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.

Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

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.

Performance improvement thought leader Dean Spitzer explains why performance measurement should be less about calculations and analysis and more about the crucial social factors that determine how well the measurements get used. Transforming Performance Measurement presents a breakthrough approach that will not only significantly reduce those dysfunctions, but also promote alignment with business strategy, maximize cross-enterprise integration, and help everyone to work collaboratively to drive value throughout your organization. Spitzer’s "socialization of measurement" process focuses on learning and improvement from measurement, and on the importance of asking such questions as: How well do our measures reflect our business model? How successfully are they driving our strategy? What should we be measuring and not measuring? Are the right people having the right measurement discussions? Performance measurement is a dynamic process that calls for an awareness of the balance necessary between seemingly disparate ideas: the technical and the social aspects of performance measurement. This book gives you assessment tools to gauge where you are now and a roadmap for moving, with little or no disruption, to a more "transformational" and mature measurement system. The book also provides 34 TMAPs, Transformational Measurement Action Plans, which suggest both well-accepted and "emergent" measures (in areas such as marketing, human resources, customer service, knowledge management, productivity, information technology, research and development, costing, and more) that you can use right away. Transforming Performance Measurement tells you not only what to measure, but how to do it -- and in what context -- to make a truly transformational difference in your enterprise.

If your company is adopting world class manufacturing techniques, you'll need new methods of performance measurement to control production variables. In practical terms, this book describes the new methods of performance measurement and how they are used in a changing environment. For manufacturing managers, as well as cost accountants, it provides the theoretical foundation for these innovative methods and is supported by extensive practical examples.

Program Evaluation and Performance Measurement: An Introduction to Practice, Second Edition offers an accessible, practical introduction to program evaluation and performance measurement for public and non-profit organizations, and has been extensively updated since the first edition. Using examples, it covers topics in a detailed fashion, making it a useful guide for students as well as practitioners who are participating in program evaluations or constructing and implementing performance measurement systems. Authors James C. McDavid, Irene Huse, and Laura R. L. Hawthorn guide readers through conducting quantitative and qualitative program evaluations, needs assessments, cost-benefit and cost-effectiveness analyses, as well as constructing, implementing and using performance measurement systems. The importance of professional judgment is highlighted throughout the book as an intrinsic feature of evaluation practice.

How can managers increase their ability to calculate price and risk data for financial instruments while decreasing their dependence on a myriad of specific instrument variants? Wolfgang Schwerdt and Marcelle von Wendland created a simple and consistent way to handle and process large amounts of complex financial data. By means of a practical framework, their approach analyzes market and credit risk exposure of financial instruments and portfolios and calculates risk adjusted performance measures. Its emphasis on standardization yields significant improvements in speed and accuracy.Schwerdt and von Wendland's focus on practical implementation directly addresses limitations imposed by the complex and costly processing time required for advanced risk management models and pricing hundreds of thousands of securities each day. Their many examples and programming codes demonstrate how to use standards to build financial instruments, how to price them, and how to measure the risk and performance of the portfolios that include them.Feature: The authors have designed and implemented a standard for the description of financial instrumentsBenefit: The reader can rely on accurate and valid information about describing financial instrumentsFeature: The authors have developed an approach for pricing and analyzing any financial instrument using a limited set of atomic instrumentsBenefit: The reader can use these instruments to define and set up even very large numbers of financial instruments.Feature: The book builds a practical framework for analysing the market and credit risk exposure of financial instruments and portfoliosBenefit: Readers can use this framework today in their work and identify and measure market and credit risk using a reliable method.

A PRACTICAL, REAL-WORLD GUIDE TO ANALYTICS FOR THE 5 MAJOR SPORTS: FOOTBALL, BASKETBALL, BASEBALL, SOCCER, AND TENNIS GAIN A COMPETITIVE EDGE! This is the first real-world guide to building and using analytical models for measuring and assessing performance in the five major sports: football, basketball, baseball, soccer, and tennis. Unlike books that focus strictly on theory, this book brings together sports measurement and statistical analyses, demonstrating how to examine differences across sports as well as between player positions. This book will provide you with the tools for cutting-edge approaches you can extend to the sport of your choice. Expert Northwestern University data scientist, UC San Diego researcher, and competitive athlete, Lorena Martin shows how to use measures and apply statistical models to evaluate players, reduce injuries, and improve sports performance. You’ll learn how to leverage a deep understanding of each sport’s principles, rules, attributes, measures, and performance outcomes. Sports Performance Measurement and Analytics will be an indispensable resource for anyone who wants to bring analytical rigor to athletic competition: students, professors, analysts, fans, physiologists, coaches, managers, and sports executives alike. All data sets, extensive code, and additional examples are available for download at http://www.ftpress.com/martin/ What are the qualities a person must have to become a world-class athlete? This question and many more can be answered through research, measurement, statistics, and analytics. This book gives athletes, trainers, coaches, and managers a better understanding of measurement and analytics as they relate to sports performance. To develop accurate measures, we need to know what we want to measure and why. There is great power in accurate measures and statistics. Research findings can show us how to prevent injuries, evaluate strengths and weaknesses, improve team cohesion, and optimize sports performance. This book serves many readers. People involved with sports will gain an appreciation for performance measures and analytics. People involved with analytics will gain new insights into quantified values representing physical, physiological, and psychological components of sports performance. And students eager to learn about sports analytics will have a practical introduction to the field. This is a thorough introduction to performance measurement and analytics for five of the world’s leading sports. The only book of its kind, it offers a complete overview of the most important concepts, rules, measurements, and statistics for each sport, while demonstrating applications of real-world analytics. You’ll find practical, state-of-the-art guidance on predicting future outcomes, evaluating an athlete’s market value, and more.

This is the first book to fully introduce a newly developed distance friction minimization (DFM) model, which is one of the new efficiency improvement projection approaches in data envelopment analysis (DEA). The DFM model can produce a most effective solution in efficiency improvement projections for inefficient spatial entities (decision-making units). The book provides a set of fresh contributions to a quantitative assessment of the performance of such policy entities. First it offers a state-of-the art overview of current DEA models and approaches, followed by the operational design of various new types of DEA models, each of them addressing weaknesses in traditional DEA approaches. Then it illustrates the assessment potential of DEA — and its new variants, in particular, the DFM model and subsequent extensions — on the basis of a broadly composed collection of empirical case studies, centering mainly but not exclusively on Japan and other Asian nations.

Businesses are collecting massive amounts of data every day as a way to better understand their processes, competition, and the markets they serve. This data can be used to increase organizational productivity and performance; however, is essential that organizations collecting large data sets have the tools available to them to fully understand the data they are collecting. Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics takes a critical look at methods for enhancing an organization’s operations and day-to-day activities through the effective use of data. Focusing on a variety of applications of predictive analytics within organizations of all types, this critical publication is an essential resource for business managers, data scientists, graduate-level students, and researchers.