Quantitative models for performance evaluation and benchmarking : data envelopment analysis with spreadsheets
著者
書誌事項
Quantitative models for performance evaluation and benchmarking : data envelopment analysis with spreadsheets
（International series in operations research & management science, 126）
Springer, c2009
2nd ed
- : hbk
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注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
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.
目次
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.
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