Quantitative Models for Performance Evaluation and Benchmarking : Data Envelopment Analysis with Spreadsheets

Joe Zhu

ISBN 10: 3319374389 / ISBN 13: 9783319374383

Published by Springer International Publishing, Springer International Publishing, 2017

About this Item

Druck auf Anfrage Neuware - Printed after ordering - The author is one of the prominent researchers in the field of Data Envelopment Analysis (DEA), a powerful data analysis tool that can be used in performance evaluation and benchmarking. This book is based upon the author's years of research and teaching experiences.It is difficult to evaluate an organization's performance when multiple performance metrics are present. The difficulties are further enhanced when the relationships among the performance metrics are complex and involve unknown tradeoffs. This book introduces Data Envelopment Analysis (DEA) as a multiple-measure performance evaluation and benchmarking tool. 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 tothe capacity of Excel Solver. It is an extremely powerful tool that can assist decision-makers in benchmarking and analyzing complex operational performance issues in manufacturing organizations as well as evaluating processes in banking, retail, franchising, health care, public services and many other industries.

Seller Inventory # 9783319374383

Bibliographic Details

• Title: Quantitative Models for Performance Evaluation and Benchmarking : Data Envelopment Analysis with Spreadsheets
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 3319374389
• ISBN 13: 9783319374383
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

The author is one of the prominent researchers in the field of Data Envelopment Analysis (DEA), a powerful data analysis tool that can be used in performance evaluation and benchmarking. This book is based upon the author’s years of research and teaching experiences.

It is difficult to evaluate an organization’s performance when multiple performance metrics are present. The difficulties are further enhanced when the relationships among the performance metrics are complex and involve unknown tradeoffs. This book introduces Data Envelopment Analysis (DEA) as a multiple-measure performance evaluation and benchmarking tool. 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 tothe capacity of Excel Solver. It is an extremely powerful tool that can assist decision-makers in benchmarking and analyzing complex operational performance issues in manufacturing organizations as well as evaluating processes in banking, retail, franchising, health care, public services and many other industries.

About the Author

Professor Joe Zhu is one of the prominent researchers in the field of Data Envelopment Analysis (DEA). His research interests are in the areas of operations and business analytics, productivity modeling, and performance evaluation and benchmarking. He has published over 100 articles in peer-reviewed journals including Operations Research, Sloan Management Review, European Journal of Operational Research, Journal of the Operational Research Society, Naval Research Logistics, IIE Transactions, Journal of Banking and Finance, OMEGA, and others. He is an Area Editor of OMEGA, an Associate Editor of INFOR, and the Associate Series Editor of Springer’s International Series in Operations Research and Management Science.

He is a Japan Society for Promotion of Science (JSPS) fellow and a William Evans Visiting Fellow  of University of Otago, New Zealand. His research has been supported by KPMG Foundation, National Institute of Health, and Department of Veterans Affairs.