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.
Highlights
Growing concern about environmental protection makes reverse logistics more important than ever
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 (3PSPs) to predict demand quantities of service parts and, mitigate risks of over- or under-stocking of service parts
The ratio could vary due to different costs of service parts
Summary
Growing concern about environmental protection makes reverse logistics more important than ever. The uncertainty in the number of defective products returned makes forecasting and inventory planning of service parts difficult, causing a backlog of returned defectives (shortage of service parts) or an increase in inventory costs (over stock of service parts) [1] This problem leads to poor after-sale repair service and customer dissatisfaction. The literature and current business practices erroneously assume that product failure rates are constant [2] or follow a particular statistical distribution [3,4] This unrealistic assumption could be very costly, causing poor after-sale service and inventory cost increase [2]. The section reviews the literature related to product failure, service parts, BTC, and MDP applications, followed by the development of a product failure rate forecasting (PFRF) model. We conclude with a discussion of the managerial and research implications of our findings, as well as research limitations
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