The Economics Of Benchmarking

The author presents the techniques and theory of benchmarking to explain how performance indices and rankings are developed and used to improve efficiency, productivity and profitability. The only software required is Microsoft Excel.

"In this book, Peter Bogetoft - THE expert on the theory and practice of benchmarking - provides an in–depth yet very accessible and readable explanation of the best way to do benchmarking, starting from the ground up." Rick Antle William S. Beinecke Professor of Accounting, Yale School of Management CFO, Compensation Valuation, Inc. "I highly recommend this well-written and comprehensive book on measuring and managing performance. Dr. Bogetoft summarizes the fundamental mathematical concepts in an elegant, intuitive, and understandable way." Jon A. Chilingerian Professor, Brandeis University and INSEAD "Bogetoft gives in his book Performance Benchmarking an excellent introduction to the methodological basis of benchmarking." Christian Parbøl Director, DONG Energy "This book is the primer on benchmarking for performance management." Albert Birck Business Performance Manager, Maersk Oil "This excellent book provides a non technical introduction for performance management." Misja Mikkers, Director, Dutch Health Care Authority "With this very well written and comprehensive introduction to the many facets of benchmarking in hand, organizations have no excuse for not applying the best and cost effective benchmarking methods in their performance assessments." Stig P. Christensen Senior R&D Director, COWI

With growing demands for increased operational efficiency and process improvement in organizations of all sizes, more and more companies are turning to benchmarking as a means of setting goals and measuring performance against the products, services and practices of other organizations that are recognized as leaders. The Benchmarking Book is an indispensable guide to process improvement through benchmarking, providing managers, practitioners and consultants with all the information needed to carry out effective benchmarking studies. Covering everything from essential theory to important considerations such as project management and legal issues, The Benchmarking Book is the ideal step-by-step guide to assessing and improving your company’s processes and performance through benchmarking.

This book unifies and extends the definition and measurement of economic efficiency and its use as a real-life benchmarking technique for actual organizations. Analytically, the book relies on the economic theory of duality as guiding framework. Empirically, it shows how the alternative models can be implemented by way of Data Envelopment Analysis. An accompanying software programmed in the open-source Julia language is used to solve the models. The package is a self-contained set of functions that can be used for individual learning and instruction. The source code, associated documentation, and replication notebooks are available online. The book discusses the concept of economic efficiency at the firm level, comparing observed to optimal economic performance, and its decomposition according to technical and allocative criteria. Depending on the underlying technical efficiency measure, economic efficiency can be decomposed multiplicatively or additively. Part I of the book deals with the classic multiplicative approach that decomposes cost and revenue efficiency based on radial distance functions. Subsequently, the book examines how these partial approaches can be expanded to the notion of profitability efficiency, considering both the input and output dimensions of the firm, and relying on the generalized distance function for the measurement of technical efficiency. Part II is devoted to the recent additive framework related to the decomposition of economic inefficiency defined in terms of cost, revenue, and profit. The book presents economic models for the Russell and enhanced graph Russell measures, the weighted additive distance function, the directional distance function, the modified directional distance function, and the Hölder distance function. Each model is presented in a separate chapter. New approaches that qualify and generalize previous results are also introduced in the last chapters, including the reverse directional distance function and the general direct approach. The book concludes by highlighting the importance of benchmarking economic efficiency for all business stakeholders and recalling the main conclusions obtained from many years of research on this topic. The book offers different alternatives to measure economic efficiency based on a set of desirable properties and advises on the choice of specific economic efficiency models.

This book provides a detailed introduction to the theoretical and methodological foundations of production efficiency analysis using benchmarking. Two of the more popular methods of efficiency evaluation are Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA), both of which are based on the concept of a production possibility set and its frontier. Depending on the assumed objectives of the decision-making unit, a Production, Cost, or Profit Frontier is constructed from observed data on input and output quantities and prices. While SFA uses different maximum likelihood estimation techniques to estimate a parametric frontier, DEA relies on mathematical programming to create a nonparametric frontier. Yet another alternative is the Convex Nonparametric Frontier, which is based on the assumed convexity of the production possibility set and creates a piecewise linear frontier consisting of a number of tangent hyper planes. Three of the papers in this volume provide a detailed and relatively easy to follow exposition of the underlying theory from neoclassical production economics and offer step-by-step instructions on the appropriate model to apply in different contexts and how to implement them. Of particular appeal are the instructions on (i) how to write the codes for different SFA models on STATA, (ii) how to write a VBA Macro for repetitive solution of the DEA problem for each production unit on Excel Solver, and (iii) how to write the codes for the Nonparametric Convex Frontier estimation. The three other papers in the volume are primarily theoretical and will be of interest to PhD students and researchers hoping to make methodological and conceptual contributions to the field of nonparametric efficiency analysis.

This report presents an in-depth comparative study of Industry-Science Relationships (ISR) in France and the United Kingdom and a special chapter on Japan.

This Handbook takes an econometric approach to the foundations of economic performance analysis. The focus is on the measurement of efficiency, productivity, growth and performance. These concepts are commonly measured residually and difficult to quantify in practice. In real-life applications, efficiency and productivity estimates are often quite sensitive to the models used in the performance assessment and the methodological approaches adopted by the analysis. The Palgrave Handbook of Performance Analysis discusses the two basic techniques of performance measurement – deterministic benchmarking and stochastic benchmarking – in detail, and addresses the statistical techniques that connect them. All chapters include applications and explore topics ranging from the output/input ratio to productivity indexes and national statistics.

This book covers recent advances in efficiency evaluations, most notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) methods. It introduces the underlying theories, shows how to make the relevant calculations and discusses applications. The aim is to make the reader aware of the pros and cons of the different methods and to show how to use these methods in both standard and non-standard cases. Several software packages have been developed to solve some of the most common DEA and SFA models. This book relies on R, a free, open source software environment for statistical computing and graphics. This enables the reader to solve not only standard problems, but also many other problem variants. Using R, one can focus on understanding the context and developing a good model. One is not restricted to predefined model variants and to a one-size-fits-all approach. To facilitate the use of R, the authors have developed an R package called Benchmarking, which implements the main methods within both DEA and SFA. The book uses mathematical formulations of models and assumptions, but it de-emphasizes the formal proofs - in part by placing them in appendices -- or by referring to the original sources. Moreover, the book emphasizes the usage of the theories and the interpretations of the mathematical formulations. It includes a series of small examples, graphical illustrations, simple extensions and questions to think about. Also, it combines the formal models with less formal economic and organizational thinking. Last but not least it discusses some larger applications with significant practical impacts, including the design of benchmarking-based regulations of energy companies in different European countries, and the development of merger control programs for competition authorities.

This book introduces readers to benchmarking techniques in the stochastic environment, primarily stochastic data envelopment analysis (DEA), and provides stochastic models in DEA for the possibility of variations in inputs and outputs. It focuses on the application of theories and interpretations of the mathematical programs, which are combined with economic and organizational thinking. The book’s main purpose is to shed light on the advantages of the different methods in deterministic and stochastic environments and thoroughly prepare readers to properly use these methods in various cases. Simple examples, along with graphical illustrations and real-world applications in industry, are provided for a better understanding. The models introduced here can be easily used in both theoretical and real-world evaluations. This book is intended for graduate and PhD students, advanced consultants, and practitioners with an interest in quantitative performance evaluation.

Benchmarks OECD Countries' industry-science relationships, identifies relevant good practices in managing the interface between public research and the business sector, and provides a framework for monitoring and assessing evolving policies in the area.