In this paper we propose a new approach to benchmarking that combines the frontier estimation techniques with clustering methods. Further, DEA is sensitive to stochastic noise, which can affect the benchmarking exercise. Moreover, the DEA benchmarks may operate in a more favorable environment than the evaluated DMU. As a result, the benchmark units may differ from the evaluated DMU in terms of their input–output profiles and the scale size. The benchmarks produced by DEA are obtained as a side-product of computing efficiency scores. Data envelopment analysis (DEA) is widely used as a benchmarking tool for improving productive performance of decision making units (DMUs).
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