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作者姓名(中文):Alvin Muhammad Ainul Yaqin
作者姓名(英文):Alvin Muhammad Ainul Yaqin
論文名稱(中文):能源產業的備用零件需求預測
論文名稱(外文):Spare Parts Demand Forecasting in Energy Industry
指導教授姓名(中文):曹譽鐘
指導教授姓名(英文):Yu-Chung Tsao
口試委員姓名(中文):林希偉
林久翔
口試委員姓名(英文):Shi-Woei Lin
Chiu-Hsiang Lin
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學號:M10701835
出版年(民國):108
畢業學年度:107
學期:2
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 forecastingspare partsstacked generalizationexternal information
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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
Altay, N., & Litteral, L. A. (Eds.). (2011). Service parts management: Demand forecasting and inventory control. London: Springer.
Amin-Naseri, M. R., & Rostami-Tabar, B. (2008). Neural network approach to lumpy demand forecasting for spare parts in process industries. In Proceedings of the 2008 International Conference on Computer and Communication Engineering (pp. 1378–1382). Kuala Lumpur: IEEE.
Boone, T., Ganeshan, R., Hicks, R. L., & Sanders, N. R. (2018). Can Google Trends improve your sales forecast? Production and Operations Management, 27(10), 1770–1774.
Breiman, L. (1996). Stacked regressions. Machine Learning, 24(1), 49–64.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Brown, R. G. (1959). Statistical forecasting for inventory control. New York: McGraw-Hill.
Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289–303.
Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2018). The operational value of social media information. Production and Operations Management, 27(10), 1749–1769.
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. In Advances in Neural Information Processing Systems 9 (Vol. 9, pp. 155–161). Denver: MIT Press.
Fildes, R. (1992). The evaluation of extrapolative forecasting methods. International Journal of Forecasting, 8(1), 81–98.
Garlock. (2019). Garlock flexseal spiral wound gaskets. Retrieved from https://www.garlock.com/sites/default/files/images/products/Flexsealcategoryimage_1200x900.png
Ghobbar, A. A., & Friend, C. H. (2003). Evaluation of forecasting methods for intermittent parts demand in the field of aviation: A predictive model. Computers & Operations Research, 30(14), 2097–2114.
Guajardo, M., Rönnqvist, M., Halvorsen, A. M., & Kallevik, S. I. (2015). Inventory management of spare parts in an energy company. Journal of the Operational Research Society, 66(2), 331–341.
Gutierrez, R. S., Solis, A. O., & Mukhopadhyay, S. (2008). Lumpy demand forecasting using neural networks. International Journal of Production Economics, 111(2), 409–420.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.
Hyndman, R. J. (2006). Another look at forecast accuracy metrics for intermittent demand. Foresight: The International Journal of Applied Forecasting, (4), 43–46.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. New York: Springer.
Keystone Indonesia. (2018). Keystone 129/239 butterfly valve. Retrieved from https://keystone.id/wp-content/uploads/2018/10/figure-129239-butterfly-valve.jpg
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1–26.
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York: Springer.
Kurniati, N., & Febriawati, E. (2011). Sparepart inventory management based on reliability at petrochemical industry. In Proceedings of the 2nd Annual Indonesian Scholars Conference in Taiwan (pp. 115–120). Taichung: AISC.
Liu, Y., Zhang, Q., Fan, Z.-P., You, T.-H., & Wang, L.-X. (2018). Maintenance spare parts demand forecasting for automobile 4S shop considering weather data. IEEE Transactions on Fuzzy Systems, 1–14.
Lolli, F., Gamberini, R., Regattieri, A., Balugani, E., Gatos, T., & Gucci, S. (2017). Single-hidden layer neural networks for forecasting intermittent demand. International Journal of Production Economics, 183, 116–128.
Pour, A. N., Tabar, B. R., & Rahimzadeh, A. (2008). A hybrid neural network and traditional approach for forecasting lumpy demand. International Journal of Industrial and Manufacturing Engineering, 2(4), 1028–1034.
Pujawan, I. N., Arvitrida, N. I., & Asihanto, B. P. (2010). Monte Carlo simulation for sparepart inventory. In Proceedings of the 11th Asia Pacific Industrial Engineering and Management Society (pp. 1–5). Melaka: APIEMS.
Raspisaniye Pogodi. (2019). Reliable Prognosis. Retrieved from https://rp5.ru/Weather_in_the_world
RStudio Team. (2016). RStudio: Integrated development for R. Boston: RStudio.
Şahin, M., Kızılaslan, R., & Demirel, Ö. F. (2013). Forecasting aviation spare parts demand using Croston based methods and artificial neural networks. Journal of Economic and Social Research, 15(2), 1–21.
Syntetos, A. A., & Boylan, J. E. (2001). On the bias of intermittent demand estimates. International Journal of Production Economics, 71(1–3), 457–466.
Syntetos, A. A., & Boylan, J. E. (2005). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303–314.
Syntetos, A. A., Boylan, J. E., & Croston, J. D. (2005). On the categorization of demand patterns. Journal of the Operational Research Society, 56(5), 495–503.
Tersine, R. J. (1994). Principles of inventory and materials management (4th ed.). Englewood Cliffs: Prentice Hall.
Teunter, R. H., Syntetos, A. A., & Babai, M. Z. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214(3), 606–615.
Wang, Z., Wen, J., & Hua, D. (2014). Research on distribution network spare parts demand forecasting and inventory quota. In Proceedings of the 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (pp. 1–6). Hong Kong: IEEE.
Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.
 
 
 
 
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