A Dynamic Failure Rate Forecasting Model for Service Parts Inventory

Abstract

This study investigates one of the reverse logistics issues, after-sale repair service for in-warranty products. After-sale repair service is critical to customer service and customer satisfaction. Nonetheless, the uncertainty in the number of defective products returned makes forecasting and inventory planning of service parts difficult, which leads to a backlog of returned defectives or an increase in inventory costs. Based on Bathtub Curve (BTC) theory and Markov Decision Process (MDP), this study develops a dynamic product failure rate forecasting (PFRF) model to enable third-party repair service providers to effectively predict the demand for service parts and, thus, mitigate risk impacts of over- or under-stocking of service parts. A simulation experiment, based on the data collected from a 3C (computer, communication, and consumer electronics) firm, and a sensitivity analysis are conducted to validate the proposed model. The proposed model outperforms other approaches from previous studies. Considering the number of new products launched every year, the model could yield significant inventory cost savings. Managerial and research implications of our findings are presented, with suggestions for future research.