Data Envelopment Analysis with Robust and Closest Targets:Integrating Full-Dimensional Efficient Facets for Risk-Resilient Benchmarking

TL;DR

This paper introduces a robust benchmarking approach in data envelopment analysis that considers risk by integrating full-dimensional efficient facets, providing decision makers with resilient targets for optimal outcomes.

Contribution

It proposes a novel robust benchmarking target based on full-dimensional efficient facets and a new efficiency measure incorporating risk considerations.

Findings

The proposed method effectively identifies risk-resilient benchmarks.

Application to Chinese universities demonstrates practical utility.

The new efficiency measure outperforms traditional approaches.

Abstract

As the external environment become increasingly volatile and unpredictable, the selection of benchmarking targets in data envelopment analysis should account for their ability to consider risks; however, this aspect has not received sufficient attention. We propose a robust benchmarking target defined by the intersection of the maximum number of full-dimensional efficient facets, each representing a unique marginal substitution relationship. These targets can serve as robust projections for decision making units that are lacking prior risk information because they incorporate the maximum number of marginal substitution relationships. This enables decision makers to adjust their production through these relationships, thereby maximizing the likelihood of achieving globally optimal outcomes. Furthermore, we propose a novel, well-defined efficiency measure based on robust and closest targets. Finally, we demonstrate the application of the proposed measure using a dataset comprising 38 universities from China's 985 Project.