Benchmarking inefficient decision making units in DEA Abstract

Benchmarking inefficient decision making units in DEA
Data envelopment analysis (DEA) is a non-parametric approach in operations research for assessing the
relativeefficiencies of a set of peer units called decision making units (DMUs) with multiple inputs and
multiple outputs.DEA provides a fair benchmarking tool that includes a technical efficiency score for
each DMU, a technicalefficiency reference set with peer DMUs, a target for inefficient DMU, and
information detailing by how muchinputs can be decreased or outputs can be increased to the improve
performance of DMUs. In this paper, wecompare DEA models to benchmark inefficient DMUs and prove
that popular models like the slack-based measure(SBM) and Charnes, Cooper and Rhodes (CCR) may not
give the acceptable results for benchmarking inefficientDMUs as strong as the weighted additive (ADD)
model. The study also warns applying those conventional DEAmodels for most of applications and
suggests using the Kourosh and Arash Method to assess the performanceevaluation of DMUs.