Service Productivity Management: Improving Service Performance using Data Envelopment Analysis (DEA)

343 Pages·2006·8.661 MB·English

by  H. David Sherman, Joe Zhu (auth.)

Service Productivity IVIanagement
Improving Service Performance using
DATA ENVELOPMENT ANALYSIS (DEA)
H. David Sherman
Northeastern University, U.S.A.
Joe Zhu
Worcester Polyteciinic Institute, U.S.A.
Service Productivity IVIanagement
Improving Service Performance using
DATA ENVELOPMENT ANALYSIS (DEA)
Includes D£AFronf/er Software
^ Sprii nger
H. David Sherman Joe Zhu
Northeastern University Worcester Polytechnic Institute
Boston, MA, USA Worcester, MA, USA
Library of Congress Control Number: 2006922584
ISBN-10: 0-387-33211-1 (HE) ISBN-10: 0-387-33231-6 (e-book)
ISBN-13: 978-0387-33211-6 (HE) ISBN-13: 978-0387-33231-4 (e-book)
Printed on acid-free paper.
© 2006 by Springer Science-i-Business Media, LLC
All rights reserved. This work may not be translated or copied in whole or in part without
the written permission of the publisher (Springer Science + Business Media, LLC, 233
Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with
reviews or scholarly analysis. Use in connection with any form of information storage
and retrieval, electronic adaptation, computer software, or by similar or dissimilar
methodology now know or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks and similar terms,
even if the are not identified as such, is not to be taken as an expression of opinion as to
whether or not they are subject to proprietary rights.
Service Productivity Management is an independent publication and is not affiliated with,
nor has it been authorized, sponsored, or otherwise approved by Microsoft Corporation
Printed in the United States of America.
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Contents
Foreword xi
Preface xv
Chapter 1 Management of Service Organization Productivity
1.1. Introduction 1
1.2. Productivity Defined vis-a-vis Effectiveness and Efficiency 2
1.3. Components of Productivity 4
1.4. Taxonomies of Service Organization 9
1.5. Classification by Characteristics of Institution 13
1.6. Other Dimensions of the Service Business Taxonomy 17
1.7. Service Productivity Management Techniques 24
1.7.1. Standard Cost Systems 25
1.7.2. Comparative Efficiency Analysis 28
1.7.3. Ratio Analysis 29
1.7.4. Profit and Return on Investment Measures 31
1.7.5. Zero-base Budgeting 32
1.7.6. Program Budgeting 35
1.7.7. Best Practice Analysis 36
1.7.8. Data Envelopment Analysis (DEA) 38
1.7.9. Peer Review 39
1.7.10. Management Reviews 39
1.7.11. Activity Analysis 40
1.7.12. Process Analysis 44
1.7.13. Staffing Models 44
1.7.14. Balanced Scorecards (BSC) 45
1.8. Conclusion 47
Chapter 2 Data Envelopment Analysis Explained
2.1. Introduction 49
viii Contents
2.2. Basic Efficiency Concepts 51
2.3.Relative Weights and Costs for Inputs and Outputs 55
2.4. Data Envelopment Analysis 57
2.4.1. How DEA Works and How to Interpret the Results 57
2.4.2. The Mathematical Formulations of DEA 63
2.4.3. Solving Envelopment DEA Model in Spreadsheets 70
2.4.4. Solving Multiplier DEA Model in Spreadsheets 85
2.5. Conclusions 88
Chapter 3 DEA Concepts for Managers: Applying and
Managing Productivity with DEA
3.1. Introduction 91
3.2. DEA Efficient and Weakly Efficient: Concepts and Examples... 92
3.3.Two Input—Two Output Example 101
3.4. Interpreting DEA Results 107
3.5. Review of the Capabilities and Limitations of DEA 108
3.6. How Management Can Apply DEA 110
3.7. Output-oriented DEA Model 113
3.8. Conclusion 119
Chapter 4 Solving DEA Using DEAFrontier Software
4.1. Introduction 121
A.2. DEAFrontier 123
4.3. Organize the Data 125
4.4. Run the D£AFr^m/er Software 127
Chapter 5 DEA Model - Extensions
5.1. Introduction 133
5.2.Returns to Scale Frontiers 133
5.3. Non-Constant Returns to scale DEA Models 135
5.4. Returns to Scale Estimation 138
5.5. Restricted Multipliers 144
5.6.Measure-Specific Models 149
5.7. Slack-Based Models 151
5.8. Other DEA Models 154
Chapter 6 Managing Bank Productivity
6.1. Introduction 159
6.2. Applying DEA to Growth Bank 160
6.3. Specifying Resource Inputs and Service Outputs 162
6.4. DEA Branch Productivity Results 164
6.5. Implementing the DEA Findings 168
Service Productivity Management ix
6.6. Conclusions 171
Chapter 7 Quality-Adjusted DEA (Q-DEA)
7.1. Introduction 175
7.2. Incorporating Quality Into DEA Benchmarking 177
7.2.1. Standard DEA Model 180
7.2.2. Quality as an Output in Standard DEA Model 181
7.2.3. Independent Quality and Productivity Dimensions 183
7.2.4. Quality-Adjusted DEA 185
7.3. Q-DEA Benchmarking Application 187
7.4. Conclusions and Future Research 197
Chapter 8 Applying DEA to Health Care Organizations
8.1. Introduction 199
8.2. DEA Applications to Health Care 203
8.2.1.Acute Care General Hospitals 203
8.2.2. Nursing Homes 210
8.2.3. Primary Care Physician Models 212
8.2.4. Hospital Physician Models 213
8.3.Benchmarking Physician Practice Patterns 215
8.3.1. Primary Care Physician (PCP) Resource Utilization 216
8.3.2. Clinical Best Practice Patterns for HMO Physicians 217
8.3.3. Potential Cost Savings 224
8.3.4. Mix of Generalist vs. Specialist PCP Practice Pattern 229
8.3.5. Physician Group Practice Patterns 237
8.4. Conclusions 241
Chapter 9 Government Productivity Management
9.1. Introduction 245
9.2. Key Issues and Findings 246
9.3. Productivity Management for Regional Acquisitions 248
9.3.1.Scope, Objectives, and Performance Evaluation 248
9.3.2. Identification of Outputs and Inputs 252
9.3.3. Compromises in Data Used for CA 253
9.3.4. Time Frame 254
9.3.5. Initial DEA Results 255
9.3.6. Refined DEA Results 258
9.4. A New System of Ratio Analysis to Control CA Productivity .. 264
9.5. Summary of Results of Initial Productivity Review 266
9.6. Subsequent Events and Their Implications 266
9.7. Field Review Findings 267
9.8. Quality of the Purchase Contracting Process 269
Contents
9.9. Alternative Approaches 272
9.10.Conclusions 274
Chapter 10 Multidimensional Quality-of-Life Measure
10.1.Introduction 275
10.2.Urban Quality of Life Analysis with DEA 276
10.3.Quality of Life Measures 279
10.4.Measuring the Quality of Life Across Cities 279
10.5.Conclusions 289
Chapter 11 Hedge Fund Performance Evaluation
U.l.Introduction 291
n.2.Background Information 293
n.3,Data and Methods 296
11.4.Results 298
11.5.Conclusions 307
References 309
Index 319
About the Authors 323
About the CD-ROM 327
Foreword
By William W. Cooper
University of Texas at Austin
As the title suggests, this is a book on uses of DEA (Data
Envelopment Analysis) to evaluate performances of firms in the
service industries. It is more general than this, however, and better
described as a user friendly introduction to DEA w^ith examples in the
service industries that can help a potential user evaluate DEA for
applications that might be of interest. The applications in this book
are accompanied by explanations and advice on what needs to be done
to ensure success in uses of DEA.
As an introductory treatment, the book begins with a review of
established methods that are already available and widely used for
evaluating performance efficiencies and effectiveness. The topics
covered include accounting techniques such as the use of standard
costs with associated "red" or "black variances" that signal deviations
below and above "efficient" performances. The discussions extend to
the use of "balanced score card" approaches to determine the
"effectiveness" of performances relative to goals established for
programs intended to implement a corporate strategy. One
shortcoming of all of these methods is that they tend to be "one at a
time measures" - as is also the case for customary ratio measures
such as "return on cost" or "return on net worth," etc.
By contrast, DEA simultaneously considers all inputs and all
outputs that may be of interest and arrives at an overall efficiency or
effectiveness score. Moreover, this is accomplished by evaluating the
performance of each entity relative to a collection of entities in ways
that extend commonly used "benchmark" procedures. In this way
DEA identifies a subset of entities best designed to serve as
benchmarks for each entity and uses them to evaluate its performance.
This results in overall scores such as "90% efficiency," which means
xii Foreword
that the evaluated entity is 10% short of what it should have been able
to accomplish. However, this overall score is only one aspect of what
is revealed in the DBA solutions. Among other things, the sources and
amounts of inefficiency in each input and output are also revealed so
that a path to achievement of full (100%) efficiency is thereby
obtained.
The mathematics underlying DBA models and their uses is kept to
a minimum in this book. Only one of the several DBA models is
formulated mathematically. The DBA literature refers to this model
as the CCR (Chames, Cooper, Rhodes) version of an "envelopment
model." This name derives from the way the model ^'envelops" the
data in order to locate a frontier where the best (i.e., 100% efficient)
performers are located. This frontier is then used to evaluate the
performances of other entities.
To each such envelopment model there is an associated "dual"
model referred to as the "multiplier" model. This model provides
further information in the form of "weights" assigned to each input
and output. These weights are referred to as "multipliers" in the DBA
literature in order to emphasize that they are not preassigned values
like the weights customarily used in the construction of index number
of prices, productivities or cost, etc. That is, the weights in DBA are
determined from the data by this multiplier model for each of the
entities that is evaluated.
Sherman and Zhu make extensive use of this dual (multiplier)
model to increase the possibility of successful use of DBA. For
example in addition to the efficiency scores, these weights can be
reported for management review where it may be found that the
weight assigned by the model to output A, for example, exceeds the
weight assigned to output B. If this is not satisfactory it can be dealt
with in a manner that does not require management to assign precise
values to these weights. Instead they only need to say that they
believe output B should receive a greater weight than output A. DBA
can be made to take this information into account and then determine
a precise numerical values for a new set of weights. The result of this
recomputation can again be reviewed by management for the
inefficiencies that are then identified. Also identified are new weights
for all inputs and outputs. That is, in general, the changes are not
confined to weights for products A and B but extend to other products
as well. These results provide insights into relations between inputs
and outputs that would not otherwise be apparent.