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題名 應用梯度提升機於供應鏈預測
The Application of Gradient Boosting Machine in Supply Chain Forecasting作者 許博淳
Hsu, Po-Chun貢獻者 張欣綠<br>莊皓鈞
Chang, Hsin-Lu<br>Chuang, Hao-Chun
許博淳
Hsu, Po-Chun關鍵詞 梯度提升機
電子零組件經銷商
Gradient boosting machine
Electronic component distributor日期 2019 上傳時間 1-Apr-2019 14:36:12 (UTC+8) 摘要 為協助亞太地區最大的電子零組件經銷商長久以來存貨量過高且達交率不如預期的情形，本研究從以下兩點做嘗試，第一項為優化訂貨策略，第二項為引入機器學習協助預測需求；在優化訂貨策略部分嘗試提出新法則去決定訂購數量，並在完美資訊下比較公司現有法則與新法則之優劣；目前個案公司採用之預測方法為移動平均法，本研究嘗試引入梯度提升機這種機器學習方法，並同時加入移動平均法與機器學習之混合模型，採用達交率、服務水準及期末存貨量三個指標，嘗試比較模型之間優劣；另外，為了要建構機器能夠學習的資料，需要事前處理資料格式與篩選內容，也需要另外加入特徵值以便機器能夠學習到需求變化的特性。本研究之目標在幫助個案公司改善預測能力，企圖使存貨量降低並且提升達交率，使個案公司之營運績效提升。
In order to solve the two main problem of our case study W company, high pressure of stock and the dissatisfied fill rate, this research aims to find a better ordering policy and use machine learning to forecast the demand. We propose a new policy to decide the order quantity and we compare the new policy to the current one under the perfect information. Nowadays, W company forecast the demand with the moving average method. We try one of the machine learning method, Gradient Boosting Machine, and we also mix the Gradient Boosting Machine and the moving average methods together to forecast. We use three indexes, fill rate, service level, and the stock quantity at the end of the period, to measure the performance. The raw data from the W company needed processing and screening and we need to add some features to make the machine capable to learn the demand pattern. Make the forecast more precise is the objective of this research. So, we want to keep the fill rate higher and minimize the inventory, which means the performance of W company will become more competitive.參考文獻 [1] Hendry, L. C., Simangunsong, E., & Stevenson M. (2011). Supply Chain Uncertainty: A Review and Theoretical Foundation for Future Research. International Journal of Production Research. 252(2016). pp. 1-26.[2] Zied, Babai, John, E. Boylan, Stephan, Kolassa & Aris, A. Syntetos(2015). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research.[3] Diane, P., Bischak, Hussein, Naseraldin & Edward, A. Silver(2008). Determining the Reorder Point and Order-Up-To-Level in a Periodic Review System So As to Achieve a Desired Fill Rate and a Desired Average Time Between Replenishments. The Journal of the Operational Research Society, 60(9), pp. 1244-1253.[4] Terry, L., Esper & Matthew, A., Waller (2014). The Definitive Guide to Inventory Management, The Principles and Strategies for the Efficient Flow of Inventory across the Supply Chain. Council of Supply Chain Management Professionals, Ch3[5] Qi, Deng, Anand, A., Paul, Yinliang (Ricky), Tan & Lai, Wei (2017). Mitigating Inventory Overstocking: Optimal Order-Up-to Level to Achieve a Target Fill Rate over a Finite Horizon. Production and Operations Management, Forthcoming,[6] S., F., Crone, R., Fildes, K., Nikolopoulos, & A., A., Syntetos (2008). Forecasting and operational research: a review. Journal of the Operational Research Society. 2008(59). Pp.1150-1172.[7]Real, Carbonneau, Kevin, Laframboise & Rustam, Vahidov (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research. 184(2008). Pp. 1140-1154.[8]Alois, Knoll & Alexey, Natekin (2013). Gradient boosting machines, a tutorial. Frontiers in NEURORBOTICS. 7(21).[9] Arno, Candel, Cliff, Click, Michal, Malohlava, Viraj Parmar & Hank, Roark (2016). Gradient Boosted Models with H2O. H2O.ai, Inc. pp.[10] Marco, Bijvank. Iris, F., A., Vis (2011). Lost-sales inventory theory: A review. European Journal of Operational Research, 215(1). pp. 1-13[11] Anna-Lena, Beutel and Stefan, Minner (2012). Safety stock planning under causal demand forecasting. International Journal of Production Economics. 140(2). pp.637 – 645.[12] Daniel Waller (2015). Method for intermittent Demand Forecasting. (Unpublished thesis). Lancaster University 描述 碩士
國立政治大學
資訊管理學系
1053560091資料來源 http://thesis.lib.nccu.edu.tw/record/#G1053560091 資料類型 thesis dc.contributor.advisor 張欣綠<br>莊皓鈞 zh_TW dc.contributor.advisor Chang, Hsin-Lu<br>Chuang, Hao-Chun en_US dc.contributor.author (Authors) 許博淳 zh_TW dc.contributor.author (Authors) Hsu, Po-Chun en_US dc.creator (作者) 許博淳 zh_TW dc.creator (作者) Hsu, Po-Chun en_US dc.date (日期) 2019 en_US dc.date.accessioned 1-Apr-2019 14:36:12 (UTC+8) - dc.date.available 1-Apr-2019 14:36:12 (UTC+8) - dc.date.issued (上傳時間) 1-Apr-2019 14:36:12 (UTC+8) - dc.identifier (Other Identifiers) G1053560091 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/122746 - dc.description (描述) 碩士 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) 1053560091 zh_TW dc.description.abstract (摘要) 為協助亞太地區最大的電子零組件經銷商長久以來存貨量過高且達交率不如預期的情形，本研究從以下兩點做嘗試，第一項為優化訂貨策略，第二項為引入機器學習協助預測需求；在優化訂貨策略部分嘗試提出新法則去決定訂購數量，並在完美資訊下比較公司現有法則與新法則之優劣；目前個案公司採用之預測方法為移動平均法，本研究嘗試引入梯度提升機這種機器學習方法，並同時加入移動平均法與機器學習之混合模型，採用達交率、服務水準及期末存貨量三個指標，嘗試比較模型之間優劣；另外，為了要建構機器能夠學習的資料，需要事前處理資料格式與篩選內容，也需要另外加入特徵值以便機器能夠學習到需求變化的特性。本研究之目標在幫助個案公司改善預測能力，企圖使存貨量降低並且提升達交率，使個案公司之營運績效提升。 zh_TW dc.description.abstract (摘要) In order to solve the two main problem of our case study W company, high pressure of stock and the dissatisfied fill rate, this research aims to find a better ordering policy and use machine learning to forecast the demand. We propose a new policy to decide the order quantity and we compare the new policy to the current one under the perfect information. Nowadays, W company forecast the demand with the moving average method. We try one of the machine learning method, Gradient Boosting Machine, and we also mix the Gradient Boosting Machine and the moving average methods together to forecast. We use three indexes, fill rate, service level, and the stock quantity at the end of the period, to measure the performance. The raw data from the W company needed processing and screening and we need to add some features to make the machine capable to learn the demand pattern. Make the forecast more precise is the objective of this research. So, we want to keep the fill rate higher and minimize the inventory, which means the performance of W company will become more competitive. en_US dc.description.tableofcontents TABLES AND FIGURES 1CHAPTER 1 INTRODUCTION 11.1 Background and Motivation 11.2 Research Questions 3CHAPTER 2 LITERATURE REVIEW 42.1 The Problems of Demand Prediction 42.2 Machine Learning for Supply Chain Demand Forecasting 5CHAPTER 3 INVENTORY MODEL AND POLICY ANALYSIS 73.1 Inventory Control System 73.2 As-Is and To-Be Policy Analysis 9CHAPTER 4 FORECASTING 144.1 Data and Feature Engineering 144.2 Forecast 154.3 The Result 22CHAPTER 5 DISCUSSION 26CHAPTER 6 CONCLUSION & LIMITATION 286.1 Conclusion 28REFERENCES 30 zh_TW dc.format.extent 1320699 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1053560091 en_US dc.subject (關鍵詞) 梯度提升機 zh_TW dc.subject (關鍵詞) 電子零組件經銷商 zh_TW dc.subject (關鍵詞) Gradient boosting machine en_US dc.subject (關鍵詞) Electronic component distributor en_US dc.title (題名) 應用梯度提升機於供應鏈預測 zh_TW dc.title (題名) The Application of Gradient Boosting Machine in Supply Chain Forecasting en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Hendry, L. C., Simangunsong, E., & Stevenson M. (2011). Supply Chain Uncertainty: A Review and Theoretical Foundation for Future Research. International Journal of Production Research. 252(2016). pp. 1-26.[2] Zied, Babai, John, E. Boylan, Stephan, Kolassa & Aris, A. Syntetos(2015). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research.[3] Diane, P., Bischak, Hussein, Naseraldin & Edward, A. Silver(2008). Determining the Reorder Point and Order-Up-To-Level in a Periodic Review System So As to Achieve a Desired Fill Rate and a Desired Average Time Between Replenishments. The Journal of the Operational Research Society, 60(9), pp. 1244-1253.[4] Terry, L., Esper & Matthew, A., Waller (2014). The Definitive Guide to Inventory Management, The Principles and Strategies for the Efficient Flow of Inventory across the Supply Chain. Council of Supply Chain Management Professionals, Ch3[5] Qi, Deng, Anand, A., Paul, Yinliang (Ricky), Tan & Lai, Wei (2017). Mitigating Inventory Overstocking: Optimal Order-Up-to Level to Achieve a Target Fill Rate over a Finite Horizon. Production and Operations Management, Forthcoming,[6] S., F., Crone, R., Fildes, K., Nikolopoulos, & A., A., Syntetos (2008). Forecasting and operational research: a review. Journal of the Operational Research Society. 2008(59). Pp.1150-1172.[7]Real, Carbonneau, Kevin, Laframboise & Rustam, Vahidov (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research. 184(2008). Pp. 1140-1154.[8]Alois, Knoll & Alexey, Natekin (2013). Gradient boosting machines, a tutorial. Frontiers in NEURORBOTICS. 7(21).[9] Arno, Candel, Cliff, Click, Michal, Malohlava, Viraj Parmar & Hank, Roark (2016). Gradient Boosted Models with H2O. H2O.ai, Inc. pp.[10] Marco, Bijvank. Iris, F., A., Vis (2011). Lost-sales inventory theory: A review. European Journal of Operational Research, 215(1). pp. 1-13[11] Anna-Lena, Beutel and Stefan, Minner (2012). Safety stock planning under causal demand forecasting. International Journal of Production Economics. 140(2). pp.637 – 645.[12] Daniel Waller (2015). Method for intermittent Demand Forecasting. (Unpublished thesis). Lancaster University zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MIS.003.2019.A05 en_US
