In companies that produce goods and services, productivity and efficiency improvements are a constant challenge. This book reviews the differences between productivity and efficiency. It proposes a new method and makes available a computational tool for implementation that contributes to facilitating the use of Data Envelopment Analysis (DEA). The book presents a discussion about productivity and efficiency, illustrating the potentials of use and conceptual differences. It covers the concepts and techniques for analysis of productivity and efficiency, analyzing critical benefits and limitations, explains in detail how to use DEA for analysis, provides innovative methods for using DEA, offers a free online computer tool with a direction guide, shows real empirical applications, and covers other techniques that can be used to complement the analysis performed. The book is for professionals, managers, consultants, students working and taking courses in productive systems of goods and services. Ancillary materials include a free online computer tool to operationalize the concepts and methods proposed in the book, a guide on how to use the method and the software developed for the DEA application. Solutions manual, instructor’s manual, PowerPoint slides, and figure slides also will be available upon qualified adoption.

Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References has been carefully designed by the authors to provide a systematic introduction to DEA and its uses as a multifaceted tool for evaluating problems in a variety of contexts. The authors have been involved in DEA's development from the beginning. William Cooper (with Abraham Charnes and Edwardo Rhodes) is a founder of DEA. Lawrence Seiford and Kaoru Tone have been actively involved as researchers and practitioners from its earliest beginnings. All have been deeply involved in uses of DEA in practical applications as well as in the development of its basic theory and methodologies. The result is a textbook grounded in authority, experience and substance.

This book presents the proceedings of the 3rd International Joint Conference – ICIEOM-ADINGOR-IISE-AIM-ASEM (IJC2017) “XXIII International Conference on Industrial Engineering and Operations Management”, “International ADINGOR Conference 2017”, “International IISE Conference 2017”, “International AIM Conference 2017” and “International ASEM Conference 2017”, which took place at UPV (Universitat Politècnica de València) from July 6th to 7th, 2017. This joint conference is the result of an agreement between ABEPRO (Associação Brasileira de Engenharia de Produção), ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización), IISE (Institute of Industrial and Systems Engineers), AIM (European Academy for Industrial Management) and ASEM (American Society for Engineering Management). Consisting of papers on new global perspectives on industrial engineering and management, the book offers an interdisciplinary view of industrial engineering and management. The topics covered include: strategy and entrepreneurship, quality and product management, modelling and simulation, knowledge and project management, logistics, as well as production, information and service systems.

1 DATA ENVELOPMENT ANALYSIS Data Envelopment Analysis (DEA) was initially developed as a method for assessing the comparative efficiencies of organisational units such as the branches of a bank, schools, hospital departments or restaurants. The key in each case is that they perform feature which makes the units comparable the same function in terms of the kinds of resource they use and the types of output they produce. For example all bank branches to be compared would typically use staff and capital assets to effect income generating activities such as advancing loans, selling financial products and carrying out banking transactions on behalf of their clients. The efficiencies assessed in this context by DEA are intended to reflect the scope for resource conservation at the unit being assessed without detriment to its outputs, or alternatively, the scope for output augmentation without additional resources. The efficiencies assessed are comparative or relative because they reflect scope for resource conservation or output augmentation at one unit relative to other comparable benchmark units rather than in some absolute sense. We resort to relative rather than absolute efficiencies because in most practical contexts we lack sufficient information to derive the superior measures of absolute efficiency. DEA was initiated by Charnes Cooper and Rhodes in 1978 in their seminal paper Chames et al. (1978). The paper operationalised and extended by means of linear programming production economics concepts of empirical efficiency put forth some twenty years earlier by Farrell (1957).

This handbook represents a milestone in the progression of Data Envelopment Analysis (DEA). Written by experts who are often major contributors to DEA theory, it includes a collection of chapters that represent the current state-of-the-art in DEA research. Topics include distance functions and their value duals, cross-efficiency measures in DEA, integer DEA, weight restrictions and production trade-offs, facet analysis in DEA, scale elasticity, benchmarking and context-dependent DEA, fuzzy DEA, non-homogenous units, partial input-output relations, super efficiency, treatment of undesirable measures, translation invariance, stochastic nonparametric envelopment of data, and global frontier index. Focusing only on new models/approaches of DEA, the book includes contributions from Juan Aparicio, Mette Asmild, Yao Chen, Wade D. Cook, Juan Du, Rolf Färe, Julie Harrison, Raha Imanirad, Andrew Johnson, Chiang Kao, Abolfazl Keshvari, Timo Kuosmanen, Sungmook Lim, Wenbin Liu, Dimitri Margaritis, Reza Kazemi Matin, Ole B. Olesen, Jesus T. Pastor, Niels Chr. Petersen, Victor V. Podinovski, Paul Rouse, Antti Saastamoinen, Biresh K. Sahoo, Kaoru Tone, and Zhongbao Zhou.

In a relatively short period of time, data envelopment analysis (DEA) has grown into a powerful analytical tool for measuring and evaluating performance. DEA is computational at its core and this book is one of several Springer aim to publish on the subject. This work deals with the micro aspects of handling and modeling data issues in DEA problems. It is a handbook treatment dealing with specific data problems, including imprecise data and undesirable outputs.