Supply Chain Performance Evaluation With Rough Two-Stage Data Envelopment Analysis Model: Noncooperative Stackelberg Game Approach

Abstract

Rapidly changing environment has affected organizations' ability to maintain viability. As a result, various criteria and uncertain situations in a complex environment encounter problems when using the traditional performance evaluation with precise and deterministic data. The purpose of this paper is to propose an applicable model for evaluating the performance of the overall supply chain (SC) network and its members. Performance evaluation methods, which do not include uncertainty, obtain inferior results. To overcome this, rough set theory (RST) was used to deal with such uncertain data and extend rough noncooperative Stackelberg data envelopment analysis (DEA) game to construct a model to evaluate the performance of supply chain under uncertainty. This applies the concept of Stackelberg game/leader–follower in order to develop models for measuring performance. The ranking method of noncooperative two-stage rough DEA model is discussed. While developing the model, which is suitable to evaluate the performance of the supply chain network and its members when it operates in uncertain situations and involves a high degree of vagueness. The application of this paper provides a valuable procedure for performance evaluation in other industries. The proposed model provides useful insights for managers on the measurement of supply chain efficiency in uncertain environment. This paper creates a new perspective into the use of performance evaluation model in order to support managerial decision-making in the dynamic environment and uncertain situations.