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This handbook compiles state-of-the-art empirical studies and applications using Data Envelopment Analysis (DEA). It includes a collection of 18 chapters written by DEA experts. Chapter 1 examines the performance of CEOs of U.S. banks and thrifts. Chapter 2 describes the network operational structure of transportation organizations and the relative network data envelopment analysis model. Chapter 3 demonstrates how to use different types of DEA models to compute total-factor energy efficiency scores with an application to energy efficiency. In chapter 4, the authors explore the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks, and chapter 5 provides a brief review of previous applications of DEA to the professional baseball industry, followed by two detailed applications to Major League Baseball. Chapter 6 examines efficiency and productivity of U.S. property-liability (P-L) insurers using DEA, while chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components. Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education. Chapter 13 measures the relative performance of New York State school districts in the 2011-2012 academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U.S. agriculture. Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period 2000 to 2014.

Business. --- Operations research. --- Decision making. --- Management science. --- Industrial engineering. --- Production engineering. --- Business and Management. --- Operation Research/Decision Theory. --- Operations Research, Management Science. --- Industrial and Production Engineering. --- Data envelopment analysis. --- Industrial efficiency --- Industrial productivity --- Mathematical models. --- Efficiency, Industrial --- DEA (Data envelopment analysis) --- Industrial management --- Linear programming --- Multivariate analysis --- Operations Research/Decision Theory. --- Management engineering --- Simplification in industry --- Engineering --- Value analysis (Cost control) --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Manufacturing engineering --- Process engineering --- Mechanical engineering --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Statistical decision --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management decisions --- Choice (Psychology) --- Decision making

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Based upon the author’s years of research and teaching experiences, this 3rd Edition introduces Data Envelopment Analysis (DEA) as a data analysis tool for multiple-measure performance evaluation and benchmarking. The focus of performance evaluation and benchmarking is shifted from characterizing performance in terms of single measures to evaluating performance as a multidimensional systems perspective. Conventional and new DEA approaches are presented and discussed using Excel spreadsheets — one of the most effective ways to analyze and evaluate decision alternatives. The user can easily develop and customize new DEA models based upon these spreadsheets. DEA models and approaches are presented to deal with performance evaluation problems in a variety of contexts. For example, a context-dependent DEA measures the relative attractiveness of similar operations/processes/products. Sensitivity analysis techniques can be easily applied, and used to identify critical performance measures. Two-stage network efficiency models can be utilized to study performance of supply chain. DEA benchmarking models extend DEA’s ability in performance evaluation. Various cross efficiency approaches are presented to provide peer evaluation scores. This book also provides an easy-to-use DEA software — DEAFrontier. This DEAFrontier is an Add-In for Microsoft® Excel and provides a custom menu of DEA approaches. This version of DEAFrontier is for use with Excel 97-2013 under Windows and can solve up to 50 DMUs, subject to the capacity of Excel Solver.

Methodology of economics --- Economics --- Operational research. Game theory --- Mathematical statistics --- Engineering sciences. Technology --- Planning (firm) --- Business management --- Business economics --- financieel management --- bedrijfseconomie --- economie --- mathematische modellen --- econometrie --- operationeel onderzoek --- ingenieurswetenschappen --- Operations research. --- Industrial engineering. --- Operations Research/Decision Theory. --- Operations Research, Management Science. --- Industrial and Production Engineering. --- Decision making. --- Management science. --- Production engineering. --- Management engineering --- Simplification in industry --- Engineering --- Value analysis (Cost control) --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Manufacturing engineering --- Process engineering --- Mechanical engineering --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Statistical decision --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management decisions --- Choice (Psychology) --- Decision making

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MODELING PERFORMANCE MEASUREMENT: Applications and Implementation Issues in DEA presents unified results from authors’ recent DEA research. These new DEA methodology and techniques are developed in application-driven scenarios that go beyond the identification of the best-practice frontier and seek solutions to aid managerial decisions. These new DEA developments are well-grounded in real world applications. Both DEA researchers and practitioners will find this book helpful. Theory is provided for DEA researchers for further development and possible extensions. However, it should also be mentioned that each theory is presented in practical terms with numerical examples, simple real management cases and verbal descriptions. It is felt that these concrete examples will be of value to researchers, students, and practitioners. This book also provides an easy-to-use DEA software — DEAFrontier (www.deafrontier.com). This DEA software is an Add-In for Microsoft Excel and provides a custom menu of DEA approaches The DEAFrontier does not set limit on the number of units, inputs or outputs. With the capacity of Excel Solver, the software can deal with large sized performance evaluation tasks MODELING PERFORMANCE MEASUREMENT: Applications and Implementation Issues in DEA… - addresses advanced/new DEA methodology and techniques that are developed for modeling unique and new performance evaluation issues, - presents new DEA methodology and techniques via discussions on how to solve managerial problems, such as business process reengineering, benchmarking and continuous improvement. It can be used as a text/case book for graduate courses on operations management and covers many DEA applications such as highway maintenance, technology implementation, a variety of R&D and allocation cost scenarios, use of DEA in a MCDM context, etc. - provides an easy-to-use DEA software — DEAFrontier (www.deafrontier.com) which is an excellent tools for both DEA researchers and practitioners. This DEA software is an Add-In for Microsoft Excel and provides a custom menu of DEA approaches.

Data envelopment analysis. --- Performance --- Measurement. --- DEA (Data envelopment analysis) --- Linear programming --- Multivariate analysis --- Operations research. --- Econometrics. --- Business. --- Engineering economy. --- Production management. --- Operations Research/Decision Theory. --- Business and Management, general. --- Engineering Economics, Organization, Logistics, Marketing. --- Operations Management. --- Manufacturing management --- Industrial management --- Economy, Engineering --- Engineering economics --- Industrial engineering --- Trade --- Economics --- Management --- Commerce --- Economics, Mathematical --- Statistics --- Operational analysis --- Operational research --- Management science --- Research --- System theory --- Decision making. --- Management science. --- Engineering economics. --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management decisions --- Choice (Psychology) --- Problem solving --- Quantitative business analysis --- Operations research --- Statistical decision --- Decision making

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In a relatively short period of time, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. It has been successfully applied to a whole variety of problems in many different contexts worldwide. The analysis of an array of these problems has been resistant to other methodological approaches because of the multiple levels of complexity that must be considered. Several examples of multifaceted problems in which DEA analysis has been successfully used are: (1) maintenance activities of US Air Force bases in geographically dispersed locations, (2) policy force efficiencies in the United Kingdom, (3) branch bank performances in Canada, Cyprus, and other countries and (4) the efficiency of universities in performing their education and research functions in the U.S., England, and France. In addition to localized problems, DEA applications have been extended to performance evaluations of 'larger entities' such as cities, regions, and countries. These extensions have a wider scope than traditional analyses because they include "social" and "quality-of-life" dimensions which require the modeling of qualitative and quantitative data in order to analyze the layers of complexity for an evaluation of performance and to provide solution strategies. DEA is computational at its core and this book by Zhu and Cook deals with the micro aspects of handling and modeling data issues in modeling DEA problems. DEA's use has grown with its capability of dealing with complex "service industry" and the "public service domain" types of problems that require modeling both qualitative and quantitative data. It is a handbook treatment dealing with specific data problems including the following: (1) imprecise data, (2) inaccurate data, (3) missing data, (4) qualitative data, (5) outliers, (6) undesirable outputs, (7) quality data, (8) statistical analysis, (9) software and other data aspects of modeling complex DEA problems. In addition, the book demonstrates how to visualize DEA results when the data is more than 3-dimensional, and how to identify efficiency units quickly and accurately.

Data envelopment analysis. --- Performance --- Measurement. --- DEA (Data envelopment analysis) --- Linear programming --- Multivariate analysis --- Operations research. --- Mathematical optimization. --- Public finance. --- Econometrics. --- Business. --- Operations Research/Decision Theory. --- Optimization. --- Public Economics. --- Business and Management, general. --- Operations Research, Management Science. --- Trade --- Economics --- Management --- Commerce --- Industrial management --- Economics, Mathematical --- Statistics --- Cameralistics --- Public finance --- Currency question --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Public finances --- Decision making. --- Management science. --- Quantitative business analysis --- Problem solving --- Statistical decision --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management decisions --- Choice (Psychology) --- Decision making

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This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others. Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data. Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.

Operations research. --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Decision making. --- Economic theory. --- Statistics . --- Operations Research/Decision Theory. --- Economic Theory/Quantitative Economics/Mathematical Methods. --- Statistical Theory and Methods. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Economic theory --- Political economy --- Social sciences --- Economic man --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Decision making