Evaluation of collection methods in reverse logistics by using fuzzy mathematics

Abstract

Purpose – The efficiency and effectiveness of reverse logistics (RL) is dependent on collection methods as the collection activities are critical in determining the economic viability of the entire recovery chain. The purpose of this paper is to evaluate the various collection methods used in RL under uncertain environment. Design/methodology/approach – An integrated fuzzy multi-criteria decision model has been developed for the evaluation of various collection methods. The evaluation has been done based on the criteria of initial investment, value added recovery, return volume, operating cost, degree of supply chain control, and level of customer satisfaction. The three alternatives used in the study are collection by the manufacturer directly from the customer, collection by the retailer, and collection by the third party. The fuzzy analytical hierarchy process has been used to compute the criteria weights and fuzzy technique for order preference by similarity to ideal solution has been used to rank the alternative collection methods. Fuzzy mathematics has been used to take care of uncertainties in the RL. Findings – Selection and evaluation of alternative collection methods is affected by multiple criteria like initial investment, value added recovery, return volume, operating cost, degree of supply chain control, and level of customer satisfaction. The utility of the proposed evaluation methodology has been validated by solving a case example from automotive company. Originality/value – The proposed methodology will provide a useful tool to the decision maker for the evaluation and selection of the alternative collection methods in RL. This will help companies in strategic decision making to prioritize and develop collection facilities accordingly.