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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/122746
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/122746

    Title: 應用梯度提升機於供應鏈預測
    The Application of Gradient Boosting Machine in Supply Chain Forecasting
    Authors: 許博淳
    Hsu, Po-Chun
    Contributors: 張欣綠

    Chang, Hsin-Lu
    Chuang, Hao-Chun

    Hsu, Po-Chun
    Keywords: 梯度提升機
    Gradient boosting machine
    Electronic component distributor
    Date: 2019
    Issue Date: 2019-04-01 14:36:12 (UTC+8)
    Abstract: 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.
    Reference: [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
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G1053560091
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.MIS.003.2019.A05
    Appears in Collections:[資訊管理學系] 學位論文

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