A risk management evaluation framework for reverse logistics organizations using failure mode and effect analysis and multi criteria decision making

Reverse Logistics (RL) is defined as all activities that return used products from the consumers and transform it to usable products to decrease landfill and protect the environment. In RL, the reprocessed used products are competing with new products in terms of quality, quantity and value. The typical processes in designing a RL network includes the collection, inspection,selection and sorting, reprocessing options and redistribution. The complexities of these processes make them vulnerable to risks, which can threaten the performance of RL, and affect investment opportunities. RL is relatively new compared to supply chain management. The risk factors relevant to RL have not been comprehensively identified and consolidated.Since RL risk factors have not been identified, classification and evaluation of these risks are still unexplored. Thus, this study aims to develop a reverse logistics risk management framework including identification, classification and evaluation and monitoring processes. Since there were no previous studies on risks factors for RL, the risks that have been identified in supply chain management and logistics literature were extracted as a basis in determining the RL risk factors. Once the risk factors were identified by RL experts in the field, the risks were classified into homogeneous groups using Self-organizing map (SOM). The number of corrective actions will be lesser and more economical for a cluster of risks than applying corrective action for each risk factor. Fuzzy Failure Mode and Effects Analysis (FMEA) were applied to evaluate RL risks factors. Finally, the clustered risk factors were ranked using the technique for order preference by similarity to ideal solution (TOPSIS) to determine the level of importance among reverse logistics risk factors. The Based on feedbacks from 22 RL experts, 41 risk factors were identified. Selforganizing map (SOM) determined three independent clusters of RL risk factors. The risk priority number (RPN) evaluated by fuzzy FMEA showed customer risk with 761, followed by long distance with 759 and business disruption with 753 as the most important RL risk factors based on the experts‘ opinion. The result of risk monitoring via TOPSIS method in ranking of the RL risks within and without clustering showed purchase risk, financial instability and inventory risk are highly ranked among the RL risk factors in the three clusters. Finally, a RL risk management framework was developed to address the risks for RL. The Reverse logistics risk management framework can assist decision makers in RL organizations to identify potential risk factors which threaten their process performance.Moreover, classification of risks in RL and their evaluation based on the proposed framework may be useful for adoption of corrective action and mitigation of the risks and consequently increasing the success of the RL organization.

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