Description
Book SynopsisA key resource and framework for assessing the performance of competing entities, including forecasting models
Advances in DEA Theory and Applications provides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models. It helps readers to determine the most appropriate methodology in order to make the most accurate decisions for implementation. Written by a noted expert in the field, this text provides a review of the latest advances in DEA theory and applications to the field of forecasting.
Designed for use by anyone involved in research in the field of forecasting or in another application area where forecasting drives decision making, this text can be applied to a wide range of contexts, including education, health care, banking, armed forces, auditing, market research, retail outlets, organizational effectiveness, transportation, public housing, and manufacturing. This vital resource:
Table of ContentsLIST OF CONTRIBUTORS xx
ABOUT THE AUTHORS xxii
PREFACE xxxii
PART I DEA THEORY 1
1 Radial DEA Models 3
Kaoru Tone
1.1 Introduction 3
1.2 Basic Data 3
1.3 Input-Oriented CCR Model 4
1.4 The Input-Oriented BCC Model 6
1.5 The Output-Oriented Model 7
1.6 Assurance Region Method 8
1.7 The Assumptions Behind Radial Models 8
1.8 A Sample Radial Model 8
References 10
2 Non-Radial DEA Models 11
Kaoru Tone
2.1 Introduction 11
2.2 The SBM Model 12
2.3 An Example of an SBM Model 15
2.4 The Dual Program of the SBM Model 17
2.5 Extensions of the SBM Model 17
2.6 Concluding Remarks 18
References 19
3 Directional Distance DEA Models 20
Hirofumi Fukuyama and William L. Weber
3.1 Introduction 20
3.2 Directional Distance Model 20
3.3 Variable-Returns-to-Scale DD Models 23
3.4 Slacks-Based DD Model 23
3.5 Choice of Directional Vectors 25
References 26
4 Super-Efficiency DEA Models 28
Kaoru Tone
4.1 Introduction 28
4.2 Radial Super-Efficiency Models 28
4.3 Non-Radial Super-Efficiency Models 29
4.4 An Example of a Super-Efficiency Model 31
References 32
5 Determining Returns to Scale in the VRS DEA Model 33
Biresh K. Sahoo and Kaoru Tone
5.1 Introduction 33
5.2 Technology Specification and Scale Elasticity 34
5.3 Summary 37
References 37
6 Malmquist Productivity Index Models 40
Kaoru Tone and Miki Tsutsui
6.1 Introduction 40
6.2 Radial Malmquist Model 43
6.3 Non-Radial and Oriented Malmquist Model 45
6.4 Non-Radial and Non-Oriented Malmquist Model 47
6.5 Cumulative Malmquist Index (CMI) 48
6.6 Adjusted Malmquist Index (AMI) 49
6.7 Numerical Example 50
6.8 Concluding Remarks 55
References 55
7 The Network DEA Model 57
Kaoru Tone and Miki Tsutsui
7.1 Introduction 57
7.2 Notation and Production Possibility Set 58
7.3 Description of Network Structure 59
7.4 Objective Functions and Efficiencies 61
Reference 63
8 The Dynamic DEA Model 64
Kaoru Tone and Miki Tsutsui
8.1 Introduction 64
8.2 Notation and Production Possibility Set 65
8.3 Description of Dynamic Structure 67
8.4 Objective Functions and Efficiencies 69
8.5 Dynamic Malmquist Index 71
References 73
9 The Dynamic Network DEA Model 74
Kaoru Tone and Miki Tsutsui
9.1 Introduction 74
9.2 Notation and Production Possibility Set 75
9.3 Description of Dynamic Network Structure 77
9.4 Objective Function and Efficiencies 80
9.5 Dynamic Divisional Malmquist Index 82
References 84
10 Stochastic DEA: The Regression-Based Approach 85
Andrew L. Johnson
10.1 Introduction 85
10.2 Review of Literature on Stochastic DEA 87
10.3 Conclusions 96
References 96
11 A Comparative Study of AHP and DEA 100
Kaoru Tone
11.1 Introduction 100
11.2 A Glimpse of Data Envelopment Analysis 100
11.3 Benefit/Cost Analysis by Analytic Hierarchy Process 102
11.4 Efficiencies in AHP and DEA 104
11.5 Concluding Remarks 105
References 106
12 A Computational Method for Solving DEA Problems with Infinitely Many DMUs 107
Abraham Charnes and Kaoru Tone
12.1 Introduction 107
12.2 Problem 108
12.3 Outline of the Method 109
12.4 Details of the Method When Z is One-Dimensional 110
12.5 General Case 113
12.6 Concluding Remarks (by Tone) 115
Appendix 12.A Proof of Theorem 12.1 115
Appendix 12.B Proof of Theorem 12.2 116
Reference 116
PART II DEA APPLICATIONS (PAST–PRESENT SCENARIO) 117
13 Examining the Productive Performance of Life Insurance Corporation of India 119
Kaoru Tone and Biresh K. Sahoo
13.1 Introduction 119
13.2 Nonparametric Approach to Measuring Scale Elasticity 121
13.3 The Dataset for LIC Operations 128
13.4 Results and Discussion 130
13.5 Concluding Remarks 136
References 136
14 An Account of DEA-Based Contributions in the Banking Sector 141
Jamal Ouenniche, Skarleth Carrales, Kaoru Tone and Hirofumi Fukuyama
14.1 Introduction 141
14.2 Performance Evaluation of Banks: A Detailed Account 142
14.3 Current State of the Art Summarized 154
14.4 Conclusion 163
References 169
15 DEA in the Healthcare Sector 172
Hiroyuki Kawaguchi, Kaoru Tone and Miki Tsutsui
15.1 Introduction 172
15.2 Method and Data 174
15.3 Results 184
15.4 Discussion 188
Acknowledgements 189
References 190
16 DEA in the Transport Sector 192
Ming-Miin Yu and Li-Hsueh Chen
16.1 Introduction 192
16.2 DNDEA in Transport 194
16.3 Extension 200
16.4 Application 207
16.5 Conclusions 212
References 212
17 Dynamic Network Efficiency of Japanese Prefectures 216
Hirofumi Fukuyama, Atsuo Hashimoto, Kaoru Tone and William L. Weber
17.1 Introduction 216
17.2 Multiperiod Dynamic Multiprocess Network 217
17.3 Efficiency/Productivity Measurement 221
17.4 Empirical Application 222
17.5 Conclusions 229
References 229
18 A Quantitative Analysis of Market Utilization in Electric Power Companies 231
Miki Tsutsui and Kaoru Tone
18.1 Introduction 231
18.2 The Functions of the Trading Division 232
18.3 Measuring the Effect of Energy Trading 235
18.4 DEA Calculation 242
18.5 Empirical Results 243
18.6 Concluding Remarks 248
References 249
19 DEA in Resource Allocation 250
Ming-Miin Yu and Li-Hsueh Chen
19.1 Introduction 250
19.2 Centralized DEA in Resource Allocation 252
19.3 Applications of Centralized DEA in Resource Allocation 261
19.4 Extension 265
19.5 Conclusions 268
References 268
20 How to Deal with Non-convex Frontiers in Data Envelopment Analysis 271
Kaoru Tone and Miki Tsutsui
20.1 Introduction 271
20.2 Global Formulation 273
20.3 In-cluster Issue: Scale- and Cluster-Adjusted DEA Score 276
20.4 An Illustrative Example 281
20.5 The Radial-Model Case 284
20.6 Scale-Dependent Dataset and Scale Elasticity 287
20.7 Application to a Dataset Concerning Japanese National Universities 289
20.8 Conclusions 294
Appendix 20.A Clustering Using Returns to Scale and Scale Efficiency 295
Appendix 20.B Proofs of Propositions 295
References 298
21 Using DEA to Analyze the Efficiency of Welfare Offices and Influencing Factors: The Case of Japan’s Municipal Public Assistance Programs 300
Masayoshi Hayashi
21.1 Introduction 300
21.2 Institutional Background, DEA, and Efficiency Scores 301
21.3 External Effects on Efficiency 304
21.4 Quantile Regression Analysis 309
21.5 Concluding Remarks 312
Acknowledgements 312
References 312
22 DEA as a Kaizen Tool: SBM Variations Revisited 315
Kaoru Tone
22.1 Introduction 315
22.2 The SBM-Min Model 316
22.3 The SBM-Max Model 318
22.4 Observations 321
22.5 Numerical Examples 323
22.6 Conclusions 330
References 330
PART III DEA FOR FORECASTING AND DECISION-MAKING (PAST–PRESENT–FUTURE SCENARIO) 331
23 Corporate Failure Analysis Using SBM 333
Joseph C. Paradi, Xiaopeng Yang and Kaoru Tone
23.1 Introduction 333
23.2 Literature Review 334
23.3 Methodology 340
23.4 Application to Bankruptcy Prediction 343
23.5 Conclusions 352
References 354
24 Ranking of Bankruptcy Prediction Models under Multiple Criteria 357
Jamal Ouenniche, Mohammad M. Mousavi, Bing Xu and Kaoru Tone
24.1 Introduction 357
24.2 An Overview of Bankruptcy Prediction Models 359
24.3 A Slacks-Based Super-Efficiency Framework for Assessing Bankruptcy Prediction Models 366
24.4 Empirical Results from Super-Efficiency DEA 372
24.5 Conclusion 376
References 377
25 DEA in Performance Evaluation of Crude Oil Prediction Models 381
Jamal Ouenniche, Bing Xu and Kaoru Tone
25.1 Introduction 381
25.2 An Overview of Crude Oil Prices and Their Volatilities 385
25.3 Assessment of Prediction Models of Crude Oil Price Volatility 388
25.4 Conclusion 401
References 402
26 Predictive Efficiency Analysis: A Study of US Hospitals 404
Andrew L. Johnson and Chia-Yen Lee
26.1 Introduction 404
26.2 Modeling of Predictive Efficiency 405
26.3 Study of US Hospitals 408
26.4 Forecasting, Benchmarking, and Frontier Shifting 412
26.5 Conclusions 416
References 417
27 Efficiency Prediction Using Fuzzy Piecewise Autoregression 419
Ming-Miin Yu and Bo Hsiao
27.1 Introduction 419
27.2 Efficiency Prediction 420
27.3 Modeling and Formulation 423
27.4 Illustrating the Application 433
27.5 Discussion 438
27.6 Conclusion 440
References 441
28 Time Series Benchmarking Analysis for New Product Scheduling: Who Are the Competitors and How Fast Are They Moving Forward? 443
Dong-Joon Lim and Timothy R. Anderson
28.1 Introduction 443
28.2 Methodology 445
28.3 Application: Commercial Airplane Development 449
28.4 Conclusion and Matters for Future Work 454
References 455
29 DEA Score Confidence Intervals with Past–Present and Past–Present–Future-Based Resampling 459
Kaoru Tone and Jamal Ouenniche
29.1 Introduction 459
29.2 Proposed Methodology 461
29.3 An Application to Healthcare 465
29.4 Conclusion 476
References 478
30 DEA Models Incorporating Uncertain Future Performance 480
Tsung-Sheng Chang, Kaoru Tone and Chen-Hui Wu
30.1 Introduction 480
30.2 Generalized Dynamic Evaluation Structures 482
30.3 Future Performance Forecasts 484
30.4 Generalized Dynamic DEA Models 487
30.5 Empirical Study 495
30.6 Conclusions 513
References 514
31 Site Selection for the Next-Generation Supercomputing Center of Japan 516
Kaoru Tone
31.1 Introduction 516
31.2 Hierarchical Structure and Group Decision by AHP 519
31.3 DEA Assurance Region Approach 521
31.4 Application to the Site Selection Problem 522
31.5 Decision and Conclusion 527
References 527
APPENDIX A: DEA-SOLVER-PRO 529
INDEX 535
