Quantitative Models for Performance Evaluation and Benchmarking: DEA with Spreadsheets and DEA Excel Solver

Citation

Maleyeff, J. (2005), "Quantitative Models for Performance Evaluation and Benchmarking: DEA with Spreadsheets and DEA Excel Solver", Benchmarking: An International Journal, Vol. 12 No. 2, pp. 180-182. https://doi.org/10.1108/14635770510593112

Joe Zhu provides a well‐crafted book on data envelopment analysis (DEA) and its application to performance analysis and benchmarking. The book includes descriptions of many specialized DEA models, examples of their use, and an Excel add‐in program that allows for the implementation of the models. While readers with the requisite technical background will likely find the book useful, many practitioners may find the coverage to be incomprehensible due to its mathematical content. This review describes the book's contents and discusses its effectiveness from a variety of target audience viewpoints.

The book is structured as follows. After reviewing the subject of DEA as applied to performance analysis, Dr Zhu describes various specific performance evaluation and benchmarking scenarios and their corresponding DEA model. The topical coverage includes DEA with preference (chapter 4), context‐dependent DEA analysis (chapter 6), models for evaluating supply chains (chapter 8), congestion (chapter 7), super efficiency (chapter 10), and sensitivity analysis (chapter 11). Dr Zhu follows a consistent pattern of describing the modeling scenario, providing the mathematical derivation of the model, then using a numerical example to illustrate the application of the model. The book ends with a detailed description of the DEA Excel add‐in (included with the book) including instructions on its use. This review will not consider the quality of the software program. But the software would appear to be helpful in providing a means to apply the material. Examples contained in the text also appear on the CD that accompanies the book.

Unlike some operations research books that may present “baby” problems (e.g. linear programming in two dimensions, scheduling two machines) and leave it up to the reader to tackle “real” problems, Dr Zhu's examples tend to be fairly comprehensive. For example, in the section on evaluating supply chain efficiency, he illustrates a comparison of ten supply chain members. It is the incorporation of Excel into the text that allows for better and more practical examples.

The book is written effectively and I did not notice any typos, misspellings, or grammatical mistakes. The material is visually appealing, including the placement and frequency of formulas and graphical displays. The table of contents contains sufficient detail, including a comprehensive list of figures and tables, many of which are duplicated on the accompanying CD. The index appears to be less detailed compared to technical books of similar scope. An instructor contemplating its use as a course textbook may find the lack of end‐of‐chapter problem sets to be problematic. However, the absence of problem sets may be offset by the availability of the DEA analysis software.

When writing a book that describes technical methods intended to solve management problems, an author must be careful in approaching the subject from the point‐of‐view of a target audience. Audience possibilities for this book include performance managers, internal analysts, external consultants, or academics interested in DEA as a field of study. In the book's preface, Dr Zhu fails to explicitly mention his target audience. He does, however, state the importance of the material to managers and suggests that this group would benefit from DEA. After reading the preface, I initially assumed that management practitioners were the intended audience.

Irregardless of his intention, Dr Zhu's book includes jargon and mathematical details that would only be accessible to readers with significant background in operations research techniques (mainly mathematical programming), along with some prior background in DEA. Specifically, I would expect a reader to possess at least a MS degree in operations research (or a similar mathematically‐based degree). The required technical sophistication extends to the example applications, where the Excel output uses jargon and notation consistent with the mathematical derivations. I would have preferred seeing more practical titling in the spreadsheets.

Dr Zhu is included as an author or co‐author in at least 22 of the 94 references listed in the book. Many chapters appear to be derived directly from his articles. In fact, reviewing this book often felt like the peer review of a technical paper for academics rather than a book review for practitioners. At times, the book reads like a collection of articles on the subject of DEA. The book remains coherent none the less, being presented with a similar style and viewpoint. The book avoids the standard redundancies and gaps that would occur in a typical collection of articles from different authors.

As an academic and business consultant with a PhD in industrial engineering and operations research (coincidently from the same program as the author, but about 20 years earlier), I can confidently state that I have rarely met a performance manager who would find this book to be readable. On the other hand, an academic, performance analyst, or consultant already familiar with DEA and having a strong technical background would likely appreciate the comprehensive development that underlies the application of DEA to performance benchmarking. I believe, however, that most practitioners would be willing to sacrifice technical detail, theoretical development, and scope of coverage for a few models that could be applied in more of a cookbook‐like fashion to problems of performance management and benchmarking typically faced by the business managers. Then again, I would think that any potential user of DEA will value the accompanying spreadsheets, which preclude the necessity to program or otherwise create computer‐based mechanisms for applying DEA models.

This review would not be complete without a comment on the book's title. The book is included in Kluwer's international series in operations research and management science. The book's main title, Quantitative Models for Performance Evaluation and Benchmarking, is shown in the publisher's catalog. Unfortunately, the subtitle, Data Envelopment Analysis with Spreadsheets and DEA Excel Solver, is much more indicative of the contents of the book. I believe that many purchasers may be misled into thinking that the text covers a multitude of different models for performance evaluation and benchmarking, not just DEA.

To conclude, the audience of Dr Zhu's book may be quite limited and the title can easily mislead some into purchasing a book that is not useful. But, qualified individuals who are looking for effective ways to evaluate the performance of an organization would find this book to be helpful. It would also make an excellent textbook for a graduate course in DEA for Masters and PhD students in operations research, management science, decision sciences, or applied mathematics. But if used, the instructor would bear the burden of developing practice problems for student assignments.