Modeling Performance Measurement: Applications and Implementation Issues in DEA presents unified results from several authors’ recent DEA research. These new DEA methodology and techniques are developed in application-driven scenarios that go beyond the identification of the best-practice frontier and seek solutions to aid managerial decisions. These new DEA developments are well-grounded in real world applications. Both DEA researchers and practitioners will find this book helpful. Theory is provided for DEA researchers for further development and possible extensions. However, it should also be mentioned that each theory is presented in practical terms with numerical examples, simple real management cases and verbal descriptions. These concrete examples will be of value to researchers, students, and practitioners.
Author(s): Wade D. Cook Joe Zhu
Edition: 1
Year: 2005
Language: English
Pages: 408
CONTENTS......Page 8
Preface......Page 16
1.1. Introduction......Page 18
1.2. Envelopment and Multiplier DEA Models......Page 19
1.3. Assurance Region DEA Models......Page 27
1.4. Slack Based DEA Models......Page 28
1.5. Measure-Specific DEA Models......Page 30
1.6.1 DEAFrontier Software......Page 31
1.6.2 Organize the Data......Page 34
1.6.3 Run the Envelopment Models......Page 36
1.6.4 Run the Multiplier Models......Page 38
1.6.5 Run the AR Models......Page 39
1.6.6 Run the Slack-based Models......Page 41
1.6.7 Run the Measure-Specific Models......Page 42
References......Page 43
2.1. Background......Page 46
2.2.1 The Model and its Factors......Page 48
2.2.2 Data and Unbounded Runs......Page 50
2.2.4 Deriving a Common Set of Weights......Page 53
2.2.5 District Runs......Page 54
2.2.6 Analysis of Various Characteristics......Page 55
2.3.1 Theoretical versus Achievable Targets......Page 57
2.3.2 Enforced Input Reduction......Page 59
2.3.3 Modeling Output Erosion......Page 60
2.4. The Application......Page 67
2.4.1 Base-Line Budget Considerations......Page 73
2.4.2 Budget Allocation Beyond the Base Line......Page 74
2.5. Discussion......Page 75
References......Page 76
3.1. Introduction......Page 78
3.2. The Problem......Page 79
3.3. Applicability of the DEA Methodology......Page 81
3.4. Application to a Sample of Safety Sections......Page 83
3.4.1 Selecting Treatments and Sections......Page 84
3.5. Conclusions......Page 87
References......Page 88
4.1. Introduction......Page 90
4.2. Variable-benchmark Model......Page 92
4.3. Fixed-benchmark Model......Page 97
4.4. Benchmarking Models in DEAFRONTIER Software......Page 98
4.5. Application to Bank Branches......Page 99
4.5.1 Identification of Benchmark Frontier......Page 101
4.5.2 Benchmarking the Ebranches Against the Traditional Branches......Page 102
4.5.3 Benchmarking within ebranches......Page 104
4.6. Conclusions......Page 107
References......Page 108
5.1. Introduction......Page 110
5.2. The Problem Setting......Page 112
5.3.1 Logistic Regression......Page 114
5.3.2 Multiple Discriminant Analysis......Page 115
5.3.4 IGP Models......Page 116
5.4. Embedding Expert Knowledge in the Additive DEA Model......Page 117
5.4.1 Linking Discriminant Techniques and the Additive DEA Model......Page 120
5.4.3 DEA Measures......Page 122
5.5.2 Estimating the Predictive Model......Page 123
5.5.3 Testing the Predictive Model......Page 125
5.6. Variables with Imposed Input and Output Status......Page 126
5.7. The Input-oriented Model......Page 130
5.8. GP Constraint– Enhanced DEA Model......Page 134
5.8.1 Imposing Nonlinear Goal Programming Constraints in an Input Oriented DEA Model......Page 137
5.8.2 Imposing Linear Goal Programming Constraints in an Input Oriented DEA Model......Page 138
References......Page 139
6.1. Introduction......Page 142
6.2.1 Multiple Functions and Shared Resources......Page 144
6.2.3 Function-Specific Performance Measures......Page 147
6.2.4 Derivation of e[sup(a)][sub(j)],e[sup(1)][sub(j)],e[sup(2)][sub(j)]......Page 148
6.2.5 An Alternative Formulation......Page 149
6.2.6 Types of Constraints in Ω[sub(2)]......Page 150
6.2.7 Special Cases......Page 151
6.3. An Application......Page 152
6.3.2 Results......Page 154
6.4.1 Addressing Some Shortcomings......Page 156
6.4.2 The General Additive Model......Page 157
6.4.3 An Additive Model for Sales and Service Components......Page 159
6.5. Application to Bank Branches......Page 164
References......Page 167
7.1. Introduction......Page 170
7.2.1 Evaluating Capital Construction Projects......Page 171
7.2.2 Selecting Automated Test Equipment at Northern Telecom......Page 175
7.2.3 Country Risk Evaluation......Page 178
7.3. The Model......Page 180
7.3.1 The Modified Model......Page 185
7.3.2 Implementation Issues......Page 190
7.4. Evaluation Relative to Partial Criteria......Page 191
References......Page 197
8.1. Introduction......Page 200
8.2.1 Ordinal Data in R&D Project Selection......Page 201
8.3. Modelling Ordinal Data......Page 204
8.4.1 R&D Project Efficiency Evaluation......Page 214
8.4.2 Evaluation of Telephone Office Efficiency......Page 215
8.5. Discussion......Page 216
References......Page 217
9.1. Introduction......Page 220
9.2. Criteria for Evaluating Research Impact......Page 221
9.3. Information Requirements from Management......Page 222
9.4. Modelling Resource Allocation – The Basic Idea......Page 224
9.5. Developing the Ratings R[sup(v)][sub(p)]......Page 226
9.5.1 Conventional Approach......Page 227
9.5.2 A Proper Evaluation of Ordinal Data......Page 228
9.5.3 Multiple Voters and Levels of Credibility......Page 230
9.6. Building Reallocation......Page 233
9.7. Application......Page 236
References......Page 239
Appendix A: Benefits......Page 241
10.1. Introduction......Page 242
10.2.2 Modelling Preliminaries......Page 243
10.2.3 A Prioritization Model......Page 246
10.2.4 An Application......Page 249
10.3. Choice of DEA Model......Page 251
10.4.1 Introduction......Page 253
10.4.2 Site Selection for a Retail Store Chain......Page 255
10.4.3 A DEA Based Model......Page 257
10.4.4 An Application......Page 262
10.5. Extentions to the Selection Model......Page 267
References......Page 269
11.1. Introduction......Page 272
11.2. Multicomponent Efficiency Measurement and Core Business Identification......Page 273
11.3.1 Multi-component Efficiency Measurement with Shared Inputs: Nonoverlapping Subunits......Page 275
11.3.2 Multi-component Efficiency Measurement with Overlapping Subunits......Page 278
11.4. Modeling Selection of Core Business components......Page 281
11.5. Application of Core Business Selection Model to a Set of Plants......Page 286
11.6. Discussion......Page 290
References......Page 291
12.1. Introduction......Page 292
12.2.1 Background......Page 293
12.2.2 Assessing the Implementation of Industrial Robotic Systems......Page 295
12.2.3 The Model Structure......Page 296
12.3. The Data......Page 298
12.4.2 Outcome from the Overall Analysis......Page 299
12.4.3 Using the Control Parameters......Page 301
12.5. Discussion and Summary......Page 305
References......Page 306
Appendix 1: Variables in Implementation Efficiency Model......Page 308
Appendix 2: Site Demographics......Page 310
Appendix 3: Data Matrix of Variables and Projects......Page 311
13.1. Introduction......Page 312
13.2.1 The Input Target Problem......Page 313
13.2.2 Unimpeded Movement of Inputs......Page 316
13.3.1 Upper Bounds on the x[sub(in+1)]......Page 318
13.3.2 Upper Bounds on the x[sub(in+1)]......Page 320
13.4. Nondiscretionary Variables......Page 322
13.4.1 Uncertainty in the Nondiscretionary Variable......Page 323
References......Page 326
14.1. Introduction......Page 328
14.2. A Fair Model for Aggregation Preferential Votes......Page 330
14.3. A Model for Ranking the Candidates......Page 335
14.4. Cross Evaluation......Page 340
References......Page 344
15.1. Introduction......Page 346
15.2. A Model for Rating Players......Page 347
15.2.1 Obtaining m th- Generation (Weighted m th- Generation) Scores......Page 350
15.3. Weak Ranking of Players......Page 353
15.3.1 Obtaining WMKS......Page 359
15.4. Multiple Tournaments......Page 362
15.5. Partial Tournaments......Page 364
References......Page 366
16.1. Introduction......Page 368
16.2. Context-dependent DEA......Page 370
16.3. Context-dependent DEA with Value Judgment......Page 373
16.4. Input-oriented Context-dependent DEA......Page 376
16.5. Context-dependent DEA Models in DEAFrontier Software......Page 379
16.6. Application......Page 381
16.7. Conclusions......Page 388
References......Page 389
17.1. Introduction......Page 390
17.2. Hierarchical Structures: Power Plants......Page 391
17.3.1 The Two-Level Hierarchy......Page 393
17.3.2 Efficiency Adjustments in a Hierarchy......Page 396
17.3.3 The Multi Level Hierarchy......Page 397
17.4. Grouping on Levels......Page 399
17.4.1 Deriving an Aggregate Rating......Page 400
17.4.2 A Common Set of Multipliers......Page 401
17.4.3 Multiple Rankings of Attributes......Page 402
17.5. Efficiency Analysis of Power Plants: An Example......Page 403
17.5.1 Hierarchical Analysis......Page 407
17.5.2 Hierarchical Analysis......Page 408
17.6. Simultaneous Evaluation Across Levels......Page 410
17.7. Analysis of Efficiency: An Example......Page 414
17.7.1 Proportional Split of Plant-level Outputs......Page 415
17.8. Conclusions......Page 416
References......Page 417
C......Page 420
E......Page 421
N......Page 422
T......Page 423
W......Page 424
