원문정보
초록
영어
In DEA, it is difficult for inefficient DMUs to be efficient by benchmarking a target DMU which has different input use. Identifying appropriate benchmarks based on the similarity of input endowment makes it easier for an inefficient DMU to imitate its target DMUs. But it is rare to find out a target DMU, which is both the most efficient and similar in input endowments, in real situation. Therefore, it is necessary to provide an optimal path to the most efficient DMU on the
frontier through several times of a proximity-based target selection process. We propose a dynamic method of stepwise benchmarking for inefficient DMUs to improve their efficiency gradually. The empirical study is conducted to compare the performance between the proposed method and the prior methods with a dataset collected from Canadian Bank branches. The comparison result shows that the proposed method is very practical to obtain a gradual improvement for inefficient DMUs while it assures to reach frontier eventually.
목차
Introduction
Literature review
Problem definition
Methodology
Evaluating efficiency score of DMUs
Obtaining neighborhood information amongDMUs
Learning an optimal path to the frontier
Empirical study
Evaluation metric
Dataset
Determination of a SOM model
Determination of parameters
Comparison with basic DEA and layer model
Relationship between efficiency score improvementand distance of input use
Conclusion
References