Quantitative models for performance evaluation and benchmarking : data envelopment analysis with spreadsheets

Author(s)

    • Zhu, Joe

Bibliographic Information

Quantitative models for performance evaluation and benchmarking : data envelopment analysis with spreadsheets

Joe Zhu

(International series in operations research & management science, 126)

Springer, c2009

2nd ed

  • : hbk

Available at  / 7 libraries

Search this Book/Journal

Note

Includes bibliographical references and indexes

Description and Table of Contents

Description

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.

Table of Contents

Envelopment DEA Models.- Multiplier and Slack-based Models.- Measure-specific DEA Models.- Non-radial DEA Models and DEA with Preference.- Modeling Undesirable Measures.- Context-dependent Data Envelopment Analysis.- Benchmarking Models.- Models for Evaluating Supply Chains.- Congestion.- Super Efficiency.- Sensitivity Analysis.- Identifying Critical Measures in DEA.- Returns-to-Scale.- DEA Models for Two-Stage Processes.

by "Nielsen BookData"

Related Books: 1-1 of 1

Details

Page Top