Handbook of Operations Analytics Using Data Envelopment Analysis (eBook)
XIII, 506 Seiten
Springer US (Verlag)
978-1-4899-7705-2 (ISBN)
This handbook focuses on Data Envelopment Analysis (DEA) applications in operations analytics which are fundamental tools and techniques for improving operation functions and attaining long-term competitiveness. In fact, the handbook demonstrates that DEA can be viewed as Data Envelopment Analytics.
Chapters include a review of cross-efficiency evaluation; a case study on measuring the environmental performance of OECS countries; how to select a set of performance metrics in DEA with an application to American banks; a relational network model to take the operations of individual periods into account in measuring efficiencies; how the efficient frontier methods DEA and stochastic frontier analysis (SFA) can be used synergistically; and how to integrate DEA and multidimensional scaling.
In other chapters, authors construct a dynamic three-stage network DEA model; a bootstrapping based methodology to evaluate returns to scale and convexity assumptions in DEA; hybridizing DEA and cooperative games; using DEA to represent the production technology and directional distance functions to measure band performance; an input-specific Luenberger energy and environmental productivity indicator; and the issue of reference set by differentiating between the uniquely found reference set and the unary and maximal types of the reference set.
Finally, additional chapters evaluate and compare the technological advancement observed in different hybrid electric vehicles (HEV) market segments over the past 15 years; radial measurement of efficiency for the production process possessing multi-components under different production technologies; issues around the use of accounting information in DEA; how to use DEA environmental assessment to establish corporate sustainability; a summary of research efforts on DEA environmental assessment applied to energy in the last 30 years; and an overview of DEA and how it can be utilized alone and with other techniques to investigate corporate environmental sustainability questions.
Shiuh-Nan Hwang is a Professor in the Department of Business Administration, and Dean of the School of Management at Ming Chuan University, Taiwan. He earned his Ph.D. in Management Science at National Chiao Tung University, an M.S. in Industrial Management at National Cheng Kung University, and a B.S. in Agriculture Economics at National Taiwan University. His research interests include Performance Evaluation and Management, Business Research Methods, and General Management.
Hsuan-Shih Lee is a Professor and Vice-President in the Department of Shipping & Transportation Management at the College of Maritime Science and Management, National Taiwan Ocean University, Taiwan. He earned his Ph.D., and M.S. in Information Engineering, and B.S. in Computer Engineering, all at National Chiao Tung University, Taiwan. His research interests include Maritime Management and Policy, Performance management, Management Information Systems, System Analysis and Design, Algorithm Analysis, and Computer Networks.
Joe Zhu is Professor of Operations Analytics in the Foisie School of Business, Worcester Polytechnic Institute. He is an internationally recognized expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA). He has published and co-edited more than 15 books focusing on performance evaluation and benchmarking using DEA. He has more than 18,000 Google Scholar citations with over 100 peer-reviewed articles. He is recognized as one of the top authors in DEA with respect to research productivity, h-index, and g-index. He is an Area Editor of OMEGA, and on the Editorial Board of European Journal of Operational Research, and Computers and Operations Research. He is the Series Associate Editor of International Series in Operations Research and Management Science.
This handbook focuses on Data Envelopment Analysis (DEA) applications in operations analytics which are fundamental tools and techniques for improving operation functions and attaining long-term competitiveness. In fact, the handbook demonstrates that DEA can be viewed as Data Envelopment Analytics.Chapters include a review of cross-efficiency evaluation; a case study on measuring the environmental performance of OECS countries; how to select a set of performance metrics in DEA with an application to American banks; a relational network model to take the operations of individual periods into account in measuring efficiencies; how the efficient frontier methods DEA and stochastic frontier analysis (SFA) can be used synergistically; and how to integrate DEA and multidimensional scaling.In other chapters, authors construct a dynamic three-stage network DEA model; a bootstrapping based methodology to evaluate returns to scale and convexity assumptions in DEA; hybridizing DEA and cooperative games; using DEA to represent the production technology and directional distance functions to measure band performance; an input-specific Luenberger energy and environmental productivity indicator; and the issue of reference set by differentiating between the uniquely found reference set and the unary and maximal types of the reference set.Finally, additional chapters evaluate and compare the technological advancement observed in different hybrid electric vehicles (HEV) market segments over the past 15 years; radial measurement of efficiency for the production process possessing multi-components under different production technologies; issues around the use of accounting information in DEA; how to use DEA environmental assessment to establish corporate sustainability; a summary of research efforts on DEA environmental assessment applied to energy in the last 30 years; and an overview of DEA and how it can be utilized alone and with other techniques to investigate corporate environmental sustainability questions.
Shiuh-Nan Hwang is a Professor in the Department of Business Administration, and Dean of the School of Management at Ming Chuan University, Taiwan. He earned his Ph.D. in Management Science at National Chiao Tung University, an M.S. in Industrial Management at National Cheng Kung University, and a B.S. in Agriculture Economics at National Taiwan University. His research interests include Performance Evaluation and Management, Business Research Methods, and General Management. Hsuan-Shih Lee is a Professor and Vice-President in the Department of Shipping & Transportation Management at the College of Maritime Science and Management, National Taiwan Ocean University, Taiwan. He earned his Ph.D., and M.S. in Information Engineering, and B.S. in Computer Engineering, all at National Chiao Tung University, Taiwan. His research interests include Maritime Management and Policy, Performance management, Management Information Systems, System Analysis and Design, Algorithm Analysis, and Computer Networks. Joe Zhu is Professor of Operations Analytics in the Foisie School of Business, Worcester Polytechnic Institute. He is an internationally recognized expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA). He has published and co-edited more than 15 books focusing on performance evaluation and benchmarking using DEA. He has more than 18,000 Google Scholar citations with over 100 peer-reviewed articles. He is recognized as one of the top authors in DEA with respect to research productivity, h-index, and g-index. He is an Area Editor of OMEGA, and on the Editorial Board of European Journal of Operational Research, and Computers and Operations Research. He is the Series Associate Editor of International Series in Operations Research and Management Science.
Preface 6
Contents 10
Contributors 12
Chapter 1: Ranking Decision Making Units: The Cross-Efficiency Evaluation 15
1.1 Introduction 15
1.2 Ranking Methods in DEA 17
1.3 The Cross-Efficiency Evaluation: The Standard Approach 18
1.4 The Choice of DEA Weights in Cross-Efficiency Evaluations 20
1.4.1 Ranking Ranges and Cross-Efficiency Intervals 25
1.4.2 Illustrative Example 27
1.5 The Aggregation of Cross-Efficiencies 30
1.5.1 Illustrative Example (Cont.) 33
1.6 Other Uses 34
1.6.1 Identification of Mavericks and All-Round Performers 34
1.6.2 Classification of DMUs and Benchmarking 35
1.6.3 Fixed Cost and Resource Allocation 35
1.7 Extensions 36
1.7.1 Cross-Efficiency Evaluation with Directional Distance Functions 36
1.7.2 Cross-Efficiency Evaluation with Multiplicative DEA Models 36
1.7.3 Cross-Efficiency Evaluation Under VRS 37
1.7.4 Fuzzy Cross-Efficiency Evaluation 38
1.7.5 Game Cross Efficiency 39
1.8 Conclusions 39
References 40
Chapter 2: Data Envelopment Analysis for Measuring Environmental Performance 44
2.1 Introduction 44
2.2 Environmental DEA Technology 45
2.3 Models for Measuring Environmental Performance 48
2.3.1 Environmental Efficiency Index 48
2.3.2 Environmental Productivity Index 50
2.3.3 Other Developments 51
2.4 Case Study 52
2.4.1 Data 52
2.4.2 Results and Discussions 53
2.4.2.1 EEI Analysis 53
2.4.2.2 EPI Analysis 58
2.5 Conclusion 59
References 61
Chapter 3: Input and Output Search in DEA: The Case of Financial Institutions 63
3.1 Introduction 63
3.2 Efficiency Modeling in Financial Institutions 65
3.3 A Case Study: American Banks 67
3.3.1 The Data Set: Three Inputs and Three Outputs 68
3.3.1.1 Labor 68
3.3.1.2 Physical Capital 68
3.3.1.3 Deposits 68
3.3.1.4 Interest and Non-interest Income 71
3.3.1.5 Loans 71
3.3.2 DEA Specification Searches Using Multivariate Methods 79
3.3.3 Results Visualization and Strategic Pattern Identification 85
3.3.4 Dissecting the Efficiency Score 94
3.4 Conclusions 95
References 96
Chapter 4: Multi-period Efficiency Measurement with Fuzzy Data and Weight Restrictions 100
4.1 Introduction 100
4.2 Crisp Network DEA with Weight Restrictions 102
4.3 Fuzzy Multi-period Efficiency with Weight Restrictions 106
4.4 Example 111
4.5 Conclusion 120
References 121
Chapter 5: Pitching DEA Against SFA in the Context of Chinese Domestic Versus Foreign Banks 123
5.1 Introduction 123
5.2 Conceptual Framework 125
5.2.1 Chinese Banking Sector 125
5.2.2 Modeling Performance to Estimate Bank Efficiency 127
5.2.3 Contextual Variables 128
5.3 Data and Method 129
5.3.1 Data 129
5.3.2 Data Envelopment Analysis (DEA) 132
5.3.3 Stochastic Frontier Analysis (SFA) 135
5.4 Results and Analysis 137
5.4.1 Testing for Scale Inefficiency Using DEA 137
5.4.2 Main DEA Results 138
5.4.2.1 Core Model (Single-Output BCC-O) 138
5.4.2.2 Extended Model (Two-Output BCC-O) 139
Overall Potential Improvements Identified by DEA Using the Extended Model 140
Assessing the Marginal Role of the Output Variables in DEA: Efficiency Contribution Measures (ECM) for the Extended Model 140
5.4.3 SFA Results 142
5.4.3.1 Core Model (Single-Output Translog Function) 142
5.4.3.2 Extended Model (Two-Output Translog Function) 146
5.4.4 Comparing DEA and SFA Results 146
5.5 Concluding Remarks 149
References 151
Chapter 6: Assessing Organizations´ Efficiency Adopting Complementary Perspectives: An Empirical Analysis Through Data Envelop... 154
6.1 Introduction 155
6.2 DEA and MDS Methodologies: A Brief Overview 156
6.2.1 The Data Envelopment Analysis Method 156
6.2.2 The Multidimensional Scaling Method 157
6.3 Data and Selection of Indicators 158
6.3.1 Our Sample 158
6.3.2 Inputs and Outputs Employed in the DEA Analysis 159
6.3.3 Indicators Included in the MDS Analysis 160
6.4 Studying HEIs´ Efficiency by Means of Data Envelopment Analysis: Results 161
6.5 Combining DEA and MDS Methodologies: Results 163
6.5.1 Preliminary Insights 163
6.6 Results 168
6.7 Concluding Remarks 171
Appendix: List of Universities Included in the Analysis and Their Acronyms 172
References 173
Chapter 7: Capital Stock and Performance of RandD Organizations: A Dynamic DEA-ANP Hybrid Approach 176
7.1 Introduction 177
7.2 Literature Review 179
7.2.1 Current Status of Taiwanese RandD Organizations 179
7.2.2 DEA Applications in RandD Organizations 180
7.3 Research Design 181
7.3.1 Three-Stage Value-Creation Process of RandD Organizations 181
7.3.2 Data Selection and Description 183
7.3.3 Dynamic Extension of Network Slack-Based Measure DEA Model 184
7.4 Results and Discussions 187
7.4.1 Performance Analysis in Value-Creation Process 187
7.4.2 The Relationship Between Capital Stock and RandD Organizations Performance 190
7.5 Conclusions 192
References 193
Chapter 8: Evaluating Returns to Scale and Convexity in DEA Via Bootstrap: A Case Study with Brazilian Port Terminals 196
8.1 Introduction 196
8.2 Efficiency Measurement and RTS Characterization 198
8.2.1 Measuring Efficiency Scores Under Different Orientations and Frontiers 198
8.2.2 Scaling or RTS Characterization 201
8.2.3 Orientation Impact on RTS Characterization 202
8.3 Estimation and Bootstrapping in DEA 203
8.3.1 Estimation 203
8.3.2 Bootstrapping Method 205
8.4 Case Study: Brazilian Port Terminals 206
8.5 Results 210
8.5.1 Initial Estimates 210
8.5.2 Preliminary Statistics Tests on Initial Estimates 213
8.5.2.1 Testing for Model Specification 214
8.5.2.2 Testing for Differences Between Container and Bulk Terminals 214
8.5.2.3 Testing for Relevant Inputs and Outputs 215
8.5.2.4 Testing for Outliers 216
8.5.3 Bootstrapped Efficiency Scores and Convexity Assumption 217
8.5.4 RTS Characterizations: CIs for SI and uo 218
8.5.5 Discussion 220
8.6 Conclusions 220
References 221
9: DEA and Cooperative Game Theory 224
9.1 Introduction 224
9.2 Cooperative Game Theory 225
9.2.1 Bargaining Problems 225
9.2.1.1 The Nash Solution 226
9.2.1.2 The Kalai-Smorodinsky Solution 228
9.2.2 Transferable Utility Games 229
9.2.2.1 The Core and Related Concepts 230
9.2.2.2 The Shapley Value 230
9.2.2.3 The Least Core and the Nucleolus 232
9.3 Nash Bargaining Approaches to DEA 232
9.4 TU Cooperative Game Approaches to DEA 236
9.5 Further Potential Applications 239
9.5.1 Nash Decomposition for Process Efficiency in Multistage Production Systems 240
9.5.2 DEA Production Games 242
References 245
Chapter 10: Measuring Bank Performance: From Static Black Box to Dynamic Network Models 249
10.1 Introduction 250
10.2 Selective Literature Review 251
10.2.1 Network DEA and Dynamic DEA 251
10.2.2 Bank Production and Risk 253
10.3 Preliminaries 254
10.3.1 Black-Box Technology 254
10.3.2 Network Technology with Bad Outputs 255
10.3.3 Dynamic Technology with Carryovers 256
10.3.4 Dynamic-Network Technology 258
10.4 DEA Implementation 260
10.5 A Choice of Variables and Regulatory Constraints 268
10.5.1 Variable Selection: An Example 268
10.5.2 Imposing Bank Regulatory Constraint 269
10.6 A Summary 271
References 271
Chapter 11: Evaluation and Decomposition of Energy and Environmental Productivity Change Using DEA 275
11.1 Introduction 276
11.2 Luenberger Productivity Indicator and Its Decomposition 278
11.3 DEA Model for Energy and Environmental Efficiency Measurement 285
11.4 Application to China´s Regional Energy and Environmental Productivity Change 289
11.4.1 Data and Variables 290
11.4.2 Results and Discussions 293
11.5 Conclusions 303
References 304
Chapter 12: Identifying the Global Reference Set in DEA: An Application to the Determination of Returns to Scale 306
12.1 Introduction 307
Part I: On Identification of the Global Reference Set 308
Part II: On Determination of the RTS 310
12.2 Background 311
12.2.1 Technology Set 311
12.2.2 The RAM Model 312
12.3 Identifying the Global Reference Set (GRS) 312
12.3.1 Definition of the GRS 312
12.3.2 Properties of the GRS 314
12.3.3 Identification of the GRS 316
12.3.4 Properties of the Proposed Approach 320
12.3.5 Numerical example 321
12.4 Determination of Returns to Scale (RTS) 323
12.4.1 Definition of RTS for an Inefficient DMU 323
12.4.2 Determination of RTS Via the BCC Model 323
12.4.3 Determination of RTS Via the CCR Model 325
12.4.4 Numerical Example 326
12.4.4.1 Determining RTS Statuses of the DMUs Using Algorithm I 327
12.4.4.2 Determining RTS Statuses of the DMUs Using Algorithm II 327
12.5 Empirical Application 328
12.5.1 Evaluation of Schools via the RAM Model 329
12.5.2 Determining RTS Statuses of the Efficient Schools 329
12.5.3 Determining RTS Statuses of the Inefficient Schools 329
12.6 Summary and Concluding Remarks 333
References 334
Chapter 13: Technometrics Study Using DEA on Hybrid Electric Vehicles (HEVs) 338
13.1 Introduction 339
13.2 Methodology 339
13.3 Research Model and Dataset 342
13.3.1 TFDEA Parameters 342
13.3.1.1 Input Variable 342
13.3.1.2 Output Variables 343
13.3.1.3 Categorical Parameter 344
13.3.2 Dataset 344
13.4 Analysis of the Technological Advancement Patterns 346
13.4.1 Two-Seaters and Compact Segments: ``Stagnated´´ 347
13.4.2 Midsize Segment: ``Flourishing´´ 348
13.4.3 Large Segment: ``Emerging´´ 349
13.4.4 SUV Segment: ``Forging Ahead´´ 351
13.4.5 Minivan Segment: ``Crossover´´ 351
13.4.6 Pickup Truck Segment: ``Steady´´ 352
13.5 Conclusion 352
Appendix: 2013 State-of-the-Art Frontiers of Different HEV Segments 353
References 354
Chapter 14: A Radial Framework for Estimating the Efficiency and Returns to Scale of a Multi-component Production System in DEA 357
14.1 Introduction 358
14.2 Radial Performance Measurement for a Multi-component System 360
14.2.1 Basic Model 361
14.2.2 Theoretical Connection with Black-Box Approach 363
14.3 Procedure for Estimating the Returns to Scale 368
14.4 Theoretical Connection Between Black Box Approach and Multi-component Approach 374
14.5 Application 375
14.5.1 Efficiency 376
14.5.2 Returns to Scale 381
14.6 Summary and Conclusion 382
Appendix 383
References 389
Chapter 15: DEA and Accounting Performance Measurement 391
15.1 Introduction 391
15.2 Accounting Information 392
15.3 Accounting Ratios for Performance Measurement 395
15.4 Accounting Information and Its Interpretation in Productivity Measurement 398
15.4.1 Model 1: Production Process 400
15.4.2 Model 2: Firm Financial Efficiency Model 401
15.4.3 Model 3: Funding Efficiency Model 401
15.5 Indexing Dollar Values and Translation of Foreign Currencies 402
15.6 Activity-Based Costing and DEA: Congenial Twins 404
15.7 DEA and the Balanced Scorecard: A New Approach to an Old Problem 408
15.8 Understanding Contextual Performance to ``Do Better´´ 410
15.9 Summary 415
References 415
Chapter 16: DEA Environmental Assessment (I): Concepts and Methodologies 419
16.1 Introduction 420
16.2 Literature Review 422
16.3 Underlying Concepts for DEA Environmental Assessment 422
16.3.1 Abbreviations and nomenclatures 422
16.3.2 Natural and Managerial Disposability 423
16.3.3 Unification Between Natural and Managerial Disposability 424
16.3.4 Desirable Congestion (DC) 426
16.4 Unified Efficiency 427
16.4.1 Unified Efficiency (UE) 427
16.4.2 Unified Efficiency under Natural Disposability (UEN) 430
16.4.3 Unified Efficiency under Managerial Disposability (UEM) 431
16.4.4 Unified Efficiency under Natural and Managerial Disposability (UENM) 432
16.4.5 Unified Efficiency under Natural and Managerial Disposability: UENM(DC) with a Possible Occurrence of Desirable Congest... 434
16.5 Investment Strategy 435
16.6 Empirical Study 436
16.7 Conclusion and Future Extensions 442
References 449
Chapter 17: DEA Environmental Assessment (II): A Literature Study 451
17.1 Introduction 452
17.2 DEA Environmental Assessment 453
17.3 Disposability Concepts 456
17.4 Electric Power Industry 462
17.5 Petroleum and Coal Industries 463
17.6 Agriculture, Fishery, Manufacturing and Transportation Industries 464
17.7 Economic Development and Corporate Strategy 465
17.8 Methodology Developments 466
17.9 Conclusion 468
References 469
Chapter 18: Corporate Environmental Sustainability and DEA 488
18.1 Introduction 488
18.2 Corporate Environmental Sustainability 489
18.3 Theory Testing and Statistical Inferencing with DEA: An Environmental Perspective 490
18.3.1 Financial and Environmental Performance Relationship 491
18.3.2 Ecological Efficiency and Technological Disposition Relationship 492
18.3.3 Environmental Practices, Performance and Risk Management 493
18.4 Benchmarking and Key Performance Indicators with DEA 494
18.5 Multiple Criteria Decision Making with DEA 496
18.5.1 Justifying and Choosing Environmental Technologies 497
18.6 Future Research Directions 498
18.7 Conclusion 500
References 501
Index 504
| Erscheint lt. Verlag | 1.7.2016 |
|---|---|
| Reihe/Serie | International Series in Operations Research & Management Science | International Series in Operations Research & Management Science |
| Zusatzinfo | XIII, 506 p. 64 illus., 35 illus. in color. |
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Technik ► Bauwesen |
| Wirtschaft ► Allgemeines / Lexika | |
| Wirtschaft ► Betriebswirtschaft / Management ► Planung / Organisation | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Schlagworte | Centralized DEA • Data envelopment analysis • DEA • DEA Models • Production Tradeoffs • Scale Elasticity |
| ISBN-10 | 1-4899-7705-8 / 1489977058 |
| ISBN-13 | 978-1-4899-7705-2 / 9781489977052 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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