A study on the selection of benchmarking paths in DEA

One of the existing DEA methods' limitations noted in the literature lies in the process of benchmarking of reference targets for inefficient DMUs. Difficulties arising in this process can be summarized to three aspects. First, the reference target might be a hypothetical DMU that does not actually exist (it is difficult and indeed unrealistic to learn from such a DMU). Second, the reference set of an inefficient DMU often has multiple efficient DMUs making it difficult to benchmark multiple best-practice DMUs simultaneously. Third, it is quite impossible for an inefficient DMU to achieve its target's efficiency in a single step, especially when the inefficient DMU is far from the efficient frontier. In order to overcome these difficulties, we propose, in place of the selection of benchmarked DMUs on the efficient frontier, a method of selecting effective benchmarking paths that direct an inefficient DMU to its target on the efficient frontier in an implementable and realistic way. The proposed method was designed based on the idea of the context-dependent DEA proposed by Seiford and Zhu (2003). It starts by clustering DMUs into several layers according to their efficiency scores, and then establishes a benchmarking path across the sequence of layers. Among the DMUs in the next layer, the most preferable one is selected as the next benchmark target, based on three criteria: attractiveness, progress, and infeasibility. We tested the proposed method by applying it to the evaluation of the relative efficiency of operations of 26 container terminals located in Asia. (C) 2010 Elsevier Ltd. All rights reserved.