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Handbook on Data Envelopment Analysis

E-BookPDF1 - PDF WatermarkE-Book
498 Seiten
Englisch
SPRINGER USerschienen am23.08.20112nd ed. 2011
This handbook covers DEA topics that are extensively used and solidly based. The purpose of the handbook is to (1) describe and elucidate the state of the field and (2), where appropriate, extend the frontier of DEA research. It defines the state-of-the-art of DEA methodology and its uses. This handbook is intended to represent a milestone in the progression of DEA. Written by experts, who are generally major contributors to the topics to be covered, it includes a comprehensive review and discussion of basic DEA models, which, in the present issue extensions to the basic DEA methods, and a collection of DEA applications in the areas of banking, engineering, health care, and services. The handbook's chapters are organized into two categories: (i) basic DEA models, concepts, and their extensions, and (ii) DEA applications. First edition contributors have returned to update their work.

The second edition includes updated versions of selected first edition chapters. New chapters have been added on: different approaches with no need for a priori choices of weights (called 'multipliers) that reflect meaningful trade-offs, construction of static and dynamic DEA technologies, slacks-based model and its extensions, DEA models for DMUs that have internal structures network DEA that can be used for measuring supply chain operations, Selection of DEA applications in the service sector with a focus on building a conceptual framework, research design and interpreting results.mehr
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Produkt

KlappentextThis handbook covers DEA topics that are extensively used and solidly based. The purpose of the handbook is to (1) describe and elucidate the state of the field and (2), where appropriate, extend the frontier of DEA research. It defines the state-of-the-art of DEA methodology and its uses. This handbook is intended to represent a milestone in the progression of DEA. Written by experts, who are generally major contributors to the topics to be covered, it includes a comprehensive review and discussion of basic DEA models, which, in the present issue extensions to the basic DEA methods, and a collection of DEA applications in the areas of banking, engineering, health care, and services. The handbook's chapters are organized into two categories: (i) basic DEA models, concepts, and their extensions, and (ii) DEA applications. First edition contributors have returned to update their work.

The second edition includes updated versions of selected first edition chapters. New chapters have been added on: different approaches with no need for a priori choices of weights (called 'multipliers) that reflect meaningful trade-offs, construction of static and dynamic DEA technologies, slacks-based model and its extensions, DEA models for DMUs that have internal structures network DEA that can be used for measuring supply chain operations, Selection of DEA applications in the service sector with a focus on building a conceptual framework, research design and interpreting results.
Details
Weitere ISBN/GTIN9781441961518
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2011
Erscheinungsdatum23.08.2011
Auflage2nd ed. 2011
ReiheInternational Series in Operations Research & Management Science
Reihen-Nr.164
Seiten498 Seiten
SpracheEnglisch
IllustrationenXXVI, 498 p.
Artikel-Nr.1719507
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Handbook on Data
Envelopment Analysis;3
1.1;Preface;7
1.2;About the Authors;11
1.3;Contents;21
1.4;Contributors;23
1.5;Chapter 1: Data Envelopment Analysis: History, Models, and Interpretations*
;27
1.5.1;1.1 Introduction;27
1.5.2;1.2 Background and History;29
1.5.3;1.3 CCR Model;33
1.5.4;1.4 Extensions to the CCR Model;44
1.5.4.1;1.4.1 Nondiscretionary Inputs and Outputs;44
1.5.4.2;1.4.2 Categorical Inputs and Outputs;46
1.5.4.3;1.4.3 Incorporating Judgment or A Priori Knowledge;47
1.5.4.4;1.4.4 Window Analysis;49
1.5.5;1.5 Allocative and Overall Efficiency;52
1.5.6;1.6 Profit Efficiency;55
1.5.7;1.7 Recent Developments;60
1.5.8;1.8 Conclusions;62
1.5.9;References;62
1.6;Chapter 2:
Returns to Scale in DEA*;66
1.6.1;2.1 Introduction;66
1.6.2;2.2 RTS Approaches with BCC Models;68
1.6.3;2.3 RTS Approaches with CCR Models;73
1.6.4;2.4 Most Productive Scale Size;79
1.6.5;2.5 Additive Models;82
1.6.6;2.6 Multiplicative Models;86
1.6.7;2.7 Summary and Conclusion;91
1.6.8;Appendix;93
1.6.9;References;94
1.7;Chapter 3:
Sensitivity Analysis in DEA*;96
1.7.1;3.1 Introduction;96
1.7.2;3.2 Sensitivity Analysis Approaches;97
1.7.2.1;3.2.1 Algorithmic Approaches;98
1.7.2.2;3.2.2 Metric Approaches;98
1.7.2.3;3.2.3 Multiplier Model Approaches;102
1.7.2.4;3.2.4 A Two-Stage Alternative;106
1.7.2.5;3.2.5 Envelopment Approach;109
1.7.3;3.3 Summary and Conclusion;115
1.7.4;References;115
1.8;Chapter 4:
Choices and Uses of DEA Weights;117
1.8.1;4.1 Introduction;117
1.8.2;4.2 Using Price Information;120
1.8.3;4.3 Reflecting Meaningful Trade-Offs;122
1.8.4;4.4 Incorporating Value Information and Managerial Goals;125
1.8.5;4.5 Choosing From Alternate Optima;129
1.8.6;4.6 Looking for Non-zero Weights;133
1.8.7;4.7 Avoiding Large Differences in the Values of Multipliers;136
1.8.8;4.8 Improving Discrimination and Ranking Units;139
1.8.9;4.9 Conclusions;144
1.8.10;References;146
1.9;Chapter 5:
Malmquist Productivity Indexes and DEA;151
1.9.1;5.1 Introduction;151
1.9.2;5.2 DEA Technologies;152
1.9.3;5.3 Projecting onto the Frontier;156
1.9.4;5.4 Productivity Indexes;162
1.9.5;5.5 A Dynamic Malmquist Productivity Index;170
1.9.6;References;172
1.10;Chapter 6:
Qualitative Data in DEA;174
1.10.1;6.1 Introduction;174
1.10.2;6.2 Problem Settings Involving Ordinal Data;175
1.10.2.1;6.2.1 Ordinal Data in RandD Project Selection;175
1.10.2.2;6.2.2 Efficiency Performance of Korean Telephone Offices;177
1.10.3;6.3 Modeling Ordinal Data;179
1.10.3.1;6.3.1 Permissible Worth Vectors;182
1.10.3.2;6.3.2 Criteria Importance;186
1.10.4;6.4 Solutions to Applications;187
1.10.4.1;6.4.1 RandD Project Efficiency Evaluation;187
1.10.4.2;6.4.2 Evaluation of Telephone Office Efficiency;188
1.10.5;6.5 Problem Settings and Issues Involving Qualitative Data;189
1.10.5.1;6.5.1 Implementation of Robotics: Identifying Efficient Implementers;190
1.10.5.2;6.5.2 A Fair Model for Aggregating Preferential Votes;190
1.10.5.3;6.5.3 Multiple Criteria Decision Modeling: Ordinal Data, Criteria Importance, and Criteria Clearness;191
1.10.5.3.1;6.5.3.1 Evaluating Vendors for Complex Systems;192
1.10.5.3.2;6.5.3.2 Country Risk Evaluation;192
1.10.5.3.3;6.5.3.3 Mutual Fund Selection;193
1.10.5.3.4;6.5.3.4 Ordinal Data in Multicriteria Modeling: Evaluation in Terms of Subsets of Criteria;193
1.10.6;6.6 Discussion;194
1.10.7;References;194
1.11;Chapter 7:
Congestion: Its Identification and Management with DEA;196
1.11.1;7.1 Congestion;196
1.11.2;7.2 Comparison of Two Literatures on Congestion;201
1.11.3;7.3 Färe, Grosskopf, and Lovell (FGL) Approach;201
1.11.4;7.4 Cooper, Thompson, and Thrall (CTT) Approach;205
1.11.4.1;7.4.1 A Numerical Example;207
1.11.5;7.5 A Unified Additive Model;209
1.11.6;7.6 Estimating the Output Effects of Congestion;211
1.11.7;7.7 Extensions;214
1.11.8;References;215
1.12;Chapter 8:
Slacks-Based Measure of Efficiency;217
1.12.1;8.1 Introduction;217
1.12.2;8.2 The SBM Model;218
1.12.2.1;8.2.1 Production Possibility Set;218
1.12.2.2;8.2.2 Input-Oriented SBM;219
1.12.2.3;8.2.3 Output-Oriented SBM;220
1.12.2.4;8.2.4 Nonoriented SBM;221
1.12.2.5;8.2.5 An Illustrative Example of SBM Models;222
1.12.2.6;8.2.6 The Dual Program of the SBM Model;223
1.12.3;8.3 Extensions of the SBM Model;224
1.12.3.1;8.3.1 Variable Returns-to-Scale Model;224
1.12.3.2;8.3.2 Weighted-SBM Model;225
1.12.3.3;8.3.3 Super-SBM Model;226
1.12.3.4;8.3.4 An Illustrative Example of Super-SBM Models;227
1.12.4;8.4 Further Extensions;227
1.12.4.1;8.4.1 Dealing with Nonpositive Data in the SBM Models;227
1.12.4.2;8.4.2 Variations of the SBM Models;229
1.12.4.3;8.4.3 A Compromise of Radial and Nonradial Measures of Efficiency;230
1.12.5;8.5 Concluding Remarks;230
1.12.6;References;231
1.13;Chapter 9:
Chance-Constrained DEA;232
1.13.1;9.1 Introduction;232
1.13.2;9.2 Efficiency and Efficiency Dominance;233
1.13.3;9.3 Stochastic Dominance and Joint Chance Constrained Efficiency;236
1.13.3.1;9.3.1 Potential Uses;239
1.13.4;9.4 Stochastic Efficiency in Marginal Chance Constrained Models;244
1.13.5;9.5 Satisficing DEA Models;252
1.13.6;9.6 Concluding Remarks;258
1.13.7;References;259
1.14;Chapter 10:
Performance of the Bootstrap for DEA Estimators and Iterating the Principle;262
1.14.1;10.1 Introduction;262
1.14.2;10.2 Efficiency and the Theory of the Firm;263
1.14.3;10.3 Estimation;265
1.14.4;10.4 A Statistical Model;268
1.14.5;10.5 Some Asymptotic Results;269
1.14.6;10.6 Bootstrapping in DEA/FDH Models;271
1.14.7;10.7 Implementing the Bootstrap;274
1.14.8;10.8 Monte Carlo Evidence;279
1.14.9;10.9 Enhancing the Performance of the Bootstrap;287
1.14.10;10.10 Conclusions;289
1.14.11;References;290
1.15;Chapter 11:
Statistical Tests Based on DEA Efficiency Scores;293
1.15.1;11.1 Introduction;293
1.15.2;11.2 Hypothesis Tests When Inefficiency is the Only Stochastic Variable;295
1.15.2.1;11.2.1 Statistical Foundation for DEA;295
1.15.2.2;11.2.2 Efficiency Comparison of Two Groups of DMUs;296
1.15.2.3;11.2.3 Tests of Returns to Scale;297
1.15.2.4;11.2.4 Tests of Allocative Efficiency;299
1.15.2.5;11.2.5 Tests of Input Separability;301
1.15.3;11.3 Hypothesis Tests for Situations Characterized by Shifts in Frontier;302
1.15.4;11.4 Hypothesis Tests for Composed Error Situations;306
1.15.4.1;11.4.1 Tests for Efficiency Comparison;307
1.15.4.2;11.4.2 Tests for Evaluating the Impact of Contextual Variables on Efficiency;308
1.15.4.3;11.4.3 Tests for Evaluating the Adequacy of Parametric Functional Forms;311
1.15.5;11.5 Concluding Remarks;313
1.15.6;References;314
1.16;Chapter 12:
Modeling DMU´s Internal Structures: Cooperative and Noncooperative Approaches;316
1.16.1;12.1 Introduction;316
1.16.2;12.2 Two-Stage Processes;318
1.16.3;12.3 Centralized Model;319
1.16.4;12.4 Stackelberg Game;322
1.16.5;12.5 DEA Model for General Multistage Serial Processes Via Additive Efficiency Decomposition;325
1.16.6;12.6 General Multistage Processes;328
1.16.6.1;12.6.1 Parallel Processes;328
1.16.6.2;12.6.2 Nonimmediate Successor Flows;329
1.16.7;12.7 Conclusions;330
1.16.8;References;331
1.17;Chapter 13:
Assessing Bank and Bank Branch Performance;333
1.17.1;13.1 Introduction;333
1.17.2;13.2 Performance Measurement Approaches in Banking;334
1.17.2.1;13.2.1 Ratio Analysis;334
1.17.2.2;13.2.2 Frontier Efficiency Methodologies;335
1.17.2.3;13.2.3 Other Performance Evaluation Methods;336
1.17.3;13.3 Data Envelopment Analysis in Banking;336
1.17.3.1;13.3.1 Banking Corporations;337
1.17.3.1.1;13.3.1.1 In-Country;337
1.17.3.1.2;13.3.1.2 Cross-Country Studies;338
1.17.3.2;13.3.2 Bank Branches;339
1.17.3.2.1;13.3.2.1 Small Number of Branches;339
1.17.3.2.2;13.3.2.2 Large Number of Branches;340
1.17.3.2.3;13.3.2.3 Branch Studies Incorporating Service Quality;341
1.17.3.2.4;13.3.2.4 Unusual Banking Applications of DEA;341
1.17.4;13.4 Model Building Considerations;342
1.17.4.1;13.4.1 Approaching the Problem;342
1.17.4.2;13.4.2 Input or Output?;342
1.17.4.3;13.4.3 Too Few DMUs/Too Many Variables;343
1.17.4.4;13.4.4 Relationships and Proxies;344
1.17.4.5;13.4.5 Outliers;344
1.17.4.6;13.4.6 Zero or Blank?;345
1.17.4.7;13.4.7 Size Does Matter;345
1.17.4.8;13.4.8 Too Many DMUs on the Frontier;346
1.17.4.9;13.4.9 Environmental Factors;346
1.17.4.10;13.4.10 Service Quality;347
1.17.4.11;13.4.11 Validating Results;347
1.17.5;13.5 Banks as DMUS;347
1.17.5.1;13.5.1 Cross-Country/Region Comparisons;348
1.17.5.2;13.5.2 Bank Mergers;349
1.17.5.2.1;13.5.2.1 Selecting Pairs of Branch Units for Merger Evaluation;351
1.17.5.2.2;13.5.2.2 Defining a Strategy for Hypothetically Merging Two Bank Branches;351
1.17.5.2.3;13.5.2.3 Developing Models for Evaluating the Overall Performance of Merged Units Through the Selection of Appropriate Input and Output Variables
;352
1.17.5.2.4;13.5.2.4 Calculating Potential Efficiency Gains;352
1.17.5.2.5;13.5.2.5 Identifying Differences in Cultural Environments Between the Merging Banks;352
1.17.5.2.6;13.5.2.6 Calculating Potential Synergies;353
1.17.5.3;13.5.3 Temporal Studies;354
1.17.5.3.1;13.5.3.1 The Models;354
1.17.5.3.2;13.5.3.2 Window Analysis;355
1.17.5.3.3;13.5.3.3 Malmquist Productivity Index;357
1.17.6;13.6 Bank Branches as DMUS;361
1.17.6.1;13.6.1 The Production Model;362
1.17.6.2;13.6.2 Profitability Model;363
1.17.6.3;13.6.3 Intermediation Model;364
1.17.6.4;13.6.4 Model Results;365
1.17.6.5;13.6.5 Senior Management Concerns;366
1.17.6.6;13.6.6 A Two-Stage Process;367
1.17.6.7;13.6.7 Targeted Analysis;368
1.17.6.8;13.6.8 New Role of Bank Branch Analysis;369
1.17.6.9;13.6.9 Environmental Effects;370
1.17.7;13.7 Validation;372
1.17.7.1;13.7.1 Validating a Method with Monte Carlo Simulation;372
1.17.8;13.8 Conclusions;373
1.17.9;References;374
1.18;Chapter 14:
Engineering Applications of Data Envelopment Analysis;380
1.18.1;14.1 Background and Context;381
1.18.2;14.2 Research Issues and Opportunities;384
1.18.2.1;14.2.1 Evaluating Design Alternatives;384
1.18.2.2;14.2.2 Disaggregated Process Evaluation and Improvement: Opening the ``Input/Output Transformation Box´´;385
1.18.2.3;14.2.3 Hierarchical Manufacturing System Performance;386
1.18.2.4;14.2.4 Data Measurement Imprecision in Production Systems;389
1.18.2.5;14.2.5 Dynamical Production Systems;391
1.18.2.6;14.2.6 Visualization of the DEA Results: Influential Data Identification;395
1.18.3;14.3 A DEA-Based Approach Used for the Design of an Integrated Performance Measurement System;396
1.18.4;14.4 Selected DEA Engineering Applications;399
1.18.4.1;14.4.1 Four Applications;399
1.18.4.1.1;14.4.1.1 Evaluating Efficiency of Turbofan Jet Engines (Bulla et al. 2000);399
1.18.4.1.2;14.4.1.2 Measurement and Monitoring of Relative Efficiency of Highway Maintenance Patrols (Cook et al. 1990, 1994) and of Highway Maintenance Operations (Ozbek et al. 2010a, b)6
;400
1.18.4.1.3;14.4.1.3 Data Envelopment Analysis of Space and Terrestrially Based Large Scale Commercial Power Systems for Earth (Criswell and Thompson 1996)
;401
1.18.4.1.4;14.4.1.4 The Relationship of DEA and Control Charts (Hoopes and Triantis 2001);401
1.18.4.2;14.4.2 The Effect of Environmental Controls on Productive Efficiency;402
1.18.4.3;14.4.3 The Performance of Transit Systems;403
1.18.4.4;14.4.4 Other Engineering Applications of DEA;404
1.18.5;14.5 Systems Thinking Concepts and Future DEA Research in Engineering;405
1.18.5.1;14.5.1 The Need for Operational Thinking;405
1.18.5.2;14.5.2 Contribution to Performance Measurement Science;406
1.18.5.3;14.5.3 Relationship of the DEA Model with the Real World;407
1.18.6;References;408
1.19;Chapter 15:
Applications of Data Envelopment Analysis in the Service Sector;420
1.19.1;15.1 Introduction;420
1.19.2;15.2 Universities1
;423
1.19.2.1;15.2.1 Introduction;423
1.19.2.2;15.2.2 Conceptual Framework;424
1.19.2.3;15.2.3 Research Design;426
1.19.2.4;15.2.4 Results and Analysis;428
1.19.2.5;15.2.5 Concluding Remarks;429
1.19.3;15.3 Hotels2
;430
1.19.3.1;15.3.1 Introduction;430
1.19.3.2;15.3.2 Conceptual Framework;431
1.19.3.3;15.3.3 A Quick Guide to Selecting Inputs and Outputs;433
1.19.3.4;15.3.4 A Numerical Example;433
1.19.3.5;15.3.5 Concluding Remarks;435
1.19.4;15.4 Real Estate Agents3
;436
1.19.4.1;15.4.1 Introduction;436
1.19.4.2;15.4.2 Research Design;437
1.19.4.3;15.4.3 Analysis of Results;439
1.19.4.4;15.4.4 Concluding Remarks;441
1.19.5;15.5 Commercial Banks5
;442
1.19.5.1;15.5.1 Introduction;442
1.19.5.2;15.5.2 Conceptual Framework;443
1.19.5.2.1;15.5.2.1 Modeling Profit Efficiency;443
1.19.5.2.2;15.5.2.2 Key Profit Centers and Estimating Corresponding Data;444
1.19.5.3;15.5.3 Methodology;446
1.19.5.3.1;15.5.3.1 Overview of Network DEA;446
1.19.5.3.2;15.5.3.2 Network Slacks-Based Measure of Efficiency;449
1.19.5.3.3;15.5.3.3 Data and Simulation;450
1.19.5.4;15.5.4 Results and Analysis;451
1.19.5.4.1;15.5.4.1 Profit Efficiency Using Traditional DEA (SBM);451
1.19.5.4.2;15.5.4.2 Profit Efficiency Using Network SBM and Simulated Profit Center Data;452
1.19.5.5;15.5.5 Concluding Remarks;454
1.19.6;15.6 Epilogue;455
1.19.7;References;457
1.20;Chapter 16:
Health-Care Applications: From Hospitals to Physicians, from Productive Efficiency to Quality Frontiers;461
1.20.1;16.1 Introduction;461
1.20.2;16.2 Brief Background and History;463
1.20.2.1;16.2.1 Acute General Hospitals and Academic Medical Centers;464
1.20.2.2;16.2.2 Nursing Homes;465
1.20.2.3;16.2.3 Department Level, Team-Level, and General Health-Care Studies;466
1.20.2.4;16.2.4 Physician-Level Studies;467
1.20.2.5;16.2.5 Data Envelopment Analysis Versus Stochastic Frontier Analysis;468
1.20.2.6;16.2.6 Reviewer Comments on the Usefulness of DEA;469
1.20.2.7;16.2.7 Summary;470
1.20.3;16.3 Health-Care Models;471
1.20.3.1;16.3.1 Clinical Efficiency Definitions;471
1.20.3.2;16.3.2 How to Model Health-Care Providers: Hospitals, Nursing Homes, Physicians;472
1.20.3.3;16.3.3 Managerial and Clinical Efficiency Models;473
1.20.3.3.1;16.3.3.1 Medical Center and Acute Hospital Models: Examples of Managerial Efficiency;475
1.20.3.3.2;16.3.3.2 Nursing Homes: Another Example of Managerial Efficiency;476
1.20.3.3.3;16.3.3.3 Primary Care Physician Models: An Example of Clinical Efficiency;477
1.20.3.4;16.3.4 Hospital Physician Models: Another Example of Clinical Efficiency;478
1.20.3.5;16.3.5 Profitability Models: A Nursing Home Example;479
1.20.4;16.4 Special Issues for Health Applications;481
1.20.4.1;16.4.1 Defining Models from Stakeholder Views;481
1.20.4.2;16.4.2 Selecting Appropriate Health-Care Outputs and Inputs: The Greatest Challenge for DEA;483
1.20.4.2.1;16.4.2.1 Take Two Aspirin and Call Me in the Morning;483
1.20.4.2.2;16.4.2.2 Using DEA to Adjust Outputs for Patient Characteristics and Case mix;484
1.20.4.3;16.4.3 Should Environmental and Organizational Factors Be Used as Inputs?;485
1.20.4.4;16.4.4 Problems on the Best Practice Frontier: A Physician Example;485
1.20.4.4.1;16.4.4.1 The Concept of a Preferred Practice Cone or Quality Assurance Region;487
1.20.4.4.2;16.4.4.2 Constant Versus Variable Returns to Scale;488
1.20.4.4.3;16.4.4.3 Scale and Scope Issues;489
1.20.4.5;16.4.5 Analyzing DEA Scores with Censored Regression Models;489
1.20.5;16.5 New Directions: From Productive Efficiency Frontiers to Quality-Outcome Frontiers;492
1.20.5.1;16.5.1 A Field Test: Combining Outcome Frontiers and Efficiency Frontiers;495
1.20.6;16.6 A Health DEA Application Procedure: Eight Steps;497
1.20.6.1;16.6.1 Step 1: Identification of Interesting Health-Care Problem and Research Objectives;497
1.20.6.2;16.6.2 Step 2: Conceptual Model of the Medical Care Production Process;498
1.20.6.3;16.6.3 Step 3: Conceptual Map of Factors Influencing Care Production;498
1.20.6.4;16.6.4 Step 4: Selection of Factors;498
1.20.6.5;16.6.5 Step 5: Analyze Factors Using Statistical Methods;499
1.20.6.6;16.6.6 Step 6: Run Several DEA Models;499
1.20.6.7;16.6.7 Step 7: Analyze DEA Scores with Statistical Methods;499
1.20.6.8;16.6.8 Step 8: Share Results with Practitioners and Write It Up;500
1.20.7;16.7 DEA Health Applications: Do´s and Don´ts;500
1.20.7.1;16.7.1 Almost Never Include Physicians As a Labor Input;500
1.20.7.2;16.7.2 Use Caution When Modeling Intermediate and Final Hospital Outputs;501
1.20.7.3;16.7.3 Do Check the Distribution of DEA Scores and Influence of Best Practice Providers on Reference Sets;503
1.20.8;16.8 A Final Word;504
1.20.9;References;506
1.21;Index;510mehr

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