Reverse Supply Chain Design in the Fast Fashion Industry for Customer Relationship Management Using Data Mining and Mathematical Modeling

Document Type : Research/ Original/ Regular Article

Authors

Department of Industrial Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

Since the short life cycle, uncertainty in demand, and speed in response are the most important characteristics of fast fashion, improving customer relationship management (CRM) is of particular importance in this industry. Recent developments in the world's clothing industry have created a competitive market, and reforming the structure of flexible supply chains in this field has become very important. This research aims to integrate CRM policies and reverse supply chain design in the field of fast fashion in order to reduce costs and increase customer satisfaction. The analysis of 659 thousand real records related to the information of the last purchase, the continuity of purchases and the value of purchases related to the customers of 21 clothing chain stores in Tehran during two years using the     K-means algorithm led to the clustering of customers into 5 groups and then the amount of discount for each group were determined. In order to determine the optimal location of the repair center and combined centers for the collection of returned goods, a mathematical model was designed and solved using the random optimization method in a scenario-oriented manner. The results showed that combined centers are not necessarily located in the area of stores with higher sales, but the return rate of goods and the amount of demand should be taken into account at the same time. According to the Pareto model diagram, the binary values of the objective function of cost and satisfaction vary between the numbers of 4.7 billion Rials for cost and 2185 for satisfaction, and 6.2 billion Rials for cost and 3156 for satisfaction. The range of minimum and maximum discounts for the purchase of new goods was calculated from 5% to 30% and for the repair of goods from 10% to 60%. The model presented in this research is generally used in fast-moving retail industries, including perishable goods.

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