A method of stepwise benchmarking for inefficient DMUs based on the proximity-based target selection

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DEA is a useful nonparametric method of measuring the relative efficiency of a DMU and yielding a reference target for an inefficient DMU. However, it is very difficult for inefficient DMUs to be efficient by benchmarking a target DMU which has different input use. Identifying appropriate benchmarks based on the similarity of input endowment makes it easier for an inefficient DMU to imitate its target DMUs. But it is rare to find out a target DMU, which is both the most efficient and similar in input endowments, in real situation. Therefore, it is necessary to provide an optimal path to the most efficient DMU on the frontier through several times of a proximity-based target selection process. We propose a dynamic method of stepwise benchmarking for inefficient DMUs to improve their efficiency gradually. The empirical study is conducted to compare the performance between the proposed method and the prior methods with a dataset collected from Canadian Bank branches. The comparison result shows that the proposed method is very practical to obtain a gradual improvement for inefficient DMUs while it assures to reach frontier eventually. (c) 2009 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2009
Language
English
Article Type
Article
Keywords

DATA ENVELOPMENT ANALYSIS; EFFICIENCY IMPROVEMENT; DEA

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.36, no.9, pp.11595 - 11604

ISSN
0957-4174
DOI
10.1016/j.eswa.2009.03.035
URI
http://hdl.handle.net/10203/94853
Appears in Collection
RIMS Journal Papers
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