Goal-oriented common benchmarking based on global and stepwise reallocation: An application to 18 ports in Korea

Common benchmarking can identify a common best practice frontier as the benchmark to assess and improve the performance of Decision-Making Units (DMUs) experiencing similar circumstances. At present, further exploration in common benchmarking is still needed. For instance, the similarity between management goals and targets belonging to a common best practice frontier. To fill this gap, we propose the following innovative work: (1) A novel Data Envelopment Analysis (DEA) model is proposed to identify a common best practice frontier on which targets closest to management goals can be found for all DMUs experiencing similar circumstances. These targets need to be achieved through a global reallocation of inputs/outputs among DMUs; (2) Considering the targets closest to management goals are too ambitious for some DMUs, an iterative algorithm is given based on another new DEA model for providing the stepwise reallocation paths. The stepwise reallocation paths can distribute the total reallocation efforts into several intermediate steps; (3) The effectiveness of the proposed model and algorithm is illustrated by applying them to a case of 18 ports in Korea. For this purpose, we use the input-output data set of these ports in 2010 and assume actual management goals for every port. The results of the case show some conclusions. First, the target of each port located on the common best practice facet is closest to its management goal. Second, global redistribution can make all ports obtain the best input-output levels. Third, ports with limited reallocation potential can monotonically and gradually achieve their distant targets through our stepwise schemes.

Automated summary

Common benchmarking can help assess and improve the performance of Decision-Making Units (DMUs) experiencing similar circumstances. Further exploration is needed, particularly in understanding the similarity between management goals and targets. This study proposes a novel Data Envelopment Analysis (DEA) model and iterative algorithm to identify a common best practice frontier and provide stepwise reallocation paths.