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In benchmarking, organizations look outward to examine others' performance intheir industry or sector. Often, they can learn from the best practices of someof them and improve. In order to develop this idea within the framework of DataEnvelopment Analysis (DEA), this paper extends the common benchmarkingframework proposed in Ruiz and Sirvent (2016) to an approach based on thebenchmarking of decision making units (DMUs) against several reference sets. Werefer to this approach as cross-benchmarking. First, we design a procedureaimed at making a selection of reference sets (as defined in DEA), whichestablish the common framework for the benchmarking. Next, benchmarking modelsare formulated which allow us to set the closest targets relative to thereference sets selected. The availability of a wider spectrum of targets mayoffer managers the possibility of choosing among alternative ways forimprovements, taking into account what can be learned from the best practicesof different peer groups. Thus, cross-benchmarking is a flexible tool that cansupport a process of future planning while considering different managerialimplications.

Cross-benchmarking for performance evaluation: looking across best practices of different peer groups using DEA