  In benchmarking, organizations look outward to examine others' performance in
their industry or sector. Often, they can learn from the best practices of some
of them and improve. In order to develop this idea within the framework of Data
Envelopment Analysis (DEA), this paper extends the common benchmarking
framework proposed in Ruiz and Sirvent (2016) to an approach based on the
benchmarking of decision making units (DMUs) against several reference sets. We
refer to this approach as cross-benchmarking. First, we design a procedure
aimed at making a selection of reference sets (as defined in DEA), which
establish the common framework for the benchmarking. Next, benchmarking models
are formulated which allow us to set the closest targets relative to the
reference sets selected. The availability of a wider spectrum of targets may
offer managers the possibility of choosing among alternative ways for
improvements, taking into account what can be learned from the best practices
of different peer groups. Thus, cross-benchmarking is a flexible tool that can
support a process of future planning while considering different managerial
implications.
