Bounded-change target-setting approach: Selection of a realistic benchmarking path

Abstract

The closest target-setting models based on data envelopment analysis (DEA) provide substantial contributions to benchmarking for requiring minimal effort to improve the performance of decision-making units (DMUs). However, these target-setting models may become impractical when several real factors, such as technology level, environmental influence, and governmental policy, are considered. In this study, we develop a novel DEA approach to target setting and benchmarking path selection by considering such real factors. A new bounded-change target-setting approach based on context-dependent DEA, in which the distance of evaluated unit to the best-practice frontier is minimized, is proposed. This approach sets benchmarks for inefficient DMUs in the short term and selects a realistic benchmarking path in the long term. Our approach also suggests a cross-level benchmarking path that few DEA-based benchmarking studies concern. This approach is applied to set sequential benchmarks and identify realistic benchmarking paths for inefficient DMUs via energy efficiency evaluation in the transport sector of China.

Keywords:

• Data envelopment analysis
• target setting
• benchmarking path
• bounded-change constraints

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research is supported by National Natural Science Foundation of China (Nos. 71871223, 71991465, 71991483, 91846301), Project of Education Department of Hunan Province (19C1053), Innovation-Driven Planning Foundation of Central South University (2019CX041).