Advances in DEA Theory and Applications With Extensions to Forecasting Models

A 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: 

• Explores the latest developments in DEA frameworks for the performance evaluation of entities such as public or private organizational branches or departments, economic sectors, technologies, and stocks
• Presents a novel area of application for DEA; namely, the performance evaluation of forecasting models
• Promotes the use of DEA to assess the performance of forecasting models in a wide area of applications
• Provides rich, detailed examples and case studies

Advances in DEA Theory and Applications includes information on a balanced benchmarking tool that is designed to help organizations examine their assumptions about their productivity and performance.

KAORU TONE is with the National Graduate Institute for Policy Studies, Japan. His contribution to DEA has a variety of attainments. He authored a classical book Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software under the co-authorship with Professor Cooper (University of Texas) and Professor Seiford (University of Michigan). He also published many papers on DEA in international journals. Kaoru Tone opened a new avenue for performance evaluation, called Slacks-based Measure (SBM) that is widely utilized over the world. His recent innovations include Network SBM, Dynamic SBM, Dynamic DEA with Network Structure, Congestion, Returns-to-growth in DEA, Ownership-specified Network DEA, Non-convex Frontier DEA, Past-Present-Future Inter-temporal DEA, Resampling DEA and SBM-Max.

LIST 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

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