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Managers are often under great pressure to improve the performance of their organizations. To improve performance, one needs to constantly evaluate operations or processes related to producing products, providing services, and marketing and selling products. Performance evaluation and benchmarking are a widely used method to identify and adopt best practices as a means to improve performance and increase productivity, and are particularly valuable when no objective or engineered standard is available to define efficient and effective performance. For this reason, benchmarking is often used in managing service operations, because service standards (benchmarks) are more difficult to define than manufacturing standards. Benchmarks can be established but they are somewhat limited as they work with single measurements one at a time. It is difficult to evaluate an organization’s performance when there are multiple inputs and outputs to the system. The difficulties are further enhanced when the relationships between the inputs and the outputs are complex and involve unknown tradeoffs. It is critical to show benchmarks where multiple measurements exist. The current book introduces the methodology of data envelopment analysis (DEA) and its uses in performance evaluation and benchmarking under the context of multiple performance measures.
Basic DEA models; Measure-specific DEA models; Returns-to-scale; DEA with preference; Modeling undesirable measures; Context-dependent Data Envelopment analysis; Benchmarking models; Models for evaluating value chains; Congestion; Super efficiency; Sensitivity analysis and its uses; DEA excel solver.
Managers are often under great pressure to improve the performance of their organizations. To improve performance, one needs to constantly evaluate operations or processes related to producing products, providing services, and marketing and selling products. Performance evaluation and benchmarking are a widely used method to identify and adopt best practices as a means to improve performance and increase productivity, and are particularly valuable when no objective or engineered standard is available to define efficient and effective performance. For this reason, benchmarking is often used in managing service operations, because service standards (benchmarks) are more difficult to define than manufacturing standards. Benchmarks can be established but they are somewhat limited as they work with single measurements one at a time. It is difficult to evaluate an organization's performance when there are multiple inputs and outputs to the system. The difficulties are further enhanced when the relationships between the inputs and the outputs are complex and involve unknown tradeoffs. It is critical to show benchmarks where multiple measurements exist. The current book introduces the methodology of data envelopment analysis (DEA) and its uses in performance evaluation and benchmarking under the context of mUltiple performance measures.
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 to the 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.
This book constitutes the proceedings of the 18th International Conference on Quantitative Evaluation Systems, QEST 2021, held in Paris, France, in August 2021. The 21 full papers and 2 short papers presented together with 2 keynote papers were carefully reviewed and selected from 47 submissions. The papers are organized in the following topics: probabilistic model checking; quantitative models and metamodels: analysis and validation; queueing systems; learning and verification; simulation; performance evaluation; abstractions and aggregations; and stochastic models.
This volume constitutes the proceedings of the Fifth International Conference on Multi-Objective Programming and Goal programming held in Nara Japan 2002. The book is dedicated to multi-objective methods in decision making. One half of the book is devoted to theoretical aspects, covering a broad range of multi-objective methods such as multiple linear programming, fuzzy goal programming, data envelopment analysis, game theory, and dynamic programming. Readers interested in practical applications, will find in the remaining parts a variety of approaches applied in numerous fields including production planning, logistics, marketing, and finance.
In today's highly competitive and rapidly evolving global landscape, the quest for efficiency has become a crucial factor in determining the success of organizations across various industries. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency is a comprehensive guide that delves into the powerful mathematical tool of DEA, is designed to assess the relative efficiency of decision-making units (DMUs), and provides valuable insights for performance improvement. This book presents a systematic overview of DEA models and techniques, from fundamental concepts to advanced methods, showcasing their practical applications through real-world examples and case studies. Catering to a broad audience, this book is designed for students, researchers, consultants, decision-makers, and enthusiasts in the field of efficiency analysis and performance measurement. Consultants and practitioners will gain practical insights for applying DEA in various contexts, and decision-makers will be equipped to make informed decisions for maximizing efficiency. Additionally, individuals with a general interest in data analysis and performance measurement will find this book accessible and informative. This book covers a wide range of topics, including mathematical foundations of DEA, DEA models and variations, DEA efficiency and productivity measures, DEA applications in various industries such as healthcare, finance, supply chain management, environmental management, education management, and public sector management.
This volume addresses advanced DEA methodology and techniques developed for modeling unique new performance evaluation issues. Many numerical examples, real management cases and verbal descriptions make it very valuable for researchers and practitioners.
Advances in Pacific Basin Business, Economics and Finance (APBBEF) is a peer-reviewed series designed to focus on interdisciplinary research in finance, economics, and management among Pacific Rim countries.
"Combat Modeling" is a systematic learning resource and reference text for the quantitative analysis of combat. After a brief overview, authors Washburn and Kress present individual chapters on shooting without feedback; shooting with feedback; target defense; attrition models; game theory and wargames; search; unmanned aerial vehicles; and terror and insurgency. Three appendices provide a review of basic probability concepts, probability distributions, and Markov models; an introduction to optimization models; and a discussion of Monte-Carlo simulations. Drawing on their many years of experience at the Naval Postgraduate School in Monterey, California, Washburn and Kress have created a reference that will provide the tools and techniques for analysts involved in the underpinnings of combat decisions. This is a book that can be used as a military manual, reference book, and textbook for military courses on this vital subject.