Performance Evaluation In Reverse Logistics With Data Envelopment Analysis

Abstract

Good reverse logistics design can save cost, increase revenues, and gain competitive edges over the rivals. Design of the optimized reverse supply chain model is a very important task to help enterprises save cost and gain benefits from their supply chains. In this study, reverse logistics is considered as a part of the Closed Loop Supply Chain (CLSC). CLSC combines forward and reverse flow together in the supply chain. Each component in a forward and reverse supply chain results in the efficiency of CLSC. Therefore, considering forward and reverse supply chain together as a CLSC will result in more benefits in improving efficiency of the supply chain than considering it separately. Since most data in the reverse supply chain are very difficult to obtain and many companies do not want to provide their reverse supply chain data due to business reasons, the data is secretly kept. Due to these reasons, there is a need to create a simulation model of CLSC to get reasonable data that can be used in this study.This research proposes a methodology to design a good reverse supply chain by using the specified parameters. The statistical experiments with Data Envelopment Analysis (DEA) were applied to obtain an optimized model. This model is used to evaluate efficiency of the reverse logistics model and also provides the opportunity to improve efficiency by varying the significant parameters. Two case studies of carpet recycling were provided as the examples to show how to apply this methodology.