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題名 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作者 陳大愚
Chen, Ta-Yu貢獻者 林我聰<br>許淳
Lin, Woo-Tsong<br>Sheu, Chwen
陳大愚
Chen, Ta-Yu關鍵詞 綠色供應鏈管理
逆物流
第三方維修服務供應商
故障率預測
服務零件
浴缸曲線理論
馬可夫決策過程
Green Supply Chains
Reverse Logistics
Third-Party Repair Service Providers
Failure Rate Forecast
Service Parts
Bathtub Curve Theory
Markov Decision Process日期 2019 上傳時間 4-三月-2019 19:30:51 (UTC+8) 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。
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.參考文獻 Ahiska, S. S. (2008). Inventory Optimization in a One Product Recoverable Manufacturing System, Ph.D. Dissertation, North Carolina.Ahiska, S. S., & King, R. E. (2010a). Inventory optimization in a one product recoverable manufacturing system. International Journal of Production Economics, 124(1), 11-19.Ahiska S. S., & King, R. E. (2010b). Life cycle inventory policy characterization for a single-product recoverable system. International Journal of Production Economics, 124(1), 51-61.Ahiska, S. S., & King, R. E. (2015). Inventory policy characterisation methodologies for a single–product recoverable manufacturing system. 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國立政治大學
資訊管理學系
101356501資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101356501 資料類型 thesis dc.contributor.advisor 林我聰<br>許淳 zh_TW dc.contributor.advisor Lin, Woo-Tsong<br>Sheu, Chwen en_US dc.contributor.author (作者) 陳大愚 zh_TW dc.contributor.author (作者) Chen, Ta-Yu en_US dc.creator (作者) 陳大愚 zh_TW dc.creator (作者) Chen, Ta-Yu en_US dc.date (日期) 2019 en_US dc.date.accessioned 4-三月-2019 19:30:51 (UTC+8) - dc.date.available 4-三月-2019 19:30:51 (UTC+8) - dc.date.issued (上傳時間) 4-三月-2019 19:30:51 (UTC+8) - dc.identifier (其他 識別碼) G0101356501 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/122380 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 101356501 zh_TW dc.description.abstract (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。 zh_TW 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.description.tableofcontents Chapter 1 Introduction 11.1 Research Background 11.2 Research Motivation 21.3 Research Purposes 4Chapter 2 Literature Review 62.1 Current Business Practices and Issues 72.2 Bathtub Curve Theory 102.3 Markov Decision Process 122.3.1 Decision Epochs and Periods 122.3.2 State and Action Sets 122.3.3 Reward and Transition Probabilities 122.3.4 Decision Rules 132.3.5 Policies 14Chapter 3 The Dynamic Product Failure Rate Forecast Model 153.1 Closed-Loop Supply Chain with Information-Shared Service Parts Planning 153.2 Problem Description and MDP formulations 183.3 Model Development 21Chapter 4 Benchmark Analysis 274.1 Initial Conditions 274.2 Results 314.3 Comparison the Three Models 33Chapter 5 Sensitivity Analysis 385.1 Increase Return Defectives 385.2 Decrease Return Defectives 445.3 Change Base Failure Base and Upper/Lower Bound 48Chapter 6 Conclusions and Further Research 576.1 Conclusions and Contributions 576.2 Future Research 59References 60Appendix 70Appendix A Notations 70Appendix B Failure Rate of Coordinate Cell (i, j) of Planning Period k 71Appendix C Generic Calculation of Failure Rate for any Coordinate Cell (i, j) at kth Planning Period 72 zh_TW dc.format.extent 2753758 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101356501 en_US dc.subject (關鍵詞) 綠色供應鏈管理 zh_TW dc.subject (關鍵詞) 逆物流 zh_TW dc.subject (關鍵詞) 第三方維修服務供應商 zh_TW dc.subject (關鍵詞) 故障率預測 zh_TW dc.subject (關鍵詞) 服務零件 zh_TW dc.subject (關鍵詞) 浴缸曲線理論 zh_TW dc.subject (關鍵詞) 馬可夫決策過程 zh_TW dc.subject (關鍵詞) Green Supply Chains en_US dc.subject (關鍵詞) Reverse Logistics en_US dc.subject (關鍵詞) Third-Party Repair Service Providers en_US dc.subject (關鍵詞) Failure Rate Forecast en_US dc.subject (關鍵詞) Service Parts en_US dc.subject (關鍵詞) Bathtub Curve Theory en_US dc.subject (關鍵詞) Markov Decision Process en_US dc.title (題名) 第三方維修服務商之保固產品動態故障率預測模式 zh_TW dc.title (題名) A Dynamic Failure Rate Forecasting Model of in-Warranty Products for Third-Party Repair Service Providers en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Ahiska, S. 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