Preface

This handbook complements the second edition of the Handbook on Data Envelopment Analysis (Cooper et al. 2011, Springer). Data envelopment analysis (DEA) is a “data-oriented” approach for evaluating the performance of a set of entities called Decision Making Units (DMUs) whose performance is categorized by multiple metrics. These performance metrics are indicated as inputs and outputs under DEA. Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management where a set of measures is selected to benchmark the performance of manufacturing and service operations. In the circumstance of benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a “production frontier,” but rather lead to a “best-practice frontier” (Cook et al. 2014). Since the publication of the second edition of Handbook on Data Envelopment Analysis, there has been a significant amount of research on DEA methodology. As pointed out in a citation-based DEA survey by Liu et al. (2013), it is expected that the literature will grow to at least double its current size. With the recent publication of Data Envelopment Analysis: A Handbook of Modeling Internal Structures and Networks (Cook and Zhu 2014) written by experts on models and applications of DEA dealing with network and internal DMU structures, the current handbook is intended to represent another milestone in the progression of DEA. Written by experts, who are often major contributors to the DEA theory, it includes a collection of 16 chapters that represent the current state-of-the-art DEA research. Chapter 1, by Färe, Grosskopf and Margaritis, provides an overview of the dual measurement of efficiency by means of distance functions and their value duals, the profit, revenue and cost functions. Chapter 2, by Cook and Zhu, discusses cross-efficiency measures in DEA. While DEA has been proven an effective approach in identifying best practice frontiers, its flexibility in weighting multiple performance measures (inputs and outputs) and its nature of self-evaluation have been criticized. The cross efficiency method is developed as a DEA extension to rank DMUs and as a peer-evaluation approach. To complement Chap. 2, Lim and Zhu discuss how to use Variable Returns to Scale (VRS) models to develop cross efficiency in Chap. 3.