the organization’s capital budget. These investment decisions need to be systematically evaluated to ensure their financial viability and their congruence with the long-term corporate goals. Consider the following examples: • A small company is exploring the feasibility of growing its business by embracing electronic commerce concepts and strategies and making use of the World Wide Web. It must determine whether the incremental revenues forecast would exceed the costs of developing, maintaining and upgrading a web site for electronic commerce. The company would also want to identify and assess the risks associated with such a venture before making a final decision. • A large global manufacturing company in the automotive industry is considering locating a new vehicle assembly plant in a developing country. The planning team must carefully examine the choice of technology, plant design and location, supply chain issues, and project financing in order to compute the likely return on this significant capital investment and evaluate the perceived risks. • A not-for-profit firm in the health-care industry is examining the acquisition of a new computer system to improve its operational effectiveness and efficiency and enable the provision of improved quality of service to its customers. It must determine whether the benefits associated with productivity and service improvements are adequate to justify the initial acquisition cost of the system and the periodic disbursements for ongoing operation and maintenance. • A large, national service company is reviewing a proposal from a group of software consultants to develop a sophisticated Membership Lapse Prediction System to improve its membership retention. The company must weigh the cost savings associated with this new system against the costs of its development, operation and maintenance. In all cases, the engineering economy techniques (DeGarmo et al, 1984; Taylor 1980; Theusen et al, 1977), developed and utilized for engineering management decision-making can be beneficially applied. These include: • simple techniques such as the payback period and rate-of return which ignore the time value of money; • discounted cash flow (DCF) techniques such as the net present value, the annual worth/cost, and the internal rate of return which include explicit consideration of the time value of money. • techniques to incorporate risk and uncertainty such as sensitivity analysis, scenario analysis, and simulation;

22

Chapter Two

This chapter will describe these techniques and their extensions, illustrate their application and show how these techniques can be integrated into a comprehensive project appraisal methodology.

2. 2 EVALUATION TECHNIQUES 2.2.1 Simple Techniques The simple techniques which can be used to evaluate the financial viability of capital projects include Payback/Payout Period, Discounted Payback Period and Return on Average Investment. These are discussed below. • Payback Period This is the period of time required to recoup the initial investment outlay associated with the project. Let C0 be the Initial Investment Outlay Rj be the Receipts (Cash Inflows) from the project in year j Dj be the Disbursement (Cash Outflows) from the project in year j n be the project life (years) j be the year Then the payback period (p) would occur when the cumulative cash flows are zero, i.e. p

When ∑ (Rj - Dj) – C0 = 0 j=1

In the example, we see that the cumulative cashflows (see Table 2.1, Row D) would be zero some time between years 2 and 3. Assuming a pattern of uniform cashflows during year 3, we can estimate the payback period (p) in this case as: Payback Period, p = (2 + 16000/25000) years = 2.64 years This is illustrated graphically in Figure 2.1 This simple index provides a crude indicator of the risk associated with the project but disregards the cashflows that occur after the payback period and ignores the time value of money. Nevertheless, it still enjoys widespread popularity among business managers (Graham, 1970). In this case, the payback period of 2.64 years indicated the initial investment of $50,000 would not be recouped until the second half of the project life: i.e. (Payback period/Project Life) > 0.5 This would indicate the existence of some project risk. • Discounted Payback Period This index represents a modification of the Payback Period to include the concept of the time value of money and can be computed as follows:-

Financial and Economic Evaluation

23

Discounted Payback Period (p*) occurs when the cumulative discounted cash flows are zero i.e. p*

When ∑ aij(Rj – Dj) – C0 = 0 j=1

aij – Discount factor for year j at a discount rate of i % per annum i.e. aij = [P/F, i%,j] = [1/(1+i)j] Example: Global Manufacturers Inc. is considering the acquisition of a new conveyor system to improve the material handling system in one of its factories. The cash flows associated with this project are summarized in Table 2.1. Simple Financial Indices • Payback Period, p = (2 + 16000/25000) years = 2.64 years • Discounted payback period, p* = [3 + 1,961/12,294] years = 3.16 years • ROR = {[(162-70)/5]/50} x 100 = 36.8% For the conveyor system in the example above, examination of the cumulative discounted cash flows (see Table 2.1, Row G) would show that they would be zero sometime between years 3 and 4. Assuming a pattern of uniform cash flows during year 4, we can estimate the discounted payback period (p*) in this case as: Discounted payback period, p* = [3 + 1,961/12,294] years = 3.16 years This is also illustrated graphically in Figure 2.1.

Table 2.1.

Summary of Cash Flows for New Conveyor Project

24

Figure 2.1.

Chapter Two

Graphical Illustration of Payback Period and Discounted Payback Period

This index provides a tougher indicator of the risks associated with the project. Like the payback period, it disregards the cash flows that occur after the discounted payback period. However, it does consider the time value of money. In the example, the discounted payback period of 3.16 years indicated that the initial investment of $50, 000 would not be recovered until the second half of the project life, i.e. (Payback period/Project life) > 0.5 This would indicate the existence of some project risk. • Return on Average Investment The rate of return (ROR) on average investment is the average net income from an investment expressed as a percentage of the average amount invested. This simple index provides a measure of the profitability of the project but ignores the timing of the cash flows. It can be computed as follows using the notation above: ROR (%) = {[(Rj –Dj)/n]/C0} x 100 For the conveyor system in the example above: ROR = {[(162-70)/5]/50} x 100 = 36.8% For the conveyor system in the example above, examination of the cumulative discounted cash flows (see Table 2.1, Row G) would show that they would be zero sometime between years 3 and 4. Assuming a pattern of uniform cash flows during year 4, we can estimate the discounted payback period (p*) in this case as: Discounted payback period, p* = [3 + 1,961/12,294] years = 3.16 This is also illustrated graphically in Figure 2.1.

Financial and Economic Evaluation

25

2.2.2 Discounted Cash Flow Techniques Discounted Cash Flow (DCF) techniques enjoy continuing popularity as effective tools for the evaluation of capital investment projects. These projects may range from the financial justification of an additional item of materials handling equipment e.g. Automated Guided Vehicles (AGVs), Robots, Automated Storage and Retrieval Systems (AS/RS), Lift trucks, Conveyors, to the in-depth, comprehensive feasibility study of the construction of a new production facility. In all cases, the analyst is requires to forecast the anticipated cash inflows and outflows over the project’s life, select an appropriate discount rate and compute DCF indices which reflect the project’s viability. Among the more widely used DCF methods are: • Net Present Value (NPV) method • Internal Rate of Return (IRR) method • Annual Worth (AW) or Annual Cost (AC) method These are defined and briefly discussed below. • Net Present Value (NPV) Method All periodic cash flows associated with the project are discounted back to a reference point at a selected discounted rate, usually the Minimum Attractive Rate of Return (MARR). The project is economically justified if the Net Present Value is positive, i.e. p*

When ∑ aij(Rj – Dj) – C0 > 0 j=1

For the conveyor system in the example above, Net Present Value @ i =10% = $19,647 • Internal Rate of Return (IRR) Method This method is among the more widely used in engineering economy studies. For a single project, it involves finding the discount rate at which the net present values of the cash flows associated with the project would be zero. This can be done by iteration. Using the notation above, the IRR is the value of i at which Net Present Value = 0 n

When ∑ aij(Rj – Dj) – C0 = 0 j=1

26

Chapter Two

The project would be acceptable if the IRR exceeded the Minimum Attractive Rate of Return (MARR) set by the company for the projects in its risk category. For the conveyor system in the example above, the Internal Rate of Return can be found using linear interpolation or extrapolation as follows: When i = 10%, NPV = +19,647 When i = 20%, NPV = +4,732 When i = 30%, NPV = -5,675 When i = IRR, NPV = 0 From the plot in Figure 2.2, the IRR would be about 24% Using i = 10 and 30% per annum, by linear interpolation, IRR = 10 + [30 –10][19647/(19647 + 5675)] = 25.52% Or, IRR = 30 + [30 –10][5675/(19647 - 5675)] = 25.52% Using i = 10 and 20% per annum, by linear extrapolation, IRR = 10 + [20 –10][19647/(19647 - 4732)] = 23.17% Or, IRR = 20 + [20 –10][4732/(19647 - 4732)] = 23.17% In general terms, IRR = i1 + [i2 –i1][NPV1/(NPV1 + NPV2)] Or, IRR = i2 + [i2 –i1][NPV2/(NPV1 + NPV2)] In practice, the Net Present Value and the Internal Rate of Return are provided as functions in many financial calculators and spreadsheet programs thus relieving the tedium of calculation. • Annual Worth (AW)/Annual Cost (AC) Method

Figure 2.2.

Graphical Estimation of the Internal Rate of Return (IRR)

Financial and Economic Evaluation

27

This method involves computing the uniform series of annual net cash flows equivalent to a schedule of cash flows associated with a particular project. If this Annual Worth (AW) is positive, the project is economically justified. If only disbursements are being considered, then the criterion is usually expressed as an Annual Cost (AC). This technique is particularly useful in comparing mutually exclusive alternatives. In general terms, the Annual Worth can be computed using the following equation: AW = NPV (A/P, i%, n) Or, AC = NPV (A/P, i%, n) Using the cash flows for our conveyor example, the Annual Worth (AW) can be computed as follows: At i = 10%, Annual Worth, AW = NPV (A/P, 10%, 5); AW = $19,647(.2638) = $5,183 The Annual Cost technique can be applied, for example, in problem 2.7.2 which considers the feasibility of acquiring a forklift truck to improve materials handling in a warehouse. The project seems to be quite profitable although there seems to be some financial risk as evidenced by the relatively long payback and discounted pay back periods.

2A. In summary, an analysis of the cash flows associated with the conveyor project would yield these financial indices.

28

Chapter Two

2.2.3 Risk and Uncertainty Overview Several techniques have been advanced for addressing the risk and uncertainty associated with capital investment projects. Sullivan and Orr (1982) identify the following six methods being quite popular in industry and government: Break-even Analysis Sensitivity Analysis Optimistic-Pessimistic Estimation Risk-adjustment Minimum Attractive Rate of Return Reduction of the Useful Life Probability Functions These techniques attempt to provide a way of addressing the uncertainty inevitably associated with any capital investment project. Variations in the project analyst’s forecasts of the project life, initial investment outlay, disbursements for operations and maintenance, receipts generated by additional sales or methods improvement, rate of inflation, taxation rate, or cost of capital could have a significant effect on the project’s acceptance. We will examine three of the more generally applicable techniques to address risk and uncertainty, viz.: Sensitivity Analysis (Eschenbach and Mckeague, 1989) Scenario Analysis Monte Carlo Simulation (Buck, 1982) Sensitivity Analysis This method can be separated into the following steps: Step #1: Identify the key project parameters i.e. those parameters whose forecasts are associated with some degree of uncertainty and whose variation may have some impact on the project’s viability. Step #2: Estimate the likely range of variation of each key project parameter. Step #3: Vary the key parameters one at a time over the identified range and determine the impact on the project’s acceptance. Step#4: Identify any sensitive parameters and develop strategies for reducing the level of uncertainty associated with the forecast. The traditional analyses used can be extended to assess the effect of variation in forecasts for operation and maintenance, asset life, discount factor and other parameters on the investment decision. The results of this analysis can be tabulated and summarized graphically using a spider plot or a tornado diagram. (Eschenbach and Mckeague, 1989)

Financial and Economic Evaluation

29

Table 2.2. Variation of Net Present Value at 10% per annum with Changes in Acquisition Costs, Operations and Maintenance Costs, and Labor Savings

Figure 2.3. Spider Plot Showing Variation of Net Present Value (NPV) at 10% per annum with Changes in Acquisition Costs, Operations and Maintenance Costs, and Labor Saving

30

Chapter Two

The results of the sensitivity analysis for the conveyor project summarized in Table 2.2 and Figure 2.3 show that the project’s viability is most sensitive to variations in Labor Savings and less sensitive to changes in the Acquisition Cost and Operation and Maintenance Costs. Table 2.3 summarizes the variation of the IRR for the conveyor project with changes in cash flows. We see that although the IRR is fairly sensitive to changes in the cash flow estimates, these estimates would have to fall by approximately 30% before the IRR drops below the minimum attractive rateof-return (MARR) of 10% p.a. Scenario Analysis The outcome of a project can be examined under three scenarios, viz. Best Case or Optimistic Scenario in which the venture’s parameters are better than forecast. Most Likely Scenario in which the venture’s parameters are similar to the forecasts. Worst Case or Pessimistic Scenario in which the venture’s parameters are worse than forecast.

Table 2.3. Variation of Internal Rate of Return (IRR) with Changes in Net Cash Flows

Financial and Economic Evaluation Table 2.4.

31

Results of Scenario Analysis for New Conveyor Project

Table 2.4 summarizes the results of a scenario analysis for the new conveyor project. This provides an estimation of the range of outcomes associated with the investment. In this case, the project will be profitable even in the worst case scenario although the relatively long payback and discounted payback periods indicate some risk. Monte Carlo Simulation This approach can be broken down into the following steps: Step #1: Identify the key project parameters. Step #2: Obtain a subjective probability distribution for each parameter. Step #3: Construct a Simulation Model to integrate the cash flows associated with the project. Step #4: Run the Simulation Model and obtain a probability distribution of the measures of investment efficiency e.g. NPV, IRR. Step #5: Estimate the risk of the project i.e. the probability of the project not attaining an acceptable level of performance. Step #6: Consider “non-quantifiable” factors. Step #7: Make final decision. Spreadsheets can greatly assist in incorporating risk and uncertainty in capital investment projects (Hobson 1983; UNIDO 1978) and can be exploited to good advantage when using sensitivity analysis, scenario analysis and Monte Carlo simulation to address risk and uncertainty. In the new conveyor project, a simulation model can be easily constructed using an EXCEL “add-in” such as @Risk for EXCEL (Evans and Olson, 2002) and Crystal Ball. (Mantel et

32 Table 2.5.

Chapter Two Results of Simulation Modeling Analysis for New Conveyor Project

al 2005). Table 2.5 shows the probabilistic inputs used in developing a simulation model and the results obtained. In this case, the results shown in Figures 2.4 and 2.5 indicate there is a very small probability of the project failing, i.e. [prob. (NPV < 0)] = 0.015 and [prob. (IRR < 0.10)] = 0.015. 2.2.4 Life Cycle Costing Models Life cycle costing models are useful when decisions concerning capital asset acquisition, replacement and retirement are being made. This model approach

Figure 2.4. Distribution of Net Present Value (NPV) at 10 % per annum for New Conveyor Project

Financial and Economic Evaluation

Figure 2.5.

33

Distribution of Internal Rate of Return (IRR) for New Conveyor Project

implies acceptance of the asset management or terotechnology concept, which was in vogue in Europe in the ‘70s. This advocates that an investment decision on a capital asset be based on a thorough examination of the asset’s expected performance through all phases of its life cycle. Adoption of the idea of “cradle-to-the-grave” management of assets requires the use of appropriate life cycle costing models to reduce the likelihood of making bad investment decisions. A typical life-cycle costing model is outlined in Figure 2.6 Where: n Asset life (years) Co Initial Investment outlay for the capital asset acquisition Ri Annual incremental receipts (cash inflows) flowing from this investment e.g. additional sales, labor savings Di Annual incremental disbursements (cash outflows) L Terminal value of the capital asset aij Discount Factor (P/F), i%, j)

34

Chapter Two

Figure 2.6.

Typical Life Cycle Costing Model

Once these periodic cash flows have been forecast, then the conventional investment appraisal indices can be computed and considerations of risk and uncertainty incorporated into the decision-making process. This modeling technique would be applicable when a relatively detailed examination of a small number of feasible alternatives is required and should be regarded as a part of a more comprehensive methodology such as the one outlined in Figure 2.7 for selecting capital equipment.

2.3 A PROJECT APPRAISAL METHODOLOGY When appraising capital investments, the individual techniques described above need to be integrated into a structured decision-making methodology to ensure that all projects would be appraised in a consistent manner. The following 10-step methodology has been found to be extremely useful to project engineers in appraising capital projects: Step #1: Define the scope of the project Step #2: Determine the study period of the project Step #3: Determine the incremental cash outflows associated with the project. The cash-flows may be measured either in constant dollars or in actual dollars. Step #4: Determine the incremental cash inflows associated with the project, e.g. Increases in sales revenues or cost savings Step #5: Select an appraisal technique e.g. DCF techniques (NPV, IRR, AW, etc) or simpler techniques (payback/payout period, Profitability Index, Return on investment) Step #6: Select an appropriate discount rate; this would be influenced by the cost of capital and the risk associated with the investment i.e. Discount Rate = Cost of Capital + Risk Premium

Figure 2.7.

Flowchart for Capital Equipment Selection Methodology

36

Chapter Two

Step #7:

Compute the appropriate financial indices e.g. NPV, IRR, ROR, BCR, Payback Period Step #8: Consider Risk and Uncertainty e.g. Sensitivity Analysis, Scenario Analysis, Monte Carlo Simulation Step #9: Consider the “non-quantifiable” factors e.g. Social, Political, Economic, Technological, Legal, Environmental issues Step #10: Make the final decision i.e. Accept the project if the anticipated Returns outweigh the Risks

2.4 FINANCIAL ENGINEERING Financial Engineering involves the application of mathematical methods and computer models to decision-making in financial markets and financial management. This field has attracted professionals from a variety of educational backgrounds e.g. business, economics, finance, industrial engineering, mathematics, computer science, statistics, and physical sciences. Career opportunities exist in a wide range of commercial settings including commercial and investment banks, brokerage and investment firms, insurance companies, consulting and accounting firms, treasury departments on non-financial corporations, government agencies, software and technology vendors providing products and services to the financial industry. In today’s information intensive business environment, financial engineers have ready access to a wide range of data sources on the World Wide Web (Ray 1996). Table 2.6 lists a sample of the online educational resources for financial engineers. These include financial news, information, and research discussion groups; financial services; free financial software; and numerous pedagogical applications. Thus, financial engineers will not only have to be experts in the use of the Internet, (Herbst 1996) but also skilled in the use of a wide range of decision models to facilitate data analysis and encourage informed and timely decision-making. The techniques outlined in this chapter can be applied to assess the financial viability of projects involving “doing business on the Net.” (Schwartz 1997).

2.5 CONCLUSION The various engineering economy techniques reviewed in this chapter have found widespread application in business. Project engineers are frequently required to provide financial and economic justification for the acquisition of new items of production or material handling equipment e.g. a CNC machin-

Financial and Economic Evaluation Table 2.6.

37

Online Educational Resources for Financial Engineering

ing center, an Automated Guided Vehicle (AGV) system, a robot a lift truck, a conveyor system. For larger projects which may require the construction of a new facility or the major refurbishing of an existing one, the engineering economic analysis would constitute an important component of a comprehensive industrial feasibility study (UNIDO, 1978) to assess the viability of the project. As companies race to adopt state-of-the-art technologies in the Information Age, Information Technology (IT) projects also need to be carefully examined. City engineers seeking to adopt an Automated Mapping (AM) or Geographic Information System (GIS) to better manage spatial data; logistics engineers seeking to adopt Electronic Data Interchange (EDI)/Bar Coding or Radio Frequency Identification (RFID) systems to improve supply chain

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management; accountants seeking to adopt new enterprise modeling software or to provide an improved environment for monitoring and controlling costs. The financial techniques outlined in this chapter and the many extensions proposed by several authors (Meredith 1986), (Canada and Sullivan, 1989) can be put to good use in analyzing these capital budgeting decisions The challenge is that of choosing the appropriate evaluation procedure consistent with the data availability, the associated risk and the degree of analytical sophistication required.

2.6 REFERENCES Buck, J.R. “Risk Analysis Method Can Help Make Firms’ Investments Less of a Gamble”, Industrial Engineering, November 1982. Canada, J.R. and W.G. Sullivan, Economic and Multi-Attribute Evaluation of Advanced Manufacturing Systems, New Jersey: Prentice Hall, 1989. DeGarmo, E.P., W.G. Sullivan and J.R. Canada, Engineering Economy, New York: Macmillan Publishing Company, 1984. Eschenbach, T.G. and L.S. Mckeague, “Exposition on Graphs for Sensitivity Analysis”, The Engineering Economist, Vol. 34, No. 4, Summer 1989, 315-333. Evans, James R., and David L. Olson, Introduction to Simulation and Risk Analysis, New Jersey: Pearson Education, Inc., 2002 Graham, P. “Cost Justification of Capital Expenditures”, Automation, 1970. Herbst, A.F.,”The Ways in Which The Financial Engineer Can Use the Internet”, Financial Practice and Education, Fall/Winter 1996, Vol. 6, No. 2, 111-121. Hobson, T., “Financial Modeling on Micro-computers”, Journal of the Operational Research Society, Vol. 34, No. 4, April 1983, 289-297. Holland, F.A., F.A. Watson and J.K. Wilinson., Introduction to Process Economics, London: John Wiley and Sons, 1983. Mantel, Samuel J., Jack R. Meredith, Scott M. Shafer, and Margaret M. Sutton, Project Management in Practice, John Wiley & Sons, 2005 Meigs, R.F. and W.B. Meigs, Accounting: The Basis for Business Decisions, 9th edition, McGraw-Hill, 1990. Meredith,J.R. (Ed.), Justifying New Manufacturing Technology, Atlanta: Industrial Engineering and Management Press, 1986. Ray, R. “An Introduction to Finance on the Internet”, Financial Practice and Education, Fall/Winter 1996, Vol. 6, No. 2, 95-101. Schwartz, E.I., Webonomics, New York: Bantam Doubleday Dell Publishing Group, Inc., 1997. Sullivan W.G. and R.G. Orr, “Monte Carlo Simulation Analyzed Alternatives in Uncertain Economy”, Industrial Engineering, November 1982. Taylor, G.S. Managerial and Engineering Economy, New York: D. Van Nostrand Reinhold Co., 1980. Thuesen, H.E., W.J. Fabrycky and G.J. Thuesen, Engineering Economy, New Jersey: Prentice Hall, 1977.

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UNIDO, Manual for the Preparation for Industrial Feasibility Studies, New York: United Nations, 1978. Financial Engineering Bibliography Cheng, M. M., A. K. Schulz, P. F. Luckett and P. Booth. 2003. The effects of hurdle rates on the level of escalation of commitment in capital budgeting. Behavioral Research In Accounting (15): 63-86 Church, P. H. and K. R. Lambert. 1993. Outside influences on capital budgeting systems. Journal of Cost Management (Fall): 54-59. Hirst, M. K. and J. A. Baxter. 1993. A capital budgeting case study: An analysis of a choice process and roles of information. Behavioral Research In Accounting (5): 187-210. Klammer, T., B. Koch, and N. Wilner. 1991. Capital budgeting practices - A survey of corporate use. Journal of Management Accounting Research (3): 113-130. Lowenstein, R. 2001. When Genius Failed: The Rise and Fall of Long-Term Capital Management. Random House. McCracken, D. E. 2001. Tie your capital budget to your strategic plan. Strategic Finance (June): 38-43. Schwan, E. S. and W. A. Remaley. 1991. Marginal return on invested capital versus internal rate of return. Journal of Cost Management (Summer): 55-58. Seitz, N. and M. Ellison. 2004. Capital Budgeting and Long-Term Financing Decisions, 4e. South-Western Educational Publishing. Swain, M. R. and S. F. Haka. 2000. Effects of information load on capital budgeting decisions. Behavioral Research In Accounting (12): 171-198. Turner, L. D. 1990. Improved measures of manufacturing maintenance in a capital budgeting context: An application of data envelopment analysis efficiency measures. Journal of Management Accounting Research (2): 127-133.

2.7 PROBLEMS/CASES 2.7.1 Bar Coding System A small manufacturer of toroidal transformers is examining the feasibility of introducing a PC-based bar-coding system to improve its inventory management. A schematic of the proposed system is shown in Figure 2.8. The choice has been narrowed down to one of the two systems, the Easy Reader or the Magic Wand. What recommendations would you make? 2.7.2 Forklift Truck Acquisition Mid-West Packaging Limited is considering the acquisition of a forklift truck to improve the material handling system in its warehousing operations. Preliminary analysis of the quotations received has narrowed down the choice to one of two models, the Boss and the Champion.

40

Figure 2.8.

Chapter Two

Schematic of Proposed Bar-Coding System

2B. The cash flows associated with the Easy Reader and the Magic Wand

Using a cost of capital of 10% per annum, compare the two alternatives using the Annual Cost method. Please state clearly any assumptions made. 2.7.3 CNC Machining Center Acquisition Beta Engineering Services Limited (BES Ltd.) is investigating the acquisition of a computer numerically controlled (CNC) machining center. Compute the after-tax internal rate of return (IRR) for the project assuming all tax payments are made in the following year. If the BES senior management has set an after-tax target rate of return of 10% per annum for capital investment projects, what recommendations would you make based on these computations?

2C.

The comparative data— the Boss and the Champion

2D. The Information Obtained by the Engineer assigned to the CNC Machining Center Acquisition

2E.

Tax Regulations Permit

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2.8 CASE STUDY: SOAP PRODUCTS LIMITED Introduction Soap Products Limited (SPL), a medium-sized company located in a developing country, is investigating the feasibility of setting up an industrial facility to satisfy the domestic market for Toilet Soap, Laundry Soap and Detergent over the forthcoming 10 year period. Project Background At present, the company imports and distributes these products through its retail outlets. The proposed venture should be commercially profitable while providing important economic and social benefits such as employment creation and foreign exchange savings by import substitution. Linkages will be established with other sub-sectors of the economy as the plant will utilize as far as possible indigenous raw materials e.g. Coconut Oil extracted from locally produced Copra, Seed Oil from Cotton Seed and Palm Kernel, Tallow from the livestock industry and Sucrose from the sugar industry to provide a sucrose-based surfactant for detergent production. Market and Plant Capacity Market Study At present, the demand for toilet soap is met in its entirety by importation whereas only part of the local market for laundry soap and detergents is met by importation. Analysis of historical data has shown that the market for both toilet and laundry soap is increasing while the market for detergent is slowly declining. Using linear regression analysis, trend lines were fitted to the data and by extrapolation, demand forecasts were obtained for the 10 year life span of the project. After consideration of the competition in the industry, SPL’s Marketing Manager proposed the adoption of a marketing strategy involving low pricing supported by heavy advertising. This was aimed at achieving easy market penetration and facilitating easy acceptance by consumers.

2F. Sales forecasts for the ten year period. Details of this computation are summarized in Table 2.7.

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Plant Capacity

2G. The nominal maximum plant capacities selected to satisfy the anticipated demand.

The proposed program to satisfy the forecasted sales is shown in Table 2.8. This program is based on an 8 hour/shift, 2 shift/day. 5 day/week, 50 week/year operation. During the first three (3) years, actual production is expected to be respectively 60, 70, and 80 percent of the nominal maximum plant capacity. Three (3) production lines are planned for toilet and laundry soap. One line would be utilized exclusively for toilet soap, another solely for laundry soap, with the third line being used for both toilet and laundry soap. The detergent plant would be expected to operate all year round on a twoshift/day production. Project Engineering Technology For soap production, the semi-boiled process has been selected as a compromise between the very simple, labor intensive cold-process and the more complex, capital intensive full-boiled process. It is felt that the semi-boiled process provides the appropriate level of technology for laundry soap production, and with good quality control of the raw material inputs, (Coconut Oil, Tallow, Caustic Soda, etc.) and good process control, toilet soap of an acceptable quality could be obtained for the domestic market. For the detergent, a relatively new sucrose surfactant process has been selected as an alternative to the traditional petro-chemical surfactant process Table 2.7.

Sales Forecast (Years 1 – 10)

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Table 2.8. Proposed Production Program—Assumes—80% Plant Availability: 50 weeks/year: 5 days/week: 2 shifts/day: 8 hours/shift

because of the potential linkages with the local edible oil industry and the sugar industry. A schematic diagram of the process is shown at Figure 2.9. Plant Organization Plant Layout A proposed floor plan of the factory shown in Figure 2.10 gives the relative location of the main production, service and storage areas and indicates the material flow from raw material storage to finished goods stores. Organizational Chart The Plant Manager will be responsible for the management of the factory. He will be assisted by a Plant Engineer, three (3) Production Supervisors and an Office Manager. A proposed Organization Chart is shown in Figure 2.11. Project Implementation A bar chart shown in Figure 2.12 outlines a proposed implementation schedule. This shows that provided the various deadlines are met, the entire plant could be completely operational after a construction period of one (1) year. Financial and Economic Evaluation Total Investment Outlay The total initial investment costs of the project are estimated at $6.9 million.

Financial and Economic Evaluation

2H.

45

Summarized Costs. Details are provided in Figure 2.13.

Project Financing The financing would consist of equity and money from a external lending institution, the supplier’s credit and the local banking facility.

2I. Expected Project Financing. Details are provided in Figure 2.14.

Production Costs A schedule of the annual production costs is shown in Figure 2.15. Financial Evaluation In computing the commercial profitability criteria, the following assumptions can be made: • The project life will be 10 years after construction • The factory will work 2 shift/day, 5 day/week and 50 week/year. • The production during the first 3 years of operation will be 60, 70 and 80 percent of feasible normal capacity. • The terms of payment required by the equipment suppliers will be an initial deposit of 15% with the order; and the remainder payable in 4 equal installments; at an interest rate- 10% per annum.

Figure 2.9.

Schematic of Sucrose Surfactant Process

Figure 2.10.

Proposed Floor Plan for Soap and Detergent Plant

Figure 2.11.

Proposed Organization Chart for Soap and Detergent factory

Financial and Economic Evaluation

Figure 2.12.

49

Proposed Implementation Schedule for Soap and Detergent Factory

The Problem From the information collected, the project seemed to offer a good investment prospect for the company and had the potential to achieve significant foreign exchange savings via import substitution. However, the company’s senior management was concerned about the selection of relatively unproven technology for detergent production and wondered if the expected returns would justify the additional risks.

50

Figure 2.13.

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Total Initial Investment Costs

There was also some feeling that the estimated sales revenues were based on too optimistic market share forecasts. Given the oligopolistic nature of the industry, any attempt to increase the company’s market share by reducing the selling prices could result in aggressive, price-cutting counter measures by the other firms in the industry. Production costs also depended heavily on the company’s ability to resist union demands for excessive wage increases and so control wage inflation to

Financial and Economic Evaluation

Figure 2.14.

Proposed Project Financing

51

52

Figure 2.15.

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Annual Production Costs

within acceptable limits. The Production Director also argued that the project life should be reduced because the proposed 2 shift operation would shorten the equipment’s useful life. The cost of loan financing was still very high, and the project would have to show a return considerably higher than the criterion rate of 15% per annum (after tax) normally used to evaluate investment projects in this risk category. The next meeting of the Board of Directors was scheduled to take place in two weeks, and these questions would have to be answered before the project would be submitted for approval.

Chapter Three

Multi-Criteria Decision Making

3.1 Introduction 3.2 Multi-Criteria Decision Making 3.3 Multi-Criteria Decision Models 3.3.1 Overview 3.3.2 Scoring Models 3.3.3 Multi-Attribute Utility Models (MAUM) 3.3.4 Analytical Hierarchy Process (AHP) 3.3.5 Goal Programming (GP) 3.3.6 Quality Function Deployment (QFD) 3.4 Other MCDM Techniques 3.5 Conclusion 3.6 References 3.7 Case Study: Planning Engineering Services at the Houston Chronicle 3.7.1 Introduction 3.7.1.1 The Houston Chronicle 3.7.1.2 Hearst Corporation 3.7.1.3 Challenges 3.7.2 The Houston Chronicle Organization 3.7.2.1 Departmental Functions 3.7.2.2 The Engineering Services Department 3.7.3 Scope of Study 3.7.4 Quality Function Deployment 3.7.4.1 A Modified QFD Process 3.7.4.2 Customer Survey–Determining Customer Needs 3.7.4.2.1 Survey Methodology 3.7.4.2.2 Analysis of Results 53

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3.7.5 Determining the HOWs–The Engineering Tools and Techniques 3.7.6 Discussion of Results 3.7.6.1 Engineering Service Areas 3.7.6.2 Engineering Tools and Techniques 3.7.6.3 Sensitivity Analysis 3.7.6.3.1 Analysis of Results 3.7.6.3.2 Analysis of Results 3.7.6.4 Further Work 3.7.6.4.1 Procedure

3.1 INTRODUCTION Decision-making in collaborative engineering teams invariably requires choosing among several alternatives while incorporating multiple, often conflicting factors. Consider the following scenarios: • Participants on a Concurrent Engineering team in the automotive industry seeking to develop the superstructure for a solar-powered car must choose between materials of differing tensile strength, physical characteristics, and cost. (SME 1993). • Aerospace engineers developing a state-of-the-art attack helicopter must select a power unit which offers the best balance among reliability, maintainability, and cost. (Kusiak 1993). • Engineers in selecting new generators to expand installed capacity in a public utility must determine which alternative best satisfies the desired equipment specifications while incurring acceptable life cycle costs. (Benjamin and Baksh, 1986). • A Logistics team seeking to select a third party logistics provider must select a company which would possess the technological and managerial sophistication required of a good partner and be capable of delivering the required service at a competitive price. (Global Logistics Summit, 1996). • A facilities planning team seeking to develop the “best” layout for a new CIM laboratory must determine the “best” space allocation plan given the competing interests of its faculty, student and industry stakeholders. (Benjamin et al, 1992). • Public sector investment programming planners must select the optimum mix of investment projects which would best contribute to the attainment of economic, social and political goals without violating resource constraints. (Al-Araimi 1993).

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55

These multi-criteria problems generally fall into the following two categories: • Multi-objective problems which require evaluation of a relatively large number of feasible alternatives described through the use of decision variables. The objectives and constraints associated with these problems are functionally related to the decision variables. • Multi-attribute problems which require evaluation of a small number of alternatives described in terms of attributes. Solution of these multi-criteria problems involves the articulation of the “decision maker’s” preference structure of the multiple criteria, and optimization over the preference structure. These criteria are attributes, objectives or goals which have been judged relevant in a given decision-making domain by an individual or group of decision-makers (Zeleny 1982). Canada and Sullivan (1989) distinguish between an “objective” which represents a preference for one or more attributes and a “criterion” which is a standard or rule that guides decision-making. The solution methods can be classified into the following three categories: • Prior articulation of preferences-the decision makers’ preference structure of the multiple criteria is obtained before the start of the optimization process; • Progressive articulation of preferences-the decision makers revise their preference structure of the multiple criteria through interaction with the analyst during the optimization process; • Posterior articulation of preferences-the decision makers formulate their preference structure of the multiple criteria after being presented with all or almost all of the non-dominated solutions to the problem; To facilitate structured decision-making and improve the quality of the resulting decisions, multi-criteria decision models can be used. These include simple scoring models, multi-attribute utility models, multi-objective or goal programming, and analytical hierarchy process. Also, these may be incorporated into multi-criteria decision-making frameworks such as quality function deployment or group decision support systems. In this chapter, we will examine the challenges typically associated with multi-criteria decision making and review the more relevant multi-criteria decision models and illustrate their application.

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3.2 MULTI-CRITERIA DECISION MAKING A structured approach to multi-criteria decision making requires the following steps: Step #1:Specify the goals, objectives and criteria to be achieved Step #2: Identify the needs to be fulfilled Step #3: Determine the constraints associated and affected by the decision Step #4: Generate decision options or alternatives Step #5: Apply appropriate multi-criteria decision model Step #6: Make final decision The multi-criteria decision-making process is also influenced by the decision-making environment, team composition, and the level of team facilitation provided. These factors are briefly discussed below. Decision-making Environment Although some organizations have established special-purpose meeting rooms to facilitate group decision-making, the current interest in industry on employee empowerment and the expanded use of intranets and the Internet for collaboration have encouraged the use of virtual teams with members distributed and participating via asynchronous decision-making. To be effective, any groupware employed should incorporate multi-criteria decision models to facilitate collaboration within teams. Team Composition Multi-criteria decision making is also influenced by the team size and the background of the team members. Contribution by participants may be inhibited in large groups or by the existence of dominant members in a group. The use of Computer Supported Collaborative Work (CSCW) systems can stimulate member participation by providing opportunities for anonymous contributions. Group Facilitator The use of a skilled facilitator can often assist in encouraging member participation, and securing group consensus through the deployment of appropriate decision models to aid the group process. These multi-criteria decision models can be incorporated in groupware.

3.3 MULTI-CRITERIA DECISION MODELS 3.3.1 Overview Several multi-criteria decision models based on prior articulation of preferences include those used to address:

Multi-Criteria Decision Making

57

• multi-attribute problems (e.g. scoring models, the analytical hierarchy process, and outranking methods) • multiple objective problems (e.g. goal programming) • both multi-attribute and multi-objective problems (e.g. utility based methods) These are reviewed and discussed in the following sections. 3.3.2 Scoring Models Scoring models are among the simplest yet most popular tools for addressing multi-attribute decision problems. Among the more widely used approaches are the scoring models for Facilities Planning and Design used by Richard Muther (1979) and those used by Kepner and Tregoe (1981) in their seminars on problem solving. A typical scoring model can be described as follows: Let n be the number of alternatives m be the number of attributes wj be the weight assigned to attribute j xij be score/rating obtained by alternative i for attribute j Xi be the score obtained by alternative i Therefore: Xi = ∑ wj.xij j=1,m

Select “best” alternative, i.e. Max (Xi) The application of a typical scoring model is illustrated in the following example. Example #3.1: Selecting a Third Party Logistics (3PL) Company (Global Logistics Summit, 1996). The Global Logistics Group (GLG) in a large, global computer equipment manufacturer has identified four companies as potential third party logistics (3PL) providers for its North American operations. The team has developed a scoring model to assist in the evaluation of these four finalists. The model uses eleven factors and records their relative importance on a four-point scale. Following site visits by the GLG team, each 3PL finalist was rated on each factor using a 5-point semantic scale, (0-poor; 4-excellent). Finally, the score computed for each alternative is used to rank the 3PL finalists. Table 3.1 below summarizes this analysis. In this case, the scores indicate that the 3rd and 4th ranked alternatives could be eliminated from further consideration. The DHL and UPS alternatives received the top two ranks. Their scores of 84 and 83 are very close and suggest further examination of these alternatives before a final selection is made.

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Table 3.1. Application of a Scoring Model to Evaluate Candidates for Third Party Logistics—Weights: 1=Least Importance to 4=Most Importance. Rating Scale: 0=Poor to 4=Excellent

Example #2: Evaluating Engineering Design Alternatives (HBS, 1988) An international project engineering team has developed a scoring model to evaluate design proposals for a large construction project which seeks to link two large communities currently separated by a large waterway. The information assembled by the team is summarized in the Table 3.2 below. In this case, the low score obtained by the Viaduct/Tunnel alternative supported its exclusion from any further consideration. Although the Suspension Bridge alternative has secured the top ranking, the close scores obtained by the Suspension Bridge and Rail Only Tunnel alternatives suggest that a closer examination is required to finalize the decision. 3.3.3 Multi-Attribute Utility Models (MAUM) MAUM models require a decomposition of the problem into logical elements, creation of a decision hierarchy, and then recombination using aggregation techniques. McCrary (1991) provides a good illustration of this technique in his development of a linear additive multi-attribute utility model to select the “best” GIS software from a shortlist of candidates. Here a score is calculated for each software package based on appropriately weighted technical attributes, functional attributes, vendor attributes, and price. This

Multi-Criteria Decision Making

59

Table 3.2. Application of a Scoring Model to Evaluate Engineering Design Alternatives— Weights: 1=Least Importance to 10=Most Importance. Rating Scale: 1=Poor, 3=Good, 5=Excellent

MAUM thus provides the basis for developing a ranked list of software alternatives. 3.3.4 Analytical Hierarchy Process (AHP) The Analytic Hierarchy Process (AHP), a popular tool for multi-attribute decision making, was first introduced by Saaty in the mid-1970s (Saaty 1980). Its implementation proceeds in the following phases: • Construction of the hierarchy network for the problem • Pairwise comparison of items within each hierarchy level • Priority evaluation of each alternative based on these pairwise comparisons. As shown in Figure 3.1, the development of a hierarchy network starts from the objective level and then goes down to the criterion and alternative levels respectively. Several levels may be used in this hierarchy. The ratios of the pairwise comparisons are put into the form of matrices. The priority assigned to each item comes from the eigenvector of the matrices. The weight for each item is then determined as the composite of the priorities of the

60

Figure 3.1.

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Decision Hierarchy for Accounting Software Selection

higher level items. The decision may then be based on the items in the lowest level (Saaty 1980; Weber 1993). Since its development, AHP has been used to address a wide range of problems which require collaboration among decision-makers. These include applications in economic planning, energy policy, health, conflict resolution, project selection and budget allocation (Zahedi 1986). The popularity of this decision–making methodology has been attributed to its flexibility, ease of use, and the availability of supporting software, Expert Choice (Liberatore and Nydick, 2003). 3.3.5 Goal Programming (GP) When objectives can be prioritized and the relationship between objectives and the decision variables can expressed mathematically, goal programming (Ignizio and Cavalier, 1994) can be used to facilitate consideration of multiple conflicting objectives. The solution approaches can be: • Pre-emptive or lexicographic goal programming in which goals are prioritized by the decision-maker and satisfied in an ordinal sequence. • Non-preemptive or weighted goal programming in which the objective is to find a solution which minimizes the weighted sum of all unwanted goal deviations. • Chebyshev or fuzzy goal programming in which the objective is to find a solution which minimizes the worst unwanted deviation from any single goal. The effectiveness of the GP technique can be increased in some instances by integration with other multi-criteria decision models. Benjamin and Ehie

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61

(1992) illustrate this synergy in developing integrated AHP/GP models for space allocation in planning facilities in academia and investment programming in the copper mining sector in Zambia (Ehie and Benjamin, 1993). 3.3.6 Quality Function Deployment (QFD) Quality Function Deployment (QFD), (Bossert 1991) first developed and applied by the Japanese in the early 1970s helps multi-functional teams identify and prioritize customer requirements and relate these needs to corresponding product or service characteristics. Over the years, QFD has attracted attention from a wide range of progressive industrial organizations in the USA including Ford Motor Company, General Motors, Rockwell International, AT&T, DEC, Hewlett-Packard, and Polaroid and has been used mainly in the area of product development and improvement. Recently, QFD has been used to facilitate planning in areas such as planning process improvement projects (Benjamin et al, 1996), planning for technology transfer on information technology projects (Khawaja and Benjamin, 1996), business planning in small companies (Ferrell and Ferrell, 1994), manufacturing strategic planning (Crowe and Cheng, 1996), and strategic planning for service improvement projects (Schubert 1989). QFD is best implemented as a multi-phase process as this approach offers the greatest potential for realizing significant benefits. Here a series of matrices link relationships and provide a graphical summary of the process. Implementation of the QFD process typically proceeds in the following steps: Step #1-Define the customer Step #2-Identify the customer wants-the WHATs and establish their relative importance Step #3-Identify the relevant product/system design attributes–the HOWs Step #4-Map the HOWs into the WHATs Step #5-Develop a House of Quality. Recently, we have seen several attempts to utilize QFD to provide a structured approach for planning in academia in areas such as developing laboratories for CIM (Benjamin et al, 1994), revising mechanical engineering curriculum (Ermer 1995), research planning (Chen and Bullington, 1993), course design (Burgar 1994), and planning enhancements to computer laboratories (Benjamin, et al, 1997). These applications all confirm the potential of QFD to facilitate effective communication, timely information transformation, and efficient resource utilization. In the following section, we describe a case study in which QFD provides a framework for planning course development in academia.

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Example: Developing a Supply Chain Management Program in Academia Background A large business school was examining the feasibility of developing a Supply Chain Management program The primary concern of the team established for this project during course development was the need to design a competitive program which would make good use of existing resources. QFD was adopted as a framework for planning this course development. Modified QFD Process The modified QFD process used to provide a rapid, structured, integrated approach to planning the new SCM program adopted the following five-step procedure: Step #1-Define the customer: The customers were the students enrolled in the undergraduate and graduate programs in the business school. Step #2-Identify the critical competencies to be delivered to the students and establish the importance of each critical competency-the WHATs: The SCM project team adopted the seven key professional competencies and their relative importance reported in the 2004 CAPS Research study by Guinipero and Handfield. Step #3-Identify relevant courses for the incorporation in the program-the HOWs: A survey of selected faculty identified the preferred courses for incorporation into both the undergraduate and graduate programs. The courses were divided into five categories: Accounting & Finance, IT & Operations, Marketing, Management and Law, and Professional Business. Step #4-Map the HOWs into the WHATs: The SCM project team mapped the HOWs into the WHATs by assigning ratings on a 1-3-9 scale (1-weak; 3medium; 9-strong) to indicate the relationship between each HOW and WHAT. Step #5-Develop a House of Quality: The SCM project team constructed a spreadsheet-based model to facilitate computation of the row and column totals and the ranking of the courses under consideration. Results The results of this analysis are summarized in Figures 3.2 and 3.3 below. Details of the analysis are provided in the QFD Charts shown in Figures 3.4 and 3.5. Analysis of the results led to a rapid identification of three course categories: Core Courses, Highly Recommended Electives, and Recommended Electives for the Graduate and Undergraduate Programs. For the undergraduate program, the top five ranked courses were considered core courses. Courses ranked 6-10 are considered highly recommended electives, and courses ranked 11-15 are recommended electives. For the graduate program, the top five courses were considered core courses. Ranks 6 through 11 are considered Highly Recommended electives, and ranks 12-20 are recommended electives.

Figure 3.2.

Courses for Proposed SCM Undergraduate Program

3.4 OTHER MCDM TECHNIQUES To improve the effectiveness of decision support environments, MCDM techniques have been complemented by other modeling paradigms such as Outranking Methods and Artificial Intelligence techniques. Outranking Methods These techniques are used to provide an ordinal ranking of alternatives. One such technique is the ELECTRE I method which allows a decisionmaker to choose the alternatives that are preferred for most of the selection criteria and do not cause an unacceptable level of any single criterion. It constructs a subset of nondominated solutions for which a degree of dissension or discord is acceptable to the decision-maker. The outranking relationships are graphed to determine the preferred alternative. However, this technique

Figure 3.3. Courses for Proposed SCM Graduate Program

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may produce only a partial ordering of alternatives. ELECTRE II, an extension of ELECTRE I, can be used to develop a complete ordering of nondominated solutions. ELECTRE II uses multiple levels of concordance and discordance to construct and graph two extreme outranking conditions depicting strong and weak relationships. These graphs are then used to obtain a complete ranking of the alternatives (Goicoechea et al, 1982). Artificial Intelligence (AI) techniques Several Artificial Intelligence (AI) techniques such as Knowledge Based Systems or Expert Systems, Artificial Neural Networks, and Fuzzy Logic, have been used to implement multi-criteria decision models. Examples of these are: • A knowledge-based system for risk evaluation on capital projects (Benjamin and Bannis, 1990). • An ANN for classifying facility location alternatives (Benjamin et al, 1994). • Fuzzy multicriteria models and decision support system for QFD (Kim 1994). Computer Supported Collaborative Work (CSCW) Systems MCDM have been incorporated into the groupware used to support several CSCW systems to enhance the decision-making process and improve decision quality. The CSCW developed for the facility location domain provides a good illustration (Chi 1994). In this case, a GDSS was developed to assist stakeholders in the group decision-making process associated with facility location decisions. Here an Artificial Neural Network (ANN) model classifies location alternatives, a scoring model evaluates and ranks location alternatives, and an Expert System assesses group consensus. The integration of these techniques resulted in a robust, effective CSCW system.

3.5 CONCLUSION The ever-increasing complexity of the decision-making environment encountered by teams in today’s industrial and organizational settings has required the development and deployment of multi-criteria decision models to promote sound decision-making through collaboration, co-operation and consensus-building. Although these models (see Table 3.3 for a summary) can be used individually, increasing emphasis on their integration and combination with techniques such as Data Mining (Groth 1999) is essential for effectiveness. An exciting development has been the incorporation of MCDM techniques into Web-based tools such as Intelligent Agents (Turban et al, 2000)

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used for decision support in E-Commerce. Multi-criteria decision-making will continue to play a significant role in operationalizing the Computer Supported Collaborative Work Systems (CSCWs) often adopted by today’s Collaborative Engineering teams.

3.6 REFERENCES Al-Araimi, S.A., An Integrated Multi-Criteria Decision Model for Manufacturing Project Selection in a Developing Country, PhD diss., University of MissouriRolla, 1993. Benjamin, C. O. and A. Baksh, “A Methodology for Capital Equipment Selection”, Proceedings, International Industrial Engineering Conference, Dallas, Texas, May 1986, 511-517. Benjamin C. O., Y. Khawaja, S. Pattanapanchai and H. Siriwardane, “A Modified QFD Planning Framework for Process Improvement Projects”, Proceedings, 47th International IIE Conference, St. Paul, Minnesota, May 18-23, 1996, 35-39. Benjamin C. O., S. Pattanapanchai, and L. Monplaisir, “QFD-A Strategic Planning Framework for CIM Laboratories”, Proceedings, ASEE Annual Conference, Alberta, Canada, June 1994. Benjamin, C. O., R. Lynch and A. Mitchell, “A Methodology for Planning Enhancements to Computer Laboratories in Academia,” Proceedings, ASEE Southeastern Conference, Marietta, Georgia, April 1997, 211-218. Benjamin, C. O. and J. Bannis, “A Knowledge-Based Approach to Project Evaluation,” Proceedings, International Industrial Engineering Conference, San Francisco, California, May 20-23, 1990, 140-145. Benjamín, C. O., S. Chi, T. Gaber and C. Riordan, “Comparing BP and ART II Neural Network Classifiers for Facility Location”, Computers and Industrial Engineering, Vol. 28, No.1, 1994, 43-50. Benjamin, C. O., I. Ehie and Y. Omurtag, “Planning Facilities at the University of Missouri-Rolla”, Interfaces, Vol. 22, No. 4, July-August 1992, 95-105. Bossert, J. L., Quality Function Deployment: A Practitioner’s Approach, Milwaukee: ASQC Quality Press, 1991. Burgar, P., “Applying QFD to Course Design in Higher Education”, Annual Quality Transactions, 1994. Canada, John R., and William G. Sullivan, Economic and Multiattribute Evaluation of Advanced Manufacturing Systems”, New Jersey: Prentice-Hall, Inc., 1989. Chen, C. L. and S. F. Bullington, “Development of a strategic plan for an academic Department through the use of Quality Function Deployment”, Computers and Industrial Engineering, Vol. 25, Nos. 1-4, 1993, 49-52. Chi, S., “An Intelligent Group Decision Support System for Facilities Location”, PhD diss. University of Missouri-Rolla, Rolla, Missouri 1994. Crowe, T. J. and C. C. Cheng, “Using Quality Function Deployment in Manufacturing Strategic Planning”, International Journal of Operations and Production Management, Vol. 16, No. 4, April 1996, 35-48.

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Ehie, I. and C. O. Benjamin, “An Integrated Multi-objective Model for Industry Planning”, European Journal of Operational Research, Vol. 68, No. 2, July 1993, 160172. Ermer, D. S., “Using QFD Becomes an Educational Experience for Students and Faculty”, Quality Progress, May 1995, 131-136. Ferrell, S. F. and W. G. Ferrell, “Using Quality Function Deployment in Business Planning at a Small Appraisal Firm,” Appraisal Journal, Vol. 62, No. 3, July 1994, 382-390. Global Logistics Summit, “DEC: Selecting a Third Party Logistics Partner”, Proceedings, World Economic Congress, Washington D.C., November, 1996. Goicoechea, A., D.R. Hansen and L. Duckstein, Multiobjective Decision Analysis with Engineering and Business Applications, John Wiley and Sons, Inc., 1982. Groth, Robert, Data Mining: Building Competitive Advantage, New Jersey: PrenticeHall, 1999. Harvard Business School, “The English Channel Fixed Link Project”, Harvard Business School Case #9-189-046, Boston: Harvard Publishing, 1988. Ignizio, J. P., and T.M. Cavalier, Linear Programming, New Jersey: Prentice Hall, 1994. Kepner, C. H. and B. B. Tregoe, The New Rational Manager, New Jersey: Princeton Research Press, 1981. Khawaja, Y. and C. O. Benjamin, “A QFD Framework for Effective Transfer of AM/FM/GIS Information Technologies to Small Communities”, URISA, Journal of the Urban and Regional Information Systems Assoc., Vol. 8, No. 1, Spring 1996, 37-50. Kim, Kwang Jae, “Fuzzy Multicriteria Models and Decision Support System for Quality Function Deployment”, IMSE Working Paper #94-116, College of Engineering, Penn State University, 1994. Kusiak, A., ed. Concurrent Engineering: Automation, Tools, and Techniques, New York: John Wiley and Sons, Inc., 1993. Liberatore, Matthew J., and Robert L. Nydick, Decision Technology, Modeling, Software & Applications, Wiley & Sons, New Jersey 2003. Monplaisir, L., C.O. Benjamin and C. Lu, “Innovative Applications of Groupware for Solving Engineering Design Problems”, Engineering Management Journal, Vol..9, No.1, March 1997, 11-16. McCrary, Steven W., Toward the Development of Decision Support Tools to Manage the CATIS Project Life Cycle, PhD diss. University of Missouri-Rolla, Rolla Missouri, 1991. Muther, R.. and L. Hales, Systematic Planning of Industrial Facilities, Kansas City: Management and Industrial Research Publications, 1979. Saaty, T. L., The Analytic Hierarchy Process, New York: McGraw Hill, 1980. Schubert, M. A., “Quality Function Deployment: A Comprehensive Tool for Planning and Development”, Proceedings, IEEE National Aerospace and Electronics Conference, 1989, 1498-1503. Society of Manufacturing Engineers (SME), Proceedings, Clinic on Concurrent Engineering, Detroit, Michigan, March 1993.

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Turban, E. J., J. Lee, D. King and H.M. Chung, Electronic Commerce: A Managerial Perspective, New Jersey: Prentice-Hall, 2000. Weber, S. F., “A Modified Analytic Hierarchy Process for Automated Manufacturing Decisions”, Interfaces, Vol. 23, No. 4, 1993, 75-84. Zahedi, F., “The Analytic Hierarchy Process–A Survey of Methods and its Applications”, Interfaces, Vol. 16, 1986: 96-108. Zeleny, M., Multiple Criteria Decision Making, New York: McGraw-Hill, 1982.

Table 3.3. Comparison of Multi-Criteria Group Decision-Making Techniques

Figure 3.4.

House of Quality for planning SCM Program Development–Undergraduate Program

Figure 3.5.

House of Quality for Planning SCM Program Development–Graduate Program

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3.7 CASE STUDY: PLANNING ENGINEERING SERVICES AT THE HOUSTON CHRONICLE 3.7.1 Introduction 3.7.1.1 The Houston Chronicle Over the years, the Houston Chronicle has provided comprehensive news coverage and has focused on delivering a quality product to meet the varying needs of its diverse readership. Table 3.4 summarizes the sections included in the traditional version of its newspapers. The Chronicle’s steady growth and profitability continued through its incorporation into the Hearst group of companies in 1987. Table 3.4 provides a chronology of the evolution of the Houston Chronicle. By the mid-1990s, the Chronicle had become one of Houston’s largest employers with over 2000 employees and hundreds more involved through private contractors. 3.7.1.2 Hearst Corporation In 1987, the privately held Hearst Corporation, one of the largest diversified communications companies in the world (www.hearstcorp.com), acquired the Houston Chronicle. Hearst’s diversified portfolio includes more than 100 separate businesses including interests in: • Magazines-largest publisher of monthly magazines (16 US titles, 96 international editions in over 100 countries). Titles include Cosmopolitan, Esquire, Good Housekeeping, Popular Mechanics, Town & Country. • Newspapers-publishes 12 daily newspapers (including the Houston Chronicle), seven weekly newspapers, maintains a Washington news bureau, and operates a publisher of yellow pages telephone directories. • Broadcasting-includes television stations, radio stations and a television production company. • Entertainment and Syndication-combines Hearst’s cable network partnerships, (e.g. A&E, The History Channel, Lifetime Television, and ESPN), television programming and distribution activities, and various syndication companies. • Books/Business Publishing-includes William Morrow & Company, a leading hardcover publisher, and Avon Books, a prominent paperback publisher, and books, business publications and database catalogs in fields as varied as electronic design and engineering, automotive and floor covering. • New Media and Technology-manages Hearst’s growing interests in new media. • Real Estate-maintains extensive real estate holdings including timberlands and agricultural operations in California, and commercial properties in New York City and San Francisco.

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Table 3.4. Product Structure – Houston Chronicle

Becoming a member of such a diversified global enterprise presented many opportunities and posed several challenges for the management of The Chronicle. 3.7.1.3 Challenges To meet the challenges of the 1990s, the Houston Chronicle pursued several business strategies including the following: • Outsourcing of several functions e.g. Distribution, Market Research, Color Printing, Facilities Maintenance to enable a corporate focus on core competencies • Developing an Internet-based version of the Houston Chronicle to establish an early presence in this new and exciting media. • Incorporating state-of-the-art Information Technology concepts into its operations

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3.7.2 The Houston Chronicle Organization 3.7.2.1 Departmental Functions The production and delivery of the newspaper is a highly complex process involving collaboration and co-operation among several departments. The major areas of responsibility include: • • • •

Editorial-Information Gathering, Art/Graphics, Editing Marketing-Promotions, Chronicle in Education, Public Affairs, Research Advertising-Sales, Service Production-Advertising Design & Services, Composing, Engraving, Pressroom, Mailroom/Packaging • Circulation-Transportation, Distribution Center, Home Delivery, Single Copy Sales. Financial, Engineering, Information Technology and Administrative Departments provide the business and technological support needed to manage the operations of the Chronicle. 3.7.2.2 The Engineering Services Department Over the years, the Engineering Services Department has delivered a wide range of project engineering services (i.e. architectural, mechanical, electrical & industrial) and has focussed on striving for excellence in the areas of Design Services, Project Management, Facilities Management, and Capital Planning. Table 3.5 summarizes the current engineering professional staff and their primary engineering discipline. 3.7.3 Scope of Study Competitive pressures in the industry require an emphasis on continuous improvement in all areas to achieve even higher levels of customer service, product quality, operating efficiencies, and overall profitability. This demands constant introspection by the various Departments to ensure their operating strategies and procedures are contributing to the attainment of the overall corporate goals. Cost centers such as the Engineering Services Department were constantly being scrutinized for opportunities to realize cost savings via outsourcing or headcount reduction. In an effort to identify ways of improving the quality of service it offers to its internal customers, the Engineering Services conducted a study to assess

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Table 3.5. Professional Staff – Engineering Services Department

customer perceptions of the Department and to identify areas of improvement. The study utilized a two-phased modified Quality Function Deployment (QFD) framework as follows: Phase #1: Planning–to identify and prioritize the engineering tools and techniques most applicable to delivering quality services to the Houston Chronicle departments. Phase #2: Design–to design an appropriate mix of training and professional development strategies to enhance professional competencies. Each phase would be implemented using the following steps: • • • • •

Step #1: Define the Customers Step #2: Survey Customer Expectations and Wants i.e. “The WHATs” Step #3: Identify relevant System Attributes i.e. “The HOWs” Step #4: Map the HOWs into the WHATs Step #5: Develop the House of Quality

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3.7.4 Quality Function Deployment 3.7.4.1 A Modified QFD Process The first phase of the modified QFD process employed was implemented using the following five-step procedure: Step #1-Define the customer: In this case, the customers were the Houston Chronicle (HC) managers. Step #2-Identify the critical Areas of Engineering Service ( the WHATs) to be delivered to the managers and establish the importance of each Area: A survey of HC managers identified thirteen critical Areas of Engineering Service. Responses from the HC managers were also used to gauge the relative importance of each WHAT. Step #3-Identify possible Engineering Tools and Techniques (the HOWs) for the Department. Following several brainstorming sessions, the Department identified twenty methodologies most applicable to HC’s Engineering Department. These were divided into four categories viz. Computer Aided Engineering, Facilities Engineering, Project Engineering, & Others. Step #4-Map the HOWs into the WHATs: Using a summer faculty consultant as a group facilitator, the engineering team mapped the HOWs into the WHATs by assigning ratings on a 1-3-9 scale (1–weak; 3–medium; 9–strong;) to indicate the relationship between each HOW and WHAT. Step #5-Develop a House of Quality. A spreadsheet-based model of the HOQ was constructed. This facilitated computation of the row and column totals and the ranking of the Engineering Tools and Techniques under consideration. 3.7.4.2 Customer Survey–Determining Customer Needs 3.7.4.2.1 Survey Methodology. In the Customer Service survey conducted, fifty Houston Chronicle managers were asked to indicate the following: • Level of Awareness of the Engineering Services Department • Quality of Service offered in the last two years • Importance of the various Areas of Engineering Services within the next two years. 3.7.4.2.2 Analysis of Results. The results revealed the following: Level of Awareness There was a Moderate level of Awareness (3.4/5.0) of the range of Engineering Services available.

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Quality of Service The Quality of Service over the last 2 years was Very Good (3.83/5.0) with the highest ratings (4.0/5.0) obtained in the areas of Space Planning, ReEngineering Projects, Automation, Productivity Studies, and Process Improvement. Importance of Engineering Services The Future Importance of the Areas of Service is High (Average 3.79/5.0) with the most important Areas being Space Planning, Capital Budgeting, ReEngineering Projects, and Equipment Evaluation. This information was used as input into the QFD model.

3.7.5 Determining the HOWs –The Engineering Tools and Techniques Using the Nominal Group Technique, we identified twenty Engineering Tools and Techniques applicable to HC’s Engineering Department. These were divided into four categories viz. Computer Aided Engineering Computer Aided Design PLC Database Drawings Database Engineering Web Page Facilities Engineering Building Automation Power Management Systems Facilities Planning Facilities Design Safety Engineering Project Engineering Cost Estimating Project Justification Project Planning & Scheduling Product Development Process Development Others Inventory Control Value Engineering Simulation Quality Function Deployment Artificial Intelligence

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The spreadsheet-based model of the HOQ shown in Figure 3.6 summarizes the results of this planning process. This facilitated computation of the row and column totals and the ranking of the Engineering Tools and Techniques under consideration. 3.7.6. Discussion of Results 3.7.6.1 Engineering Service Areas The WHATs–Engineering Service Areas The results from the Customer Service Survey indicated that the greatest importance should be assigned to the following Engineering Service Areas:Space Planning, Capital Budgeting, Equipment Evaluation, and ReEngineering Projects. On a five-point weighting scale, these received weights > 4.0. The Engineering Service Areas which emerged as being moderately important were Project Management, Process Improvement, Energy Management, Cost Analysis, and Automation. These received weights > 3.5 and ≤ 4.0. The lowest importance was accorded the following Engineering Service Areas:Furniture Management, Inventory Management, Product Development, and Productivity Studies. These received weights ≤ 3.5. 3.7.6.2 Engineering Tools and Techniques The HOWs-Engineering Tools and Techniques The scores obtained by the twenty Engineering Tools and Techniques examined ranged from a low of 1.15% to a high of 9.08 %. Computer Aided Design, Cost Estimating, Facilities Design, Project Justification, and Value Engineering obtained scores in the top quartile (9.08%, 8.26%, 7.70%, 7.37%, and 7.31%). On the other hand, the Engineering Tools and Techniques which received scores in the bottom quartile (3.32%, 2.55%, 2.51%, 1.54% and 1.15%) were Safety Engineering, Artificial Intelligence, Drawings Database, Power Management Systems, and PLC Database. The remaining ten Engineering Tools and Techniques which emerged as being moderately important received scores ranging from 6.90% to 3.59%. 3.7.6.3 Sensitivity Analysis 3.7.6.3.1 Analysis of Results. Sensitivity tests were conducted to ascertain the impact of variations in the weights assigned to the customer needs (the WHATs) and the rating scale used to map the HOWs into the WHATs. Four scenarios were investigated. Scenario 1 used the weights obtained from the original customer survey data and a rating scale of 1-3-9 to map the HOWs into the WHATs. In Scenario 2, all weights adopted in Scenario 1 were

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reduced by 30%. In Scenario 3 all WHATs were assumed to be of equal importance and were assigned a weight of four (Average/High importance) on a five-point scale. In the final case, Scenario 4, the weights of the WHATs were similar to those obtained in the original survey. In this case, however, a 1-35 rating scale was used (1-weak; 3-medium; 5-strong) to map the HOWs into the WHATs. 3.7.6.3.2 Analysis of Results. In Table 3.6, the Engineering Tools and Techniques are grouped in ABC categories to reflect their level of importance. The results summarized in Figure 3.7 confirmed that the proposed planning framework was very robust. Although the four scenarios investigated incorporated significant changes in the input planning data, there was little impact on the output-the ranking of the HOWs–The Engineering Tools and Techniques. The HOWs that occupied the top quartile were the same, viz. Computer Aided Design, Cost Estimating, Facilities Design, Project Justification, and Value Engineering. Those in the bottom quartile (Safety Engineering, Artificial Intelligence, Drawings Database, Power Management Systems, and Table 3.6. ABC Classification of Engineering Tools and Techniques

Figure 3.6.

QFD Chart for Planning Engineering Services-Phase #1 Baseline Mode—Rating Scale: 1 –Weak; 3–Moderate; 9–Strong

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Figure 3.7. Results of Sensitivity Analysis— Code: A–Most Important; B–Important; C–Least Important

PLC Database) also retained their rank while there was slight shifting of the positions of the HOWs placed in the second and third quartiles. 3.7.6.4 Further Work 3.7.6.4.1 Procedure. In the second phase, we focused on designing an appropriate mix of training and professional development strategies to enhance professional competencies. This was implemented using the following five-step process:Step #1-Define the customer: In this case, the customers were the Houston Chronicle engineers. Step #2-Identify the critical Engineering Tools and Techniques (the WHATs) to be employed by the Engineering staff and establish their importance. This represents the output from Phase 1. Step #3-Identify possible Training and Professional Development Strategies (the HOWs) for the Department. Using a nominal group technique, the Department identified twelve Training and Professional Development strategies applicable to HC’s Engineering Department. Step #4-Map the HOWs into the WHATs: Using a summer faculty consultant as a group Facilitator, the engineering team mapped the HOWs into the WHATs by assigning ratings on a 1-3-9 scale (1–weak; 3–medium; 9–strong) to indicate the relationship between each HOW and WHAT.

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Step #5-Develop a House of Quality. A spreadsheet-based model of the HOQ was constructed. This facilitated computation of the row and column totals and the ranking of the Training and Professional Development Strategies under consideration. The partially completed QFD chart shown in Figure 3.8 summarizes the results of Steps #1-#4 of the process. However, before any recommendations could be made, Step #5 now needs to be completed. Then sensitivity analysis should be conducted and the results analyzed and interpreted before recommendations could be offered to the Manager of the Engineering. Services Department and the Senior Vice President, Engineering, Technology and Administrative Services. 1902-The Chronicle buys and consolidates another afternoon newspaper, the Daily Herald.

Figure 3.8.

Partially Completed QFD Chart for Phase #2

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1908-Jesse Jones is engaged to build the ten-story Chronicle Building at the corner of Texas and Travis in exchange for part interest in the paper. 1912-The Chronicle takes the lead in Houston newspaper advertising. 1926-Marcellus Foster sells his remaining interest in The Chronicle to Jones, who then becomes the sole owner. 1940-Circulation reaches 117,000. 1956-Jesse Jones dies after transferring Chronicle ownership to the Houston Endowment, a charitable foundation which he and his wife, Mary Gibbs Jones, established. Sunday circulation sets a record of 354,000. 1974-Saturday Sunrise, a morning edition, is introduced and becomes an immediate success. 1979-The first daily morning edition is published, adding significantly to The Chronicle’s circulation. 1985-The Chronicle buys Promotional Printing Corporation-an independent printer known for high volume printing-and makes it a wholly owned subsidiary. 1987-The Hearst Corporation buys The Chronicle from Houston Endowment for Mn $415. 1995-The Chronicle’s largest rival, The Houston Post, ceases operations, leaving Houston the nation’s largest one-newspaper city. In an ensuing agreement, the Hearst Corporation purchases some of the Post’s assets including its buildings and printing presses.

Chapter Four

Project Management

4.1 The Evolution of Project Management 4.2 Project Planning, Scheduling and Control Techniques 4.3 Network Analysis 4.3.1 An Overview 4.3.2 Phases in Application 4.3.3 Methods of Presentation 4.3.4 Critical Path Algorithm 4.3.5 Resource Analysis 4.4 Project Management Software 4.5 Applications in Business 4.6 References 4.7 Worked Example: Pump Installation Project 4.7.1 Assignment 4.7.2 Analysis Using EXCEL 4.7.3 Analysis Using Microsoft Project Software 4.8 Assignment–The CIM Laboratory Facility 4.9 Case Study: Developing an AI-Based Membership Retention System at ALA

4.1 THE EVOLUTION OF PROJECT MANAGEMENT A project can be defined as a temporary endeavor consisting of a set of interrelated activities undertaken to create a unique product or service. This definition covers a wide range of business activities encountered by engineering and business professionals. These include: 82

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• • • • • •

83

Planning and implementing an external audit Implementing a new Enterprise Resource Planning (ERP) system Developing a feasibility study for a hotel and convention center. Installing a new printing press Launching a new product Constructing a new office building

The project management philosophy encourages a clear focus on on-time, within-budget project completion while striving to ensure the completed project attains the desired level of operating performance. As shown in Figure 4.1 and Table 4.1, the field of Project Management in the past 20 years, has steadily evolved (Morris, 1999), moving from the Project Management Institute’s narrow focus on project implementation in the mid-1980s (www.pmi.org), to a broader focus by the Association for Project Management (www.apm.org.uk) to achieve project delivery while meeting customer or sponsor requirements. This shift has been supported by research agencies such as the Centre for Research in the Management of Projects (www.umist.ac.uk/CRMP) which advocate enlargement of APM’s body of knowledge to include a stronger focus on meeting customer requirements. Today’s increasing emphasis on globalization now requires even greater expansion of Project Management’s body of knowledge to incorporate adequate consideration of global issues when planning and implementing global projects. The challenges of managing global projects have attracted the attention of several researchers who seek to address issues related to managing cultural differences (Anbari et al, 2004), developing strategies for global project evaluation (Patel 2004), managing relationships in international business partnerships (Endrissat and Kuehlmann, 2004), and understanding linkages between Project Management and business performance (Morris 2002). Continuous

Figure 4.1. Evolution of Project Management

84 Table 4.1.

Chapter Four Comparison of Project Management Bodies of Knowledge

Improvement (CI) of the traditional courses in a Project Management curriculum is vitally important in academia to maintain a high quality of service to “customers”- the students, faculty, and industry stakeholders. Among the innovations proposed for curriculum enhancement at the School of Business and Industry (SBI) at Florida A&M University (FAMU) is the expansion of traditional project management techniques to incorporate global concepts which would better enable project managers to operate in a global environment (Gray et al, 2006). Several techniques have been employed by project managers to aid decision-making in all phases of the project life cycle. These include idea generation techniques such as Brainstorming (Burns and Bush, 2000), Brain Writing (Couger 1995) and Quality Function Deployment (Benjamin and Thompkins, 2003), which are used in the early phases of the project life cycle. Also employed are economic and financial evaluation techniques such as the Internal Rate of Return (DeGarmo et al, 1997) and Cost Benefit Analysis (Newman 1991) which are used to assess the viability of new ventures, and project planning and scheduling techniques such as Network Analysis (Mantel et al, 2005), which provide the theoretical basis for numerous project management software developed. We will discuss Network Analysis in detail in the following section.

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4.2 PROJECT PLANNING, SCHEDULING AND CONTROL TECHNIQUES The techniques used in the planning, scheduling and control of projects are summarized in Table 4.2. In today’s inter-connected global community, there will be an ever-increasing need to solicit input in a cost-effective manner from project team members distributed throughout the world thus avoiding the constraints of time and place. These virtual teams now have ready access to a range of powerful technologies that can greatly facilitate their work. Three of the more promising technologies available to facilitate the operation of these distributed project teams are Teleconferencing and Videoconferencing, (www Table 4.2.

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.webopedia.com/TERM/v/videoconferencing.html. Accessed April 2005); Group Decision Support Systems (Benjamin et al, 1996), and the Access Grid (Binns et al, 2000). In industry, Tele/Videoconferencing systems and Group Decision Support Systems have experienced steady gains in popularity and teams seeking to use these technologies can choose from several mature commercial systems. In academia, the Access Grid technology offers great promise as a cost-effective enabling technology for facilitating collaboration among members in distributed project teams.

4.3 NETWORK ANALYSIS 4.3.1 An Overview Network Analysis techniques were first introduced as project planning and scheduling aids in the late 1950s. CPM (Critical Path Method) (Levy et al, 1963), evolved from efforts initiated originally by the Du Pont Company to facilitate cost control on its large maintenance projects. Each project was broken down into a number of activities and an arrow diagram used to display the sequence in which these activities could be executed. Deterministic time estimates were made of activity durations and an attempt was made to include, as an integral part of the overall scheme, a procedure for time/cost trade-off to minimize the sum of direct and indirect project costs. Around the same time, the United States Navy Special Projects Office set up a joint team which developed the PERT (Program Evaluation and Review Technique) methodology (Malcolm et al, 1959) to assist in managing the large complex Polaris missile project and help avoid the time and cost overruns that had plagued other similar development programs. The early completion of this project was partly attributed to the use of this technique which differed from the CPM method in that it used probabilistic time estimates for activity durations. The mean and variance of each activity duration were computed from three time estimates viz. an “optimistic” time, a “pessimistic” time and a “most likely” time. This statistical treatment of uncertainty in activity durations enabled the computation of probability estimates of meeting specified scheduled dates. The PERT and CPM techniques represented an improvement over the Line of Balance (LOB) approach (Turban 1968), a basic project management tool which has never attained the popularity of Network Analysis. The advantages of network analysis include: 1. Precedence or sequence dependency is readily apparent. 2. The activities (tasks) and the events (completion points) are distinguished, thus stressing critical accomplishment.

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3. It permits ready consideration of alternative plans and can be analyzed using readily available project management software. 4. It can be tied into cost control and resource analysis. 5. It provides excellent control for tight sequencing of many critical events. Today, network analysis techniques, extended and refined, provide the basis for numerous project management software packages (Kezsbom et al, 1989). In this chapter, we review the fundamentals of Network Analysis and illustrate its application in managing projects in business.

4.3.2 Phases in Application Network Analysis techniques can be applied to assist in the planning, scheduling and control of projects. The following activities are associated with the various phases: Phase #1: Planning • • • • •

Prepare a Job List Determine Immediate Predecessors Estimate Job Duration Estimate Resource Requirements Determine Logic Network

Phase #2: Scheduling • Do Forward Pass in Network (Obtain ES, EF times) (ES-Early Start; EF-Early Finish) • Do Backward Pass in Network (Obtain LS, LF times) (LS-Latest Start; LF-Latest Finish) • Compute Activity Slack Times • Identify Critical Path(s) • Include Resource Considerations • Prepare Bar /Gantt Chart Phase #3: Control • Monitor Implementation • Revise and Update Network The planning phase should result in a job list similar to that shown in Table 4.3 for a small pump installation project.

88 Table 4.3.

Chapter Four Job List for Pump Installation Project

4.3.3 Methods of Presentation In the planning phase, a decision must be made on the method of presenting the logic network diagram. The popular alternatives are: • The Activity-on-the-Arrow (AOA) Diagram • The Activity-on-the-Node (AON) Diagram The choice is influenced in part by the preference of the project planner and the dictates of the project management software which will be used to analyze the network. Whereas earlier software supported the AOA method, more recent packages offer the user the option of using either the AON or AOA method of presentation. These alternative conventions of presenting the network diagram are illustrated in Figures 4.2 and 4.3 using data from a small

Figure 4.2. Activity on the Node (AON) Network Diagram for Pump Installation Project

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Figure 4.3. Activity on the Arrow (AOA) Network Diagram for Pump Installation Project

pump installation project. In the AOA method of presentation, dummy arrows are sometimes required to preserve the logic of the network. Dummy activities have zero duration and may also be used to improve the layout of the network diagram. 4.3.4 Critical Path Algorithm Once a logic network diagram has been drawn to depict the precedence relationships that exist among project activities, a temporal analysis of the network diagram can be obtained by applying the Critical Path Algorithm (Wiest and Levy, 1974).

Table 4.4.

Early and Late Activity Start and Finish Times for Pump Installation Project

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In this algorithm, the activities are first listed in technological order. Then the following calculations are performed for each activity in a forward pass through the network (from left to right): S = start time for project (usually=0) ES (a) = S for all beginning activities , or ES (a) = max {EF(all predecessors of a)} EF (a) = ES (a) + t(a) T = max {EF(all jobs)} = earliest finish time for project. Then, in a backward pass through the network, the following are calculated: LF (a) = T for all ending jobs, or LF (a) = min (LS (all successors of a)} LS (a) = LF (a)-t (a) TS (a) = LS (a)-ES (a) = LF (a)-EF (a) FS (a) = min {(ES (all of immediate successors of a)}-EF (a). The critical path algorithm when applied to a network diagram thus results in the following information: • • • • •

The ES (Early Start) and EF (Early Finish) activity times The LS (Late Start) and LF (Late Finish) activity times The Free Slack (FS) and Total Slack (TS) for each activity The critical activities (i.e. those with TS = 0) The critical path(s) i.e. the longest path(s) through the network.

Note that Free Slack (FS) represents the period of time an activity can be delayed without impeding the start of a succeeding activity and, Total Slack (TS) represents the period of time an activity can be delayed without impeding the completion of the entire project. Table 4.4 summarizes the early times (ES, EF) and late times (LS, LF) computed using the critical path algorithm and the planning data assembled in Table 4.3 for the pump installation project. A Bar/Gantt Chart can also be prepared using information from the temporal analysis to provide a visual summary of the proposed implementation schedule for the project. Figure 4.4 shows the Gantt chart developed for the pump installation using Visio. 4.3.5 Resource Analysis In the temporal analysis of network diagrams outlined above, resource considerations are not explicitly incorporated. Estimates of activity durations assume “normal” resource requirements based on generally accepted work methods and ignore resource constraints. Projects often have to be

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Figure 4.4. Gantt Chart for Pump Installation Project using Visio

implemented in an environment in which resources are limited and cannot be acquired and re-deployed instantaneously. Project planning has therefore to be extended to incorporate resource analyses to ensure project completion with a minimum of time and cost slippages and disruption of ongoing operations. This can be effected in the following ways: • Resource Aggregation • Resource Leveling • Time/Cost Trade-Off These alternative approaches will be discussed below. Resource Aggregation This represents the simplest approach to resource analysis in a project network. Using the early start bar chart developed from the temporal analysis of the network, resource loading charts can be prepared showing the profile of resource use required over the project’s duration. This will facilitate identification of periods of peaks and troughs in resource requirements and prompt the development of appropriate strategies for resource acquisition or re-deployment to maintain the desired implementation schedule. A typical resource loading chart is shown in Figure 4.5. Resource Leveling This approach to resource analysis starts with an examination of the resource loading charts and attempts to smooth out wide fluctuations in periodic resource requirements. This is illustrated in Figure 4.6.

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Figure 4.5. Typical Resource Loading Chart

Several heuristics have been used and incorporated into computer programs to provide an automated resource leveling feature in many project management software packages. In some instances, when rigid resource limits are imposed, it may be necessary to extend the project duration to conform to the constraints. Resource-constrained project scheduling constitutes an important part of project planning and can assist planners in implementing projects with a minimum of disruption while meeting budget and schedule targets. Time/Cost Trade-Off Project planners must determine the optimum pace at which a project should be implemented to conform with time, cost, and performance targets.

Figure 4.6. Resource Leveling with a Resource Constraint of Two Analysts

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At the activity level, if the work is to be executed using a “normal” crew/team size, with “normal” work methods, during “normal” working hours, a “normal” duration can be estimated for each activity. However, jobs can be rapidly “expedited” and completed, if required, in a shorter “crash” duration. The time savings obtained often requires additional direct costs resulting from the increased resource allocation e.g. additional manpower, increased mechanization, overtime work. The challenge to the project planner is that of determining the activity durations which best fit his overall project objectives of time, cost, and performance. This time/cost trade-off at the activity level is illustrated in Figure 4.7. In this diagram, the time/cost variation is assumed linear between the “normal” and “crash” points. A similar trade-off must be made at the project level. If all project activities are executed in their “normal” durations, the entire project will be completed in a “normal” time (Dn). Associated with this implementation pace will be lower direct activity costs but higher indirect costs e.g. overheads, penalty costs, etc. If activities are crashed, then the project can conceivably be completed in as short a time as Dc, the crash duration. This will however incur inevitable direct costs but may typically result in lower indirect costs. The optimum project duration which minimizes total project costs is as shown in Figure 4.8. The time/cost trade-off can be formulated as a linear programming (LP) problem if linearity assumptions about the time/cost variation at both the activity and project level are valid. A typical LP formulation is shown in Figure 4.9. The optimum project and activity durations can thus be determined from

Figure 4.7. Time/Cost Trade-off – Activity Level

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Figure 4.8. Time/Cost Trade-off – Project Level

such a model. These results should be regarded more as a guide to the project planner in developing a sound project implementation plan.

4.4 PROJECT MANAGEMENT SOFTWARE Over the years, several surveys have been done of commercially available project management software. Walton and Staffurth (Project Management Institute, 1980) report the results of a United Kingdom survey done in the early 1970s to find, and if possible, test and assess publicly available computer packages for network analysis. Most (23) of the 36 computer packages evaluated provided resource analysis facilities. Similar surveys have been carried out in the USA by the Project Management Institute (Smith and Mahler, 1978), and Smith and Mills (1982). In recent years, the major area of growth has been PC-based software. As PC hardware has improved in processing speed and information storage capacity, project management software has been developed to address important areas of project management such as resource leveling, project costing, reporting. Software surveys in the professional journals and the PC magazines, (O’Neal 1987; Fersko-Weiss, 1989; Davis and Martin, 1985), can provide a useful overview of the important features of the various project man-

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Figure 4.9. LP formulation of Time/Cost trade-off

agement software. Each software package has its peculiar strengths and weaknesses and thus selection of an appropriate software package can present a formidable challenge to the project engineer or manager. Many packages include features to facilitate network construction, project scheduling, cost control, resource analysis, and report generation. Managers today have access to a wide range of commercially available Project Management (PM) software which can significantly aid project planning and control (Elliott, 2001) These software range from low-cost programs such as Milestones Simplicity which have limited functionality to the more expensive programs such as Artemis Views and Primavera P3e which offer a comprehensive suite of features (see Figure 4.10). Programs such as Microsoft Project (2003) have gained popularity among industry practitioners because they provide adequate flexibility at an affordable cost, are fairly easy to use and are readily integrated with conventional office productivity tools. More recently, “add-ins” have been developed (Grossman 2002) to expand

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Figure 4.10. Price/Functionality Comparison of Project Management Software Alternatives

the capability of project management software to incorporate areas of Risk Management, an area of special interest on large, complex projects. In this chapter, we illustrate the application of the Microsoft Project software to develop a project plan for a small pump installation project. In this example, we show that once planning information has been assembled, the Project Manager can systematically proceed with developing a project schedule, conducting resource and cost analyses, incorporating risk and uncertainty, and making appropriate plan revisions to ensure conformance with company objectives and constraints. Among the factors that influence the choice of project management software are:• • • • • • • •

Features of the projects Users of the software Cost analysis features Cost of the software Users of the reports Resource Planning and Scheduling Hardware limitations Software documentation

4.5 APPLICATIONS IN BUSINESS As shown in Table 4.5, project management techniques have widespread application in business ranging from small, simple projects such as planning a

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small pump installation to larger, complex projects such as the design and construction of a deluxe hotel and convention center in the Caribbean. In all cases, these projects present exciting challenges to managers in all functional areas of an organization. The techniques outlined in this chapter should provide an introduction to the ever-evolving project management body of knowledge and stimulate the development of frameworks for adopting the project management philosophy to improve all facets of business performance.

4.6 REFERENCES Anbari, F.T., E.V. Khilkhanova, M.V. Romanova, & S. A. Umpleby, “Managing Cultural Differences in International Projects”, Journal of International Business & Economics, V. II, No. 1, ‘04, p. 267-74. Association for Project Management, www.apm.org.uk, Accessed June, 2004. Benjamin, C. O., L. Monplaisir, S. Chi, L., Lahndt-Hearney, & C. Riordan, “Group Decision Support Systems: A Review of Industrial Applications”, Proceedings, 47th International Industrial Engineering Conf., St.Paul/Minneapolis, Minnesota, May 18-23, ‘96, 197-02. Benjamin, C.O. & G. Thompkins, “Developing an Integrated Engineering for Business Curriculum”, Review of Business Research, Vol. 1, No. 1, 2003, 43-55 Binns, J. et al., The Access Grid (2000). Available at: http://www.accessgrid.org

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Burns, Alvin C., and Ronald F. Bush, Marketing Research, Prentice-Hall, New Jersey, 2000. Centre for Research in the Management of Projects, www.umist.ac.uk/CRMP, Accessed Jun. 2004. Couger, J. Daniel, Creative Problem Solving and Opportunity Funding, Boyd and Fraser Publishing, Danvers, MA, 1995. Davis E. W. and R.D. Martin, “Project Management Software for the Personal Computer: An Evaluation,” Project Management Journal, 1985, Vol. 16, No. 5,100-125. DeGarmo, E. P., W.G. Sullivan, J.A. Bontadelli and E.M. Wicks, Engineering Economy, 10th Edition, Prentice-Hall, Inc., New Jersey, 1997. Elliott, Monica (2001), Buyer’s Guide Project Management, IIE Solutions, August, 45-52. Endrissat, Nadia and T.M. Kuehlmann, “Risk, Control, and Trust-Building in International Business Partnerships”, Journal of International Business and Economics, Vol. 1, No. 1, 2004, 148-161. Fersko-Weiss, H. “High-End Project Managers make the Plans,” PC Magazine, Vol. 8, No. 9, May 1989. Gray, K., C. Benjamin and N. Shrestha, “Teaching International Business through Global Project Management”, Proceedings, Academy of International Business, US Southwest Chapter (AIB–SW) Meeting, Oklahoma City, Oklahoma, March 1-4, 2006. Grossman, Thomas A., “Spreadsheet Add-Ins for OR/MS”, OR/MS Today, Aug., 2002. Kezsbom, D. S. D.L. Schilling and K.A. Edward, Dynamic Project Management: A Practical Guide for Managers and Engineers, Chapter 11, Project Management Software Systems, 317-345, John Wiley & Sons, New York, 1989. Levy, F. K., G.L. Thompson and J.D. Wiest, “The ABCs of the Critical Path Method,” Harvard Business Review, September-October, 1963. Loughborough University of Technology, NCC Publications, Manchester, England, 1974. Malcolm, D. G. J.H. Rosenbloom, C.G. Clark and W. Fazar, “Application of a Technique for Research and Development Program Management,” Operations Research, Sept.-October, 1959. Mantel, Samuel J., Jack R. Meredith, Scott M Shafer, & Margaret M. Sutton, Project Management in Practice, 2nd edition, John Wiley & Sons, 2005 MicroSoft Corporation, MS Project Software, 2003. Morris, P. “What project managers need to know”, IEE Review, July 1999, pp. 173175. Morris, Peter W., “Research Trends in the 1990s: The Need to Focus on the Business Benefits of Project Management”, in The Frontiers of Project Management Research, edited by Dennis P. Slevin, David I. Cleland, and Jeffrey K. Pinto, PMI Inc., 2004, Chapter 2, pp. 31-56. Newman, Donald G., Engineering Economic Analysis, 4th ed., Engineering Press, Inc., San Jose, California, 1991. O’Neal, K. R. “Project Management Microcomputer Software Buyer’s Guide,” Industrial Engineering, January 1987, 53-63.

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Patel, Bhavesh, Project Management: Strategic Financial Planning, Evaluation and Control, Vikas Publishing House, New Delhi, 2000. Project Management Institute, www.pmi.org, Accessed June 2004. Smith L. A. and P. Mahler, “Comparing Commercially Available CPM/PERT Computer Programmes,” Industrial Engineering, April 1978. Smith L. A. and J. Mills, “Project Management Network Programmes,” Project Management Quarterly, June 1982. Teleconferencing and VideoConferencing, Video Conferencing (2004). Available at: http://www.webopedia.com/TERM/v/videoconferencing.html. Accessed April 2005. Turban, E. “The Line of Balance-A Management by Exception Tool,” Journal of Industrial Engineering, September 1968. Wiest J. D. and F.K. Levy, “A Management Guide to PERT/CPM”, 2nd edition, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1977. 6. H. Walton and C. Staffurth, “Programmes for Network Analysis-A United Kingdom Survey,” Joint report by Internet (UK) and Project Management Institute, “Survey of CPM Scheduling Software Packages and Related Project Control Programmes,” Project Management Quarterly, January 1980. Wood, L. “The Promise of Project Management,” Byte, November 1988, 180-192.

4.7 WORKED EXAMPLE: PUMP INSTALLATION PROJECT 4.7.1 Assignment You have been appointed Project Co-ordinator for the pump installation project described in Table 4.6. You have been asked by the Plant Manager to develop a plan to assist in completing the installation successfully, without any cost over-run or time slippage. 1. Prepare an implementation schedule in the form of a Network Diagram and a Bar/Gantt Chart for the project. (Use the data provided in Table 4.7 and assume a start date of January 1). 2. What recommendations would you make based on this analysis? 3. How would your schedule change if your installation crew was limited to one skilled worker and one unskilled worker? 4.7.2 Analysis Using EXCEL We can begin by developing an implementation schedule in the form of a Network Diagram and a Gantt Chart for the project (Figures 4.11 & 4.12). Based on the information obtained from this, we have identified the critical path and total project duration (Figures 4.11 & 4.12). Using Microsoft Excel, we

100 Table 4.6.

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developed a resource profile for skilled workers (Figure 4.13), unskilled workers (Figure 4.14) and skilled and unskilled workers combined (Figure 4.15) over the project’s duration. Next, we prepare a simple budget for the project, using Excel. The daily rates for the skilled workers and unskilled workers are $120 and $80 respectively, with a 50% premium for overtime. In addition, indirect costs are incurred over the duration of the project at a rate of $100/ day (factory overhead, project administration, etc). The total budget for the project comes to $8,160 (Figure 4.16).

Figure 4.11. Network Diagram for Pump Installation Project using Visio

Figure 4.12. Gantt Chart for Pump Installation Project

Figure 4.13. Resource Aggregation–Skilled Labor

Figure 4.14. Resource Aggregation–Unskilled Labor

Figure 4.15. Resource Aggregation–skilled and unskilled workers

Figure 4.16. Budget for Pump Installation Project

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Figure 4.17. Set Project Start Date

4.7.3 Analysis Using Microsoft Project Software We can also utilize Microsoft Project to prepare a project plan. After opening the software, we will begin by setting the project beginning date. We do this by selecting the “Project” drop down menu, and selecting “Project Information.” From here we can enter our starting date of Monday, August 2, 1999 (See Figure 4.17). From here, we can input our resources (our materials, laborers, and indirect costs). Select the “View” drop down menu, and select “Resource Sheet” From here we input our skilled and unskilled laborers, and their corresponding daily rates. In addition, we input our material costs for each task, and our indirect cost rate ($100/day) (Figure 4.18). It is important to note there is also a place for an overtime rate, which can be converted to an hourly rate of $22.50/hr and $15/hr for skilled and unskilled workers respectively. By selecting “View” and the “Gantt Chart,” we can now input our task information. We can input our task description and the corresponding duration of the task. (See Figure 4.19).

Figure 4.18. Resource Sheet

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Figure 4.19. Input Task Information

After inputting our task information, we can assign the appropriate resources to each task. Click on the resource icon on the taskbar. The Resource Assignment window will appear.

4A.

Resource Icon

You will see those resources we input in the Resource Sheet. Click on the appropriate task and use the window to assign the correct resource(s). Remember, those resources expressed as rates (skilled and unskilled labor and indirect costs) should be entered in their corresponding percentages. For example, if you have 2 skilled workers for a particular task, you would assign the resource at 200%, if you have one skilled worker, it would be 100%. In addition, remember that only those tasks that are on the critical path should be assigned the indirect costs, as they constitute the duration of the project with no overlap.

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4B.

Resource Assignment Window

Next, we want to establish relationships between the tasks (i.e. predecessors, successors). We can do this easily by selecting “View” and then “Network Diagram”. Here we see the visual representation of our project, much in the same way as the Network Diagram (see Figure 4.11). To make things easier, we can right click the background area, and select “Layout.” From here, select the radio button at the top of the window titled “Allow Manual Box Positioning.” Click ok. Now we can move the boxes how we like to organize our diagram. There are two ways to create the relationship. First, we can simply click on a box, and while continuing to hold the mouse down we can drag our mouse to the task that directly follows it. Our second approach is to right click the task box, and select “Task Information.” We can click on tab titled predecessors, and input our preceding tasks (Figure 4.20). After establishing the relationships between tasks, we can order the Network Diagram how we wish. In addition, the critical path should be displayed for us in red. If we switch back to the Gantt Chart, we should see that our Task dispersion across the calendar looks a little different, and it should project an ending date for us, given the duration and relationship constraints we have entered. The Gantt Chart should also display the critical path in red. (If not, select “Tools” and then “Options,” and select the “Schedule” tab. Select “Fixed Duration” for “Default Task Type” and uncheck the box labeled “Effort Driven”) You can also right click the Gantt Chart, and select “Layout Wizard” to adjust the look of your chart.

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Figure 4.20. Task Information

After double checking our inputs to ensure accuracy (duration, resources, relationships) we can now format and print our Network Diagram and Gantt Chart (Figures 4.21 and 4.22). We can also view a project budget by selecting “View”, “Reports”, “Costs” and the select “Budget” (Figure 4.23). Our budget via MS Project should match those from our Excel generated spreadsheet. Lastly, we can view our resource histograms, to understand our utilization on a per resource basis (Select “View”, “Resource Graph”). Figure 4.24 displays the resource usage for skilled workers.

Figure 4.21. Network Diagram for Pump Installation Project using Microsoft Project

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Figure 4.22. Gantt Chart for Pump Installation Project using Microsoft Project

Scenario Analysis We can also use MS Project to help analyze various scenarios for the same project. Figure 4.25 summarizes the project budget in three possible scenarios for our Pump Installation Project.

Figure 4.23. Budget for Pump Installation Project using Microsoft Project

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Figure 4.24. Resource Histogram for Pump Installation Project using Microsoft Project

Figure 4.25. Project Budgets Under Three different scenarios

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Using MS Project will help analyze these scenarios. For instance, with the second and third scenarios, we can place limits on our resources to one skilled and unskilled laborer. We can select this limit in MS Project via the resource sheet (select 100% for Max column on both resources). Then, we can use the resource histogram to tell us when resources are over-allocated. This will tell us how many additional man-days will be required for each resource to compensate for the peaks. In scenario 2, we take the approach that we will pay overtime for those additional days, and we see the resultant impact on the budget. In scenario 3, however, we decide to level our resources and extend our project, so that we can meet our resource constraints. As shown in Figure 4.26, the project duration will now be increased to 26 working days.

4.8 ASSIGNMENT—THE CIM LABORATORY FACILITY The Computer Integrated Manufacturing (CIM) laboratory in a technological university is being upgraded to better enable the attainment of the goals of excellence in engineering education through high-quality teaching, research and extension activities. Approval has been obtained from university administration for this facilities planning project which requires the procurement and installation of new laboratory equipment and the establishment of a dual-purpose classroom for lecturers and presentations. A job list showing the activities required, their estimated durations, and their immediate predecessors is provided in Table 4.7. You have been appointed Project Manager with the responsibility of effecting the smooth implementation of the project.

Figure 4.26. Gantt Chart After Leveling for Pump Installation Project using Microsoft Project

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Job List for CIM Laboratory Project

Questions 1. Using the information in Table 4.7, prepare a network diagram for the project. 2. Assuming a start day in mid-May and a five-day work week, determine the completion date for the project. 3. Draw a Gantt chart to provide a visual summary of the implementation plan.

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4.9 CASE STUDY: DEVELOPING AN AI-BASED MEMBERSHIP RETENTION SYSTEM AT ALA Introduction Over the years, the American Leisure Association (ALA), a large, national, service company had experienced strong competitive pressures. Several companies were offering alternatives to the range of products and services traditionally offered. The cost of acquiring new members had increased steadily over the years. Non-renewal rates in the first three years of membership were high. The group’s top management established a Shared Technology Group (STG) with representatives from several clubs to explore and disseminate innovative technologies, which showed good potential to improve membership retention. The STG determined that a cooperative system development effort would be highly desirable. It was determined that a “Marketing Information System” represented the highest priority for a shared effort among the eight participating ALA Clubs. Marketing and MIS representatives from Southern California, Iowa, Michigan, Mid-Atlantic, Cal State, South Missouri, and National met and selected two potential projects from a list of alternatives. These had been developed in accordance with specific project selection criteria. These projects were: 1. Membership Lapse Prediction System–This was proposed by ALA Missouri. They reported encouraging results from a Pilot Project on Membership Retention conducted by the Intelligent Systems Center, University of Missouri-Rolla as part of their Industrial Liaison Program. In this study, a lapse prediction accuracy of 79% was obtained by the application of Artificial Intelligence techniques to the Missouri Club’s membership records. 2. Direct Mail Automation and Tracking System–This was proposed by ALA Michigan. This system would be developed externally and would incorporate traditional marketing techniques (e.g. direct mailing, telephone surveys). After considerable discussion and debate, a feasibility study was requested to determine which, if either, of the proposed systems would be the more appropriate project to be implemented. The Feasibility Study The feasibility study was conducted by a small in-house team aided by an external consultant. The team conducted several telephone conferences and made a site visit to obtain additional information required to evaluate the alternatives. The primary conclusion derived from the feasibility study of both of the proposed systems was that both systems appeared to be viable and desirable.

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However, the Lapse Prediction System was preferred for the following reasons: 1. Focus–Its primary purpose was clearly focused toward improving member retention. Retention was a top business priority. A decline in membership lapse from 14% to 13% was estimated to yield incremental revenues of about $300,000. 2. Complexity and Cost–The risks resulting from the use of unfamiliar AI technologies, the uncertainty regarding the system’s ultimate ability to generate reliable lapse predictions and the relatively high cost of the system would render the project unsuitable for implementation by a single club. However, by sharing the risks and cost of this project, several clubs could together pursue the highly promising business potential of this system. A Depth Study of the Lapse Prediction System was initiated. The proposed solution entailed the use of Artificial Intelligence (AI) techniques, Expert Systems and Artificial Neural Networks, to predict likely lapsers. The system would be developed by the University of Missouri-Rolla, Intelligent Systems Center. A pilot project performed for the Missouri Leisure Club had obtained prediction accuracies of 79% using a limited number of data elements. During the course of this Depth Study, the STG team took the opportunity to solicit proposals from professional consultants, viz. R.L. Polk, Andersen Consulting, and IBM. These companies proposed alternative systems incorporating techniques such as logistic regression analysis, fuzzy logic, focus groups, and telephone surveys. The cost of these proposals ranged from a low of $80,000 to $300,000 and the implementation periods ranging from 3 months to 12 months. The principal features of each proposal and the group evaluation are summarized in Figure 4.27. From this evaluation, the UMR Lapse Prediction System again emerged as the preferred alternative. Its primary advantages were its low cost and its clear, unambiguous focus on improving membership retention–the major business goal of the STG. The UMR Lapse Prediction System The UMR team proposed the development of an AI-based lapse prediction system geared towards providing the ALA Clubs with a strategic competitive edge. A simplified flow chart of the proposed system is shown in Figure 4.28. In the development of this system, statistical analysis (logistic regression) would be used to analyze samples of membership data from each club to identify the significant predictor variables. These would then be used to develop and test Artificial Neural Networks which would be used to assess the likelihood of members lapsing based on their membership profile and pattern of product and service usage. Figure 4.29 outlines a typical ANN architecture. An Expert System (see Figure 4.30) would then be used to prescribe what ac-

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Figure 4.27. Summary of Alternative Proposals

tion should be taken to retain the member. This approach would facilitate the introduction of an intelligent, proactive approach to improving membership retention. Figure 4.31 contains an overview of the proposed approach to system development. The Demonstration Prototype Because of the risk and uncertainty associated with the project, the ALA Clubs asked that a demonstration prototype be developed within the next ten weeks. This would demonstrate “proof of concept” of the feasibility of developing an AI-based Lapse Prediction System. This would be a logical fol-

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low-up to the pilot study originally completed with Missouri data, and was the next cycle in the spiral modeling approach to system development proposed by the UMR team (see Figure 4.32). The leader of the UMR project team recognized that a project plan was urgently required to ensure that the prototype would be completed by the mid-May deadline, within budget, and performing as expected. Successful completion of this prototype would go a long way towards securing ALA support and approval for the subsequent phases of the project. The project represented the application of novel AI technologies to address a traditional business problem requiring the analysis of large sets of data. There was limited previous experience in the industrial application of AI technologies among the members of the project team. This fact coupled with the absence of objective historical data on similar projects added considerable risk and uncertainty to the system development process. In view of the tight completion deadline required by the ALA, the development of the ANN and ES modules would have to be conducted in parallel. The statistical analysis required to identify key predictor variables would require the timely provision of samples of membership data by the STG. The UMR team leader prepared a job list for this phase of the project. This included the immediate predecessors and estimates of activity durations. This information is summarized in Table 4.8. Graduate students with expertise in Artificial Neural Networks, Expert Systems, Database Management Systems, and Computer Programming would be recruited. Preliminary estimates of the resources required for each task are summarized in Table 4.9. A budget would have to be finalized for submission to the STG, the project sponsors. The Project Management Challenge As he sat in his office, one Saturday afternoon in early March, the Project Leader had mixed feelings about the project. On one hand, it represented a unique opportunity for fame and fortune. Successful completion would go a long way towards enhancing the image of UMR and securing a place of prominence for the Intelligent Systems Center. On the other hand, industry often had serious reservations about the capability of universities to provide project deliverables in a timely and cost-effective manner. The proposed system would have to be developed by graduate students with expertise in Artificial Neural Networks, Expert Systems, Database Management Systems, Statistical Analysis, and Computer Programming. How many students should be recruited? How long would they be required to work? How much money should be requested from the STG for this phase of the project? How should the system development be monitored? These questions would have to be answered very soon. The STG had asked that he deliver his project plan on Monday.

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IBM Planning session; Knowledge analysis; model development 1. Predict lapse (fuzzy logic) 2. Determine characteristics of lapsers and renewers (host-based fuzzy model) 3. Combine characteristics with marketing judgments, statistical measures and business insights to identify likely lapsers 4. Download results to marketing workstations 5. Develop before-the-fact marketing plan and a full ad hoc reporting capability

Project Management Table 4.9.

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Resource Requirements for Pilot Project

R. L. Polk 1. A three club pilot test 2. Add Polk data 3. Draw samples 4. Logit models to determine chance of renewal 5. Classify into three groupings (likely renewers, unlikely renewers, fence sitters) 6. Conduct focus groups and surveys (to learn why members don’t renew)

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Anderson Consulting Based on AAA awareness of likely lapsers, implement a Marketing Workbench 1. Select members to contact 2. Download member data 3. Queue calls and collect information 4. Analyze results University of Missouri-Rolla 1. Use statistical package to understand, reduce, classify data 2. Use ANN to predict lapse 3. Use Expert System to determine Marketing tactics (based on rules provided by human experts)

Figure 4.28. Conceptual Flow Chart showing Artificial Neural Network (ANN)/ES Integration

Figure 4.29. Diagram of Selected Artificial Neural Network (ANN) Architecture

Figure 4.30. Overall Structure of Knowledge Base for Expert System Module

Figure 4.31. Schematic of Data Analysis Procedure for Prototyping STG/AAA System

Figure 4.32. Spiral Modeling Approach to System Development

Chapter Five

Applications Software for Business

5.1 Introduction 5.2 Types of Application Software 5.3 A Methodology for Selecting Applications Software 5.3.1 Overview 5.3.2 Phases 5.3.3 Implementation 5.4 Applications in Business 5.5 References 5.6 Assignment— Selecting Applications Software 5.6.1 Selecting Vehicle Routing Software 5.6.2 Selecting Simulation Software 5.7 Case Study: Selecting a GIS System at the Bureau of Mine Reclamation

5.1 INTRODUCTION A wide range of Applications Software is available to progressive Engineering Managers to enhance global competitiveness. These include inter alia software for Business Planning, Business Process Mapping, Computer Aided Design (CAD), Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Ergonomics, Facilities Planning and Design, Geographic Information Systems (GIS), Project Management, Scheduling, Simulation Modeling, Supply Chain Management (SCM), Vehicle Routing Systems, and Work Measurement. When effectively deployed, these software can facilitate the design and implementation of efficient business processes which can result in cost savings, quality improvement, and enhanced customer 125

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service. These factors will ultimately have a significant impact on a company’s survival and long-term growth and profitability. The literature periodically provides surveys and case studies which contain profiles of commercially available software. Although some authors (Collins 1999) have addressed the challenge of selecting the right software, few guidelines are available to permit a systematic evaluation of the many software options and enable a logical determination of the alternative which represents the best fit with a company’s needs. In this chapter, we describe a generalized methodology that can be used to help an organization select the right applications software and illustrate the robustness of this framework using scenario analysis.

5.2 TYPES OF APPLICATION SOFTWARE Software used in Engineering for Business applications can be grouped into the following four categories: • • • •

Industrial & Engineering Management (IEM) Management Science/Operations Research (MS/OR) Information Systems (IS) Production and Operations Management (POM)

Table 5.1 provides a summary description of each type of applications software and an example of commonly available software in each area.

5.3 A METHODOLOGY FOR SELECTING APPLICATIONS SOFTWARE 5.3.1Overview Several general strategies have been proposed to guide managers through the software selection process. Engle (IIE Solutions, 2005) has suggested that a systematic process and attention to detail can reduce the risks associated with selecting software and can lead to an optimal decision. He proposes the adoption of a structured process to guide managers through the myriad of software choices available and avoid a wrong choice. The Capterra enterprise software center (www.capterra.com) offers a software selection methodology to guide a potential buyer through the complexities encountered in software selection. Their approach proceeds in four phases, viz. Planning, Identification, Evaluation and Selection.

Applications Software for Business Table 5.1.

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Sample of Commercially Available Applications Software

5.3.2 Phases Our proposed software selection methodology follows the general framework suggested by Engle (2005) but incorporates specific screening heuristics to identify the software finalists and a multi-criteria model to compare and rank finalists. Figure 5.1 details in graphical form the methodology proposed for selecting the most appropriate applications software. Once the organization specifies its needs, this proposed methodology will proceed using the following four phases: • Phase #1: Compile a “Long List” of Alternatives–The software selection team should compile a listing of available applications software, detailing

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various vendor and product specifications. Information collected may include the product name, price range, average implementation time, supported entity size, supported operating systems, and any other attributes deemed appropriate by the selection team. • Phase #2: Develop a Shortlist of Feasible Software Candidates–This can be accomplished by screening the software products assembled to identify the top 3-5 candidates which offer the full range of characteristics needed by the organization. • Phase #3: Compare Alternatives Using A Multi-Criteria Decision Model (MCDM)–During this phase, the selection team should use a multi-criteria decision model incorporating the specific goals and needs of the organization to rank the “short list” of products. Evaluation criteria may include price, ease of use (Collins 1999) and security (Rushinek et al, 1995) • Phase #4: Make the Final Decision-After obtaining a ranking of the software candidates on the shortlist, the selection team should analyze the results further, incorporating any variables that could not be captured by the decision model. Based on this analysis, the optimum software product should be identified for implementation. Once the preferred software has been selected, the implementation phase can begin. In this phase, it is important to ensure that the original goals of the project are met. 5.3.3 Implementation In implementing the software selection methodology, a database can be developed containing the vendor and product data for commercially available software. From here, the database can be queried using criteria such as price, average implementation time, size of the organization supported, or a myriad of other criteria. This will ultimately derive a “short list” of products, that match the specific requirements of the organization. A multiple criteria decision model may be employed to compare and rank the “short list” of products. The Analytic Hierarchy Process (AHP) (Saaty 1990) is a useful tool for this task. Through this process, the user can make pairwise comparisons of the software finalists based on selected criteria and obtain a ranking of the final products. The evaluation criteria adopted for this selection process may include price, ease of use (Carlton 1999) and functionality. One benefit of the AHP is that it allows the user to utilize subjective judgment of the products relative to each other. The AHP model can be readily implemented using an EXCEL spreadsheet model or the Expert Choice software (Liberatore 2003).

Figure 5.1. Software Selection Methodology

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5.4 APPLICATIONS IN BUSINESS The software selection methodology outlined above has been used by several organizations in identifying the particular software solution which represented the best fit with the company’s needs. Examples of these are: 1. Cisco System in selecting an ERP system (Austin et al, 2003) Here, Cisco Systems identified three finalists from an initial long list of ten software vendors for developing its new ERP system. After a structured selection process, Cisco selected Oracle as the preferred vendor. 2. Florida Bureau of Mine Reclamation selecting a GIS system (Benjamin, 1998) Five GIS software which emerged as finalists after a structured selection process were compared using a multi-criteria decision model. It was determined that PcArcInfo provided the best Price/Performance relationship. 3. The BioTech Company selecting a Vehicle Routing System (VRS) to transport hazardous waste (Brown and Benjamin, 2004). In this case, this small but rapidly expanding company worked with a university team to identify a small set of feasible VRS software alternatives. These were compared using an Analytical Hierarchy Process model. 4. Selecting Accounting Software (Benjamin et al, 2005) Here a general framework was developed to help companies select the accounting software which represents the best fit with a company’s needs.

5.5 REFERENCES Austin, R.A., R.L. Nolan and M.J. Cotteleer, “Cisco Systems, Inc.: Implementing ERP”, Harvard Business School Case # 9-699-022, May 2002, Harvard Business School Publishing, Cambridge, MA, USA. Benjamin, C. O., “Selecting a GIS system at the Bureau of Mine Reclamation,” Working paper, (1998), School of Business & Industry, Florida A & M University, Tallahassee. Benjamin, C. O., E. Gillman & Ira Bates, “A Framework for Selecting Accounting Software”, Proceedings, American Soc. of Business & Behavioral Sciences (ASBBS) Annual Conference, Las Vegas, Vol. 12, No. 1,152-162, Feb. 26-28, 2005. Brown A. and C.O. Benjamin, “A Methodology for Vehicle Routing System (VRS) Software Vendor Selection”, Proceedings, Tenth International Conference on Industry, Engineering, and Management Systems (IEMS), Cocoa Beach, Florida, March , 2004, 562-567. Capterra, The enterprise software center, “The High-Level Software Selection Guideline”, www.capterra.com/selection_methodology, Accessed April 30, 2005.

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Collins, J, Carlton, “How to Select the Right Accounting Software”, Journal of Accountancy, October 1999, 67-77. Engle, Paul. “Select the Right Software”, IIE Solutions, April 2005, 26. Liberatore, Matthew J., Robert L. Nydick, (2003), Decision Technology: Modeling, Software, and Applications, John Wiley & Sons. Rushinek, Avi, Rushinek, Sara F. (1995). “Accounting Software Evaluation: Hardware, Audit Trails, Backup, Error Recovery and Security”, Managerial Auditing Journal, 10(9), 29. Saaty, T.L., (1990). “How to Make a Decision: The Analytic Hierarchy Process”, European Journal of Operational Research, Vol. 48, pp. 9-26.

5.6 ASSIGNMENT–SELECTING APPLICATIONS SOFTWARE 5.6.1 Selecting Vehicle Routing Software Your company has been asked to recommend a Vehicle Routing Software that could be used by its many distributors throughout the USA to improve the operating performance of their distribution truck fleets. The software should already be installed in several companies and be capable of being deployed on several operating systems (viz. Windows, UNIX, Linux, Mac OS and Web). It should also provide all routing functions and Geographic Information Systems (GIS) capabilities and incorporate soft time windows and automatic forecasting of deliveries. It will be used on several types of truck fleets and should accommodate a very large number of stops, vehicles, and routes. Which commercially available Vehicle Routing Software would you recommend? Use the software survey available at www.lionhrtpub.com, (OR/MS Today, June 2004) as a starting point and apply a structured software selection process to arrive at your recommendation. Develop a flowchart to summarize the software selection process used and provide supporting charts, figures & tables. 5.6.2 Selecting Simulation Software You have been asked to develop a simulation model to evaluate the operating performance of your company’s supply chain. Which commercially available simulation software would you recommend for use on this project? The software should be moderately priced (standard price: $15,000–$20,000); focus on supply chain, logistics, or warehousing; enable input distribution fitting and real-time viewing of the animation; and run on a Windows XP or 2000 operating system. Use the simulation software survey published by OR/MS Today (www.lionhrtpub.com, 2003) as a starting point. What would be your shortlist of simulation software? Develop a scoring model to facilitate the ranking of the finalists.

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5.7 CASE STUDY: SELECTING A GIS SYSTEM AT THE BUREAU OF MINE RECLAMATION Introduction Project Background The Florida Bureau of Mine Reclamation (BOMR) is responsible for regulating the mining in waters of the state (wetlands primarily), the reclamation of lands distributed by mining, and the management and storage of surface waters in mines. In regulating the mines, BOMR takes a holistic approach in analyzing the impact of mining activities on adjacent environmental resources. An examination of BOMR’s mission statement (see Figure 5.2) would reveal the critical importance assigned to the provision of a high quality level of service to its several groups of customers–the general public, the policy-makers, the mining community, and other stakeholders. BOMR’s management was eager to explore the applicability of Information Technology–enhanced solutions to enable continuous improvement of its services. Geographic Information Systems (GIS) seemed to offer opportunities for realizing significant quality improvements through innovative approaches to information management.

Figure 5.2.

Mission Statement–Bureau of Mine Reclamation

Figure 5.3.

Economic Model for Evaluating GIS Investment

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Figure 5.4.

Chapter Five

Baseline Economic Model for GIS Acquisition

A GIS has been defined as “a computer-based system designed to collect, store, retrieve, manipulate, and display spatial data” (Martin et al, 1994). A GIS links data to maps so that the spatial characteristics of the data can be easily comprehended. GISs have been used as Decision Support Systems (DSSs) in domains ranging from fast food site selection to delivery route selection. The information management challenges associated with mine reclamation require the collection, storage, retrieval, manipulation, and display of considerable amounts of graphic and textual data. Thus, BOMR’s Bureau Chief was anxious to examine the feasibility of employing a GIS to achieve productivity enhancements in the Bureau’s activities and elicited the support of the School of Business and Industry (SBI), Florida A&M University, in conducting a feasibility study.

Figure 5.5.

Project Network for GIS Study

Figure 5.6.

Gantt Chart for GIS Study

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Project Brief The study would examine BOMR’s information requirements and develop and evaluate a proposal for a Decision Support System for their needs. The specific focus was on identifying the GIS which would best meet the Bureau’s needs by reviewing the critical success factors based on the Bureau’s mission statement and conducting complementary analyses using a “bottomup” approach and an “enterprise analysis”. Then, the additional costs associated with acquiring, operating, and maintaining the system would be compared with the anticipated incremental benefits to determine whether a business case could be made for such an investment. Project Planning The project, a collaborative effort between the BOMR and SBI, would be conducted during the period of September 15 to November 15, and would proceed in the following phases: • Phase #1: Analysis of Existing System–to assess the strengths and weaknesses of the existing Information System; • Phase #2: User Information Survey–to ascertain the user needs for information processing; • Phase #3: GIS Software Evaluation–to identify and compare a short list of GIS software alternatives; • Phase #4: Economic Evaluation–to assess the economic viability of the preferred GIS alternative; The project network and Gantt Chart developed for the project are shown in Figures 5.6 and 5.7. The progress of the entire project was monitored through weekly team meetings. It was envisaged that the project would culminate with a presentation of the team’s findings and recommendations to key BOMR personnel. Analysis of Current System Phase #1: Needs Assessment In the first phase of the study, a survey was conducted of BOMR’s professionals to assess the Information Management needs of the Bureau of Mine reclamation. According to the survey, most respondents made regular use of both manual and computer-based information sources. The BOMR staff were quite familiar with Geographic Information Systems and were dissatisfied with the current system. Phase #2: User Information Survey The survey revealed that, in order to satisfy BOMR’s needs, a new GIS system must include the following primary features: • Permits-tracking functions • Review of site plans

Figure 5.7.

Data Flow Diagram

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• Property mapping • Ownership tracking • Parcel mapping Some secondary features include: • • • •

Field data collection Database creation Complaint tracking Fees & expenditure monitoring

The data flow diagram shown in Figure 5.4 gives a graphical representation of the activities involved in processing forms in BOMR. Of the estimated 27,220 person-hours spent annually on applications processing, approximately thirty percent of those involved the processing of mapping data. It was in this area that any prospective GIS would have its primary impact by improving the efficiency of the application processing system. Phase #3: GIS Software Evaluation The GIS Sourcebook (ESRI 1995) and the GIS Book (Korte 1994) provided the majority of the information concerning software evaluation criteria. The major criteria selected for this GIS software evaluation were: • Price–Although the average price of the GIS software products was approximately $6,000, approximately 50% of them were priced below $2,500 and about 25% were priced above $5,000. ARC/Info, with a price tag of $18,000 was the most expensive GIS software. • Software Functionality–This is a measure of the software’s versatility and encompasses aspects such as operating system compatibility, external database interfaces, database compatibility, and mapping ability. • Data Input–This should be as quick and accurate as possible and may include manual digitizing, scanning, and a Global Positioning System. These data input options can enhance the flexibility of the GIS system. • Data Output–The GIS must produce both visual and hard-copy output of data and should be capable of processing output through both printer and plotter interfaces. • Vendor Reputation–This could be assessed by examining the year founded, ownership, products offered, support service, and the number of current installations/users. • Other–This captures any other related factors which may impact the purchase decision.

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After a review of the GIS literature, the following five popular GIS software, whose features are summarized in the Software Functionality Matrix in Table 5.3, were short-listed for further comparison and examination: • ARC/INFO–a full function GIS system for industrial applications. With an estimated base price of $18,000, this system was the most expensive. However, it was the most versatile of the group and enjoyed a leadership position in the GIS industry. • Geo/SGL–a full-function application development platform for GIS with a base price of $9,500. Its corporate sponsor, Generation 5, was not recognized as one of the leaders in the GIS field. • MGE–this GIS system facilitated data capture, validation, editing, analysis, management, data output. The corporate sponsor, Intergraph, enjoyed a highly respected position within this industry and had nearly 200,000 estimated users worldwide. • MapInfo–this displayed corporate data geographically from worldwide to street-level views enabling visualization and analysis to support key strategic decisions. This system provided a majority of the important features at a relatively low base price. • IDRISI–this provided complete geographic analysis, image processing, and spatial statistics software. This system was relatively inexpensive and under-powered. The results of the multi-criteria decision model used to compare these software alternatives are summarized in Table 5.2. This decision model indicated that MGE should be the preferred software. However, when price was eliminated from the criteria used, ARC/INFO secured the top ranking. This Price/Performance relationship is summarized in Figure 5.8. Phase #4: Economic Evaluation • Economic Model–The economic model used to evaluate the GIS investment is outlined in Figure 5.3. In this case, it was assumed that the ARC/INFO GIS would be acquired for ten users with appropriate hardware upgrades. The economic model provides a structured approach to facilitate comparison of the additional costs associated with acquiring, operating, and maintaining a GIS with the anticipated incremental benefits. Thus, BOMR would be able to determine whether a business case could be made for investment in a GIS.

Table 5.1.

Software Functionality Matrix

Table 5.2.

Multi-Criteria Decision Model for GIS Software Evaluation

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• Spreadsheet Model–The economic model was implemented using an EXCEL spreadsheet. The baseline model is shown in Figure 5.4. Using a fiveyear study period, the following economic indices were computed:

The relatively long payback period indicated the existence of some project risk. However, the large, positive Net Present Value, the high Internal Rate of Return, and large Benefit/Cost Ratio supported investment in the GIS Project. The Investment Decision The BOMR’s Bureau Chief was heartened by the favorable economic indices reported by the economic model. However, he was well aware that the

Figure 5.8.

Price/Performance Comparison for GIS Software

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project’s viability was closely linked to forecasts of the levels of productivity improvements to be realized in the processing of applications. Sensitivity analyses should be conducted to assess the impact of changes in the model’s inputs on the project’s economic viability. Scenario analysis could also be used to determine the spread of the Net Present Value and Internal Rate of Return associated with optimistic, most likely, and pessimistic scenarios. A simulation model could be constructed to provide a quantitative measure of the project’s risk. Although he was acutely aware of the enormous potential of a GIS, he wondered whether BOMR should venture into the GIS arena relying on its limited internal IT resources or whether these services should be outsourced. If the GIS services were to be developed in-house, what implementation strategy would be most likely to succeed? Bibliography GIS World Inc., The GIS World Sourcebook 1996, 7th edition, Fort Collins, Colorado, 1995. Korte, G.P., The GIS Book, 3rd edition, Santa Fe, New Mexico: High Mountain Press, 1995. Martin, E.W., D.W. DeHayes, J.A. Hoffer, and W.C. Perkins, Managing Information Technology, 2nd edition, Macmillan, New York, NY: Macmillan, 1994.

Chapter Six

Technology Commercialization

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8

Introduction Proposed Evaluation Framework Applications in Business Software Support for Business Planning Discussion and Conclusion References Assignment—Commercializing New Technologies Case Study—Evaluating a NASA Technology

6.1 INTRODUCTION Commercializing new technologies poses many challenges in today’s fast paced, competitive business environment (Allen 2003), (Parker and Mainelli, 2001). Seigel et al (1995) propose a strategy to accelerate technology commercialization efforts through improved co-operation via strategic alliances and the use of expert panels. Duke (1995) examines organization conflict arising from management and innovator interest in commercializing technology and suggests key issues to be considered in developing market approaches for technologies generated in university or federal laboratories. Walsh (2002) evaluates Value Chain Management, a decision support system developed by the Advanced Technology Division of Lucent Technologies’ Bell Laboratories to measure and manage the technology commercialization process. In the USA, non-profit organizations such as the Kauffmann foundation (Snyder et al, 2004) and NASA Langley Research Center (http://www.sti .nasa.gov/tto) provide support for collaborative efforts to accelerate technol144

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ogy transfer and commercialization initiatives in the Life and Health Sciences and Engineering. International agencies such as United Nations Industrial Development Organization (UNIDO) (http://www.unido.org) and the Organization for Economic Co-operation and Development (OECD) (http://www.oecd.org) provide training and support for industrial development efforts in developing countries. As shown in Figure 6.1, successful technology commercialization efforts typically evolve through constant iterations between a new technological capability and a market need and require critical, objective and unbiased assessment of the proposed technology as it progresses through the phases of the technology development cycle. Desai and Andrews (2003), in observing best practices in commercializing new technologies, report that successful companies involve both technologists and business users and require due diligence to understand business and technology issues and market dynamics. Several researchers have examined the challenges associated with evaluating new technologies. For example, Fagan (2001) has proposed a framework for evaluating Global Information Technology Transfer; Brown and Wallnau (1996) offer an experimental framework for evaluating software technology by systematically examining features in relation to its peers and competitors; Lubbe and Remenyi (1999) and Sarkis and Sundarraj (2003) have focused on Information Technology evaluation. Other researchers have proposed techniques for planning new business ventures (Timmons 2003), and have formulated metrics for evaluating technologies (Geisler 2002). However, there has been no consensus on a generalizable methodology which would provide a replicable framework for evaluating new technologies.

Figure 6.1.

Information Flow in Technology Commercialization Process

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This chapter describes a replicable, structured four-phase methodology to provide a framework for evaluating the commercial potential of new technologies and identifies several tools and techniques available to facilitate decision-making in each phase of the evaluation process.

6.2 PROPOSED EVALUATION FRAMEWORK The proposed evaluation framework provides a robust approach to assess the feasibility of developing a commercially viable business venture using a new technology and proceeds in the following phases: • Phase #1: Market Analysis to identify and assess the more promising applications of the technology. This requires a combination of brainstorming activities to identify potential applications and professional market research studies to provide a more focused assessment and in-depth market analysis. • Phase #2: Technology Assessment to gauge the competitive advantage offered by the technology. This requires comparison with competing technologies and independent evaluation from domain experts. • Phase #3: Financial Evaluation to assess the technology’s profitability potential based on the expenditure associated with getting the product to market and revenue forecasts. This requires the development of a financial model to enable the computation of appropriate financial indices to measure the commercial viability of the technology. • Phase #4: Risk Analysis to provide an assessment of the risks associated with commercialization efforts. This requires the extension of the financial model to incorporate considerations of risk and uncertainty and facilitate a final decision on the acceptance of the technology commercialization project. Figure 6.2 provides an overview of the proposed four-phased framework for conducting technology commercialization studies. It provides a structured, controlled evaluation process with feedback loops and incorporates the following features: Concurrent execution of the Market Analysis and Technology Assessment phases • Critical review milestones at the end of each phase. • Opportunity for feedback and project modification. • Project termination is possible if targets are not met.

Technology Commercialization

Figure 6.2.

Proposed Methodology for a Technology Commercialization Study

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6.3 APPLICATIONS IN BUSINESS Table 6.1 summarizes the decision tools and techniques used when the proposed framework for evaluating new technologies was applied in three case studies. Case #1 NASA–An ice detection gauge was developed to monitor the accumulation of ice on aircraft surfaces and initiate de-icing procedures. Case #2 BioMed–An intelligent monitor was designed to track an infant’s vital signs and alert guardians when problems arose. Case #3 ELLIPSE–an intelligent personnel scheduling system was designed incorporating robust scheduling heuristics and Artificial Neural Networks.

6.4 SOFTWARE SUPPORT FOR BUSINESS PLANNING Analysts seeking to assess the commercial potential of technology-based ventures have access to a wide range of business planning software which can facilitate the timely development of a business plan by providing “wizards” and

Table 6.1.

Decision Tools & Techniques used in Technology Evaluation Framework

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“templates” that prompt users through a detailed list of potential topics. Among the more popular business planning software are: • Business Plan Pro Premier Edition distributed by Palo Alto Software. This product, developed by Tim Berry, a business planner/consultant since 1974, has secured benefits from many years of use and improvement. • PlanWrite Expert Edition developed by Business Resource Software, Inc. (BRS) has received favorable reviews. BRS, founded in 1989, stresses the utilization of its expertise in artificial intelligence as well as competitive strategy and industry analysis. They cite Michael Porter and Boston Consulting as two of the many “gurus” whose work/principles are incorporated into their software/analytical tools. • Automate Your Business Plan 11.0, sold by Out of Your Mind and Into the Marketplace™, is another promising business planning software. It is produced by an entrepreneur with 17 years experience as a business planner, consultant, and software developer. Reviews indicate this software is very user friendly and that it has the elements needed to produce a complete and professional looking business plan. • COMFAR III software suite is developed and sold by the United Nations Industrial Development Organization (UNIDO). The software is used to facilitate the development of opportunity, pre-feasibility and feasibility studies using the well-proven UNIDO project evaluation methodology. Table 6.2 provides a listing developed of 26 vendors of business planning software and the 56 products they sell. Most of the vendors were located in the United States, with a small number in international locations, namely Ireland and Austria. The domestic vendors were geographically dispersed across the entire USA with the largest number (nearly 40%) being located in the state of California. Most vendors maintained physical locations with headquarters. However, some were only internet business operations without a physical presence. Many vendors offered one product directly related to business plan development, with a select few offering multiple versions of the same product. The price of the 56 business planning software examined ranged from $20 to $4500. The wide range in price was a result of the significant variations in level of detail and functionality of the various software products.

6.5 DISCUSSION AND CONCLUSION New business development often requires the systematic evaluation of new technologies whose profiles are readily available in patent offices such as the

150 Table 6.2.

Chapter Six List of Business Planning Software and Vendors

US Patent and Trademark Office (http://www.uspto.gov), the European Patent Office (http://www.epo.co.at), and Japanese Patent Office (http://www .jpo.go.jp). The approach outlined in this chapter facilitates the integration of well-proven decision models (Liberatore et al, 2003) and group processes (Benjamin et al, 1996) into a replicable, structured decision-making framework to evaluate the viability of commercializing new technologies. This phased approach allows for periodic review and adjustment at the end of each

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phase of the process and facilitates participation from the stakeholders. It enables a clear focus on the risk factors associated with a particular technology thus permitting the deployment of appropriate risk reduction strategies and providing an invaluable precursor to the development of business plans. The final investment decision would require careful assessment by the prospective investors of the risk/return tradeoff to determine if the anticipated returns of the commercial venture under consideration would be adequate to justify the perceived risks. This framework has been effective in evaluating technology commercialization initiatives in Aerospace Engineering, Biomedical Engineering and Management Engineering.

6.6 REFERENCES Allen, Kathleen R., Bringing New Technology to Market, Pearson Education, Inc., New Jersey, 2003. Benjamin, C. O., L. Monplaisir, S. Chi, L. Lahndt-Hearney, & C. Riordan, “Group Decision Support Systems: A Review of Industrial Applications”, Proceedings, 47th International Industrial Engineering Conference, St.Paul/Minneapolis, Minnesota, May 18-23, 1996, 197-202. Brown, Alan W. and Kurt C. Wallnau, “A Framework for Evaluating Software Technology”, IEEE Software, Vol. 13, No. 5, 1996, 39-49. Desai, Gautam and Linda Andrews, “A Framework for Better Decisions”, Report No. 110703018, Thomson Gale, Gale Group, MI, December 2003. Duke, Charles R. “Organizational Conflicts Affecting Technology Commercialization from Non-profit Laboratories”, The Journal of Product and Brand Management, Vol. 4, Issue 5, 5-13, 1995. European Patent Office, Patent Information Centres, August 8, 2003, http://www.epo.co.at. Fagan, Mary Helen, “Global Information Technology Transfer: A Framework for Analysis”, Journal of Global Information Technology Management, Vol. 4, Issue 3, 2001, 5-26. Geisler, Eliezer, “The Metrics of Technology Evaluation: Where we stand and where we should go from here”, International Journal of Technology Management, Vol. 24, No. 4, 2002. Japanese Patent Office, Industrial Property Digital Library, August 8, 2003, http://www.jpo.go.jp. Liberatore, Matthew J., and Robert L. Nydick, Decision Technology, Modeling, Software & Applications, Wiley & Sons, 2003. Lubbe, Sam and Dan Remenyi. “Management of Information Technology EvaluationThe Development of a Managerial Thesis.”Logistics Information Management, Vol. 12, Issue 1/2, 1999, 145-146. NASA Langley Research Center, Technology Applications Group, “Spotlight on Langley Research Center”, http://www.sti.nasa.gov/tto, Retrieved on August 30, 2004.

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Organization for Economic Co-operation and Development, Publications and Documents, August 8, 2004, http://www.oecd.org. Parker, Kevin and Michael Mainelli, “Great Mistakes in Technology Commercialization.”, Strategic Change, Vol. 10, 2001, 383-390. Sarkis, Joseph and R.P. Sundarraj, “Evaluating Componentized Information Technologies: A Multiattribute Modeling Approach”, Information Systems Frontiers, Vol. 5, No. 3, 2003, 309-313. Siegel, Richard A, Sten-Olof, Hansen, and Lars H. Pellas, “Accelerating the Commercialization of Technology: Commercialization through Co-operation,” Industrial Management + Data Systems, Vol. 95, Issue 1; 18, 1995. Snyder, Solomon, Michael M.E. Johns, James J. Morgan, & James R. Utaski, “Accelerating Technology Transfer and Commercialization in the Life and Health Sciences”, www.kauffman.org, Ewing Kauffman Foundation, Retrieved on March 31, 2004. United Nations Industrial Development Organization, Investment Opportunities, July 15, 2004, http://www.unido.org. United States Patent and Trademark Office, Patents, May 20, 2003, http:// www.uspto.gov Timmons, Jeffry A. and Spinelli, Stephen, New Venture Creation: Entrepreneurship for the 21st Century, 6th edition, Irwin/McGraw-Hill, New York, 2004. Walsh, Steven, “Portfolio Management for the Commercialization of Advanced Technologies.” Engineering Management Journal, Vol. 13, No.1, 2001, 33-37.

6.7 ASSIGNMENT—COMMERCIALIZING NEW TECHNOLOGIES Your consulting company, Engineering Management Associates, has been asked to examine the viability of commercializing a technology developed to facilitate the intelligent scheduling of personnel at banks, groceries, post offices, supermarkets. Specifically, you are required to: 1.Using the patent database at the US Patents and Trademarks Office (www.uspto.gov), develop a ranked shortlist of the more competitive technologies available to develop a system for personnel scheduling. 2. Compare the more promising technologies using an appropriate scoring model. What recommendations would you make?

6.8 CASE STUDY—EVALUATING A NASA TECHNOLOGY Technology Commercialization–An Overview Studies on technology commercialization in the USA have indicated relatively poor performance levels (Leonard-Barton et al, 1994) with many technol-

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ogy-based inventions never moving beyond the conception stage (Jolly 1997). Several companies have realized that successful technology commercialization efforts require ongoing concurrent collaboration, cooperation and teamwork among research scientists, engineers, designers and many business functions during all phases of the commercialization process (Nevens et al, 1990). Most ideas evolve through constant iterations between a new technological capability and a market need (Jolly, 1997). NASA’s broad technology commercialization efforts through initiatives such as the Commercial Opportunities Program (COOPPR) and Software and Patent and Licensing Programs (NASA LaRC, 1999) facilitate this interaction. These programs provide unique opportunities for interested parties to participate in the successful technology commercialization processes required to maintain America’s international competitiveness. Over the years, NASA-Langley Research Center has developed over 170 patents and has been interested in commercializing the more promising ones. One of the more interesting technologies patented is an Ice Thickness Gauge Technology (NASA Tech Briefs, 1995), which addresses the hazards posed by ice build-up on aircraft, buildings and other structures. The Ice Thickness Gauge Technology The Ice Thickness Gauge Technology developed by NASA-Langley Research Scientist, Dr. Leonard Weinstein and registered as US Patent No. 4,766.369 dated August 23, 1988 was developed to measure the buildup of ice over a detector. A profile of this patent is included as Figure 6.5. This technology offered promise because of its ability to distinguish between ice and water and to measure the thickness of ice with a high level of accuracy. Its versatility suggested good potential for deployment in a wide range of applications. These factors prompted its selection as a candidate for a preliminary technology commercialization study. Evaluation Methodology The study conducted by the School of Business & Industry, Florida A&M University sought to assess the feasibility of a company developing a commercially viable business venture using the NASA Ice Thickness Gauge technology. This gauge could allow the monitoring of ice accumulation on buildings and other structures, and in some cases, to initiate de-icing. The study incorporated the following activities: • Market Analysis to identify the more promising applications for the technology; • Technology Assessment to gauge the competitive advantage offered by the NASA Ice Gauge technology; • Cost Analysis to develop a profile of the costs associated with getting the product to market for the more promising applications;

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• Financial Evaluation to provide a preliminary assessment of the risks and return associated with commercialization efforts targeted at a particular segment; The schedule for executing the study is detailed in the Network Diagram and Gantt Chart shown as Figures 6.3 and 6.4.

6A.

The project milestones and target dates established for the project.

Market Analysis Figure 6.5 summarizes in a flowchart the phases of market research conducted by the SBI project team to identify potential applications for the technology by brainstorming a novice panel comprised of SBI students and an expert panel of professional engineers. Novice Panel The aircraft industry was a prime candidate for applying the Ice Gauge Technology to provide a system to monitor ice accumulation on aircraft wings and initiating de-icing procedures. However, during several brainstorming sessions, a novice panel of sixty SBI 2-3 person student teams unearthed other potential applications for the Ice Gauge Technology. Among the top-ranked applications identified were measuring and monitoring ice accumulation in automobiles, lakes and ponds, pipelines, roadways and bridges, buildings and refrigeration devices. Table 6.3 summarizes the market potential assigned to these application areas using a Staple scale (Churchill, 1995) ranging from - 4 (poor) to + 4 (excellent).

Figure 6.3.

Network Diagram for NASA/SBI Technology Commercialization Project

Figure 6.4.

Gantt Chart for NASA/SBI Technology Commercialization Study

Figure 6.5.

Methodology for Market Analysis

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Table 6.3.

Summary of High Potential Applications of Ice Gauge Technology

Expert Panel A virtual expert panel of twelve experienced engineering professionals was formed to identify potential applications for the ice gauge technology and to provide an assessment of its technological capabilities.

6B.

The twelve application areas suggested by the expert panel.

The expert panel also provided an overall rating for the suggested applications and assessed them in terms of market potential, ease of manufacture, performance, and serviceability using a Staple scale ranging from - 4 (poor) to + 4 (excellent).

6C.

Average Ratings Received

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The results of both panels indicate that the ice gauge device had the potential to satisfy a number of customer-driven needs across several diverse applications and industries. However, the aircraft industry seemed to offer the best potential for establishing a commercial venture. This was the focus adopted in developing a financial model to assess the financial viability of commercializing this technology. Technology Assessment The major steps involved in the evaluation of this technology were: Step #1: Identify a longlist of competing technologies Step #2: Develop a shortlist of acceptable technologies using appropriate screening criteria Step #3: Compare finalists using a multi-criteria decision model A flowchart summarizing the steps in this evaluation is shown in Figure 6.6. First, an examination of the patent databases at the United States Patents and Trademarks Office (www.uspto.gov/go/ptdl) (USPTO) and the IBM Intellectual Property Network (www.womplex.patents.ibm.com) yielded a longlist of twelve competing technologies. Next, these were screened using the following three criteria: • Accuracy–Can the technology provide the requisite accuracy in measuring ice thickness? • Reliability–Can we depend on the technology to work consistently? • Versatility–Can the technology be used in a wide range of applications? This filtering process identified five patents which met these criteria viz. Patents #4766369, #5095754, #5354015, #5551288, & #5821826. These were shortlisted for further evaluation. Profiles of the competing patents are shown in Figures 6.8-6.11. Finally, the five finalists were compared using a scoring model which incorporated eight criteria viz. Accuracy, Versatility, Reliability, Ease of Use, Ease of Maintenance, Ease of Installation, Ease of Construction, and Price.

6D. The score and rank obtained by each of the 5 patents described in Figures 6.7 through 6.11.

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Figure 6.6.

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Methodology for Technology Assessment

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Figure 6.7.

Technology Profile – U.S. Patent #4,766,369

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Figure 6.8.

Technology Profile – U.S. Patent #5,095,754

Figure 6.9.

Technology Profile – U.S. Patent #5,354,015

Figure 6.10.

Technology Profile – U.S. Patent #5,551,288

Figure 6.11.

Technology Profile – U.S. Patent #5,821,826

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Table 6.4. Scoring Model for Comparing Competing Technologies. Weights: 1-low, 3moderate, 5-high. Rating: 1-poor, 3-average, 5-excellent

The NASA technology, US Patent #4766369 and US Patent #5551288 tied for the top position with a score of 125, significantly better than the scores of 107, 96 and 92 obtained by the other finalists. Cost Analysis Technology Costs Companies seeking to use a patented NASA-Langley technology may apply for one the following: • An exclusive license • A partially exclusive license • A non-exclusive license Each company will have to negotiate the specific terms of any licensing agreement it secures. These terms are influenced by the perceived potential of the patent, the age of the patent, the competitive environment in the industry, and will affect the fields in which the technology could be used, the downpayment or up-front money to be paid, and the royalty payments to NASA. Table 6.5 summarizes the parameters proposed for use in this study in developing a model to assess the financial viability of commercializing the NASA technology focusing on the airline industry. Development Costs Development costs refer to the expenditure incurred in developing a prototype of the proposed product. Allowance must be made for the cost of components, assembly, installation, labor, and contingencies. This activity would

Technology Commercialization Table 6.5.

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help to establish the feasibility of manufacturing products which would incorporate the ice gauge technology and be capable of satisfying perceived market needs. Figure 6.12 summarizes the manufacturing and technology development costs used in this study. Product Costs The percent of sales approach for cost allocation is one of the more popular rule-of-thumb methods and owes its appeal largely to its simplicity. The firm simply takes a percentage figure and applies it to either past or future sales. This approach is justified in terms of the following argument: (a) the costs in question are needed to generate sales; (b) a number of cents, that is, the percentage used, out of each dollar of sales should be devoted to those costs in order to generate needed sales; and (c) the percentage is easily adjusted and can be readily understood by other executives. Production costs, marketing and selling, distribution, and research and development costs for each period were allocated using a percent-of-currentsales method. Percentages were assigned to each category of cost based on a study of the financial statement of a sample of companies with technologyintensive product lines. Costs were then allocated 40 percent, 15 percent, 10 percent, and 20 percent of sales to production, marketing and selling, distribution, and research and development costs respectively. Financial Evaluation Model Formulation The financial evaluation of the feasibility of commercializing the new technology was completed using a discounted cash flow model which examined the operations of the project over a study period of five years. It is not assumed, however, that the technology will cease to be marketable after five years. Parameters involving revenue streams (inflows), disbursements (outflows), and product costs were input into the financial model as a result of the previously discussed marketing and technology assessments, and cost analysis.

168

Figure 6.12.

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Manufacturing and Technology Development Cost

The financial model ultimately measures the feasibility of the project based on four selected financial indices: (1) payback period; (2) discounted payback period; (3) net present value; and (4) internal rate of return. The payback period is simply the time it takes for the firm to recover its original investment. The discounted payback period is similar except that it discounts the annual cash flows based upon a rate of interest. The net present value method compares the present value of the cash outflows with the present value of the cash

Figure 6.13. Technology

Financial Model for Evaluating the Feasibility of Commercializing New

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inflows. The internal rate of return (IRR) is the interest rate received for an investment consisting of outflows and inflows that occur at regular periods. For the purpose of financial modeling, a tax rate of 50 percent was assumed. Figure 6.13 summarizes the logic that guided the construction of the financial model. Financial Indices Table 6.6 summarizes the deterministic cash flow forecasts over a five-year study period. Application of the financial model outlined in Figure 6.7 showed that in a most likely scenario, the investment would yield an after-tax Internal Rate of Return (IRR) of 64% per annum using constant dollar cash flows, and a Payback Period of 1.6 years. These results suggested that a commercial venture for the ice gauge device targeted at the airline industry would be very profitable with a moderate level of risk. Further investigation was reTable 6.6.

Summary of Cash Flows for Commercializing New Technology

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quired to incorporate considerations of risk and uncertainty. Scenario analysis could assist in showing the range of investment outcomes in pessimistic, most likely, and optimistic scenarios. Sensitivity analysis could assist in identifying high risk factors and prompt the formulation of appropriate risk management strategies. Simulation modeling could extend the deterministic financial model to incorporate probabilistic input parameters. Investment Recommendation Estimation of the cash flows used in the financial evaluation of the feasibility of commercializing the Ice Gauge measurement patent was based on the study team’s assessment and forecast of market and cost parameters. Variations in these forecasts could result in a significant change in the venture’s profitability. These risk factors should be identified, their significance assessed, and appropriate risk management strategies initiated to minimize any adverse impact on the commercial venture. A special effort would be required to identify suitable industry partners for commercializing the technology. A careful assessment was required of the risk/return tradeoff to determine if the anticipated returns of this commercial venture would be adequate to justify the perceived risks.

6.9 REFERENCES Churchill, Gilbert A., Marketing Research: Methodological Foundations, 6th ed. The Dryden Press Series in Marketing, Fort Worth: Harcourt Brace College Publishers, 1995. IBM’s Intellectual Property Network, www.womplex.patents.ibm.com. Jolly, Vijay K., Commercializing New Technologies, Harvard Business School Press, Boston: 1997. Leonard-Barton, Dorothy, Edith Wilson and John Doyle, Commercializing Technology: Imaginative Understanding of User Needs, Harvard Business School Teaching Note #9-694-102, Harvard Business School Publishing, Boston 1994. NASA Langley Research Center, “Technology Transfer Process”, http://tag.larc.nasa .gov/tag/tschtransfer.html, February 1999. NASA Tech Briefs, “Measuring Thickness of Ice When Liquid is Present,” The Digest of New Technology 19, No.4 , 1995. Nevens, T. Michael, Gregory L. Summe, Bro Uttal. “Commercializing Technology: What the Best Companies Do.” Harvard Business Review, May-June 1990, 154162. U.S. Patent and Trademark Office, www.uspto.gov/go/ptdl.

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Management Science Techniques

7.1 Introduction 7.2 Mathematical Programming 7.2.1 Mathematical Programming Techniques 7.2.2 Linear Programming Software 7.2.3 A Case Study–Course Planning in Academia 7.3 Simulation Modeling 7.3.1 Overview 7.3.2 Techniques 7.3.3 Phases of a Simulation Study 7.3.4 Simulation Software 7.3.5 A Case Study–The DemMaTech Flexible Manufacturing Cell (Benjamin et al, 1995) 7.4 Other Techniques 7.5 Applications in Business 7.6 References 7.7 Case Study—Warehouse Consolidation at the Coca Cola Company

7.1 INTRODUCTION Management Science has been widely used in business to facilitate a quantitative approach to decision-making (Taylor 2004). It requires the development of a mathematical model of a system and its analysis using one of the several management science techniques such as Mathematical Programming, Simulation Modeling, Network Analysis, Decision Analysis, Inventory Models, and Queuing Theory, and interpretation of the results obtained to inform decision making. Analysts seeking to use these techniques have access to a 172

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wide range of commercially available software and can also use spreadsheetbased models to facilitate model solution. The term Management Science is used interchangeably with Operations Research which grew out of successful applications of the scientific approach to solving military operational problems encountered during World War II (Lawrence & Pasternak, 2002). Today, management science techniques are used in many areas of business to solve problems ranging from schedule planning in the airline industry (Subramanian et al, 1994) to the design and analysis of complex supply chains (Liberatore and Nydick, 2003). In all cases, their effective deployment requires a clear statement of the problem to be tackled, identification of the management science technique(s) to be used, formulation of a model of the system to be analyzed, collection of relevant data to operationalize the model, model solution using appropriate software, analysis and interpretation of the results, and presentation of the final recommendations to company management for action. In this chapter, we will examine two of the more popular and powerful management science techniques, mathematical programming and simulation and illustrate their application to structured decision making.

7.2 MATHEMATICAL PROGRAMMING 7.2.1 Mathematical Programming Techniques As shown in Figure 7.1, mathematical programming models can be broadly separated into two categories, viz. deterministic and probabilistic (Eppen et al, 1998). Of these, linear programming, one of the deterministic models, is the most commonly used. Application of these models requires a three-phased process, viz. Phase #1 - Model Formulation: Define decision variables, specify objective function, identify constraints Phase #2 - Model Solution: Select solution algorithm and solve using appropriate software. Phase #3-Interpretation of Results: Analyze results and prepare reports summarizing recommendations 7.2.2 Linear Programming Software Many software programs have been developed to provide assistance to analysts seeking to solve linear programming (LP) problems. A survey of the linear programming software conducted by OR/MS Today (www.lionhrtpub .com) provides profiles of 44 LP related software distributed by 30 vendors.

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Figure 7.1.

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Types of Mathematical Programming Models

The products surveyed incorporate two complementary but fundamentally different types of software (Fourer 2003). Solver software takes an instance of an LP model as input, applies one or more solution methods, and returns the results. Modeling software, typically designed around a computer modeling language for expressing LP models, offers features for reporting, model management and application development, in addition to a translator for the language. As shown in Figure 7.2, these software programs range in price from a low of $20 to a high of $30,000 with many vendors offering discounts for educational use. Ninety-seven percent of the software products can be used with the Windows operating system with products also being developed for the Linux, Solaris and Unix operating systems. However, as shown in Figure 7.3, most of the products (55%) are designed to run on one or two operating systems with a few products (16%) designed for use on seven or more platforms. Nonlinear and linear solvers are among the many features and functions offered by the software. 7.2.3 A Case Study–Course Planning in Academia Overview A professional school whose total enrollment is 250, consisting of 110 lower division students (freshman and sophomores), 70 upper division stu-

Management Science Techniques

Figure 7.2.

Variation of Prices for LP Software

Figure 7.3.

Operating Systems Available for LP Software

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dents, and 70 graduate professional students is trying to build a revenue optimization model to aid its operational planning process. The school receives revenues as follows: Lower division students - $5000 per full time equivalent (FTE) (15 hours per semester); Upper classmen - $10,000 per FTE; and graduate students - $15,000 per FTE. There are approximately 30 teaching faculty and 300 desks in the school. Classes are scheduled such that all rooms are used by the faculty at the rate of 60% of capacity.

7A

Other Assumptions

Model Development and Solution The Linear Programming model developed to solve this problem is as follows: • Let Xij be the number of lower/upper/graduate classes to be offered in Fall and Spring semesters • i denotes Lower (1), Upper (2), and Graduate classes (3) • j denotes Fall (1) and Spring (2) semesters Objective Function Maximize Revenues per Academic Year (Fall and Spring Semesters): MAX = (( 3 * (X11 + X12) / 15) * 110 * 5000) + (( 3 * (X21 + X22) / 15) * 70 * 10000) + (( 3 * (X31 + X32) / 15) * 70 * 15000) Specify Constraints Course Availability: offer at least a minimum number of courses at each level X11 ≥ 11; X12 ≥ 7; X21 ≥ 10; X22 ≥ 11; 31 ≥ X31 ≥ 16; 28 ≥ X32 ≥ 14 Classroom Availability Fall: ((3 * (X11 + X21)) / 8 + (2*X31) / 6) / 5) ≤ 0.60 * 20 Spring: ((3* (X12 + X22)) / 8 + (2*X32) / 6) / 5) ≤ 0.60 * 20

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Faculty Availability Fall: (0.5 * (X11 + X21 + X31)) ≤ 30 Spring: (0.5 * (X12 + X22 + X32)) ≤ 30 Seating Constraint Fall: (3 * (X11 + X21 + X31)) / 5) * 14 ≤ 300 Spring: (3* (X12 + X22 + X32)) / 5) * 14 ≤ 300 Non-negativity Constraint All Xij ≥ 0 All Integer Variables (Xij ) The solution was obtained using the LINGO mathematical programming software (Fourer 2003). The results obtained in the most likely, optimistic, and pessimistic scenarios are summarized in Tables 7.1 and 7.2 below. Table 7.3 shows the increase in Annual Revenues with decreases in FTE (credit hours per semester). Conclusion The results of this planning model were examined under pessimistic, most likely, and optimistic scenarios. The optimal solution associated with each scenario was found by taking into account changes in course availability, classroom availability, faculty availability, and seating constraints. The results summarized in Table 7.1 show that annual revenues ranged from a low of Mn$13.45 to a high of $Mn16.75. As shown in Table 7.2, the best performance indices were obtained in the optimistic scenario which assumes greater availability of resources. The planning model developed can serve as a useful starting point in planning in academia and can be developed into a flexible tool for rapidly exploring the outcomes of alternative planning scenarios associated with policy alternatives. To enable effective implementation of the optimal solution developed, this model needs to be complemented by Table 7.1.

Summary of Results Obtained in Alternative Planning Scenarios

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Table 7.2.

Performance Indices under Various Planning Scenarios

Table 7.3.

Variation of Annual Revenues with FTE (Credit Hours per Semester)

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other planning tools for course scheduling (Abboud et al, 2004; Busam 1967; Mooney et al, 1995), timetabling (Abramson 1991; Gu’eret et al, 1996; Mirrazavi et al, 2003), space allocation (Benjamin et al, 1992), and classroom scheduling software (Stallaert 1997).

7.3 SIMULATION MODELING 7.3.1 Overview Simulation modeling entails the development of a dynamic model of a system to evaluate its operating performance. In recent years, simulation modeling has enjoyed increasing popularity among business analysts. This has been caused by the following factors: • Greater emphasis in industry on complex automated business systems which can only be analyzed by a powerful tool like simulation. • Reduced computing costs and improvements in simulation languages which have reduced model development time. • The availability of graphical animation which has resulted in greater understanding and acceptance of simulation by managers. • The widespread availability of increasingly powerful personal computers, pc-based simulation and animation software, and easy-to-use spreadsheet “add-ons”. • The existence of a larger pool of skilled operations analysts, management scientists, industrial engineers, systems consultants, and engineering managers. Surveys have consistently indicated that Operations Research (OR) practitioners in US industry report simulation modeling as one of the most widely used OR technique and one most respondents are eager to master. For today’s analysts, simulation modeling provides an invaluable tool for reducing the risk and uncertainty associated with the design and operation of complex, integrated manufacturing and service systems. 7.3.2 Techniques Simulation modeling falls within a continuum of modeling techniques available to system planners and designers. These techniques, listed in Figure 7.4 (Borchelt and Alptekin, 1990), range in decreasing level of abstraction from mathematical models through computer simulation to a pilot plant. In general, as system design progresses, modeling techniques which more closely

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approximate the realities of the working system become more appropriate and relevant. Computer simulation with animation falls in the middle of this spectrum and can provide the system designer with an improved understanding of the system’s operational performance without incurring the additional costs associated with a physical simulator or a pilot plant. Simulation models can be either discrete or continuous types and are event or process driven. Discrete simulation is used when discrete change predominates in the system being modeled, e.g. starting and stopping of Automated Guided Vehicles (AGVs) in an automated material handling system, or arrival and departures of customers in a supermarket. Mathematical-logical expressions are used to define the instantaneous state transitions that occur at discrete time intervals. On the other hand, continuous simulation is used when

Figure 7.4.

Continuum of Simulation Modeling Techniques

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continuous change predominates, e.g. modeling tool wear in a machining operation. Here the system can be represented by algebraic difference/differential equations. The theoretical fundamentals of developing, verifying and validating simulation models are discussed in excellent texts (by Banks et al, 2001) and (Law and Kelton, 2000) and will not be examined here. Other authors, (Taylor 2004) (Lawrence and Pasternack, 2002) have provided good coverage of the application of simulation in modeling manufacturing and service systems. 7.3.3 Phases of a Simulation Study A sound simulation study should typically follow the following four phases: Phase 1: Orientation 1. Formulate the problem. 2. Examine external considerations and plan the study. Phase 2: System Modeling 1. Collect the relevant data and define a model 2. Establish the validity of the model 3. Construct and verify a computer-based version of the model 4. Make pilot runs of the model 5. Establish the model’s validity Phase 3: System Analysis 1. Design simulation experiments 2. Make production runs 3. Analyze output data 4. Interpret results and make decisions Phase 4: Implementation 1. Document, get funding and implement result 7.3.4 Simulation Software One of the major challenges faced by analysts in developing a simulation model is the selection of appropriate simulation software. Surveys of commercially available simulation software are conducted by professional organizations such as the Institute of Industrial Engineers (www.iie.net) (Elliot 2001) and the Operations Research/Management Science (OR/MS) Today (www.lionhrtpub.com) (Fourer 2003). Selecting simulation software requires consideration of the dimensions of the user environment (application area, system users) and the software characteristics (features available, ease-of-use) to ensure the “best” fit is obtained.

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This matching must take place within the constraints imposed by the user’s budget, time available for system development, level of modeling detail and sophistication required. The three fundamentals of good simulation software selection are thus: 1. Understanding of the user needs/requirements 2. Knowledge of available simulation software 3. Choice of software with characteristics to match user needs 7.3.5 A Case Study–The DemMaTech Flexible Manufacturing Cell (Benjamin et al, 1995) A not-for-profit corporation, the Demonstration of Manufacturing Technology (DemMaTec), was formed to implement a program to assist small and medium-sized manufacturers in the mid-west USA in adapting and implementing advanced manufacturing technologies and modern management systems. One of its immediate goals was the development of a 6000 square foot flexible manufacturing cell whish would serve as a pilot facility to test the computer software, hardware and management systems which would be used in a larger 80,000 square foot production facility. The design team employed a range of quantitative tools and system design methodologies to analyze generate and evaluate system design alternatives. Once the cell layout, materials handling system and communication system were selected, a simulation model was developed using the SIMPROCESS manufacturing simulation software (Elliott 2001) to obtain an a priori assessment of the flexible manufacturing cell’s performance. An operations process chart was developed to show the sequence and duration of operations required to produce a typical part. This information was used in modeling the cell’s performance over a typical forty hour work week and yielded quantitative measures of cell performance, e.g. throughput, equipment utilization, work-in-process. These results greatly assisted the Project Director in developing realistic plans to meet the center’s objectives.

7.4 OTHER TECHNIQUES Other Management Science techniques have been applied to address a wide range of business problems. Examples of these are: • Forecasting–to determine short-term stock prices (Pemberton et al, 2005) • Decision Analysis–to develop the optimal investment strategy for securing additional power transmission capability (Borison 1995)

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• Network Analysis–to develop a schedule for implementing a plant maintenance program (Benjamin et al, 1984). • Statistical Analysis–to develop customer profiles from market research data (Benjamin et al, 2005) • Scheduling–to determine the optimal schedule for implementing capital projects in an investment portfolio (Benjamin 1987)

7.5 APPLICATIONS IN BUSINESS Management Science techniques have found widespread applications in business as companies seek to find the optimum trade-off among the often competing goals of increasing profits, improving resource utilization, and enhancing customer service. Some examples of these in the literature are: • Manufacturing–(Jungthirapanich and Benjamin, 1995). A knowledgebased system is developed to aid decision makers in identifying the best USA location for new manufacturing facilities. • Finance–(Mulvey 1994). Here, Pacific Financial Asset Management Company developed a nonlinear optimization model to balance the risk and rewards of the strategic investment decisions in concert with the movement of projected liabilities. • Operations–(Ehie and Benjamin, 1993). A planning model integrating Analytic Hierarchy Process and Goal Programming is used to facilitate operations planning in the Zambian copper mines. • Marketing–(Carlisle et al, 1987). Network optimization is used by Marshalls, a rapidly expanding off-price clothing retailer, to find the most economical way of routing merchandise from suppliers to warehouses and from warehouses to retail stores in their retail chain. • Human Resources–(Evans 1988). A Decision Support System (DSS) which integrates an assignment scheduling model, statistical routines and a database program is used to facilitate the scheduling of umpiring crews in the American League. Management science techniques will continue to play a significant role in addressing a wide range of business problems that confront managers in all business sectors in both for-profit and not-for-profit organizations. These techniques will be complemented by computer science techniques to conduct data analysis required to develop realistic solutions to complex business problems. Data analysts are encouraged to explore the integration of these tools and techniques in order to meet the challenge of effective data analysis for informed business decisions.

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7.6 REFERENCES Abboud, Nicolas; Sakawa, Masatoshi; Inuiguchi, Masahiro; Sahinidis, Nikolaos V. “School Scheduling Using Threshold Accepting.” Mathematical Programming; Apr 2004, Vol. 99 Issue 3, 563-591. Abramson, D. “Constructing School Timetables using Simulated Annealing: Sequential and Parallel Algorithms”, Management Science, Vol 37, No 1, Jan 1991, 98–113. Banks, J., J.S. Carson, B.L. Nelson, and D.M. Nichol, Discrete-Event Simulation, 3rd edition, New Jersey, Prentice-Hall, 2001. Benjamin, Colin “A Heuristic Algorithm for Scheduling Capital Investment Projects”, Project Management Journal, 1987, Vol. XVIII, No. 4, 87-93. Benjamin, C. O. and Basso, R., “Network Analysis - The Theory/Practice Gap in Local Manufacturing Industry”, West Indian Journal of Engineering, 1984, Vol. 9, No. 2, 28-38. Benjamin, C. O., Ehie, I., and Omurtag, Y., “Planning Facilities at the University of Missouri-Rolla”, Interfaces, Vol. 22, No. 4, July-Aug. 1992, 95-105. Benjamin, Z., P. Nkansah and C. O. Benjamin, “Data Mining in Business Planning”, Proceedings, Eleventh International Conference on Industry, Engineering, and Management Systems (IEMS), Cocoa Beach, Florida, March 14- 16, 2005, 199206. Borchelt, R.D. and S. Alptekin, “Physical Modeling–A Tool for CIM Educators”, International Journal of Applied Engineering Education, 1990, Vol. 6, No. 5, 557565. Borison, Adam, “Oglethorpe Power Corporation Decides about Investing in a Major Transmission System,” Interfaces, Vol. 25, No, 2, 1995, pp. 25-36. Busam, Vincent A., “An Algorithm for Class Scheduling With Section Preference.” Communications of the ACM, Sept 67, Vol. 10, Issue 9, p 567-569. Carlisle, David Kenneth Nickerson, Stephen Porbst, Denise Rudolph, Yosef Shetti, and Warren Powell, “A Turnkey Microcomputer-Based Logistics Planning System,” Interfaces, Vol. 17, No. 4, July-August 1987. Ehie, I., and Benjamin, C. O., “An Integrated Multi-objective Model for Industry Planning”, European Journal of Operational Research, Vol. 68, No. 2, Jul. ‘93, 160-172. Elliott, Monica Simulation Software Buyer’s Guide, IIE Solutions, May 2001, 41-50. Eppen, G.D. F.J. Gould, C.P. Schmidt, J.H. Moore, and L.R. Weatherford, Introductory Management Science, 5th edition, Prentice-Hall, 1998. Evans, James “A Microcomputer-Based Decision Support System Scheduling Umpires in the American Baseball League,” Interfaces, Vol. 18, No. 6, November-December, 1988. Fourer, Robert. “2003 Simulation Software Survey”, OR/MS Today, Institute for Operations Research and the Management Sciences. 2003; Available at www.lionhrtpub.com. Gu’eret, C., Jussien N., Boizumault P., and Prins C. “Building University timetables using Constraint Logic Programming.” 130—145, 1996.

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Jungthirapanich, C. and C .O. Benjamin, “A Knowledge-based Decision Support System for Locating a Manufacturing Facility”, IIE Transactions on Research, 1995, Vol. 27, 789-799. Law, A. M. and W.D. Kelton, Simulation Modeling Analysis, 3rd edition, New York, McGraw-Hill, 2000. Lawerence John A.. Jr. and Barry A. Pasternak, Applied Management Science, second edition, Wiley & Sons, New Jersey, 2002. Liberatore, Matthew J. and Robert L. Nydick, Decision Technology: Modeling, Software, and Applications, John Wiley and Sons, Inc., 2003. Massay, L, Benjamin, C. O., and Omurtag, Y., “Design Axioms for Cellular Manufacturing System Design”, Proceedings, ISRAM ‘94 Fifth International Symposium on Robotics & Manufacturing, Maui, Hawaii, Aug. 15-17, 1994, 151-6. Mirrazavi S. K., Mardle S. J., Tamiz M. “A Two-Phase Multiple Objective Approach to University Timetabling Utilizing Optimization and Evolutionary Solution Methodologies.” Journal of the Operational Research Society, Nov 2003, vol. 54, no. 11, 1155-1166. Mooney, Edward, Rardin, Ronald, Paramenter, W.J. “Large Scale Classroom Scheduling”, IIE Transactions, Vol. 28, No. 5, 1995, 369-378. Mulvey, John “An Asset-Liability Investment System,” Interfaces, Vol. 24, No.3, 1994, 22-33 Pemberton, C., C.O. Benjamin, T. Davis and S. Stephens, “Models for Short-Term Stock Price Forecasting,” International Journal of Industrial Engineering, Vol. 12, No. 2, 2005, 171-178. Stallaert, Jan. “Automated Timetabling Improves Course Scheduling at UCLA”, Interfaces. Vol. 27, July/August 1997, 67-81. Subramanian, Radhka, Richard Scheff, John Quillinan, Steve Wiper, Roy Marsten, “Coldstart: Fleet Assignment at Delta Air Lines,” Interfaces, Vol. 24, No. 1, 1994, pp. 104-120. Taylor, Bernard W. III, Introduction to Management Science, Eighth edition, Prentice Hall, New Jersey, 2004.

7.7 CASE STUDY—WAREHOUSE CONSOLIDATION AT THE COCA COLA COMPANY Introduction The Coca-Cola Company is the global leader in the soft drink industry. Headquartered in Atlanta, GA, this company and its subsidiaries sell over 160 soft-drink brands in almost 200 countries. Because of its heavy global presence, over 1 billion servings of Coca-Cola’s products are consumed daily. Domestically, Coca-Cola North America Fountain (CCNAF) is responsible for syrup manufacturing, syrup distribution, sales, marketing, and associated services for the fountain business. Included in its list of services is the managing of fountain dispensing equipment. CCNAF works closely with equipment

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manufacturers to develop and commercialize dispensing equipment. They then make the equipment available to various fountain customers and coordinate the arrangements for installation, maintenance, and removal of equipment. Management of the inventory and the movement of equipment to customer locations are the responsibility of the Supply Management Organization’s Equipment Group. Current State There are four key players in the process of moving equipment to the customer. They are the account installation coordinator (AIC), the Coca-Cola independent service agent, the equipment group, and the logistics provider. Currently, an order from the customer is placed with the AIC. It becomes the AIC’s responsibility to manage the communication among all parties in an effort to have a successful, complication-free installation. The AIC schedules a site survey with the service agent to ensure the site is capable of supporting the equipment ordered. An order of equipment is also placed by the AIC with the Equipment Group. When new equipment is ordered, it is taken from stock stored in the warehouse. The Equipment Group coordinates with the logistics provider to move the equipment to the service agent who then takes it to the customer location for installation. This process, which takes an average of 12 business days, is diagrammed in Figure 7.5. A description of the current equipment supply chain is as follows: An order for several fountain dispensing units is placed with our equipment suppliers, primarily Lancer, Cornelius, and Shur-Flo. The supplier ships these units to one of five warehouses where the units await a future order from the

Figure 7.5.

Current Process Flow

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customer. Once an order is received, the equipment is shipped from the closest warehouse having the correct model and specifications in stock to the service agent assigned to do our customer’s installation. The supplier and equipment warehouse locations are shown in Figure 7.6. Many challenges to efficiencies lie within the current supply chain for equipment. Once the inventory is in the warehouse, Coca-Cola owns and therefore pays associated carrying costs on the equipment. The build to stock inventory model causes high inventories in each of the five warehouses. If the requested equipment is not in the closest warehouse, the units ship from one of the other warehouses that have the proper equipment in stock. Another inefficiency lies in the communication among the five warehouses and all involved parties in the Coca-Cola system. The last issue is inefficient and antiquated legacy systems and processes used for asset tracking and freight management systems. To further complicate the situation, there are several additional factors that signal a change is needed. They are: • • • •

Customer demand has moved to mass customization Pressure to complete installations faster has caused a compressed timeline Cost of present inventory management practices can not be sustained Transportation costs continue to escalate and directly affect the overall installation costs to the customer

Figure 7.6.

Map of Service Agent and Supplier Locations

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• There are increasing pressures to track the progress of equipment toward fulfilling customer orders. This becomes a huge customer added value initiative • Currently, with multiple shipping points, all elements of the customer’s order arrive at the assigned service agent at any time prior to and sometimes after the scheduled install date. This causes lost items, mixed orders, loss of assets and multiple trips to the customer outlet to complete the installation Future State Coca-Cola has introduced a new fountain dispenser to its customers. The iFountain dispensing unit is state of the art due to its customization potential and its ability to stabilize the variables that affect drink quality. The iFountain plan is more than just a new unit as it encompasses new business processes to support the unit. The process objectives specific to the Equipment Group are increased customer service, reduced cost to serve, ability to capture costs of doing business, and improve asset tracking. To achieve the objectives, several decisions have been made. In the future state of the supply chain for the iFountain units, the units will ship directly to customers from the manufacturer. The equipment package consists of fountain dispensers and backroom equipment coming from separate suppliers. Because the future state has to address the issue of different arrival times for the various parts, the Equipment group has decided to use crossdocking to consolidate the equipment in route to the customer. The supply chain for iFountain is given in Figure 7.7. The suppliers and warehouses will act under a build to order system over the current build to stock system to allow for desired customization of the iFountain unit. The equipment built before the commercialization of iFountain, called legacy equipment, will not be in production after 2002. Customers will be encouraged to install iFountain and customize the unit to their specifications. Legacy equipment is currently, and will continue to be, remanufactured. It will be offered to smaller customers that do not have the demand that the iFountain unit is designed to satisfy. The supply chain for legacy equipment in the future state is given in Figure 7.8. In the future state, legacy equipment will still be in the market, but the amount will decline as it is replaced. With iFountain units shipping directly to the customer, the implication for the warehouses is a decreasing stock of equipment composed of mostly remanufactured legacy equipment. The decision has been made to consolidate the warehouses. The investment in asset tracking systems to use with the Equipment Group’s inhouse logistics personnel would be high. The best way to approach the improvements would be through the use of a third party logistics provider

Management Science Techniques

Figure 7.7.

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Supply Chain for the iFountain System

(3PL). A request for proposal was sent to logistics providers. After a careful evaluation of the proposals submitted, it was decided that the 3PL would be UPS. One key business decision focuses around determining the number and locations for the crossdocks and warehouses. It has already been decided that there will be five crossdocks located in Los Angeles, San Antonio, Chicago,

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Figure 7.8.

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Proposed Supply Chain for Legacy Equipment

Atlanta, and Scranton. The diagram of crossdocks and their service areas are on Figure 7.9. It is the purpose of this case to determine the best location for the proposed warehouse(s). The Case For the purposes of this case, the following assumptions have been made: • • • • • •

There are no crossdocks. There will be one warehouse. There are 50 service agents in the Coca-Cola System. All inbound volume comes from the suppliers. All outbound volume goes to the service agents. Outbound volume grows at a rate of 5% each year. Each warehouse costs the same amount to operate. Each expense is the same for each warehouse. • It takes 10 Coca-Cola employees to run a warehouse. Salaries per warehouse average $410,000. • Based on the pricing of the 3PL provider chosen by the Equipment Group the following analysis has been made: • Inbound cost per piece moved: $6.40 • Outbound cost per piece moved: $6.33

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Figure 7.9. Proposed Integrated Supply Chain

The following information has been provided: • A spreadsheet of the location of the service agents and suppliers and their estimated volume for 2002 (Figure 7.10) • A map of the 50 service agents and suppliers (Figure 7.11) • Volume projections for 2001 and 2002 (Figure 7.12) • A consolidated budget overview for 2001 and 2002 (Figure 7.13) As the Warehousing Manager, you are charged with the responsibility of determining the “best” warehouse location and analyzing the effect of both warehouse consolidation and the use of a 3PL. In a presentation to your manager, you seek to answer the following questions: • Where should the warehouse be located? Why? • What effect will consolidating and using a 3PL have on the projected warehouse budget for 2002? • What other costs need to be considered? • What effect will this have on Coca-Cola’s, particularly the warehouses’, stakeholders?

Figure 7.10.

Service Agent and Supplier Locations and Volume

Figure 7.11.

Service Agent and Supplier Locations

Figure 7.12.

Volume (Actual and Projected)

Figure 7.13.

Equipment Group Budget Overview

Figure 7.14.

Transportation Averages 2000

Chapter Eight

Supply Chain Management

8.1 Introduction 8.2 SCM Academic Programs 8.2.1 US Programs 8.2.2 International Programs 8.3 Professional SCM Organizations 8.4 Challenges for SCM Professionals 8.5 Discussion and Conclusion 8.6 References 8.7 Case Study—Global Sourcing at Otis Elevators

8.1 INTRODUCTION Supply Chain Management seeks to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that goods and services are produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system-wide costs while satisfying service level requirements. (Simchi-Levi et al, 2000). In recent years, many universities have developed Supply Chain Management (SCM) curricula to provide formal education and training for aspiring purchasing and supply management professionals (www.ism.ws 2004). Professional SCM organizations such as ISM, APICS, CAPS, and SOLE provide ample opportunities for industry/academia interaction to stimulate innovations in university SCM curricula. Good models of mutually beneficial industry/academia collaboration in advancing industry research and practice in Supply Chain Management through Research Centers are provided by the CAPS Research Center, Cranfield’s Centre for 195

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Logistics and Supply Chain Management, MIT’s Center for Transportation and Logistics, Penn State’s Centre for Supply Chain Research, and the Ryder Center for Supply Chain Systems established at Florida International University.

8.2 SCM ACADEMIC PROGRAMS 8.2.1 US Programs The ISM website (www.ism.ws) provides information on 165 institutions offering SCM programs in 40 states in the USA. Table 8.1 which summarizes the availability of SCM Programs and Institutions relative to a state’s population shows that a few of the more populous states such as Florida and New York offer relatively limited opportunities for the training of SCM professionals. Figure 8.1 shows the geographic distribution of the SCM programs offered in the 9 regions and the 50 states in the USA. The summary in Table 8.2 indicates that SCM programs are widely available throughout all regions in the USA with the Central and West regions offering the most programs, 72 and 60 respectively. As shown in Table 8.3, California, with 47 SCM programs offered by 22 institutions, has the highest availability of SCM programs of any state. A few states have no programs. US World and News Report (www.usnews.com) provides annual rankings of university programs in Supply Chain Management. One of the top-ranked programs is offered by Ohio State University’s Fisher School of Business, which differentiates its SCM program through the explicit integration of business and engineering in its curriculum. 8.2.2 International Programs SCM programs are available throughout the world. Table 8.4 lists a sample of these programs. These programs have developed strategic alliances with industry and professional purchasing and supply management organizations.

8.3 PROFESSIONAL SCM ORGANIZATIONS Several professional Purchasing and Supply Management organizations (e.g. Institute of Supply Management (ISM) www.ism.ws, American Production and Inventory Control Society (APICS), www.apics.org., Center for Advanced Purchasing Studies (CAPS) www.capsresearch.org, and the International Society of Logistics (SOLE), www.sole.org, have identified the professional competencies required of SCM professionals. Table 8.5 summarizes the cer-

Table 8.1.

Availability of SCM Programs and Institutions in the USA

198

Figure 8.1.

Table 8.2.

Chapter Eight

Geographic Distribution of SCM Programs in the USA

Regional Distribution of SCM Programs in the USA

Supply Chain Management Table 8.3.

199

Distribution by State of Institutions Offering SCM Programs

tification offered and the core competencies and skills emphasized by the professional SCM organizations. To increase the realism and relevance of Supply Management education, universities can pursue collaboration with professional Purchasing and Supply management organizations. A good model of mutually beneficial industry/academia collaboration in advancing industry research and practice in Supply Management is provided by the CAPS Research Center. (www.capsresearch.org). A good example is the report by Guinipero and Handfield (CAPS Research, 2004) which examines the Purchasing and Education Training requirements for Supply Management professionals. The authors, in their survey of industry professionals, found that the most critical areas of need in the training for Supply Management professionals were: Team Building, Strategic Planning Skills, Interpersonal Communication Skills, Technical Skills, Broader Financial Skills, Relationship Management Skills, and Legal Issues, Contract Writing and Risk Mitigation in a Global Environment. These core competencies and associated skills are listed in Table 8.6. The researchers also identified key knowledge areas for Purchasing and Supply Management professionals. These areas are listed and defined in Table 8.7.

Table 8.4.

Sample of International SCM Programs

Table 8.5.

Certification and Core Competencies Required by Professional P&SM Organizations

202 Table 8.6.

Chapter Eight Core Competencies Required by P&SM Professionals

8.4 CHALLENGES FOR SCM PROFESSIONALS A 2005 study by ProLogis (www.prologis.com/researchreports) to ascertain current best practices used by supply chain leaders presents their results in five research reports based on interviews conducted with supply chain executives at 31 major companies. The ten major themes that emerged from the interviews are: 1. Key competitive advantages: Supply chains have become one of the central elements in many companies’ overall competitive strategies. 2. Network re-designs: Supply chain executives reported frequently that network re-designs remain the number one challenge that they face. 3. Outsourcing to offshore manufacturing facilities: Numerous strategies are being used today to mitigate the negative effects of offshore outsourcing on lead-times and customer service. 4. Increasing customer service requirements: Increasingly stringent customer service requirements are one of the main drivers necessitating network re-design.

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203

5. Growing reliance on supply chain software applications: Software applications are being used extensively to provide full visibility across the entire supply chain. 6. Aggregation evolving into a key supply chain strategy: Aggregation strategies designed to facilitate supply chain synergies have become one of the foremost drivers of M&A transactions. 7. Distribution centers getting bigger: Mega-sized facilities incorporating the latest innovations in warehouse design, layout, and management systems are obviating the need for costly automated materials handling systems. 8. Heightened emphasis on cash flow Many executives are using cash-tocash cycle time as a key metric in designing and managing their companies’ supply chains. 9. Manufacturing and distribution networks are merging: Several companies are merging their manufacturing and distribution facilities in order to position inventories closer to their customers. 10. Striving for external visibility: Supply chains are becoming more externally focused, with companies teaming up and collaborating with their supply chain partners.

8.5 DISCUSSION AND CONCLUSION Supply Chain Management represents one of the more exciting areas in industry and offers numerous opportunities for the synergistic fusion of business and engineering. This chapter has emphasized the global nature of Supply Chain Management and the broad scope of decision-making required, the core competencies and knowledge areas required by today’s purchasing and supply management professionals, and the critical challenges to be addressed by P&SM practitioners. Details of several of the quantitative tools and techniques employed by P&SM professionals are covered in the earlier chapters of this book. Readers interested in coverage of the more qualitative topics such as Ethics, Supplier Development, Business Negotiations, are referred to several good textbooks which provide adequate coverage of these areas e.g. (Gourdin 2006); (Handfield & Nichols, 1999); (Turban et al, 2002); (Chopra & Meindl, 2001).

8.6 REFERENCES Barteis, Andrew, Ryan Hudson. And Tom Pohlmann, “ISM/Forrester Report on Technology in Supply Management”, October 2003, Available at www.ism.ws/ISMReport/Forrester. Accessed September 2004. American Production and Inventory Control Society (APICS), www.apics.org. Accessed September 2004.

Table 8.7.

Definition of Key Knowledge Areas for P&SM Professionals

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Center for Advanced Purchasing Studies Research Report, 2004, www.capsresearch.org. Accessed September 2004. Chopra, Sunil & Peter Meindl, Supply Chain Management: Strategy, Planning, and Operation, Prentice Hall, 2001. Cranfield School of Management, Centre for Logistics and Supply Chain Management, www.som.cranfield.ac.uk. Accessed September 2004. Florida International University, Ryder Center for Supply Chain Systems, http://cba.fiu.edu/centers.htm Accessed September 2004. Giunipero, Larry and Robert B. Handfield, “Purchasing Education and Training II”, Center for Advanced Purchasing Studies Research Report, 2004, www.capsresearch.org. Accessed September 2004. Gourdin, Kent, Global Logistics Management, Blackwell Publishing,Williston Vermont, 2006. Handfield, Robert B., & Ernest L. Nichols, Jr., Introduction to Supply Chain Management, Prentice Hall, 1999. Institute of Supply Management, 2004 Supply Management Education: Institutions Offering Programs and Curriculum in SupplyManagement, www.ism.ws/ISMMembership/SchoolsOfferingCourses.cfm, accessed September 2004. Massachusetts Institute of Technology, Center for Transportation and Logistics, http://web.mit.edu/ctl. Accessed September 2004. Pennsylvania State University, Smeal College of Business, Center for Supply Chain Research, www.smeal.psu.edu/cscr. Accessed September 2004. ProLogis, Supply Chain Review, February 2005, www.prologis.com/researchreports. Simchi-Levi, David, Philip Kaminsky, & Edith Simchi-Levi, Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies, McGraw-Hill, 2000. The International Society of Logistics, www.sole.org. Accessed September 2004. Turban, E., D. King, J. Lee, M. Warkentin, & H.M. Chung, Electronic Commerce: A Managerial Perspective, Prentice-Hall, 2002. US News & World Report, America’s Best Graduate Schools, 2006. www.usnews .com Accessed June 5th, 2005.

8.7 CASE STUDY—GLOBAL SOURCING AT OTIS ELEVATORS Introduction The ISM/Forrester 2003 Report on Technology in Supply Management (Barteis et al, 2003) confirms that companies have been steadily increasing their adoption of online purchasing (OLP), with 85% of the 294 companies surveyed reporting some progress toward adopting this mode of purchasing. Several surveys have documented the wide range of OLP Systems available to progressive Supply Managers seeking to adopt these tools to enhance global competitiveness (Dietel et al, 2001), (Banham 2000), (Antonette et al, 2003).

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Online Auctions systems (Nakache 1998), marketed by companies such as Ariba, who acquired FreeMarkets in July 2004, CommerceOne.com and Oracle, have gained popularity in recent years as companies seek to realize significant savings in their annual spends. Electronic Reverse Auctions (e-RAs) are used in a One Buyer –> Many Suppliers scenario to enable a company to implement an on-line procurement system which allows pre-qualified suppliers to bid in an online, real-time dynamic auction. This technique has been used on global sourcing projects by large companies like Otis Elevator Company, Sony and GE that can attract many suppliers. One of the industry leaders in providing Electronic Reverse Auction services is Ariba (www.ariba.com) who organizes interactive, online bidding among pre-qualified suppliers responding to a highly specific request for quotation (RFQ) for custom parts. Others are software providers such as Commerce One (www.commerceone.com), and Oracle (www.exchange.oracle.com). A recent CAPS research study (Beall et al, 2003) reports that e-RAs have increasingly found an appropriate niche in companies’ strategic sourcing toolkits. Buyers indicate that e-RAs can allow companies to realize cost savings by efficiently sourcing goods and services which are easily described, have sufficient spend volumes, can be replicated by a reasonable number of qualified competitors, and have quantifiable switching costs. They can also lead to a fairer process of awarding business by leveling the playing field through increased transparency. A Case Study from SBI/FAMU SBI/Industry Partnership The School of Business and Industry (SBI) at Florida A&M University (FAMU) faced with the ongoing challenge of developing and maintaining an innovative business curriculum has aggressively fostered collaboration with industry partners. In the area of Supply Chain Management, Otis Elevator Company (OTIS), a subsidiary of United Technologies Corporation (UTC), has been one of its most valued partners. This collaboration has resulted in several class presentations at SBI/FAMU by Otis Supply Management professionals on topical issues in Supply Chain Management, a workshop and mini-case competition on Value Engineering for MBA students in Global Logistics, and more recently, class presentations on Online Purchasing from Ariba, one of Otis’ strategic partners. The very positive feedback provided by the MBA students after Ariba’s presentation and the interest generated by their novel business model prompted examination of ways to extend this beneficial collaboration. Our strategy was to facilitate further examination of Online Sourcing by simulating a Competitive Bidding Event using Ariba’s QuickSource online bidding software. This CBE simulation would be the last phase of a larger spend man-

Supply Chain Management

Figure 8.2.

207

Industry/Academia Collaboration in Developing the CBE Simulation

agement project in which students would be required to develop a project plan to guide the timely and cost-effective acquisition of globally sourced components. As shown in Figure 8.2, success would require close academia/industry collaboration. Table 8.8 provides a tabular summary of the profiles of SBI/FAMU (www.famu.edu/sbi), OTIS (Avery 2000), and Ariba (www.ariba.com), the three partners in this collaboration. Among the benefits envisaged to be reaped by the students were an increased awareness of Online Purchasing fundamentals, improved teamwork skills, and enhanced analytical and logical thinking. To realize these benefits, careful attention would have to be given to planning the global sourcing project and the associated CBE simulation to enable the seamless integration of this module into the graduate Supply Chain Management curriculum, clearly specifying students’ roles and responsibilities, offering support to student teams to address areas of uncertainty in a timely manner, monitoring the schedule to ensure on-time implementation, and providing incentives for students to invest adequate time and thought in working on the simulation.

208 Table 8.8.

Chapter Eight Profiles of Partners in Industry/Academia Collaboration in CBE Simulation

The Global Sourcing Project At OTIS, Online Auctions are employed as one of the several tools used to facilitate strategic sourcing as part of its enterprise-wide 4 star Spend Management program (Avery 2000). The program’s objectives included making Supply Chain Management a strategic function, reducing the supplier base to 250 by 2003 from 3,500 in 1998. The program also aimed to increase on-time delivery to 95% and produce cost savings of at least $200 million. Table 8.9 summarizes the project planning information typically assembled by Otis Elevator for globally sourced components using an online auction with Ariba as a partner. This project information was provided to the graduate class in Global Logistics at SBI/FAMU early in the Spring 2004 semester to heighten awareness of the considerable planning required to complete a successful Online Auction using Ariba as a partner. Student teams analyzed the project information provided using commercially available project management software and developed a project plan to facilitate project imple-

Supply Chain Management Table 8.9. Auction

209

Planning Information for the Global Sourcing Project with an Online

mentation. The estimates of resource requirements and costs were supplied by the instructor to provide students with typical representative data for analysis. In real life, this global sourcing project conducted by Otis involved a commodity spend of approximately Mn $40 involving 7 currencies and the resources of approximately 30 associates worldwide. Successful implementation required the consolidation of commodity spends at fourteen buying locations, identification of high-quality suppliers, collaboration with Ariba to publish Supplier Outreach Documents and RFQs, pre-qualification of suppliers to participate in the CBE, provision of software training for suppliers, managing the CBE, and making the final awards to realize costs savings, and ensure on-schedule, within-budget project completion. Figures 8.3 - 8.6 show the Network Diagram, Gantt Chart, Project Budget, and Resource Histogram developed using MS Project 2000 software. This analysis conducted by the student teams indicated that the Global Sourcing Project could be completed in 45 weeks with a budget of $709,100, and would have a peak resource requirement of six analysts. Once a start date is selected, project milestones can be readily established to assist in monitoring implementation. If the project is started on 03/01/04 and experiences no schedule slippage, the CBE will be executed on 10/04/04, and the entire project completed on 01/07/05. Also, “what-if” analyses can be performed to develop a project plan consistent with management’s schedule, budget and

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quality objectives. To complement this high-level view of the process involved in planning a Global Sourcing Project, a more detailed examination of the steps involved in planning and implementing the Competitive Bidding Event was proposed. This would help to promote better understanding of the dynamics of e-RAs, examine the capabilities of the Ariba QuickSource sourcing software being used, highlight the need for careful pre-bid and post-bid planning, and encourage the focus required on error-free implementation. The planning of the CBE simulation is discussed below. The CBE Simulation The activities involved in planning the CBE simulation and the associated responsibilities are summarized in Table 8.10. As shown in Figure 8.8, students, organized into 2 large, twelve-student mock Buyer Teams located at Tallahassee and Pittsburgh, were given the challenge of securing contracts for two lots of Printed Circuit Assemblies using an online auction to secure bids from pre-qualified Suppliers organized into 5 six-student teams. The suppliers, two incumbent and three new candidates, were located in Philadelphia, Pennsylvania; Monterrey, Mexico; Shanghai, China; Prague, Czech Republic; and Savannah, Georgia. They all had different cost structures. The Buyer Teams were required to complete Ariba’s QuickSource software tutorial for Buyers, develop a sourcing strategy, design the Auction, submit bid documents to the 5 pre-qualified suppliers, participate in the CBE, and prepare a final report analyzing all team performance. The supplier teams reviewed Ariba’s QuickSource software tutorial for Suppliers, reviewed their cost structure and examined the additional project information provided. Then each supplier team developed and refined a bid strategy to be implemented during the CBE, and prepared a final report summarizing their team’s performance. As shown in the Gantt chart in Figure 8.7, the CBE simulation was completed over a 30-day period with the following critical milestones: • • • • • •

Project Start Date:03/01/04 Project Briefing: 03/03/04 Participate in CBE: 03/19/04 Prepare Written Report: 03/25/04 Peer Evaluation: 03/26/04 Post Project Appraisal: 04/01/04

Table 8.11 summarizes the results of the CBE simulation. Three separate auctions were conducted using different formats with each auction being conducted over a compressed fifteen minute time frame. As expected, most of the bidding activity took place in the final minutes of the auctions. This auction

Table 8.10.

Planning Information used for conducting the CBE Simulation

Table 8.11.

Results of the CBE simulation

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format favored low-cost suppliers, and because of the switching costs, incumbent suppliers had an initial advantage. During the auctions, our industry partners provided technical support to the teams using the QuickSource software. On completion of the CBE, a review was conducted in class to debrief the student teams and sharpen the focus on the lessons learned. Lessons Learned Feedback provided by the students during an after-action review of the CBE simulation indicated the following lessons were learned: • There is a need for careful planning by the buyers • There is a need for the development of a bid strategy by suppliers • Careful evaluation of a company’s cost structure is required in developing a bid strategy • Software application training is important for users of the QuickSource software • Good IT support is critical to a successful CBE implementation In addition, the student teams were required to develop their skills in the most critical areas of training for Supply Management professionals, viz. Team Building, Strategic Planning, Interpersonal Communication, Technical, Finance, Relationship Management, and Legal Issues, Contract Writing and Risk Mitigation in a Global Environment (Giunipero and Handfield, 2004) Discussion & Conclusions To realize the benefits from the CBE simulation, close co-operation and collaboration is required among all participants. Students were very enthusiastic about the opportunity to use state-of-the-art commercial software for online bidding and invested considerable time in developing their team-working skills. The excellent leadership provided by the industry partners, Otis and Ariba, enabled timely clarification of the procedures associated with a CBE and facilitated speedy resolution on IT problems encountered during implementation. The development of robust interactive software for use in academia to simulate a global sourcing project incorporating a CBE simulation should be a great addition to the arsenal of Supply Chain Management educators. However, even greater benefits can be attained when these tools are used within the framework of pursuing mutually beneficial industry/academia collaboration in training the next generation of Supply Management professionals.

Figure 8.3.

Network Diagram for the Global Sourcing Project using an Online Auction

Figure 8.4.

Gantt Chart for the Global Sourcing Project using an Online Auction

Figure 8.5.

Budget for the Global Sourcing Project using an Online Auction

Figure 8.6. Resource Histogram for Analysts for Global Sourcing Project using an Online Auction

Figure 8.7.

Gantt Chart for Implementing CBE Simulation

Figure 8.8.

Buyer/Supplier Network for CBE Simulation

Bibliography Antonette, Gerald, Larry Giunipero & Chris Sawchuk, e-Purchasing Plus, JCG Enterprises, New York, 2003. Ariba, www.ariba.com. Accessed September 2004 Avery, Susan, “Global Sourcing Strategy: Supply Chain Management at Otis Reins in Global Spending”, Purchasing, June 2000, 53-61 Banham, Russ; “The B-to-B Virtual Bazaar”, Journal of Accountancy, July 2000, 2630. Barteis, Andrew, Ryan Hudson. And Tom Pohlmann, “ISM/Forrester Report on Technology in Supply Management”, October 2003, Available at www.ism.ws/ISMReport/Forrester. Accessed September 2004. Beall, Stewart, Craig Carter, Philip Carter, Thomas Germer, Thomas Hendrick, Sandy Jap, Lutz, Kaufmann, Debbie Maciejewski, Robert Moncka, and Ken Petersen, “The Role of Reverse Auctions in Strategic Sourcing”, Center for Advanced Purchasing Studies Research Report, 2003, www.capsresearch.org. Accessed Sept. 2004.

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Dietel, M., P. J. Dietel & K. Steinbuhler; e-Business and e-Commerce for Managers, Prentice-Hall, New Jersey, 2001. Elliot, Monica, “Buyer’s Guide Project Management Software”, IIE Solutions, August 2001, 45-52. Florida A&M University, School of Business & Industry, www.famu.edu/sbi. Accessed September 2004. Giunipero, Larry and Robert B. Handfield, “Purchasing Education and Training II”, Center for Advanced Purchasing Studies Research Report, 2004, www.capsresearch.org. Accessed September 2004. Nakache, Patricia, “What You Need to Know About Purchasing Online”, Harvard Management Update, July 1998. University of Western Ontario, Richard Ivey School of Business, www.ivey.ca/cases, Accessed July 2004.

Chapter Nine

Facilities Planning

9.1 9.2 9.3 9.4

9.5 9.6 9.7 9.8

Introduction Systematic Methods Facilities Planning Software Facility Location Models 9.4.1 Overview 9.4.2 Location Factors 9.4.3 Location Models Applications in Business References Assignments Case Study–Mid-West Furniture Company

9.1 INTRODUCTION The goal of facilities planning is to develop a facilities plan that maximizes the efficiency of a facility. Maximizing efficiency should result in a decrease in overall costs, an increase in the quality of the product and an improvement in the quality of working life of the employees, thus enabling an organization to be more competitive. The facility planning problem must be addressed by many companies of varying size, in both the private and public sectors. Warehouses, depots and retail outlets have to be re-located; office buildings for banks, insurance companies and other service institutions need to be located optimally; factory expansion plans need to be finalized; additional schools, hospitals and other social infrastructure need to be properly sited.

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The phases of planning for any type of facility can be ordered in the following hierarchy (Hales 1984): 1. Capacity Planning–to determine the throughput requirements and other operating requirements of the proposed facility 2. Facility Location–to determine the “best” geographic region to place the facility 3. Site Selection–to identify the “best” site to accommodate the facility 4. Site Layout–to determine the “best” arrangement of the buildings and auxiliary facilities on the site 5. Building/Facility Layout–to determine the optimal allocation of space within each building on the proposed site 6. Department Layout - to determine the optimal allocation of space within each department in each building 7. Workplace Design–to determine the “best” workplace layout and environment 8. Implementation–to plan and implement the new facility plan in a timely and cost-effective manner These phases are all integrated as decisions made in one phase must be carried through to the next phase. Sound decision-making in each phase requires collaboration between business professionals and engineers and can be encouraged through the use of a systematic approach incorporating manual as well as computer-based methods. Typically, business analysts would be expected to take the lead in the early and final phases of the decision-making process, i.e. phases 1- 3 and 8, with engineers directing decision-making in the middle phases, phases 4–7. In this chapter, we will review the tools and techniques typically used in facility planning and conduct a broad review of facility location models available for use in the facilities planning process.

9.2 SYSTEMATIC METHODS The facility planning phases focus primarily on developing the ‘best’ arrangement of a facility after considering major operational activities and key economic issues. This requires careful consideration and integration of the following key components of a facility: 1. Overall facility layout plan 2. Overall materials handling plan 3. Basic communication/controls plan 4. Primary utilities/auxiliary, distribution plan 5. Preliminary building plans

Facilities Planning

219

Achieving effective integration of these components is in fact quite complicated, requiring input from many different levels in the organization. Several structured methodologies have been developed to assist in this complex planning process. A few of the more popular approaches are as follows: A. Manual Methods Systematic Layout Planning (SLP) (Muther 1973) Simplified Systematic Layout Planning (SSLP) (Muther 1962) Systematic Handling Analysis (SHA) (Muther and Haganas, 1969) Systematic Planning of Industrial Facilities (SPIF) (Muther 1980)] B. Computer Based Methods (Moore 1974; Tompkins and Moore, 1978)] Construction Algorithms e.g. CORELAP, PLANET, CASS, COLO2, COMP2 Improvement Algorithms e.g. CRAFT, COSFAD, GRASP, OFFICE, PREP Each approach has its peculiar assumptions, limitations and constraints. Before any method can be chosen, the problem at hand must be carefully defined to enable the adoption of the most appropriate planning approach. The manual methods listed above, all pioneered by Richard Muther, have enjoyed considerable popularity in the industry. They offer the facilities planner simple, structured, replicable methodologies for arriving at a good facilities plan. The computer-based facilities planning methods have been based on either: • Improvement algorithms, which improve an existing layout, or • Construction algorithms, which generate a layout from data provided. CRAFT–Computerized Relative Allocation of Facilities Technique is an example of a computer-based facility planning methodology which utilizes an improvement algorithm. The improvement algorithm requires the following 3 inputs: a. From/To chart for material flow b. Material Handling cost matrix c. Initial layout CRAFT’s objective function is the minimization of material handling costs. To evaluate how well this goal is reached, the CRAFT program outputs the initial layout and its associated material handling costs, the departments interchanged at each interaction, the improved layout and the revised material handling cost, and finally the percent improvement achieved in material handling cost.

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CORELAP–COmputerized RElationship LAyout Planning is a computerbased facility planning methodology based on a construction algorithm that requires the following 3 inputs: a. Department areas b. Maximum building length to width ratio c. A summary of the relationship chart This algorithm utilizes procedures similar to Muther’s SLP and has as its main objective minimizing a total closeness rating (TCR) for the facility layout.

9.3 FACILITIES PLANNING SOFTWARE A wide range of software is available for use by facilities planners. Figure 9.1 presents profiles of facilities planning software based on a survey conducted by the Institute of Industrial Engineers (http://solutions.iienet.org, 2000). This shows that eleven software products distributed by eleven companies range in price from a low of fifty dollars (MHAND) to a high of $35,000 (Workplace Designer). All of the products used the Windows 95/98/NT operating system and focused on a particular aspect of facilities planning, e.g. materials handling analysis, layout planning, workplace design, or simulation modeling. One of the more popular facilities planning software is VisFactory, a product marketed by Engineering Animation Inc. This software has an easy-to-use interface and enables the concurrent engineering of an entire factory through Design Suite modules. The first module, FactoryCAD, is for layout modeling and links to discrete-event simulation. FactoryPlan and FactoryFlow are for use in layout analysis, VisSim is for 3D factory visualization and animation. The VisFactory Suite handles the graphical and logical detail needed for modeling large factories.

9.4 FACILITY LOCATION MODELS 9.4.1 Overview Facility location decisions are of major importance to a company’s competitive position. The suitability of a site rests on how well those responsible in the decision-making process assess the impact of the facility on the fulfillment of corporate objectives. A suitable location can provide favorable contributions to a company’s market competitiveness. Facility location models incorporating location factors can range from simple techniques such as scoring models, dimensional analysis, and the center-of-gravity model to the

Facilities Planning

Figure 9.1.

221

Sample of Facility Planning Software— (http://solutions .iienet.org,2000)e

more advanced techniques such as regression analysis, decision analysis, mathematical programming, and group decision support systems. These will be examined in the following sections. 9.4.2 Location Factors Jungthirapanich and Benjamin (1995), in a review of location studies for manufacturing plants in the USA, observed that in the early days of location research, only a small number of easily quantified location factors were considered. This approach led logically to the use of operations research type models. Later, interest shifted to include a wide range of both quantifiable and non-quantifiable location factors. Quantifiable factors, i.e. economic and technological factors, must be supplemented with non-quantifiable factors, such as sociological factors, in selecting the best facility location. The nonquantifiable factors cannot be evaluated directly by quantitative location models but require subjective judgment to estimate their effects. As shown in

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Figure 9.2, facility location factors can be classified in the following eight groups: • • • • • • • •

Market Transportation Labor Site Considerations Raw Materials and Services Utilities Governmental Concerns Community Environment

9.4.3 Location Models Scoring Models The following procedure may be followed for plant location/site selection decision-making: 1. Identify desirable site attributes/factors. 2. Assign a weight to each factor 3. Score values for each site i.e. rate each site for each factor. 4. Compute a suitability index for each site. 5. Select ‘best’ site. This procedure can be adapted to compare countries, regions, locations and individual sites. Mathematically, the scoring procedure can be stated as follows: Let i = factor number j = candidate location number m = number of candidate locations n = number of evaluation factors wi = weight assigned to factor i xij = rate/score assigned to factor i for location j Xj = suitability index computed for location j n

therefore Xj = ∑ wixy i=1

[] m

The best location is candidate j such that Max Xj

j=1

Dimensional Analysis Dimensional Analysis (Wild 1973) represents a modification of the procedure outlined above to combine measurable cost information and scores or

Figure 9.2.

Classification of Facility Location Factors

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ratings assigned to non-quantifiable factors. The procedure can be stated as follows: Let: Oi1, Oi2 . . . Oin be costs/scores associated with factors 1–n for location i; w1, w2 . . . wn be weights to be assigned to factors 1, 2 . . . n; Mj . . . merit of candidate location j. Then Mj = Owiji x Ow2j2 . . . . . . .x Ownjn Therefore comparing locations a & b, we get: Ma/Mb = (Oa1/Ob1) w1 x (Oa2/Ob2)w2 x . . . x (Oan/Obn)wn This ranking procedure represents a slight improvement over the scoring model outlined earlier in the chapter in that it takes advantage of measurable cost information where available and complements this data with subjective rankings and scores of the non-measurable factors. Center of Gravity Model This technique tries to find the optimum facility location by determining the location coordinates which would minimize the materials handling moment, i.e. the product of the material flows and the distances traveled. This can be expressed as follows: m

Minimize ∑ wi [(x–ai) 2 + (y–bi) 2] 1/2 i=1

Where m = number of market areas to be served by the plant (x,y) = coordinate location of new plant (ai,bi) = coordinate location of market area i wi = market demand for area i. This technique is also referred to as ‘Least Cost Center Analysis’ (Wild 1973). It is useful where the problem is finding the market location of a single facility to best satisfy specified markets. Its major limitations are: • The ‘best’ location is determined on the basis of the single objective of minimizing the transportation and distribution costs of the finished goods. • Transportation and distribution costs are assumed to be linear and directly proportional to the distances. The technique can be extended to incorporate factors which may be significant in a particular situation e.g. • Transportation of raw materials to the facility. • Use of different freight rates to better represent the varying modes of transportation.

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225

The data requirements for this ‘center of gravity’ approach are more than for the procedures discussed earlier. In this case, specific forecasts would be required for: • • • •

Location of market regions and material suppliers. Sales volume by region/location. Material flows (quantity or intensity) from suppliers. Transportation means/modes and distribution freight-rate charges.

Sensitivity analyses can be performed to assess the potential impact on the final recommendation of changes in parameters such as the sales forecast, material flows, modes of transportation and distribution, etc. The benefits of this technique are similar to those offered by the ranking procedures. It provides a way of rapidly evaluating alternatives to select the ‘best’ location. However, more detailed evaluation is required to incorporate strategic and other ‘non-quantifiable’ considerations into the final location decision. Advanced Techniques Several sophisticated quantitative techniques have been developed and applied to the facility location/site selection problem. These include: • Mathematical programming models (viz. linear, integer, mixed integer, goal, and heuristic) • Regression analysis • Decision analysis • Group Decision Support Systems These advanced techniques, covered at length elsewhere in the literature, are discussed briefly below to make the reader aware of their potential applicability to facility location/site selection problems. Despite the use of these tools, considerable managerial judgment is still required in: • Identifying good prospective candidate locations. • Interpreting and analyzing the results from the model. • Integrating strategic ‘non-quantifiable’ considerations into the decisionmaking process. Mathematical Programming The literature on plant location/site selection contains numerous mathematical programming formulations developed (see Francis & White, 1974,

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Love et al. 1988; Brandiau and Chiu, 1989). A typical linear programming formulation (Salvendy 1981) is presented below: Let m = number of factories n = number of sales regions ai = production capacity of factory i bj = demand in sales for region j cij = total cost of producing a unit in factory i and shipping it to sales region j xij = amount of region i sales demand to be supplied from factory j (to be determined) Then select facility location configuration such that: m

m

∑ xj cij xij

...

Objective function

i=1 j=1 m

Subject to: ∑ xij < bj

...

Sales constraints

i=1

xij < ai . . . Production constraints All xij > 0 . . . Non-negativity constraints This formulation could be used to decide the best arrangement of a proposed facility configuration. A solution can be obtained using any standard linear-programming package. Thus, the economic consequences of alternative proposals for plant location and related capacity and distribution can be explored with relative ease. Regression Analysis In some situations, the location of a facility can have a significant impact on sales and thus revenue generation. Once the important determinants of sales are established, these can be used as independent variables in a regression equation of the form: For location i, yi = (aixi + . . . + anxn) + b Where yi = forecast sales associated with location i xj = independent variable determining sales aj = coefficient of variable xj b = constant term Decision Analysis The problem of facilities location/site selection has also been addressed using Decision Analysis (Raiffa 1968). This technique may be particularly useful when the operating risks are substantial such as the siting of a nuclear power station (Kirkwood 1982). In such instances, a decision analysis model can serve as a means of evaluating the risks associated with alternative candidate locations, thus providing a good basis for finalizing the location decision.

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Application of this technique to a facilities location/site selection problem would typically proceed as follows: 1. Identify the evaluation measures/attributes (x1 . . . xn) to be used to determine the desirability of each location. 2. Assess a multi-attribute function u (xi . . . xn) which represents the facilities planner’s attitude toward risk-taking and trade-offs among the attributes. 3. Determine the specific level xi of each evaluation measure Xi that would result from selecting each site. Uncertainty about these levels can be encoded as a probability distribution p (x1 . . . §n1a) representing the desirability of candidate location a with respect to the evaluation measures. 4. Rate sites in order of their expected utilities U (a) where U (a) = x1, x2, . . . xn u (x1, . . . xn) p (x1, . . . xn1a) 5. Carry out sensitivity analysis and make final decision. Group Decision Support Systems Group Decision Support Systems (GDSS) or Computer Supported Collaborative Work (CSCW) systems (Benjamin et al, 1996) are groupware, software designed to facilitate communication and information sharing, which can facilitate intra-team and inter-team communication and collaboration within organizations by enabling the use of shared applications, access to corporate databases, real-time brainstorming, and consensus building. A GDSS prototype (Chi et al., 1997) was developed for locating manufacturing facilities in one of the 48 contiguous states in the USA. The GDSS solicited inputs from several users and facilitated mediation and consensus building among various user groups. It adopted a four-phased decision-making process entailing data collection, individual evaluation, data classification, and establishment of group consensus. Decision models utilizing Artificial Intelligence (AI) and Operations Research (OR) techniques were integrated with Database Management Systems (DBMS) to provide an integrated group decision-making environment. An Artificial Neural Network (ANN) model classified location alternatives, a multi-criteria decision model (MCDM) evaluated location alternatives, and an Expert System (ES) assessed group consensus. The prototype received very favorable user evaluations when tested in the development environment, confirming the considerable potential it offered for enhancing the group decision-making process in the facility location domain.

9.5 APPLICATIONS IN BUSINESS The following case studies illustrate the challenges encountered by analysts in addressing facility planning problems in industry.

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Case #1 General Motors (GM) Saturn Plant Early in January, GM announced it was looking for a site for its Saturn project, a new plant to build a sub-compact car as inexpensively as the Japanese. This project would attempt to revolutionize the automotive industry by incorporating state-of-the-art Computer Integrated Manufacturing (CIM) concepts. It would utilize more intensive and sophisticated levels of manufacturing automation, integrated through computer systems which would also provide relevant information in a timely manner to all functions of the company. The new facility would cost $5 billion and would ultimately create up to 20,000 assembly and component jobs. The facility would require approximately 1000 acres of land, proximity to railroads, highways, airports and an aluminum foundry. During operation, it would consume 80 megawatts of electricity, 4 million gallons of water per day, 400 million cubic feet of natural gas and 80,000 tons of coal per year. The GM management hoped to finalize the site selection by May 1. Following the announcement, GM Chairman Roger Smith was besieged by high-powered groups of lobbyists from Illinois, Michigan, Iowa, Texas, Kansas, Missouri, Minnesota, Ohio, Pennsylvania, and several other states. All were seeking to attract the large capital investment and the associated economic benefits. State officials offered tax abatements, financing assistance, cheap land, improved housing, educational and recreational facilities for Saturn employees. Finally, in August of that year, GM announced its decision to locate the Saturn facilities and its 6,000 workers on a 2,400 acre site, near a small town of Spring Hill, south of Nashville, Tennessee. State officials claimed that no tax breaks had been requested or given. Instead, GM was lured by Tennessee’s strategic location, climate, ample supplies of water and electricity, low property taxes and its eager workforce. Its chief asset, however, was its close proximity to GM’s suppliers and its location within 500 miles of 76% of the U.S. population. Low freight costs, cited as the most important economic factor in this selection process, were assured by the site’s proximity to three interstate highways, a rail link to the 234-mile Tennessee-Tombigbee Waterway, and a new $2 billion barge canal to the Gulf of Mexico. Case Study #2 Motorola location for its new Cellular phone plant (WSJ, 1997) Motorola Inc. looked around for a location for a new plant for its growing cellular phone business, including sites in Scotland and China. After a lengthy consideration, it chose the German city of Flensburg, a spot in one of the most expensive and least competitive countries in the world. Although burdened by the same prohibitive labor costs, powerful trade unions, utopian worker benefits and crippling corporate taxes that deterred many foreign companies from

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locating facilities in Germany, the city was still an attractive location for Motorola and other capital-intensive manufacturers who did not rely on a lot of labor. Germany was Europe’s single biggest market and possessed a large skilled and educated workforce. In addition, the state of Schleswig Holstein, where the city was located, offered 8.6 million marks of subsidies in return for Motorola’s guarantee to employ for at least seven years, one worker for every 95,000 marks provided in subsidies. Motorola was also persuaded to site its new plant in Flensburg because of the excellent performance of its 11-year old plant already located in the German city. Case Study #3 Locating a Deluxe Hotel and Convention Center in the Caribbean Global Logistics Consultants were retained to conduct a location study to assess the suitability of eight English-speaking countries in the Caribbean as possible locations for a proposed Deluxe Hotel and Convention Center. The countries examined were Antigua, Barbados, Grenada, Guyana, Jamaica, St. Kitts, St. Lucia, and Trinidad. Using data obtained from UNICEF, the World Bank Reports, and the CIA Factbook, country profiles were created based on 16 factors divided into four categories, viz. Economic, Infrastructure, Social, and Transportation. A scoring model was developed to compare the island locations. The chart shown in Figure 9.3 summarizes the country score and rank achieved by each

Figure 9.3.

Chart of Country Ranks and Grades

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location upon completion of the assessment. The scores were categorized as follows: • EXCELLENT (Score > 3.4) • VERY GOOD (3.4 > Score > 3.0) • GOOD (3.0 > Score > 2.6) The results of this study showed that the scores obtained by the country locations varied over a relatively small range (3.46-2.65). This suggested that no location had a significant advantage. However, the larger countries were all ranked in the upper 50 percent. Trinidad was the only country classified as Excellent, while Jamaica, Barbados, Guyana, and Antigua were determined to be Very Good. St. Kitts, Grenada, and St. Lucia were assigned with a Good rating. All countries scored greater than 2.5 indicating that good opportunities existed in all of the eight country locations examined. Moreover, there was considerable flexibility for investors seeking a good location for the deluxe hotel and convention center. Figure 9.4 displays the scores obtained by each country.

9.6 REFERENCES Benjamin, C. O., L. Monplaisir, S. Chi, L., Lahndt-Hearney, & C. Riordan, “Group Decision Support Systems: A Review of Industrial Applications”, Proceedings,

Figure 9.4.

Chart of Country Scores

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47th International Industrial Engineering Conference, St.Paul/Minneapolis, Minnesota, May 18-23, 1996, 197-202. Brandeau, M. and S.S. Chiu, “An Overview of Representative Problems in Location Research”, Management Science, Vol. 35, No. 6, June 1989, 645-674. Cameron, Bonnie and Patrice Raithel, “Software Buyer’s Guide, Facilities Planning”, IIE Solutions, Jamuary 2000, 43-45, http://solutions.iienet.org, 2000. Chi, S. C., Benjamin, C. & Riordan, C. A., “A GDSS for Locating Manufacturing Facilities”, Proceedings, 6th Industrial Engineering Research Conference, May 1718, 1997, Miami Beach, Florida, 169-174. Francis, R.L. and J.A. White, Facility Layout and Location: An Analytical Approach, Prentice-Hall, Englewood Cliffs, NJ, 1974. Hales, H Lee, Computer-Aided Facilities Planning, Marcel Dekker, Inc., New York, 1984 Jungthirapanich, C. and C. O. Benjamin, “A knowledge-based decision support system for locating a manufacturing facility”, IIE Transactions, 1995, Vol. 27, 789799. Kirkwood, C.W., “A Case History of Nuclear Power Plant Site Selection”, Journal of the Operational Research Society, Vol. 33, No. 4, 353-364, April 1982. Love, R.F., J.G. Morris, and G.O. Wesolowsky, Facilities Location: Models and Methods, Elsevier Science Publishing Company, New York, 1988. Moore, James M., Computer Aided Facilities Design, An International Survey. International Journal of Production Research, Vol. 12, Jan. 1974. Muther, Richard and John D. Wheeler, Simplified Systematic Layout Planning, Management and Industrial Research Publications, Kansas City, Missouri 1962. Muther, Richard and Knut Haganas, Systematic Handling Analysis, Management and Industrial Research Publications, Kansas City, Missouri 1969. Muther, Richard, Systematic Layout Planning, Second Edition, CBI Publishing Co., Inc., Boston, MA, 1973 Muther, Richard, Systematic Planning of Industrial Facilities, Vols I & II, Management and Industrial Research Publications, Kansas City, MO 1980. Raiffa, H., Decision Analysis, Addison-Wesley, Massachusetts, 1968. Rhoads, C., “A Contrarian Motorola Picks Germany”, Wall Street Journal, Friday, October 10, 1997, A18. Salvendy, G. (Ed.), Handbook of Industrial Engineering, Wiley, 1982. George M. Parks, Chap. 10.1, Location: Single and Multiple Facilities, 10.1.6-10.1.14. Tompkins, J.A. and J. M. Moore, Computer-Aided Layout: A User’s Guide, IIE, Norcross, GA, 1978. Wild, R., The Techniques of Production Management, Holt, Rinehart and Winston Ltd., London, 1973.

9.7 ASSIGNMENTS Question 1. International Motors Ltd., an automobile manufacturer, is planning a new assembly facility to supply a new range of vans for the North

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American market. The possible locations for the facility have been reduced to two, viz. St. Louis and Seattle.

9A.

Information assembled by the company’s facilities planners

Compare the relative merits of the two sites using: a) scoring model b) dimensional analysis What recommendation would you make? Question 2. Scientific Technologies’s New Business Development team is using the center-of-gravity model to identify the best location for a new centralized warehouse. This will be used to facilitate distribution of its new Millennium+ chip to USA market locations in Boston, Kansas City, Seattle, and Tallahassee. The following information has been obtained.

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Calculate the x, y coordinates of the optimal location Question 3. Give three examples of specific areas for potential application of at least two different location models. Question 4. What would be the important location factors in the following situations? • Location of a warehouse in a replacement parts or technical books distribution system. • Location of a fork lift truck assembly plant. • Location of a new McDonald’s restaurant. • Location of a science and research center for a national research institution. Question 5. Discuss the usefulness and limitations of decision support systems in facility location decision-making.

9.8 CASE STUDY—MID-WEST FURNITURE COMPANY A Case Study in CIM System Design Introduction Mid-West Furniture Company is a small manufacturing company which produces high-end, contract office and institutional furniture in Marquand, Missouri. Its products are mainly handmade and high quality. Mid-West currently has six skilled employees and annual sales of around $200,000. Mid-West has the ability to sell more product than its current facility can produce. The company wants to expand its facility and increase its sales to the multimillion dollar level. It has spent the last eight years conducting the product and market research necessary to create a new furniture manufacturing and marketing company. Mid-West is also interested in building other plants in foreign countries after the new plant has been proven successful. This will help it produce high quality products that will be competitive worldwide. Mid-West currently produces a wide variety of furniture, each in a relatively small volume. The furniture is of two basic types: table, and storage or casegoods. The tables are of several types, including occasional, conference, boardroom, and work tables. The storage or casegood furniture includes bookcases, file cabinets, credenzas, and beds. In total, Mid-West produces approximately fifty different products. Each of the products has three possible surfaces: high pressure plastic laminate, wood veneer, and solid hardwood. Skilled laborers operate standard, manually-controlled machines. A list of the machines and work areas is given in Appendix A. Simply expanding its current facility is not a viable way to achieve its sales goals. Economies of scale are not possible and skilled labor is difficult to find in its rural location.

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Adopting fixed or dedicated automation is also not practical. Each product requires different machine operations and settings. Many changeovers would be needed to produce the variety of products that Mid-West sells. These changeovers would be very time consuming and expensive if standard automation was used. Mid-West wants to use automation, but the automation must be flexible enough to accommodate frequent change-overs and short production runs. Computer Integrated Manufacturing (CIM) technology meets these needs. Computer Integrated Manufacturing (CIM) is concerned with providing computer assistance, control and high level integrated automation at all levels of the manufacturing industries, by linking islands of automation into a distributed processing system. The technology applied in CIM makes intensive use of distributed computer networks and data processing techniques, Artificial Intelligence and Data Base Management Systems (Ranky, 1986). CIM is typically applied in situations where between 2 and 800 different parts exist and where annual production volumes are between 15 and 15,000 parts per year (Groover and Zimmers, 1984). The Mid-West Furniture operation fits these parameters and the owner has requested the assistance of the University of Missouri-Rolla (UMR) in planning and developing a CIM system for Mid-West Furniture’s new plant. The process of planning and implementing a CIM facility can be divided into four phases as shown in Figure 9.5. The orientation phase, Phase I, begins with the definition of the project and its objectives. It involves an evaluation of all constraints and influencing factors, such as: machinery to be used, projected capacity, and costs. A preliminary economic analysis should be done in this phase also. Phase II, the overall facility plan, involves the development plant layout. This layout may be revised upon evaluation. Phase III, the detailed facility plan, includes a second more accurate economic analysis and the development of detailed layouts. This phase also includes development of the computer software for integrating and controlling the system elements. Implementation is Phase IV; in this phase the plant is built, debugged and put into operation. The owner of Mid-West Furniture had completed Phase I before he contacted UMR. This case study describes activities related to Phase II. The first step in Phase II is to develop an initial layout. The owner of MidWest Furniture Company had developed a tentative layout for the new CIM plant based on his knowledge of the business. This layout is shown in Figure 9.6. While this layout has straight aisles for good flow, it also has some negative aspects. All machines are shown as being the same size and shape, when, in fact, they vary considerably. Some machines also need significant

Facilities Planning

Figure 9.5.

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Phases of Integrated Facilities Planning

space for loading and unloading materials to be processed. The location of some machines does not lead to efficient part routings. Because of its location the dust generated in the hand sanding area could interfere with the finishing area, where the proper finish is applied to the furniture. These problems need to be corrected. A PC based software package, called STORM, and a mainframe based package, called CRAFT, were tried in attempting to improve the initial layout. Both of these packages had drawbacks. STORM could not handle the number of departments required or their different sizes. CRAFT draws departments in irregular shapes which are impractical. Since neither of these programs was satisfactory, a different methodology, Systematic Layout Planning (SLP), was used. SLP is a manual method for designing an efficient plant layout, developed by Richard Muther (Muther 1973). The diagram in Figure 9.7 shows the steps of SLP. Figures 9.9–9.13 contain the list of machines and work areas, the typical daily production, routing charts for sample products, departmental areas, a product flow matrix, a relationship chart, and the relationship diagram for

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Mid-West. These were used in the SLP method to develop several alternative layouts. The alternatives were evaluated on the following factors: 1. Ease of product flow and reduction of congestion 2. Minimization of movement 3. Utilization of available space 4. Ease of maintenance 5. Flexibility 6. Safety 7. Efficiency Through this evaluation, the “best” layout alternative was chosen. The improved layout for Mid-West is shown in Figure 9.8. The next consideration is the material handling system. The future plant will require a system that can allow the product to accumulate and can move the product without damaging it. Also, the system should be computer controlled in order to incorporate CIM. Mid-West recommended the use of transfer carts in combination with roller conveyers. In the furniture industry, roller conveyors and transfer carts are typically used in combination to move the product, enabling the system to be flexible. Also, many other furniture companies have been successful using this type of system. To further evaluate the layout and the material handling, system, simulation can be used. Computer simulation of a future production system can be beneficial for a number of reasons. Simulation can uncover short-falls in the layout, such as bottlenecks. It can help determine if the material handling system is adequate and whether the required production volumes can be achieved. Simulation can also be used to increase the confidence of planners and investors, which can help generate funds for the project. A simulation study should follow the same four phase process discussed earlier. Phase I-Orientation, begins with the formulation of the problem. The external considerations should be examined and the study planned. Phase II-System Modeling. In this phase, data is collected and the model is defined. Then the model is validated. This phase includes the construction and verification of a computer-based version of the model. Pilot runs of the model are made and the validity is again checked. Phase III-System Analysis, includes designing simulation experiments and making production runs. The output data is analyzed, results interpreted, and decisions made. Phase IV-Implementation is the final phase. Here the model and work is documented. Funding for the project is acquired now and the results are implemented.

Figure 9.6.

Initial Plant Layout

Figure 9.7.

SLP Pattern of Procedures

Figure 9.8.

Improved Plant Layout

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There are several software packages available for developing a simulation model. These packages range in price from $500 to $100,000 and must be evaluated on several factors: animation features, underlying simulation language, brand and type of hardware, and the special class of problems which can be modeled. For the simulation in Phase II of developing the Mid-West CIM facility, PCModel was used. This software simulates discrete systems where the products move as individual parts. PCModel has interactive debugging and uses event scheduling for its structure. Event scheduling is a method in which the user defines and develops the simulation. In most other simulation packages the user describes the flow and this is translated into events by the software. It takes longer to develop a model with event scheduling, but there are fewer constraints on the model. This model was developed to evaluate the plant layout and the material handling system. The improved layout was used with transfer carts and roller conveyers as the material handling system. In order to create the simulation model, the user must supply certain information about the production process. First, the user must construct a layout of the system, called an Overlay Screen. It gives a pictorial representation of the system as viewed from above. Then, various data about the production process are entered. This information is summarized in Table 9.1. Once the Overlay Screen has been created and the information has been entered, the system can be simulated. The Mid-West System was simulated with two different products, radius hardwood table tops (R-2600) and cube tables (C-1000). The routing and the process times for each of the products are given in Figure 9.11. For the simulation, 32 cube tables and 1 radius table were produced. The time required to create the simulation was approximately two days being required for each new product that was added. This is mainly due to the complex interaction between the products and the transfer carts. When a part arrives at a transfer cart it tells the cart it is waiting. When the transfer cart moves, it checks for waiting parts. If a part is waiting, the cart stops and tells the part that it is ready. Then the part moves toward the cart and to a hiding place. The part moves to a hiding place because the part and the transfer cart cannot occupy the same space. In order for the part to appear to be on the cart, it is moved to a hiding place. The transfer cart moves and when it reaches the location where the part is to move off of the cart, the cart stops and gives a signal to the part and the part moves off. When several parts are involved, the same transfer cart is used several times. This lengthy process results in a more complex program. Based on the simulation, the plant will be required to operate at least nine hours a day in order to complete the furniture started that day. The cube ta-

Facilities Planning Table 9.1.

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Model Section

bles are completed relatively quickly, but the radius tables are produced at a slower pace. This is because they have to travel through the finishing system. The products remain in the finishing system approximately seven hours. This is a large part of the production time. A bottleneck was discovered between the CNC panel saw and the press. The bottleneck was caused by an inadequate amount of material handling equipment, which results in a large number of parts waiting for transfer. This can be taken care of by adding an additional transfer cart or by installing roller conveyors from the saw to the press. If there is still a bottleneck and the production is not up to the desired level, then an additional saw should be added. As the planning of the CIM facility goes into Phase III, simulation can be used again. In this phase, simulation can assist in the development of detailed layouts and integrating the system. It will also help in developing the logic for controlling the flow of products through the plant. The first step would be to develop a simulation with more products and accurate times. The times used in the initial simulation were estimated based on Mid-West Furniture’s experience. In order to add enough products to simulate a typical day’s production with PCModel, it will take two or three weeks of programming.

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There are a few limitations with simulation. First, any simulation requires a lot of programming time. In PCModel, the part routings are difficult to change. These problems are currently being addressed with programs which allow the user to develop a model without actually programming. Secondly, simulation cannot optimize the system. It can only describe the results of a “what-if” question. Finally, the simulation model is only as accurate as the input data and it can only describe system characteristics that have been explicitly modeled. Simulation does provide several benefits, though. The first benefit is its usefulness in evaluating plant layouts and material handling systems. As in this case, simulation can identify bottlenecks and other areas of inefficiencies. This is important in the development of a new plant. Simulation can also lay the groundwork for the computer integration of the facility. Finally, simulation is an important communication tool. It can be used to justify expenditures and help get funding. With animation, it is also useful in explaining the production process to people unfamiliar with the process. Simulation can also be used after the plant has begun operations. In an operating plant, simulation assists management in scheduling and staffing. It can also determine equipment availability and estimate production capacity. Although there are some limitations in simulation, the benefits to be attained outweigh them. Simulation is a very effective way to evaluate new ideas and can save a company much time and money. Bibliography Benjamin, Colin O.,”Simulation: Teaching Note”, Course EMGT 357, Advanced Facilities Planning, Dept. of Engineering Management, University of Missouri-Rolla, January 1990. Culbreth, C. Thomas, Russell E. King and Ezat T. Sanli, “A Flexible Manufacturing System for Furniture Production.” Manufacturing Review, December 1989, 257265 Grant, John W. and Steven A Weiner, “Factors to Consider in Choosing a Graphically Animated Simulation System”, Industrial Engineering, August 1986. Groover, M.P. and E.W. Zimmers, CAD/CAM: Computer Aided Design and Manufacturing, New Jersey: Prentice-Hall Inc., 1984 Jones, William Lester, “Developing Computer Integrated Manufacturing in a Furniture Manufacturing Company”, Unpublished M. Sc. Thesis, Dept. of Engineering Management, University of Missouri-Rolla, July 1989 Muther, Richard, Systematic Layout Planning, Missouri: Management & Industrial Research Publications, 1973 Ranky, P.G., Computer Integrated Manufacturing, New Jersey: Prentice-Hall Inc, 1986

Included are routing charts for two sample products: radius hardwood table tops (R-2600) and cube tables (C-1000) (Figure 9.11).

Figure 9.9.

List of Machines and Work Areas

Figure 9.10.

Typical Daily Production

Figure 9.11.

Routing Charts for Sample Products

Figure 9.11.

Continue

Figure 9.11.

Continue

Figure 9.11.

Continue

Figure 9.12.

Departmental Areas

Figure 9.13.

Product Flow Matrix

Figure 9.14.

Relationship Chart

Figure 9.15. gram

Relationship Dia-

Chapter Ten

Contemporary Techniques

10.1 Introduction 10.2 Techniques 10.2.1 Data Mining 10.2.2 Quality Management 10.2.3 Business Process Mapping 10.2.4 Other Tools and Techniques 10.3 Applications in Business 10.4 Discussion and Conclusion 10.5 References 10.6 Case Study–Developing a Business Plan for BioMed Inc.

10.1 INTRODUCTION To remain competitive, business executives are continually undertaking review and re-examination of their systems and processes. In previous chapters, we have reviewed several engineering tools and techniques which have been widely applied to facilitate analysis of operations and to improve decisionmaking in business. In this chapter, we review a few of the more contemporary techniques and illustrate their applications. We examine Data Mining, Quality Management, Business Process Mapping and identify other promising tools and techniques.

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10.2 TECHNIQUES 10.2.1 Data Mining An Overview Data Mining has been traditionally defined as “the automated extraction of valuable information from large databases” (Groth 2000). Data Mining integrates techniques from Statistics, Artificial Intelligence, Pattern Recognition, Visualization, and Database Management Systems and is derived from Mining Engineering in which a large area is investigated to find a vein of valuable ore. In today’s data-rich business environment with its accelerated process of data accumulation stimulated in part by the Internet and e-business applications, there has been a search for new tools of data analysis. Data mining tools attempt to integrate and analyze data from a variety of sources to look for relations that would not be easily discerned. Analysts have access to a wide range of data mining software to facilitate data analysis. Data Mining is now seen as an exciting new technology with great potential for tackling new problems and enabling valuable commercial and scientific discoveries. Indeed, Berry and Linoff (1997) provide vignettes of data mining applications in the fields of business, medicine, pharmacy, and law enforcement. The literature reports several data mining applications in business in the areas of retailing and sales, banking, marketing, accounting, airlines, and broadcasting (see Turban et al, 2002; Calderon et al, 2003; Morgan, 2005; Wipro, 2003; Siegel, 2005). Data Mining Software A wide range of data mining software is available for use by business analysts. When effectively deployed, Data Mining (DM) software can facilitate data analysis and lead to the design and implementation of efficient business processes which can result in cost savings, quality improvement, and enhanced customer service. The development of several EXCEL “add-ins” such as XL Miner and XLStat has enabled the substitution of low-cost DM software for business analysis for more expensive high-end DM software such as IBM’s Intelligent Miner, SAS’s Enterprise Miner, SPSS’s Clementine, and Oracle’s Darwin. Table 10.1 provides a listing of a sample of the more popular data mining software and EXCEL “add-ins”. Swain (2005) in the 2005 survey of statistical software products provides profiles of 45 products from 32 vendors. Of these, 16 (50%) of the vendors reported 22 (49%) products with data mining capabilities. These are summarized in Table 10.2. An important part of a data mining application is choosing the appropriate tool from the large collection of tools and techniques available. This is not an easy task because of the rapidly evolving data mining market. When choosing between actual software products rather than abstract algorithms, Berry and Linoff (1997) suggest we carefully consider price, availability, scalability, support, vendor relationships, compatibility, and platform-independence.

Table 10.1. ins”

Sample of Commercially Available Data Mining Software and EXCEL “add-

Table 10.2. Statistical Software with Data Mining Capabilities— Source: Swain, James J., “2005 Statistical Software Products Survey: Essential Tools of the Trade”, OR/MS Today, February 2005, www.lionhrtpub.com, Accessed February 10th, 2005.

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On the other hand, Two Crow (1999) suggests we examine the three main groups of data mining products. The first group covers tools that are analysis aids for On-Line Analytical Processing (OLAP) where the analyst generates a series of hypothetical patterns and relationships and uses queries against a database to verify or disprove them (i.e. a deductive process). Leading tools in this category include Business Objects, Business Miner, and Cognos Scenario. The second group includes the “pure” data mining products in which the analyst uses the data itself to uncover such patterns (i.e. an inductive process). Leading tools in this category include (in alphabetical order) IBM Intelligent Miner, Oracle Darwin, SAS Enterprise, SGI MineSet, and SPSS Clementine. The lowend in this group includes XLMiner, Query Tool, Tiberius, and Monarch Pro. The last category covers those analytic applications which implement specific business processes for which data mining is an integral part. 10.2.2 Quality Management The quality revolution experienced by business organizations spawned many new tools and techniques which have transformed business practice. Brocka and Brocka (1992) provide a good analysis of over 30 Total Quality Management (TQM) tools including statistical measurement, benchmarking, goal setting, and quality function deployment. Quality management today seeks to improve an organization’s effectiveness by improving the quality of managing, operating, and integrating the customer service, marketing, production, delivery, information, and financial areas throughout an organization’s quality value chain (Summers 2005). Quality initiatives adopted by businesses include: Six Sigma–a methodology for improving profitability through the systematic reduction of process variability by locating and eliminating sources of process error. Malcolm Baldrige National Quality Award–this award established in 1987 by the United States Congress and managed by the American Society for Quality (www.asq.org), sets a national standard for quality excellence in the areas of Business, Education, and Health Care. The award sets standards to be used as baselines and benchmarks for total quality management in seven areas, viz., Leadership Strategic Planning Customer and Market Focus Measurement, Analysis, Knowledge Management Human Resource Focus Process management Business Results ISO 9000–a series of international standards initiated in 1979 to facilitate multinational exchange of products and services by providing a clear set of

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quality requirements. The ISO 9000: 2000 standards include eight key principles, viz. Leadership Involvement of people Process approach Systems approach to management Continual improvement Factual approach to decision-making Mutually beneficial supplier relationships Lean Manufacturing–improvement initiatives that focus on the elimination of waste from systems and processes. 10.2.3 Business Process Mapping Business Process Mapping (BPM) s a powerful tool that allows an analyst to get a good understanding of a business process, effectively find ways for the process to be more successful, and ensure that true value is being provided to customers. (Jacka and Keller, 2002). BPM is a four-step process, viz. Process Identification–allows company to view the process from an internal perspective as well as from the perspective of their customers. Information Gathering–Define objectives of the process, risks of the process, key risk controls, and identify success measures. Review information sources required to describe the process, obtain buy-in from process owners Interviewing and Mapping–Gather information from the process owners and record all steps of the process and the owners of the process. Analysis–Analyze the process to eliminate unnecessary steps and remove duplication Analysts can choose from over 180 BPM software program to assist in mapping the business process. These include: Visio, SmartDraw, QMAP, and Triaster. 10.2.4 Other Tools and Techniques Industry’s ongoing search for productivity improvements has encouraged the deployment of a wide range of tools and techniques not addressed in this text. These include: Industrial Engineering and Engineering Management • Manpower Scheduling (deSilva 2005) • Value Engineering (Shillito and DeMarle, 1992) • Design of Experiments (Schmidt and Launsby, 2004)

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Management Science/Operations Research • Decision Analysis (Taylor 2004) • Game Theory (Lawrence and Pasternack, 2002) • Forecasting (Hanke and. Reitsch, 1998) Information Technology/Computer Science • Text Mining (Hand et al, 2001) • Data Warehousing (Van Slyke and Belanger, 2003) • Radio Frequency Identification (RFID) Systems (Davis and. Jones, 2004)

10.3 APPLICATIONS IN BUSINESS The three vignettes below are illustrative of the widespread application in business of Data Mining, Quality Management, and Business Process Mapping. Data Mining (Turban et al, 2002) British Telecom, a large telecommunication company in the United Kingdom, provides 4,500 products and services to its 1.5 million business users who make about 90 million calls a day. The company developed a customer data warehouse and used neural computing technology to analyze buying habits of its customers to better understand customer needs and target market opportunities. Using the system, the company identified purchasing profiles for individual products, packages of products, and customers. Data mining facilitated the identification of customers at risk of capture by the competition and trends in products that have a high sales value. This improved the relationship between the marketing and sales functions, and enabled more informed resource allocation decisions by providing ready access to up-to-date marketing information. Quality Management (Eckes 2001) Jack Welch, General Electric’s legendary CEO in the 1990s, championed quality management and regarded Six Sigma as the most important initiative undertaken by GE. Six Sigma success stories abounded within GE. One story is the case of GE Medical Systems’s introduction of a $1.25 million diagnostic scanner, a product designed from start to finish using Six Sigma design principles. A chest scan which took 3 minutes was reduced to 17 seconds. Another of the many GE success stories was reported at GE Plastics where a Six Sigma team significantly improved the production process for plastics and

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generated substantial revenue increases and the acquisition of major new contracts. Six Sigma has assisted GE in improving its standing as the most successful corporation in history. Business Process Mapping (Microsoft Corporation, 2003) JetBlue Airways, a rapidly expanding low-fare passenger airline, with a mission of providing high-quality customer service powered by leading-edge technology, was looking for a method of managing its dynamic business processes. The company selected Microsoft Visio to diagram its increasingly complex technology network and business processes. Because the airline standardized on Microsoft Windows XP and Office XP, the JetBlue staff was able to use the familiar Visio interface to get up and running quickly, import data from common sources, and share files with Office software like Word, Excel and PowerPoint. Visio allowed JetBlue employees to create graphical representations of everything from flowcharts and timelines to directory structures and network architectures–and to update those diagrams frequently without starting over. JetBlue credits Visio with helping the airline achieve consistently high marks for efficiency, productivity and customer satisfaction.

10.4 DISCUSSION AND CONCLUSION Analysts are constantly searching for innovative solutions to business problems. This quest often requires the deployment of techniques traditionally applied to problem solving in engineering domains. Network analysis, for example, first applied in aerospace and maintenance engineering, now provides the basis for a wide range of commercially available project management software which enjoy widespread applications in business. In this concluding chapter, we briefly reviewed three techniques not covered in the previous sections of the book. These techniques will continue to be used in business, individually or combined, to develop good solutions to complex business problems. Effective integration of business and engineering will be required to ensure the success of these efforts.

10.5 REFERENCES Benjamin, Colin, “A Framework for Evaluating New Technologies”, International Journal of Technology Transfer, [Vol. 5, No. 3, 2006, p. 181–94]. Berry, M. J. A. and Linoff, G. (1997) Data Mining Techniques for Marketing, Sales, and Customer Support. New York, NY: Wiley. Berthold, M. and Hand, D. J. (1999) Intelligent Data Analysis: An Introduction. New York, NY: Springer.

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Brocka, Bruce and Suzanne Brocka, Quality Management, Irwin/McGraw-Hill, New York, 1992. Calderon, Thomas G., Cheh, John J., and Kim, Il-Woon, “How Large Corporations Use Data Mining to Create Value”, Management Accounting Quarterly, Winter, 2003. Davis, Howard C. and Steven R. Jones. “RFID Technology: Is the Capability a Boon or Burden for DoD?”, Air Force Journal of Logistics, Winter 2004: 1-7. Desai, Gautam and Linda Andrews, “A Framework for Better Decisions”, Report No. 110703018, Thomson Gale, Gale Group, MI, December 2003. deSilva, Anura, “Labor Force Schedule Optimization System–Technology Profile, www.planitusa.com, Accessed December 28, 2005. Eckes, G., The Six Sigma Revolution, John Wiley & Sons, New York, 2001. Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases”, (1996), AI Magazine 17(3), 37-54. Frawley, W., Piatetsky-Shapiro, G., and Matheus, C. (1992) “Knowledge Discovery in Databases: An Overview”, AI Magazine, 213-228. Groth, Robert , Data Mining: Building Competitive Advantage, Prentice Hall, New Jersey, 2000. Hand, D., H. Mannila, and P. Smyth, (2001) Principles of Data Mining. Cambridge, MA: MIT Press. Hanke, John E. and Arthur G. Reitsch, Business Forecasting, Prentice Hall, New Jersey, 1998 Jacka, J. M. and P. J. Keller, Business Process Mapping: Improving Customer Satisfaction, John Wiley & Sons, New York, 2002. Lawrence, J.A. and B.A. Pasternack, Applied Management Science, 2nd edition, John Wiley & Sons, New York, 2002. Microsoft Corporation, Microsoft Visio Customer Solution: jetBue Airways, Microsoft Corporation, 2003. Morgan, Martin, “Unearthing the Customer: Data Mining Is No Longer the Preserve of Mathematical Statisticians. Marketeers Can Also Make a Real, Practical Use of It–Revenue-Generating Networks”, Telecommunications International, May, 2003. Piatetsky-Shapiro, G. (1999) Expert Opinion: The Data Mining Industry: Coming of Age. IEEE Intelligent Systems, Vol. 14, No 6. Rawlings, Ian, “Using Data Mining and Warehousing for Knowledge Discovery”, Computer Technology Review, September, 1999. Schmidt, S. and R. Launsby, Understanding Industrial Designed Experiments, 4th edition, Air Academy Press, Colorado Springs, Colorado, 2004. Sharp, Alec and Patrick McDermott, Workflow Modeling: Tools for Process Improvement and Application Development, Artech House, Boston, 2000. Shillito, M. L. and D. J. DeMarle, Value Engineering–Its Measurement, Design & Management, John Wiley & Sons, New York, 1992. Siegel, Eric, “Predictive Analytics with Data Mining: How It Works”, DM Review, February, 2005. Swain, James J., “2005 Statistical Software Products Survey: Essential Tools of the Trade”, OR/MS Today, February 2005, www.lionhrtpub.com, Accessed February 10th, 2005.

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Summers, Donna, Quality Management, Pearson Education, Inc, Upper Saddle River, New Jersey, 2005. Taylor, B.W., Introduction to Management Science, 8th edition, Pearson Education, Upper Saddle River, New Jersey, 2004. Turban, Efraim, David King, Jae Lee, Merrill Warkentin, and H. Michael Chung, Electronic Commerce 2002: A Managerial Perspective, Pearson Education, New Jersey, 2002. Two Crows Corporation (1999) Introduction to Data Mining and Knowledge Discovery, Third Edition. Potomac, MD. Van Slyke, C. and F. Belanger, E-Business Technologies, John Wiley and Sons, New York, 2003. Wipro Technologies, “Mining for Gold”, White paper for DM Review, April, 2003.

10.6 CASE STUDY–DEVELOPING A BUSINESS PLAN FOR BIOMED INC. Introduction A small biomedical engineering start-up company was engaged in the development and marketing of a home and self-care health system which incorporated an ultra intelligent, vital signs - real time monitor to operate as a wireless baby monitor. This system would enable the user to interpret readings of vital signs and heart rate when the monitor was in use and, as a preventive measure, track and notify guardians in the event of an emergency. One application of the technology was to reduce the incidence of Sudden Infant Death Syndrome (SIDS). To assess the market potential of the home and self-care health system developed, a survey was conducted to gauge the level of consumers’ knowledge of, concern about, and interest in SIDS. Of particular interest was ascertaining the profile of the consumer who would be most interested in purchasing the baby bio-monitor and identifying the factors that would influence their purchasing decision. Using a combination of convenience and judgmental, non-probability sampling, a survey was conducted of 125 adults (potential and actual parents) in the North Florida area using nursery schools and daycares as the primary sites for data collection. A 5-point semantic scale was used to elicit responses from the survey participants. The survey instrument developed and administered to the panel included three sections. Section One recorded the demographic information of the respondent. Section Two used a 5-point semantic scale (1 - Low; 3- Moderate; 5–High) to ascertain the respondent’s knowledge of SIDS, concern with SIDS and their interest in receiving related information. Section Three centered on identifying factors in the marketing mix that would influence a consumer’s decision to purchase a unit. Data mining can be helpful in the Market Analysis phase in unearthing patterns in historical consumer data or in the analysis of market research data

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obtained in primary market research. This will be illustrated in the case study that follows. Methodology Software The data mining analysis was performed using XLSTAT. XLSTAT is a data analysis and statistical solution add-in for Microsoft EXCEL. The XLSTAT add-in offers over 40 different functions to enhance the analytical capabilities of Excel. XLSTAT runs on all EXCEL versions from version 5.0 to version 2002, part of the new Office XP package. A fully functional 30-day trial version is available for download from www.xlstat.com. A predictive model was generated using logistic regression. Logistic regression extends the ideas of multiple linear regression to the situation where the dependent variable, y, is binary (for convenience we often code these values as 0 and 1). As with multiple linear regression the independent variables x1, x2 . . . xk may be categorical or continuous variables or a mixture of these two types. Data Preparation and Partitioning The raw data from the market research survey consisted of 125 responses to a 14-item questionnaire. To facilitate this analysis the raw data was reformatted to lend itself to logistic regression. Specifically, the target question “Would you be interested in purchasing an electronic device designed to monitor the pulse of infants while they are asleep?” allowed for 5 possible responses ranging from “Not Interested” to “Extremely Interested”. This was transformed into a binary response variable whereby responses from “Interested” to “Extremely Interested” were categorized as a positive response, while “Not Interested” or “Somewhat Interested” was categorized as a negative response. Additionally, data redundancies in the independent variables were resolved and responses with missing data were excluded. The reformatted raw data were then partitioned into Training, Validation and Test datasets. The training dataset consisted of roughly 60% of the observations and was used to develop the predictive model. The validation dataset consisted of approximately 30% of the observations and was used to test the effectiveness of the predictive model generated using the training data. The Test dataset consisted of the remaining 10% of observations and was intended to be used in a final blind evaluation of the predictive model. Predictive Model Logistic Regression produced the following predictive model. Predicted Purchase Intent = Exp(L(x))/[1 + Exp(L(x)], Where, L(x) = -1.39 + 3.66*children_ages_Newborn to 1-yr - 1.37*children_ages_two to three years–1.89*children_ages_four to five years 1.98*children_ages_older than six years -4.03*personal_computer + qualitative variables factor

Contemporary Techniques Table 10.3.

263

Model Parameters

The confidence in the model parameters above is summarized in Table 10.3. In this table we can see from looking at the probability of the Chisquares that the variables most influencing purchase interest are: • • • • •

children_ages_Newborn to 1-yr personal_computer How Many Children (Qualitative)–One Income (Qualitative) - $40k-$60k Healthcare Websites visitor (Qualitative)–Often

Therefore, if we are confident in our model, we can say that these are the factors that make up the profile of a respondent who is most likely to indicate interest in purchasing an electronic device designed to monitor the pulse of infants while they are asleep. Model Performance In order to assess the effectiveness of the predictive model generated from logistic regression of the training data, we applied the model to the validation dataset and evaluated how accurate the predictions were. The predictive model generated a response rate of 70% when applied to the validation data. This response rate is equal to the percentage of respondents the model predicted would indicate purchase interest that actually did indicate purchase interest. This is quite good. The complete results of the predictive model can be summarized using a confusion matrix as shown in Table 10.4. Another way to graphically evaluate the effectiveness of our model is by generating a cumulative lift plot as shown in Figure 10.1.

264 Table 10.4.

Chapter Ten Confusion Matrix Summarizing Results of the Predictive Model

In this graph, the x-axis represents all survey responses, sorted by the model’s predicted probability that a given respondent will indicate purchase interest. The y-axis represents the cumulative number of respondents that actually indicated purchase interest. The reference line represents pure chance. That is, without a predictive model, the reference line represents the response rate we can expect. The higher the lift above the reference line, the more effective our predictive model is. Discussion and Conclusion Model Results Analysis of data collected from the infant home health device market research survey indicates that the user most likely to indicate interest in purchasing an electronic device designed to monitor the pulse of infants while they are asleep would: • Have one child less than one year old • Earn between $40,000 - $60,000 annually

Figure 10.1.

Cumulative Lift Plot

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• Not own a personal computer • Visit healthcare websites often These findings when complemented by those obtained from analyzing other consumer survey responses and the feedback provided by a focus group provided a solid platform for formulating a marketing strategy for the new venture. Other Survey Findings The overall results for the consumer survey indicated that adults with children dominated our survey. Of the 125 respondents, • Most (70%) had children and the vast majority (90.7%) had 1–3 children; • Adults with 2 or 3 children were the largest category of respondents with children, i.e. 54 % of the 90.7% • 47% of the respondents’ children were over the age of six; • 34% of the adults surveyed had a household income of $20,000 to $40,000 and 28% of the adults surveyed had a household income range of $40,000 to $60,000; Among the major findings were: • Over 50% of respondents were knowledgeable of SIDS • Nearly 75% of them were at least concerned with the infant affliction • More than 70% of the individuals would like more information about SIDS, but only 40% of them would be interested in purchasing the product • 70% of the respondents with infant children (14 out of 20) were interested in a device that would monitor infant pulse. • Respondents’ doctors would have the most influence on their decision to purchase this product, with their pharmacist and friends coming in a close second and third. • Consumers would be more interested in purchasing this product through their doctor, a retail store chain, or a drug store/pharmacy. • There was not a significant statistical correlation between the household income and the interest in purchasing the product or the highest price the respondent was willing to pay for the product. Consumer Focus Group A focus group was conducted to better understand the factors that would influence consumers’ purchasing decisions and to obtain their preliminary assessment of a prototype developed. Participants for this meeting were also chosen at random, with a focus on including parents of infants or potential parents. In the focus group, the participants were asked preliminary questions on their general opinion of SIDS and current baby monitoring devices. Then

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they were given a demonstration of the product prototype, after which more specific questions were asked about the product. Among the major findings from our focus group were: • A doctor’s recommendation would be a major influence on the purchasing decision; • Ongoing product development is required to incorporate desired features into the product; • Innovative market strategies will have to be employed to market the product nationally As the company moved through the technology commercialization process, the business development team planned to conduct other complementary market research activities such as additional focus groups to refine the product design and ensure that desirable features deemed important by prospective customers were incorporated in the final product.

Index

ALA case study, 114–24; alternative proposals, 114, 116; artificial intelligence, 114, 115; artificial neural network, 114, 115, 122; expert systems, 114, 117, 122; feasibility study, 114; flow chart, 115, 121; Intelligent Systems Center, 114, 115, 117; job list, 117, 118; logistic regression, 115; membership lapse prediction, 114; resource requirements, 119; spiral modeling, 117, 124; system development, 116, 117; UMR, 115, 117; University of Missouri–Rolla, 114, 115 alternative project proposals, 115, 116 applications software, 125–30; business planning, 125; business process mapping, 125; computer aided design, 125, 127; customer relationship management, 125, 127; enterprise resource planning, 125, 127, 130; ergonomics, 125, 127; facilities planning, 125, 127; geographic information systems, 125, 127; industrial engineering and management, 125; information systems, 126; management science/operations research, 126; production and operations

management, 126; project management, 125; scheduling, 125, 127; simulation, 125, 127; supply chain management, 125; vehicle routing system, 125, 127; work measurement, 125, 127 Ariba, 206, 207, 208 artificial intelligence, 115 artificial neural network, 115, 117, 121, 122 assignments: bar coding, 39–40; CIM laboratory facility, 112; CNC machining center acquisition, 40–41; forklift truck acquisition, 39; location decision-making, 233; location models, 233; personnel scheduling, 152; project planning, 95–96; pump installation project, 99–112; simulation software selection, 131, 181–82; site selection, 222, 225; vehicle routing software selection, 131 bar coding, 39–40 BioMed Inc case study, 261–66; BioMed Inc, 261; biomedical engineering, 261; data mining analysis, 262; data preparation, 262; focus groups, 266; logistic

267

268

Index

regression, 262; model parameters, 263; XLSTAT, 262; SIDs, 261; survey findings, 265 business planning software, 148–49; Business Planning Pro, 149; COMFAR III, 149 business process mapping, 257–59; applications, 258–59; definition, 257; software, 257; steps, 257 Bureau of Mine Reclamation case study, 132–43; data flow diagram, 138; economic evaluation, 133, 134, 141; GIS software, 140; GIS, 134; investment decision, 142; mission, 132; phases, 137–39; schedule–Gantt chart, 136; schedule–network diagram, 135; scoring model, 141; software functionality, 141 capital equipment selection flowchart, 35 case study topics: CBE (Competitive Bid Event), 206–15; CIM (Computer Integrated Manufacturing), 112–13; data mining, 254–56; electronic reverse auctions, 206–15; engineering services, 70–81; facilities planning, 217–52; GIS (Geographic Information Systems), 125–30; global sourcing, 205–15; industrial feasibility study, 42–52; online auction, 206–15; project management, 114–24; project planning/scheduling, 114–24; software selection, 125–30; SPIF (Systematic Planning of Industrial Facilities), 233–52; technology evaluation, 152–71; warehouse consolidation, 185–94; warehouse location, 185–94 cash flow forecasts, 170 CBE (Competitive Bidding Event), 205–15 CIM lab facility, 112–13 CIM system design, 233–52

CIM (Computer Integrated Manufacturing), 233–52 CNC machining center acquisition, 40–41 Coca Cola case study, 185–94; 3PL (third party logistics), 189; crossdocks, 189–90; supply chain, 188–91; warehouse consolidation, 185–94; warehouse location, 185–94; warehouses, 186–91 CommerceOne.com, 206 Computer Integrated Manufacturing, 233–52 Competitive Bid Event, 205–15 contemporary techniques, 253, 261–66; British Telecom, 258; General Electric, 258–59; JetBlue Airways, 259; quality management, 258 cross docks, 188–90 data flow diagram, 138 data mining analysis, 262 data mining, 254–56; applications, 254; definition, 254; model parameters, 263; software, 255; statistical analysis, 255 data preparation, 262 economic evaluation, 137, 140 electronic reverse auctions, 206 engineering/business interfaces, 2, 3, 4; academia, 6; accounting, 3, 5, 6; architecture, 3; business plan competitions, 6; customer service, 3; enterprise resource planning, 5; ERP systems, 5; finance, 3, 5, 6; human resources, 3, 5; information technology, 5; legal, 3, 4, 5; operations, 4, 5, 6; research and development, 5; sales and marketing, 3, 5 engineering/business integration, 6; SBI/FAMU, 7, 18 engineering services, 70–81 e-RAs, 206, 210

Index

evaluation criteria, 128, 139 expert panel, 5, 144, 154, 158 expert systems, 64, 115, 117, 227 engineering economy techniques: annual cost/worth, 21; @Risk for EXCEL, 31; Crystal Ball, 31; discounted cashflow techniques, 25–27; discounted payback, 22–24, 31; EXCEL add-ins, 31; graphical simulation outputs, 32, 33; internal rate of return, 21, 25, 26, 40, 81, 142, 168, 170; linear interpolation/extrapolation, 26; Monte Carlo simulation, 28, 31, 36; net present value, 21, 25, 26, 142, 143, 168; payback, 22–24, 27, 31, 34, 36; payback calculation, 23; payback graphical illustration, 24; return on investment, 22, 24, 31; risk and uncertainty, 28–32; scenario analysis, 21, 28–31, 36; sensitivity analysis, 21, 28–31, 36; simple techniques, 21–24 engineering disciplines, 2; contributions, 2 engineering teams: aerospace engineering, 54; concurrent engineering, 54; engineering design, 60; facility planning, 54; life cycle costing, 54; public sector investment programming, 54; third party logistics, 54, 57 facility location models, 220–27; center of gravity, 220, 224–25, 232; decision analysis, 221, 225; group decision support system, 227; mathematical programming, 225; regression analysis, 225, 226; scoring models, 222 facilities planning, 217–52 financial evaluation, 84, 146, 167 financial model, 146, 159, 167–71 flowchart, 35, 131, 154, 159 focus groups, 115, 119, 266

269

forklift truck, 27, 39 Freemarkets, 206 facilities planning: computer-based methods, 218; construction algorithms, 219; CORELAP, 219; CRAFT, 219, 235; goal, 219; improvement algorithms, 219; key components, 218; manual methods, 219; phases, 218, 234–35; systematic methods, 218 facilities planning software: MHAND, 220; software survey, 220; VisFactory, 220 feasibility study, 3, 25, 37, 83, 114, 134 financial engineering: CFA (Chartered Financial Analyst), 37; Chartered Financial Analyst, 37; definition, 36; online resources, 36–37 financial evaluation illustrations, 21 Florida A&M University, 6, 7, 18, 84, 134, 153, 206 Gantt chart, 99, 101, 108–12, 136–37, 156, 213, 215 Gantt chart with Visio, 91 Geographic Information Systems, 125, 131, 132–43 GIS software, 130, 137–42 GIS (Geographic Information Systems), 125, 131, 132–43 global project management, 84 global sourcing case study, 205–16; Ariba, 206–10; e-RAs, 206, 210; CommerceOne.com, 206; competitive bid event, 208–12, 215; FreeMarkets, 206; Gantt chart, 209, 210, 213, 215; global sourcing, 205–7; MS Project, 209; network diagram, 209, 213; online auctions, 206, 208; Oracle, 206; Otis Elevators, 205, 206, 208; planning information, 209; project budget, 209; resource histogram, 209, 214; SBI/FAMU, 206, 207, 208

270

Index

Houston Chronicle case study, 70–81; ABC classification, 77; computer aided engineering, 74–75; engineering services, 70, 72–75, 78; engineering tools and techniques, 73–77, 79; facilities engineering, 74–75; modified QFD process, 74; other engineering tools & techniques, 75; product structure, 71; project engineering, 72, 74–75; QFD–House of Quality, 78, 80; QFD process, 74; scenario analysis, 79; sensitivity, 79 IBM intellectual property network, 159 Ice gauge technology, 153–54, 158, 167 integrated facilities planning, 235 Intelligent Systems Center, 114–15, 117 investment decisions, 21, 33, 183; ecommerce, 21, 65; global manufacturing, 21; minimum attractive rate of return, 25–26, 30; non-profits, 21; service companies, 21; SPL, 42–52. See also Soap Products Limited investment recommendations–NASA, 171 ISC (Intelligent Systems Center), 114–15, 117 job list–ALA case, 118 job list–global sourcing, 211 licensing agreement, 166, 167 life cycle costing models, 32–34 linear programming: case study from academia, 174–79; phases, 173; software, 173–75; time/cost tradeoff, 93 location decision-making, 233 location factors, 220–23; applications in business, 227–30; convention center, 229–30; GM Saturn, 228; Motorola, 228–29 location models, 222–27 logistic regression, 115, 262

management science techniques, 172–83; decision analysis, 172, 182; deterministic models, 173; forecasting, 182; inventory models, 172; mathematical programming, 172–74; network analysis, 172, 183; probabilistic models, 173; queuing theory, 172; scheduling, 182; simulation modeling, 172, 179–80; statistical analysis, 183 market analysis flowchart–NASA case, 157 membership lapse prediction, 114 Microsoft Project–global sourcing, 209 Midwest Furniture–case study, 233–52; CIM system design, 233–52; CIM, 233–34, 236, 240–41; integrated facilities planning, 235; Muther, Richard, 235; PC Model, 240, 241, 242; product flow matrix, 235, 250; relationship chart, 235; relationship diagram, 235; routing charts, 235; simulation, 236, 240–42; simulation package, 240; study phases, 235; SLP, 235, 236, 237; Systematic Layout Planning, 231, 235 mission statement, 132, 137 multi-attribute decision making, 59 multi-criteria decision making, 55, 56, 65; steps, 56; virtual teams, 56 multi-criteria decision models, 55, 56–64; AHP–Expert Choice, 60; analytical hierarchy process, 57, 59; artificial intelligence, 63, 64; goal programming, 57, 60; Kepner and Tregoe, 57; multi-attribute utility models, 61; computer supported collaborative work, 65; Electre I and II, 64; Quality Function Deployment (QFD), 61–63; QFD in academia, 63–65; QFD–House of Quality, 61, 62, 68, 69; QFD industry applications, 61; scoring models, 57; scoring model applications, 57–59

Index

multi-objective decision models, 57 Muther, Richard, 235 NASA technology–case study, 153–71; cash flow forecasts, 170; evaluation criteria, 159; expert panel, 158; financial evaluation, 167; financial model, 169; Gantt chart, 156; IBM intellectual property network, 159; ice gauge technology, 158, 167; investment recommendations, 171; licensing agreement, 167; market analysis flowchart, 157; network analysis, 155; NASALaRC, 153; novice panel, 154; patent profiles, 161–65; royalties, 167; SBI/FAMU, 153; scenarios, 167; scoring model, 166; technology assessment flowchart, 160; Weinstein, 153 NASALaRC, 153 network analysis, 86, 155, 172, 183; activity on the arrow, 89; activity on the node, 88; advantages, 86–87; CPM, 86; critical path algorithm, 89–90; critical path method, 86; Du Pont, 86; line of balance, 86; LOB, 86; PERT, 86; phases, 87; Program Evaluation Review Technique, 86; resource analysis–aggregation, 91; resource analysis–leveling, 91, 92; resource analysis–loading chart, 92; resource analysis–time/cost tradeoff, 92, 93, 94; US Navy, 86 network analysis–NASA case, 155 network diagram, 135, 154, 209, 213 novice panel, 154 online auction, 208–15 online auctions, 206, 208 Oracle, 206 organizations: American Leisure Association, 114–24; Ariba, 206–15; BioMed Inc, 261–66; Bureau of Mine Reclamation, 132–43; Coca Cola, 185–94; Florida A&M

271

University, 134, 153, 206–8; Houston Chronicle, 70–81; MidWest Furniture, 233–52; NASA Langley Research Center, 153–71; Otis Elevators, 206–15; School of Business & Industry, 134, 153, 206–8; Soap Products Limited, 42–52; University of Missouri–Rolla, 115 other contemporary tools and techniques: IE & engineering management, 257; IT & computer science, 258; management science/operations research, 258 Otis Elevators, 205, 207 patent evaluation, 152 patent profiles, 161–65 PC Model, 240 personnel scheduling, 152 phases, 137 product flow matrix, 250 pump installation project, 99–112 project: definition, 82; examples, 86; global, 85; life cycle, 84 project appraisal: ten-step methodology, 34–36 project budget–global sourcing, 209, 214 project management, 205–15 project management organizations, 83–84; APM, 84; Association for Project Management, 83, 84; PMI, 83, 84; Project Management Institute, 83, 84; CRMP, 83, 84; Centre for Res. in the Mgmt. of Projects, 83, 84; PMBoK, 84; SBI/FAMU, 83, 84 project management, 82–83; evolution, 82–83 project management software, 94; surveys, 94–95; Microsoft Project, 94–95 project planning, 112–13, 114–24

272

Index

project planning and scheduling, 84, 86, 206–15; applications in business, 97; techniques, 86–87 project teams, 85, 86 pump installation project, 99–112; Assignment, 99; EXCEL analysis, 99–105; EXCELbudget, 105; EXCELGantt chart, 101; EXCELnetwork diagram, 100; EXCELresource histogram, 102; Microsoft Project analysis, 106–12; MS Project budget, 110; MS Project Gantt chart, 110; MS Project network diagram, 109; MS Project resource histogram, 111; MS Project scenario analysis, 111 quality function deployment, 7–19, 61–65, 74–79; applications in academia, 7; course design, 9–11; course implementation, 11–12; course planning, 8–9; critical business competencies, 12; engineering for business courses, 12, 15; engineering tools and techniques, 11, 14, 17; sensitivity analysis, 15–17; seven step procedure, 8; teaching methodologies, 9, 13–14; three-phased process, 7 Quality Management, 256–57; ISO 9000, 256; lean manufacturing, 257; Malcolm Baldridge, 256; Six Sigma, 256; Total Quality Management, 256 relationship chart, 251 relationship diagram, 252 resource histogram–global sourcing, 209, 214 resource requirements, 119 routing charts, 245–48 royalties, 167 SBI/FAMU, 154, 206, 207, 208 scenario analysis, 28, 30, 31, 110, 126, 143

School of Business and Industry, 7, 18 scoring model, 1, 37, 159 SIDs, 261 simulation modeling, 240–42 simulation software selection, 131 simulation software, 181, 182, 240 simulation study phases, 226 site selection, 227 SLP (Systematic Layout Planning), 235, 238 Soap Products Limited, 42–52; market study, 42, 43; organization chart, 48; plant capacity, 43; plant layout, 44, 47; process diagram, 46; project engineering, 43, 47; project financing, 45, 50, 51; project schedule, 49; technology selection, 43 software functionality, 139–40 software selection (BOMR), 132–43 software selection, 126–31; accounting information systems, 130; enterprise resource planning, 130; geographic information systems, 130; methodology, 127, 129; phases, 127–28; vehicle routing systems, 130 SCM academic programs: availability, 197–99; core competencies, 201, 202; key knowledge areas, 201, 202; professional challenges, 202–3; rankings, 197; international programs, 200 SCM professional organizations, 196, 199; APICS, 196; CAPS, 196; ISM, 196; research centers, 195; SOLE, 196 Simulation Modeling: @Risk for EXCEL, 31; case study–DemaTech, 182; Crystal Ball, 31; definition, 179; EXCEL add-ins, 31; graphical outputs, 32, 33; Monte Carlo Simulation, 28, 31–33; phases, 181; software surveys, 181; techniques, 180

Index

273

simulation applications, 181–83; finance, 183; human resources, 183; manufacturing, 183; marketing, 183; operation, 183 SPIF (Systematic Planning of Industrial Facilities), 233–52 spiral modeling, 117, 124 supply chain, 186, 189, 191 Supply Chain Management definition, 195 survey findings, 265 Systematic Planning of Industrial Facilities, 233–52 system development, 117 Systematic Layout Planning, 235, 238

technology evaluation, 148–51; BioMed Inc, 148; Ellipse, 148; framework, 148; NASA, 148; patent office–Europe, 150; patent office–Japan, 150; Phases, 146; USPTO, 152 third party logistics (3PL), 188

technology assessment flowchart, 160 technology commercialization: organizations–Kauffmann, 144; organizations–NASA, 144; organizations–OECD, 145; organizations–UNIDO, 145

warehouse consolidation, 185–91 warehouse location, 187–91 warehouses, 189 Weinstein, 153

UMR, 115 University of Missouri–Rolla, 115 vehicle routing software selection, 131 virtual teams, 85

XLSTAT, 262