- 學位論文
第三方維修服務商之保固產品動態故障率預測模式
A Dynamic Failure Rate Forecasting Model of in-Warranty Products for Third-Party Repair Service Providers
摘要
本研究探討逆物流問題之一，即保固內產品的售後維修服務。售後維修服務對客戶服務和客戶滿意度至關重要。儘管如此，退回的不良產品數量的不確定性使得服務零件的預測和庫存規劃變得困難，這導致退回的不良產品積壓或零件庫存成本增加。基於浴缸曲線（Bathtub Curve, BTC）理論和馬可夫決策過程（Markov Decision Process, MDP），本研究發展了一個動態產品故障率預測（Product Failure Rate Forecast, PFRF）模型，使第三方維修服務提供商能夠有效預測服務零件的需求，從而減輕服務零件庫存過多或庫存不足的風險影響。本研究從一3C（電腦、通信和消費性電子）公司收集的數據進行模擬實驗，並進行敏感度分析以驗證所提出的模型，提出的PFRF模型優於先前研究的其他方法。考慮到每年推出的新產品數量，該模型可以節省大量的庫存成本。最後介紹了研究結果的管理意涵，並提出了未來研究的方向與建議。
並列摘要
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.