Abstract
Remanufacturing is widely recognized as an effective strategy to address the negative environmental impacts of product disposal and minimize costs across the entire value chain. Short life cycle product like smartphone manufacturers and their e-commerce partners are offering tempting incentives to exchange obsolete handsets to gain market share. This increase in exchange programs has created issues about returns management, notably remanufacturing and disposal regulations. Due to rapid technology changes that make components obsolete in 2-3 years, anticipating spare part needs is harder. An effective remanufacturing policy should replenish a percentage of spare parts inventory through returns recovery to solve these problems and optimize inventory levels. We propose two steps. First, we use Bayesian Estimation to predict returns and spare parts. This reduces production risk. Second the return quality function determines the spare part manufacturing curve. This twostep technique reduces production uncertainty and optimizes inventory. This study also aims to identify and analyze the trade-offs associated with component commonality in the design of product families across multiple generations. By utilizing developed models, we conduct numerical analysis to showcase the practicality of our approach and derive valuable insights for managers. Furthermore, in the long term, these results may serve as a theoretical basis for expediting the development and integration of design, manufacturing, and services. This, in turn, can improve the responsiveness and efficiency of the overall design process, benefiting both technical and economic aspects.
