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

TL;DR

This paper introduces a cross-benchmarking approach within Data Envelopment Analysis (DEA) that allows organizations to compare against multiple peer groups, providing flexible performance targets and insights for improvement.

Contribution

It extends existing DEA benchmarking methods by enabling the selection of multiple reference sets for more comprehensive performance evaluation.

Findings

Provides a procedure for selecting reference sets in DEA

Formulates models for setting closest targets to multiple reference groups

Enhances decision-making flexibility through multiple benchmarking options

Abstract

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.