Comprehensive performance measurement and causal-effect decision making model for reverse logistics enterprise

Product returns are becoming inevitable across all industries and returns can occur at any time during the product lifecycle. Consequently, the importance of reverse logistics (RL), has grown significantly in recent years. In order to maintain effective and efficient RL operations, enterprises adopt various approaches to improve their performance, such as Balanced Scorecard (BSC). In this research paper, a comprehensive RL performance measurement model is first developed by integrating BSC, and performance prism, thus, rectifying the drawbacks in previous frameworks while incorporating their strengths. Moreover, the RL performance is affected by different factors, for example resources utilization, productivity, and it is always difficult for decision-makers to improve all aspects at the same time. Another factor from the published frameworks assumes independence of performance factors. Nonetheless in the real world, such performance factors are seldom independent. In view of the constraint of various resources, this paper brings forward an important issue on how to enhance RL performance by clustering complex yet influential factors into groups to improve them in a stepwise way. To address this concern, an effective method called decision-making trial and evaluation laboratory (DEMATEL), is utilized. Considering the interdependence among these factors, the DEMATEL method produces a cause and effect relationship diagram. The performance factors are divided into these cause and effect groups, which enable the handling of inner dependences within a set of factors. The following proposed model contributes to enhance this RL enterprise performance, provides milestones for a performance measurement system design, and achieves targets of RL operations. Furthermore, the causal model development can help in the decision-making process as well as proposing suggestions to improve the enterprise performance. 2013 Elsevier Ltd. All rights reserved.