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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 col

Benchmarking nonprofit performance can be challenging, constraining the ways nonprofits can use operational data to learn from each other and highlight organizational progress. Valid output or outcome data are scarce, and there is much to learn about measuring performance even when these data are available. Data envelopment analysis (DEA) is a mathematical linear programming technique that can be used to measure performance in a way that not only produces valid efficiency scores but also allows for benchmarking nonprofits with similar service missions. Using financial and production data from the nonprofit transportation sector, we walk through an example to explore DEA as a tool to measure and benchmark nonprofits. We conclude with suggestions for practice, emphasizing that DEA is useful for stakeholders looking to benchmark nonprofits by underscoring production and performance.

AbstractData envelopment analysis is a linear programming–based method that has clear advantages over competing approaches. However, its own limitations should not be overlooked.

This second edition systematically presents the underlying theory, model development, and applications of network Data Envelopment Analysis (DEA). It discusses the models used to measure the efficiency of systems in specific network structures and introduces readers to the latest applications. It demonstrates how the network DEA approach helps identify and manage the specific components that cause inefficiencies in the overall system. The existing models for measuring the efficiency of systems in specific network structures are also discussed, and the relationships between system efficiency and component efficiency are explored. Moreover, the book provides an advanced exposition on performance evaluation of systems with network structures. It explores the networked nature of most production and operation systems, and explains why network analyses are necessary. Accordingly, the book will inspire new research and applications based on the state of the art. In this new edition, the latest research advances and discoveries are discussed. Two new chapters on Linkage Efficiency and External and Internal Evaluations have been added. This book is mainly aimed at researchers and graduate students who are interested in performance evaluation, DEA, and multi-criteria decision analysis. Practitioners who want to measure the performance of production, operation, or any type of decision-making units will also find it useful.