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Data Envelopment Analysis (DEA) is widely used as a benchmarking tool forimproving performance of organizations. For that purpose, DEA analyses provideinformation on both target setting and peer identification. However, theidentification of peers is actually a by-product of DEA. DEA models seek aprojection point of the unit under evaluation on the efficient frontier of theproduction possibility set (PPS), which is used to set targets, while peers areidentified simply as the members of the so-called reference sets, which consistof the efficient units that determine the projection point as a combination ofthem. In practice, the selection of peers is crucial for the benchmarking,because organizations need to identify a peer group in their sector or industrythat represent actual performances from which to learn. In this paper, we arguethat DEA benchmarking models should incorporate into their objectives criteriafor the selection of suitable benchmarks among peers, in addition toconsidering the setting of appropriate targets (as usual). Specifically, wedevelop models having two objectives: setting the closest targets and selectingthe most similar reference sets. Thus, we seek to establish targets thatrequire the less effort from organizations for their achievement in addition toidentifying peer groups with the most similar performances, which are potentialbenchmarks to emulate and improve.

Benchmarking within a DEA framework: setting the closest targets and identifying peer groups with the most similar performances