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研究生： Alvin Muhammad Ainul Yaqin
Alvin Muhammad Ainul Yaqin
論文名稱： 能源產業的備用零件需求預測
Spare Parts Demand Forecasting in Energy Industry
指導教授： 曹譽鐘
Yu-Chung Tsao
口試委員: 林希偉
Shi-Woei Lin
林久翔
Chiu-Hsiang Lin
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 43
中文關鍵詞： 需求預測 、備件 、堆疊泛化方式 、外部信息
外文關鍵詞： demand forecasting, spare parts, stacked generalization, external information
相關次數： 點閱：235  下載：22 
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    • 本論文研究能源行業的零部件需求預測問題。因為一般備件需求的需求規模和需求間隔的高度變化特性，備件的需求預測有其自身的困難與挑戰。在本研究中，提出了兩種處理零部件需求的預測方法：在第一種方法中，使用堆疊泛化方式結合傳統的時間序列預測方法和機器學習方法; 在第二種方法中，利用外部資訊來改進第一種方法的預測，從而產生更準確的預測。為了測試這些方法的性能，本研究考慮了天然氣液化公司的案例。在案例研究中，這些方法用於預測公司維護操作中使用的零部件月需求。在案例研究中還使用了幾種傳統的時間序列預測方法（包括Simple Moving Average，Single Exponential Smoothing，Croston’s Method，Syntetos-Boylan’s Approximation和Teunter-Syntetos-Babai’s Method）和幾種機器學習方法（包括Multiple Linear Regression，Elastic Net，Neural Network，Support Vector Machine和Random Forests）來比較其性能。 提出的方法。最後，結果表明，本文提出的方法是有希望的。研究成果顯示我們所提出的方法優於傳統的時間序列預測方法與機器學習方法。

    This paper deals with spare parts demand forecasting problem in energy industry. Forecasting parts demand has its own challenges because in general spares demand is characterized by high variation in its demand size and in its inter-demand interval. In this study, two forecasting approaches to deal with spare parts demand are proposed: in the base approach, traditional time series forecasting methods and machine learning methods are combined using stacked generalization; in the improved approach, external information is utilized to improve the predictions from the base approach, resulting in more accurate predictions. To test the performance of these approaches, a case study in a natural gas liquefaction company is provided in this research. In the case study, these approaches are employed to forecast the monthly demand of parts used in the company’s maintenance operations. Several traditional time series forecasting methods (including Simple Moving Average, Single Exponential Smoothing, Croston’s Method, Syntetos-Boylan’s Approximation, and Teunter-Syntetos-Babai’s Method) and several machine learning methods (including Multiple Linear Regression, Elastic Net, Neural Network, Support Vector Machine, and Random Forests) are also utilized in the case study to compare the performance of the proposed approaches. In the end, results showed that the approaches proposed in this paper are promising.

    摘要 i ABSTRACT ii ACKNOWLEDGEMENT iii TABLE OF CONTENTS iv LIST OF TABLES vi LIST OF FIGURES vii CHAPTER 1 INTRODUCTION 1 1.1. Research background 1 1.2. Research objectives 4 1.3. Research organization 4 CHAPTER 2 LITERATURE REVIEW 6 2.1. Spare parts demand forecasting using traditional time series forecasting methods 6 2.2. Spare parts demand forecasting using machine learning methods 7 2.3. Spare parts demand forecasting using hybrid traditional time series forecasting-machine learning methods 7 2.4. Spare parts demand forecasting by utilizing external information 8 CHAPTER 3 FORECASTING METHODS 9 3.1. Traditional time series forecasting methods 9 3.1.1. Simple Moving Average 9 3.1.2. Single Exponential Smoothing 9 3.1.3. Croston’s Method 10 3.1.4. Syntetos-Boylan’s Approximation 10 3.1.5. Teunter-Syntetos-Babai’s Method 11 3.2. Machine learning methods 12 3.2.1. Multiple Linear Regression 12 3.2.2. Elastic Net 12 3.2.3. Neural Network 13 3.2.4. Support Vector Machine 14 3.2.5. Random Forests 15 3.3. Proposed approaches 15 3.3.1. Base approach 16 3.3.2. Improved approach 17 CHAPTER 4 CASE STUDY 20 4.1. Overview 20 4.2. Settings 21 4.3. Forecast errors metrics 23 4.3.1. Mean absolute scaled error 24 4.3.2. Relative geometric root mean squared error 24 4.4. Results and discussion 25 4.4.1. Forecast errors results 25 4.4.2. The importance of external information 28 CHAPTER 5 CONCLUSION AND FUTURE RESEARCH 31 5.1. Conclusion 31 5.2. Future research 31 REFERENCES 33

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