Quantitative Models for Performance Evaluation and Benchmarking Data Envelopment Analysis with Spreadsheets - Zhu, Joe.; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2014)
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.
Chapter 1: Data Envelopment Analysis -- Chapter 2: Envelopment DEA Models -- Chapter 3: Multiplier DEA Model -- Chapter 4: DEA Cross Efficiency -- Chapter 5: Slack-Based DEA Models -- Chapter 6: Measure-Specific DEA Models.- Chapter 7: Non-radical DEA Models and DEA with Preference -- Chapter 8: Modeling Undesirable Measures -- Chapter 9: Context-dependent Data Envelopment Analysis -- Chapter 10: Super Efficiency -- Chapter 11: Sensitivity Analysis -- Chapter 12: Benchmarking Models -- Chapter 13: Returns-to-Scale -- Chapter 14: DEA Models for Two-Stage Network Processes -- Chapter 15: Models for Evaluating Supply Chains and Network Structures -- Chapter 16: Congestion.- Chapter 17: Identifying Critical Measures in DEA.- Chapter 18: Interval and Ordinal Data in DEA.- Chapter 19: DEAFrontier Software.
Keywords: Operations research; Decision making; Management science; Industrial engineering; Production engineering; Operations Research/Decision Theory; Operations Research, Management Science; Industrial and Production Engineering