OPTIMIZING THE REPAIR AND REFURBISH NETWORKS FOR REVERSE LOGISTICS Rajesh Piplani, Jumpol Vorasayan and Ashish Saraswat Center for Supply Chain Management School of Mechanical and Aerospace Engineering Nanyang Technological University, Singapore Email: mrpiplani@.ntu.edu.sg , jumpolv@ntu.edu.sg , ashish@ntu.edu.sg ABSTRACT This paper describes a reverse supply chain from the point of view of a company providing after-sales service for the electronic products. The objective is to decide the facility locations and product flow to support two types of networks: a repair network for faulty products and refurbish network for commercial returns. We propose a mixed integer linear programming (MILP) model to maximize total profit subject to conservation and flow constraints. The binary variables represent the use of facility for the networks while the continuous variables represent the amount of flow. The optimal decisions depend on several factors e.g. rate of product returned, percent of faulty products, demand of refurbished products, warranty, distance between facilities, and refurbishment fractions. The model will be tested on real- life data for validation. KEYWORDS Sustainable Supply Chain, Reverse Logistics, Network Design 1. Introduction A considerable amount of electronic products are returned back to sellers after sales. They can be separated into two categories: (1) faulty returns where products are returned for repairing due to their malfunctions and (2) commercial returns where products are returned within their return period of 30, 60 or 90 days. They are returned not only by their imperfection but also compassionate reasons such as dissatisfaction and remorseful Guide et al. [4]. Refurbishment and repair are two of five recovery processes defined by Thierry [8]. In electronic products, the term “repair” generally means restoring faulty products to their working order. Repairing incurs at either original equipment manufacturers (OEMs) or repair vendors (RVs). On the other hand, “refurbishment” means bringing products to their original manufacturing specifications White and Naghibi [11]. The amount of commercial returns is around 12% of total new product sales [9]. Returned products in this category preserve value nearly as high as brand new one due to lightly uses in short period of time. Refurbishing and reselling these returns will generate very high profit due to the high margin between processing cost and price of refurbished products. Leading electronic product companies such as Dell, Apple, Nikon successfully implement refurbishment process and resale their returned products. In theory, Vorasayan and Ryan [10] show scenarios that refurbishment can be profitable and it can be a better option than selling dismantled modules as they are. Setting up reverse channel in an exiting forward channel is economically viable due to low set up cost and very reasonable because products acquisition and returns can be done in either retailers or service centers along with refurbishment can be carried out at the exiting facilities, in our case, e.g. service centers. The decision of using third party, exiting retailers or self collect for collecting used products has been previously studied by Savaskan [7]. Although the refurbished products might perform the same or nearly as well as new products, they are classified and priced lower than new products. The lower perceived quality of refurbished products comes from the view that they have been used and manufactured more than once. In this paper, we assume demand for refurbished products comes solely from the secondary market. Fleischmann and Beullens [2] address the impact of product recovery networks to the original one and propose MILP model for designing the optimal flows and infrastructures with two numerical examples of paper recycling and photo copier. Kusumastuti et al. [5] use MILP to optimally design a repair network in a case study of closed-loop supply chain for a computer manufacturer in Asia pacific. Sarkis and Sundarraj [6] conduct a case study at a major computer manufacturer to determine the locations of repair-parts warehouses by considering both qualitative and quantitative perspectives. They proposed analytical network process to downsize the number of potential sites based on qualitative parameters, whereas a