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題名 A Dynamic Failure Rate Forecasting Model for Service Parts Inventory
作者 Chen, Ta-Yu
Lin, Woo-Tsong
林我聰
Sheu, Chwen
貢獻者 資管系
關鍵詞 green supply chains; reverse logistics; third-party repair service providers; failure rate forecast; service parts; bathtub curve theory; Markov Decision Process
日期 2018-07
上傳時間 9-十一月-2018 15:07:18 (UTC+8)
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.
關聯 SUSTAINABILITY, 10(7), 2408
資料類型 article
DOI http://dx.doi.org/10.3390/su10072408
dc.contributor 資管系
dc.creator (作者) Chen, Ta-Yu
dc.creator (作者) Lin, Woo-Tsong
dc.creator (作者) 林我聰
dc.creator (作者) Sheu, Chwen
dc.date (日期) 2018-07
dc.date.accessioned 9-十一月-2018 15:07:18 (UTC+8)-
dc.date.available 9-十一月-2018 15:07:18 (UTC+8)-
dc.date.issued (上傳時間) 9-十一月-2018 15:07:18 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/120870-
dc.description.abstract (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.en_US
dc.relation (關聯) SUSTAINABILITY, 10(7), 2408
dc.subject (關鍵詞) green supply chains; reverse logistics; third-party repair service providers; failure rate forecast; service parts; bathtub curve theory; Markov Decision Process
dc.title (題名) A Dynamic Failure Rate Forecasting Model for Service Parts Inventoryen_US
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.3390/su10072408
dc.doi.uri (DOI) http://dx.doi.org/10.3390/su10072408