Quantitative Models for Performance Evaluation and Benchmarking (eBook)

Data Envelopment Analysis with Spreadsheets

(Autor)

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2008 | 2nd ed. 2009
XIII, 327 Seiten
Springer US (Verlag)
978-0-387-85982-8 (ISBN)

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Quantitative Models for Performance Evaluation and Benchmarking - Joe Zhu
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Managers are often under great pressure to improve the performance of their organizations. To improve performance, one needs to constantly evaluate operations or processes related to producing products, providing services, and marketing and selling products. Performance evaluation and benchmarking are a widely used method to identify and adopt best practices as a means to improve performance and increase productivity, and are particularly valuable when no objective or engineered standard is available to define efficient and effective performance. For this reason, benchmarking is often used in managing service operations, because service standards (benchmarks) are more difficult to define than manufacturing standards. Benchmarks can be established but they are somewhat limited as they work with single measurements one at a time. It is difficult to evaluate an organization's performance when there are multiple inputs and outputs to the system. The difficulties are further enhanced when the relationships between the inputs and the outputs are complex and involve unknown tradeoffs. It is critical to show benchmarks where multiple measurements exist. The current book introduces the methodology of data envelopment analysis (DEA) and its uses in performance evaluation and benchmarking under the context of multiple performance measures.

Joe Zhu is Associated Professor of Operations, Department of Management at Worcester Polytechnic Institute, Worcester, MA. His research interests include issues of performance evaluation and benchmarking, supply chain design and efficiency, and Data Envelopment Analysis. He has published over 70 articles in journals such as Management Science, Operations Research, IIE Transactions, Annals of Operations Research, Journal of Operational Research Society, European Journal of Operational Research, Information Technology and Management Journal, Computer and Operations Research, OMEGA, Socio-Economic Planning Sciences, Journal of Productivity Analysis, INFOR, Journal of Alternative Investment and others. He is the author of Quantitative Models for Evaluating Business Operations: Data Envelopment Analysis with Spreadsheets (Kluwer Academic Publishers, 2003). He developed the DEAFrontier software which is a DEA add-in for Microsoft Excel. Professor Zhu has also co-authored two books on modeling performance measurement and evaluating hedge funds. He is a co-editor of the DEA handbook. He is an Associate Editor of OMEGA and The Asia-Pacific Journal of Operational Research. He is also a member of Computers & Operations Research Editorial Board. For more information on his research, please visit www.deafrontier.com.


Managers are often under great pressure to improve the performance of their organizations. To improve performance, one needs to constantly evaluate operations or processes related to producing products, providing services, and marketing and selling products. Performance evaluation and benchmarking are a widely used method to identify and adopt best practices as a means to improve performance and increase productivity, and are particularly valuable when no objective or engineered standard is available to define efficient and effective performance. For this reason, benchmarking is often used in managing service operations, because service standards (benchmarks) are more difficult to define than manufacturing standards. Benchmarks can be established but they are somewhat limited as they work with single measurements one at a time. It is difficult to evaluate an organization's performance when there are multiple inputs and outputs to the system. The difficulties are further enhanced when the relationships between the inputs and the outputs are complex and involve unknown tradeoffs. It is critical to show benchmarks where multiple measurements exist. The current book introduces the methodology of data envelopment analysis (DEA) and its uses in performance evaluation and benchmarking under the context of multiple performance measures.

Joe Zhu is Associated Professor of Operations, Department of Management at Worcester Polytechnic Institute, Worcester, MA. His research interests include issues of performance evaluation and benchmarking, supply chain design and efficiency, and Data Envelopment Analysis. He has published over 70 articles in journals such as Management Science, Operations Research, IIE Transactions, Annals of Operations Research, Journal of Operational Research Society, European Journal of Operational Research, Information Technology and Management Journal, Computer and Operations Research, OMEGA, Socio-Economic Planning Sciences, Journal of Productivity Analysis, INFOR, Journal of Alternative Investment and others. He is the author of Quantitative Models for Evaluating Business Operations: Data Envelopment Analysis with Spreadsheets (Kluwer Academic Publishers, 2003). He developed the DEAFrontier software which is a DEA add-in for Microsoft Excel. Professor Zhu has also co-authored two books on modeling performance measurement and evaluating hedge funds. He is a co-editor of the DEA handbook. He is an Associate Editor of OMEGA and The Asia-Pacific Journal of Operational Research. He is also a member of Computers & Operations Research Editorial Board. For more information on his research, please visit www.deafrontier.com.

Contents 7
Preface 11
Chapter 1 Envelopment DEA Models 14
1.1 Performance Evaluation, Tradeoffs, and DEA 14
1.2 Envelopment Model 18
1.3 Envelopment Models in Spreadsheets 27
1.4 Solving DEA Using DEAFrontier Software 48
REFERENCES 54
Chapter 2 Multiplier and Slack-based Models 56
2.1 Multiplier Model with Weight Restrictions 56
2.2 Multiplier Models in Spreadsheets 58
2.3 Slack-based Model 64
2.4 Slack-based Models in Spreadsheets 66
2.5 Solving DEA Using DEAFrontier Software 70
REFERENCES 74
Chapter 3 Measure- specific DEA Models 75
3.1 Measure-specific Models 75
3.2 Measure-specific Models in Spreadsheets 76
3.3 Performance Evaluation of Fortune 500 Companies 78
3.4 Solving DEA Using DEAFrontier Software 87
REFERENCES 88
Chapter 4 Non- radial DEA Models and DEA with Preference 89
4.1 Non-radial DEA Models 89
4.2 DEA with Preference Structure and Cost/Revenue Efficiency 91
4.3 DEA/Preference Structure Models in Spreadsheets 95
4.4 DEA and Multiple Objective Linear Programming 97
4.5 Solving DEA Using DEAFrontier Software 102
REFERENCES 107
Chapter 5 Modeling Undesirable Measures 108
5.1 Introduction 108
5.2 Efficiency Invariance 109
5.3 Undesirable Outputs 110
5.4 Undesirable Inputs 114
5.5 Solving DEA Using DEAFrontier Software 115
APPENDIX: NEGATIVE DATA 116
REFERENCES 118
Chapter 6 Context- dependent Data Envelopment Analysis 119
6.1 Introduction 119
6.2 Stratification DEA Method 121
6.3 Input-oriented Context-dependent DEA 125
6.4 Output-oriented Context-dependent DEA 133
6.5 Solving DEA Using DEAFrontier Software 137
REFERENCES 139
Chapter 7 Benchmarking Models 140
7.1 Introduction 140
7.2 Variable-benchmark Model 141
7.3 Fixed-benchmark Model 152
7.4 Fixed-benchmark Model and Efficiency Ratio 155
7.5 Minimum Efficiency Model 159
7.6 Buyer-seller Efficiency Model 162
7.7 Solving DEA Using DEAFrontier Software 167
REFERENCES 169
Chapter 8 Models for Evaluating Supply Chains 170
8.1 Supply Chain Efficiency 170
8.2 Supply Chain Efficiency 172
8.3 Cooperative and Non-Cooperative Approaches 183
REFERENCES 195
Chapter 9 Congestion 196
9.1 Congestion Measure 196
9.2 Congestion and Slacks 202
9.3 Slack-based Congestion Measure 204
9.4 Solving DEA Using DEAFrontier Software 210
REFERENCES 212
Chapter 10 Super Efficiency 213
10.1 Super-efficiency DEA Models 213
10.2 Infeasibility of Super-efficiency DEA Models 217
10.3 Solving DEA Using DEAFrontier Software 231
REFERENCES 233
Chapter 11 SensitivityAnalysis 234
11.1 DEA Sensitivity Analysis 234
11.2 Stability Regiona 237
11.3 Infeasibility and Stability 249
11.4 Simultaneous Data Changeb 253
11.5 Solving DEA Using DEAFrontier Software 271
REFERENCES 273
Chapter 12 Identifying Critical Measures in DEA 275
12.1 Introduction 275
12.2 Performance Evaluation and DEA 275
12.3 Identifying Critical Output Measures 280
12.4 Identifying Critical Input Measures 281
12.5 Numerical Example and Extension 283
12.6 Application to Fortune E-Companies 284
REFERENCES 293
Chapter 13 Returns- to-Scale 294
13.1 Introduction 294
13.2 RTS Regions 295
13.3 RTS Estimation 296
13.4 Scale Efficient Targets 304
13.5 Solving DEA Using DEAFrontier Software 306
REFERENCES 308
Chapter 14 DEA Models for Two-Stage Processes 309
14.1 Introduction 309
14.2 VRS Two-Stage Model 310
14.3 CRS Two-Stage Model 316
14.4 Solving DEA Using DEAFrontier Software 321
REFERENCES 322
Author Index 323
A 323
B 323
C 323
D 323
F 323
G 324
H 324
J 324
K 324
L 324
M 324
N 324
P 324
R 324
S 324
T 325
V 325
W 325
Z 325
Topic Index 326

Erscheint lt. Verlag 20.10.2008
Reihe/Serie International Series in Operations Research & Management Science
International Series in Operations Research & Management Science
Zusatzinfo XIII, 327 p.
Verlagsort New York
Sprache englisch
Themenwelt Wirtschaft Allgemeines / Lexika
Wirtschaft Betriebswirtschaft / Management Unternehmensführung / Management
Wirtschaft Volkswirtschaftslehre Mikroökonomie
Wirtschaft Volkswirtschaftslehre Ökonometrie
Schlagworte Benchmarking • data envelopment • Data envelopment analysis • Data-Envelopment-Analysis • DEA (data envelopment analysis) • Efficiency • Evaluation • Health Care • Modeling • organization • Performance • Sensitivity Analysis • Service
ISBN-10 0-387-85982-9 / 0387859829
ISBN-13 978-0-387-85982-8 / 9780387859828
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