Earticle

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

Abstract
 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

저자

• Hee Seok Song [ Department of Management Information Systems., Hannam University ]
• Shaneth A. Estrada [ Department of Management Information Systems., Hannam University ]
• Young Ae Kim [ Business School, Korea Advanced Institute of Science and Technology (KAIST) ]
• Su Hyeon Namn [ Department of Management Information Systems., Hannam University ]
• Shin Cheol Kang [ Department of Management Information Systems., Hannam University ]

참고문헌