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研究生： 陳愷謙
Chen, Kai-Chien
論文名稱： 最終訂單問題的備料需求預測
Spare Parts Demand Forecasting in the Final Order Problem
指導教授： 李育杰
Lee, Yuh-Jye
口試委員: 徐南蓉
Hsu, Nan-Jung
黃文瀚
Hwang, Wen-Han
學位類別： 碩士
Master
系所名稱： 理學院 - 統計學研究所
Institute of Statistics
論文出版年： 2018
畢業學年度： 107
語文別： 英文
論文頁數： 42
中文關鍵詞： 最終訂單問題 、備料 、需求預測 、移動平均 、隨機森林演算法
外文關鍵詞： the final order problem, spare part, demand forecasting, moving average model, random forest algorithm
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    • 售後服務為現今越來越重要的一個議題。為了提供好的售後服務給客戶，
    公司需要確保備料的存量能讓維修服務正常運作。
    在產品停產之後，相對應的料件也會停止供應，在供應截止之前，供應商
    會給公司最後一次下單的機會。最終訂單的時間範圍包括一年保固、兩年保修
    以及四個月的緩衝，總共四十個月。我們提供的模型提供最終定單未來四十個
    月的預測用量。
    模型中以移動平均的概念結合隨機森林演算法引入近期的資訊，讓預測更
    為準確。比起傳統用於預測需求量移動平均的方法，有顯著的進步。

    After-sales service is an increasingly important issue nowadays. In order to
    provide their customers good after-sales service, companies need to ensure the
    amount of spare parts needed in maintenance service.
    After the product ends its manufacturing, the corresponding parts are not
    available for ordering. The suppliers give the last chance to the company to order
    spare parts before the end of the supply. The time horizon of the final order
    includes a three-year warranty and a four-month buffer, a total of forty months.
    Our model predicts the demand of the final order over the next forty months.
    The model combines the concept of the moving average model with random
    forest algorithm to introduce the latest information, making the prediction more
    accurate. Compared with moving average, the conventional method for demand
    forecasting, the prediction of our model progresses significantly.

    1 Introduction 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Literature Review 3 2.1 Characteristics of Spare Part . . . . . . . . . . . . . . . . . . . . . 3 2.2 Demand Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 Final Order Problem . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Aggregation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3.1 Blending . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3.2 Random Forest Algorithm . . . . . . . . . . . . . . . . . . . 9 3 Methodology 11 3.1 Problem Definition and Assumption . . . . . . . . . . . . . . . . . . 11 3.2 Repair Data Description . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Prediction Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3.1 Short-term Prediction . . . . . . . . . . . . . . . . . . . . . 14 3.3.2 Long-term Prediction . . . . . . . . . . . . . . . . . . . . . 17 3.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4 Real Data Analysis 24 4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 Model Fitting and Evaluation . . . . . . . . . . . . . . . . . . . . . 25 5 Conclusion and Future Work 33 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 References 35 Appendix 37

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