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研究生:許博淳
研究生(外文):Hsu, Po-Chun
論文名稱:應用梯度提升機於供應鏈預測
論文名稱(外文):The Application of Gradient Boosting Machine in Supply Chain Forecasting
指導教授:張欣綠張欣綠引用關係莊皓鈞莊皓鈞引用關係
指導教授(外文):Chang, Hsin-LuChuang, Hao-Chun
口試委員:孔令傑
口試日期:2019-01-24
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:31
中文關鍵詞:梯度提升機電子零組件經銷商
外文關鍵詞:Gradient boosting machineElectronic component distributor
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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.
TABLES AND FIGURES 1
CHAPTER 1 INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Research Questions 3
CHAPTER 2 LITERATURE REVIEW 4
2.1 The Problems of Demand Prediction 4
2.2 Machine Learning for Supply Chain Demand Forecasting 5
CHAPTER 3 INVENTORY MODEL AND POLICY ANALYSIS 7
3.1 Inventory Control System 7
3.2 As-Is and To-Be Policy Analysis 9
CHAPTER 4 FORECASTING 14
4.1 Data and Feature Engineering 14
4.2 Forecast 15
4.3 The Result 22
CHAPTER 5 DISCUSSION 26
CHAPTER 6 CONCLUSION & LIMITATION 28
6.1 Conclusion 28
REFERENCES 30
[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
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