Benchmarking inefficient decision making units in DEA
Data Envelopment Analysis (DEA) is a non-parametric approach to operations research for assessing the relative efficiencies of a set of peer units, called Decision Making Units (DMUs), with multiple inputs and multiple outputs. DEA provides a fair benchmarking tool that includes a technical efficien...
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| Main Authors: | , , |
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| 格式: | Article |
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TEXTROAD Publishing Corporation
2012
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| 在線閱讀: | http://eprints.utm.my/id/eprint/46641/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69458 |
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| 總結: | Data Envelopment Analysis (DEA) is a non-parametric approach to operations research for assessing the relative efficiencies of a set of peer units, called Decision Making Units (DMUs), with multiple inputs and multiple outputs. DEA provides a fair benchmarking tool that includes a technical efficiency score for each DMU, a technical efficiency reference set with peer DMUs, a target for a technically inefficient DMU, and information detailing by how much inputs can be decreased or outputs can be increased to improve performance of DMUs. In this paper, we compare DEA models to benchmark technically inefficient DMUs, and prove that popular models like the Slack-Based Measure (SBM) and Charnes, Cooper and Rhodes (CCR) may not give the acceptable results for benchmarking technically inefficient DMUs as strong as the weighted Additive (ADD) model. The study also warns against applying the conventional DEA models for most of applications and suggests using the Kourosh and Arash Method to (KAM) assess the performance evaluation of DMUs. |
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