Track: Inventory Management

Abstract

In a quest to grab the market share of smartphones, manufacturing companies and partnered e-commerce sites are offering greater benefits on exchange products. This has severely increased the concern of returns management through better remanufacturing and disposal policy. Though we know a lot about the demands of new products there is no guarantee about the quality and the quantity of the returned products. Spare parts requirement is also equally difficult to gauge with the technological advancement making the components obsolete within 2-3 years span. An effective remanufacturing policy could be replenishing a portion of spare parts inventory through the returns recovery. To reduce the production uncertainty and maintain optimal inventory levels we propose a two-step methodology. First, we obtain a good forecast of the return quantity and spare parts requirement by comparing results obtained using Bayesian Estimation and Adaptive Network-based Fuzzy Inference System (ANFIS). Secondly, based on the return quality function, the production curve for the spare parts is determined. The study is concluded by presenting numerical cases to illustrate its usage.