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題名 機器學習結合集群與時間聚合方法應用於半導體供應鏈之需求預測 - 以 W 企業為例
The Integration of Machine Learning with Grouping Policy and Temporal Aggregation for Demand Forecasting in Semiconductor SCM: Using W Company as an Example作者 邵晙瑋
Shao, Chung-Wei貢獻者 張欣綠<br>杜雨儒
Chang, Hsin-Lu<br>Tu, Yu-Ju
邵晙瑋
Shao, Chung-Wei關鍵詞 電子供應鏈
需求預測
資料探勘
機器學習
Electronic Supply Chain
Demand Forecasting
Data Mining
Machine Learning日期 2022 上傳時間 2-Sep-2022 14:48:33 (UTC+8) 摘要 全球對於電子性產品的大量需求，帶動著半導體產業的快速發展。而零組件通路商在供應鏈中游扮演重要的角色，須給予下游穩定的需求供給，同時也需要控制庫存水位以降低存貨持有成本。本研究目標為利用機器學習方法，預測未來下游客戶廠區的產品需求，以幫助通路商提前規劃其訂貨策略。\n\n本研究與亞洲最大的半導體零組件通路商(簡稱W公司)進行合作，使用過往下游客戶實際取貨(Call-off)去預測未來需求。經過資料探索，我們發現有主要兩個問題：整體資料因產品需求特性，呈現高度稀疏性、變異性，本研究針對以上問題，提出解決方法。首先，針對資料的高度稀疏性，我們設定了不同的預測區間，並使用時間聚合方法將資料顆粒度放大，降低資料稀疏程度，希望進而提升預測績效；針對不同產品的高變異性資料，我們使用集群方法將各產品做分組，組內的產品因擁有相似的需求特性，能夠更好地配適機器學習模型；最後使用不同的機器學習模型(XGBoost, SVM, Deep Neural Network)進行預測。期望找出最佳的需求預測資料組合模式，期望能幫助合作公司提升預測準確度，為半導體產業鏈帶來一些新的需求預測方法進行參考。
As the high demand for electronic products has driven the development of the semiconductor industry, distributors play an important role in the supply chain because they need to meet downstream customer demand and control inventory levels to lower holding costs. The goal of this study is to use machine learning methods to predict future demand from downstream customers’ plants to help distributors schedule ordering policies.\n\nThe study cooperates with Asia’s largest semiconductor component distributor (referred to as W Company) and uses its historical data to implement demand forecasting. We find two problems during the exploratory data analysis: high sparsity\nand high volatility due to the characteristics of customer demand. We solve problems by proposing a framework that includes temporal aggregation to lower data sparsity and grouping policies across different products to lower volatility and better fit machine learning models. We then employ three machine learning models — XGBoost, SVM, and Deep Neural Network — with different parameter settings. Our goal is to find the best forecasting combinations that are better than W company’s original method (8WMA).參考文獻 Abolghasemi, M., Beh, E., Tarr, G., and Gerlach, R. 2020. "Demand Forecasting in\nSupply Chain: The Impact of Demand Volatility in the Presence of\nPromotion," Computers & Industrial Engineering (142), p. 106380.\nAburto, L., and Weber, R. 2007. "Improved Supply Chain Management Based on\nHybrid Demand Forecasts," Applied Soft Computing (7:1), pp. 136-144.\nAl-Bakri, N. F., & Hashim, S. H. (2018). Reducing data sparsity in recommender\nsystems. Al-Nahrain Journal of Science, 21(2), pp. 138-147.\nAnderson Jr, E. G., Fine, C. H., & Parker, G. G. (2000). Upstream volatility in the\nsupply chain: The machine tool industry as a case study. Production and\nOperations Management, 9(3), pp. 239-261.\nBerndt, D. J., and Clifford, J. 1994. "Using Dynamic Time Warping to Find Patterns\nin Time Series," KDD workshop: Seattle, WA, USA:, pp. 359-370.\nBissmark, J., and Wärnling, O. 2017. "The Sparse Data Problem within Classification\nAlgorithms: The Effect of Sparse Data on the Naïve Bayes Algorithm."\nBox, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. 2015. Time Series\nAnalysis: Forecasting and Control. John Wiley & Sons.\nBoylan, J. E., Syntetos, A. A., and Karakostas, G. C. 2008. "Classification for\nForecasting and Stock Control: A Case Study," Journal of the operational\nresearch society (59:4), pp. 473-481.\nBrown, R. G., and Meyer, R. F. 1961. "The Fundamental Theorem of Exponential\nSmoothing," Operations Research (9:5), pp. 673-685.\nCachon, G. P., and Lariviere, M. A. 2001. "Contracting to Assure Supply: How to\nShare Demand Forecasts in a Supply Chain," Management science (47:5), pp.\n629-646.\nCarbonneau, R., Laframboise, K., and Vahidov, R. 2008. "Application of Machine\nLearning Techniques for Supply Chain Demand Forecasting," European\nJournal of Operational Research (184:3), pp. 1140-1154.\nChatfield, C. 2000. Time-Series Forecasting. CRC press.\nChen, T., and Guestrin, C. 2016. "Xgboost: A Scalable Tree Boosting System,"\nProceedings of the 22nd acm sigkdd international conference on knowledge\ndiscovery and data mining, pp. 785-794.\nChien, C.-F., Chen, Y.-J., and Peng, J.-T. 2010. "Manufacturing Intelligence for\nSemiconductor Demand Forecast Based on Technology Diffusion and Product\nLife Cycle," International Journal of Production Economics (128:2), pp. 496-\n509.\n61\nCrook, T. R., and Combs, J. G. 2007. "Sources and Consequences of Bargaining\nPower in Supply Chains," Journal of operations management (25:2), pp. 546-\n555.\nFerreira, K. J., Lee, B. H. A., and Simchi-Levi, D. 2016. "Analytics for an Online\nRetailer: Demand Forecasting and Price Optimization," Manufacturing &\nService Operations Management (18:1), pp. 69-88.\nGardner, E. S. 1990. "Evaluating Forecast Performance in an Inventory Control\nSystem," Management Science (36:4), pp. 490-499.\nGonçalves, J. N., Cortez, P., Carvalho, M. S., and Frazão, N. M. 2021. "A\nMultivariate Approach for Multi-Step Demand Forecasting in Assembly\nIndustries: Empirical Evidence from an Automotive Supply Chain," Decision\nSupport Systems (142), p. 113452.\nHill, T., O`Connor, M., and Remus, W. 1996. "Neural Network Models for Time\nSeries Forecasts," Management science (42:7), pp. 1082-1092.\nJha, A., Ray, S., Seaman, B., and Dhillon, I. S. 2015. "Clustering to Forecast Sparse\nTime-Series Data," 2015 IEEE 31st International Conference on Data\nEngineering: IEEE, pp. 1388-1399.\nKim, K.-j. 2003. "Financial Time Series Forecasting Using Support Vector\nMachines," Neurocomputing (55:1-2), pp. 307-319.\nKleijnen, J. P., and Smits, M. T. 2003. "Performance Metrics in Supply Chain\nManagement," Journal of the operational research society (54:5), pp. 507-\n514.\nLee, H. L., Padmanabhan, V., and Whang, S. 1997. "Information Distortion in a\nSupply Chain: The Bullwhip Effect," Management science (43:4), pp. 546-\n558.\nMakridakis, S., Spiliotis, E., and Assimakopoulos, V. 2018. "Statistical and Machine\nLearning Forecasting Methods: Concerns and Ways Forward," PloS one\n(13:3), p. e0194889.\nMeddahi, N., and Renault, E. 2004. "Temporal Aggregation of Volatility Models,"\nJournal of Econometrics (119:2), pp. 355-379.\nMüller, M. 2007. "Dynamic Time Warping," Information retrieval for music and\nmotion), pp. 69-84.\nMurray, P. W., Agard, B., and Barajas, M. A. 2015. "Forecasting Supply Chain\nDemand by Clustering Customers," IFAC-PapersOnLine (48:3), pp. 1834-\n1839.\nNikolopoulos, K., Syntetos, A. A., Boylan, J. E., Petropoulos, F., and\nAssimakopoulos, V. 2011. "An Aggregate–Disaggregate Intermittent Demand\n\nApproach (Adida) to Forecasting: An Empirical Proposition and Analysis,"\nJournal of the Operational Research Society (62:3), pp. 544-554.\nNoble, W. S. 2006. "What Is a Support Vector Machine?," Nature biotechnology\n(24:12), pp. 1565-1567.\nPappas, S. S., Ekonomou, L., Karamousantas, D. C., Chatzarakis, G., Katsikas, S.,\nand Liatsis, P. 2008. "Electricity Demand Loads Modeling Using\nAutoregressive Moving Average (Arma) Models," Energy (33:9), pp. 1353-\n1360.\nPfeffer, J., and Pfeffer, J. 1981. Power in Organizations. Pitman Marshfield, MA.\nReinsel, G. C., and Ahn, S. K. 1992. "Vector Autoregressive Models with Unit Roots\nand Reduced Rank Structure: Estimation. Likelihood Ratio Test, and\nForecasting," Journal of time series analysis (13:4), pp. 353-375.\nRostami‐Tabar, B., Babai, M. Z., Syntetos, A., and Ducq, Y. 2013. "Demand\nForecasting by Temporal Aggregation," Naval Research Logistics (NRL)\n(60:6), pp. 479-498.\nSchmidhuber, J. 2015. "Deep Learning in Neural Networks: An Overview," Neural\nnetworks (61), pp. 85-117.\nSenin, P. 2008. "Dynamic Time Warping Algorithm Review," Information and\nComputer Science Department University of Hawaii at Manoa Honolulu, USA\n(855:1-23), p. 40.\nSyntetos, A. A., and Boylan, J. E. 2005. "The Accuracy of Intermittent Demand\nEstimates," International Journal of forecasting (21:2), pp. 303-314.\nTerwiesch, C., Ren, Z. J., Ho, T. H., and Cohen, M. A. 2005. "An Empirical Analysis\nof Forecast Sharing in the Semiconductor Equipment Supply Chain,"\nManagement science (51:2), pp. 208-220.\nTeunter, R. H., Syntetos, A. A., and Babai, M. Z. 2011. "Intermittent Demand:\nLinking Forecasting to Inventory Obsolescence," European Journal of\nOperational Research (214:3), pp. 606-615.\nVan Steenbergen, R., and Mes, M. R. 2020. "Forecasting Demand Profiles of New\nProducts," Decision support systems (139), p. 113401.\nWang, C.-H., and Chen, J.-Y. 2019. "Demand Forecasting and Financial Estimation\nConsidering the Interactive Dynamics of Semiconductor Supply-Chain\nCompanies," Computers & Industrial Engineering (138), p. 106104.\nWillemain, T. R., Smart, C. N., Shockor, J. H., and DeSautels, P. A. 1994.\n"Forecasting Intermittent Demand in Manufacturing: A Comparative\nEvaluation of Croston`s Method," International journal of forecasting (10:4),\npp. 529-538.\n\nWillmott, C. J., and Matsuura, K. 2005. "Advantages of the Mean Absolute Error\n(Mae) over the Root Mean Square Error (Rmse) in Assessing Average Model\nPerformance," Climate research (30:1), pp. 79-82.\nWinters, P. R. 1960. "Forecasting Sales by Exponentially Weighted Moving\nAverages," Management science (6:3), pp. 324-342.\nZhao, X., Xie, J., and Wei, J. C. 2002. "The Impact of Forecast Errors on Early Order\nCommitment in a Supply Chain," Decision Sciences (33:2), pp. 251-280.\nZheng, H., Yuan, J., and Chen, L. 2017. "Short-Term Load Forecasting Using EmdLstm Neural Networks with a Xgboost Algorithm for Feature Importance\nEvaluation," Energies (10:8), p. 1168.\nZhu, X., Ninh, A., Zhao, H., and Liu, Z. 2021. "Demand Forecasting with Supply‐\nChain Information and Machine Learning: Evidence in the Pharmaceutical\nIndustry," Production and Operations Management (30:9), pp. 3231-3252. 描述 碩士
國立政治大學
資訊管理學系
109356027資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109356027 資料類型 thesis dc.contributor.advisor 張欣綠<br>杜雨儒 zh_TW dc.contributor.advisor Chang, Hsin-Lu<br>Tu, Yu-Ju en_US dc.contributor.author (Authors) 邵晙瑋 zh_TW dc.contributor.author (Authors) Shao, Chung-Wei en_US dc.creator (作者) 邵晙瑋 zh_TW dc.creator (作者) Shao, Chung-Wei en_US dc.date (日期) 2022 en_US dc.date.accessioned 2-Sep-2022 14:48:33 (UTC+8) - dc.date.available 2-Sep-2022 14:48:33 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2022 14:48:33 (UTC+8) - dc.identifier (Other Identifiers) G0109356027 en_US dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=160928 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 109356027 zh_TW dc.description.abstract (摘要) 全球對於電子性產品的大量需求，帶動著半導體產業的快速發展。而零組件通路商在供應鏈中游扮演重要的角色，須給予下游穩定的需求供給，同時也需要控制庫存水位以降低存貨持有成本。本研究目標為利用機器學習方法，預測未來下游客戶廠區的產品需求，以幫助通路商提前規劃其訂貨策略。\n\n本研究與亞洲最大的半導體零組件通路商(簡稱W公司)進行合作，使用過往下游客戶實際取貨(Call-off)去預測未來需求。經過資料探索，我們發現有主要兩個問題：整體資料因產品需求特性，呈現高度稀疏性、變異性，本研究針對以上問題，提出解決方法。首先，針對資料的高度稀疏性，我們設定了不同的預測區間，並使用時間聚合方法將資料顆粒度放大，降低資料稀疏程度，希望進而提升預測績效；針對不同產品的高變異性資料，我們使用集群方法將各產品做分組，組內的產品因擁有相似的需求特性，能夠更好地配適機器學習模型；最後使用不同的機器學習模型(XGBoost, SVM, Deep Neural Network)進行預測。期望找出最佳的需求預測資料組合模式，期望能幫助合作公司提升預測準確度，為半導體產業鏈帶來一些新的需求預測方法進行參考。 zh_TW dc.description.abstract (摘要) As the high demand for electronic products has driven the development of the semiconductor industry, distributors play an important role in the supply chain because they need to meet downstream customer demand and control inventory levels to lower holding costs. The goal of this study is to use machine learning methods to predict future demand from downstream customers’ plants to help distributors schedule ordering policies.\n\nThe study cooperates with Asia’s largest semiconductor component distributor (referred to as W Company) and uses its historical data to implement demand forecasting. We find two problems during the exploratory data analysis: high sparsity\nand high volatility due to the characteristics of customer demand. We solve problems by proposing a framework that includes temporal aggregation to lower data sparsity and grouping policies across different products to lower volatility and better fit machine learning models. We then employ three machine learning models — XGBoost, SVM, and Deep Neural Network — with different parameter settings. Our goal is to find the best forecasting combinations that are better than W company’s original method (8WMA). en_US dc.description.tableofcontents CHAPTER 1: INTRODUCTION 8\nCHAPTER 2: LITERATURE REVIEW 13\n2.1. Demand forecasting 13\n2.2. Grouping policy for demand forecasting 17\n2.3. Temporal aggregation for demand forecasting 19\nCHAPTER 3: METHODOLOGY 21\n3.1. Data Description and Preprocessing 21\n3.2. Research Method 25\nCHAPTER 4: EXPERIMENT RESULTS 33\n4.1. Initial Data Analysis 33\n4.2. Implementation of Temporal Aggregation 37\n4.3. Forecasting Results 37\nCHAPTER 5: DISCUSSION 54\nCHAPTER 6: CONCLUSION 58\nREFERENCE 60 zh_TW dc.format.extent 2021974 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109356027 en_US dc.subject (關鍵詞) 電子供應鏈 zh_TW dc.subject (關鍵詞) 需求預測 zh_TW dc.subject (關鍵詞) 資料探勘 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) Electronic Supply Chain en_US dc.subject (關鍵詞) Demand Forecasting en_US dc.subject (關鍵詞) Data Mining en_US dc.subject (關鍵詞) Machine Learning en_US dc.title (題名) 機器學習結合集群與時間聚合方法應用於半導體供應鏈之需求預測 - 以 W 企業為例 zh_TW dc.title (題名) The Integration of Machine Learning with Grouping Policy and Temporal Aggregation for Demand Forecasting in Semiconductor SCM: Using W Company as an Example en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Abolghasemi, M., Beh, E., Tarr, G., and Gerlach, R. 2020. "Demand Forecasting in\nSupply Chain: The Impact of Demand Volatility in the Presence of\nPromotion," Computers & Industrial Engineering (142), p. 106380.\nAburto, L., and Weber, R. 2007. "Improved Supply Chain Management Based on\nHybrid Demand Forecasts," Applied Soft Computing (7:1), pp. 136-144.\nAl-Bakri, N. F., & Hashim, S. H. (2018). Reducing data sparsity in recommender\nsystems. Al-Nahrain Journal of Science, 21(2), pp. 138-147.\nAnderson Jr, E. G., Fine, C. H., & Parker, G. G. (2000). Upstream volatility in the\nsupply chain: The machine tool industry as a case study. Production and\nOperations Management, 9(3), pp. 239-261.\nBerndt, D. J., and Clifford, J. 1994. "Using Dynamic Time Warping to Find Patterns\nin Time Series," KDD workshop: Seattle, WA, USA:, pp. 359-370.\nBissmark, J., and Wärnling, O. 2017. "The Sparse Data Problem within Classification\nAlgorithms: The Effect of Sparse Data on the Naïve Bayes Algorithm."\nBox, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. 2015. Time Series\nAnalysis: Forecasting and Control. John Wiley & Sons.\nBoylan, J. E., Syntetos, A. A., and Karakostas, G. C. 2008. "Classification for\nForecasting and Stock Control: A Case Study," Journal of the operational\nresearch society (59:4), pp. 473-481.\nBrown, R. G., and Meyer, R. F. 1961. "The Fundamental Theorem of Exponential\nSmoothing," Operations Research (9:5), pp. 673-685.\nCachon, G. P., and Lariviere, M. A. 2001. "Contracting to Assure Supply: How to\nShare Demand Forecasts in a Supply Chain," Management science (47:5), pp.\n629-646.\nCarbonneau, R., Laframboise, K., and Vahidov, R. 2008. "Application of Machine\nLearning Techniques for Supply Chain Demand Forecasting," European\nJournal of Operational Research (184:3), pp. 1140-1154.\nChatfield, C. 2000. Time-Series Forecasting. CRC press.\nChen, T., and Guestrin, C. 2016. "Xgboost: A Scalable Tree Boosting System,"\nProceedings of the 22nd acm sigkdd international conference on knowledge\ndiscovery and data mining, pp. 785-794.\nChien, C.-F., Chen, Y.-J., and Peng, J.-T. 2010. "Manufacturing Intelligence for\nSemiconductor Demand Forecast Based on Technology Diffusion and Product\nLife Cycle," International Journal of Production Economics (128:2), pp. 496-\n509.\n61\nCrook, T. R., and Combs, J. G. 2007. "Sources and Consequences of Bargaining\nPower in Supply Chains," Journal of operations management (25:2), pp. 546-\n555.\nFerreira, K. J., Lee, B. H. A., and Simchi-Levi, D. 2016. "Analytics for an Online\nRetailer: Demand Forecasting and Price Optimization," Manufacturing &\nService Operations Management (18:1), pp. 69-88.\nGardner, E. S. 1990. "Evaluating Forecast Performance in an Inventory Control\nSystem," Management Science (36:4), pp. 490-499.\nGonçalves, J. N., Cortez, P., Carvalho, M. S., and Frazão, N. M. 2021. "A\nMultivariate Approach for Multi-Step Demand Forecasting in Assembly\nIndustries: Empirical Evidence from an Automotive Supply Chain," Decision\nSupport Systems (142), p. 113452.\nHill, T., O`Connor, M., and Remus, W. 1996. "Neural Network Models for Time\nSeries Forecasts," Management science (42:7), pp. 1082-1092.\nJha, A., Ray, S., Seaman, B., and Dhillon, I. S. 2015. "Clustering to Forecast Sparse\nTime-Series Data," 2015 IEEE 31st International Conference on Data\nEngineering: IEEE, pp. 1388-1399.\nKim, K.-j. 2003. "Financial Time Series Forecasting Using Support Vector\nMachines," Neurocomputing (55:1-2), pp. 307-319.\nKleijnen, J. P., and Smits, M. T. 2003. "Performance Metrics in Supply Chain\nManagement," Journal of the operational research society (54:5), pp. 507-\n514.\nLee, H. L., Padmanabhan, V., and Whang, S. 1997. "Information Distortion in a\nSupply Chain: The Bullwhip Effect," Management science (43:4), pp. 546-\n558.\nMakridakis, S., Spiliotis, E., and Assimakopoulos, V. 2018. "Statistical and Machine\nLearning Forecasting Methods: Concerns and Ways Forward," PloS one\n(13:3), p. e0194889.\nMeddahi, N., and Renault, E. 2004. "Temporal Aggregation of Volatility Models,"\nJournal of Econometrics (119:2), pp. 355-379.\nMüller, M. 2007. "Dynamic Time Warping," Information retrieval for music and\nmotion), pp. 69-84.\nMurray, P. W., Agard, B., and Barajas, M. A. 2015. "Forecasting Supply Chain\nDemand by Clustering Customers," IFAC-PapersOnLine (48:3), pp. 1834-\n1839.\nNikolopoulos, K., Syntetos, A. A., Boylan, J. E., Petropoulos, F., and\nAssimakopoulos, V. 2011. "An Aggregate–Disaggregate Intermittent Demand\n\nApproach (Adida) to Forecasting: An Empirical Proposition and Analysis,"\nJournal of the Operational Research Society (62:3), pp. 544-554.\nNoble, W. S. 2006. "What Is a Support Vector Machine?," Nature biotechnology\n(24:12), pp. 1565-1567.\nPappas, S. S., Ekonomou, L., Karamousantas, D. C., Chatzarakis, G., Katsikas, S.,\nand Liatsis, P. 2008. "Electricity Demand Loads Modeling Using\nAutoregressive Moving Average (Arma) Models," Energy (33:9), pp. 1353-\n1360.\nPfeffer, J., and Pfeffer, J. 1981. Power in Organizations. Pitman Marshfield, MA.\nReinsel, G. C., and Ahn, S. K. 1992. "Vector Autoregressive Models with Unit Roots\nand Reduced Rank Structure: Estimation. Likelihood Ratio Test, and\nForecasting," Journal of time series analysis (13:4), pp. 353-375.\nRostami‐Tabar, B., Babai, M. Z., Syntetos, A., and Ducq, Y. 2013. "Demand\nForecasting by Temporal Aggregation," Naval Research Logistics (NRL)\n(60:6), pp. 479-498.\nSchmidhuber, J. 2015. "Deep Learning in Neural Networks: An Overview," Neural\nnetworks (61), pp. 85-117.\nSenin, P. 2008. "Dynamic Time Warping Algorithm Review," Information and\nComputer Science Department University of Hawaii at Manoa Honolulu, USA\n(855:1-23), p. 40.\nSyntetos, A. A., and Boylan, J. E. 2005. "The Accuracy of Intermittent Demand\nEstimates," International Journal of forecasting (21:2), pp. 303-314.\nTerwiesch, C., Ren, Z. J., Ho, T. H., and Cohen, M. A. 2005. "An Empirical Analysis\nof Forecast Sharing in the Semiconductor Equipment Supply Chain,"\nManagement science (51:2), pp. 208-220.\nTeunter, R. H., Syntetos, A. A., and Babai, M. Z. 2011. "Intermittent Demand:\nLinking Forecasting to Inventory Obsolescence," European Journal of\nOperational Research (214:3), pp. 606-615.\nVan Steenbergen, R., and Mes, M. R. 2020. "Forecasting Demand Profiles of New\nProducts," Decision support systems (139), p. 113401.\nWang, C.-H., and Chen, J.-Y. 2019. "Demand Forecasting and Financial Estimation\nConsidering the Interactive Dynamics of Semiconductor Supply-Chain\nCompanies," Computers & Industrial Engineering (138), p. 106104.\nWillemain, T. R., Smart, C. N., Shockor, J. H., and DeSautels, P. A. 1994.\n"Forecasting Intermittent Demand in Manufacturing: A Comparative\nEvaluation of Croston`s Method," International journal of forecasting (10:4),\npp. 529-538.\n\nWillmott, C. J., and Matsuura, K. 2005. "Advantages of the Mean Absolute Error\n(Mae) over the Root Mean Square Error (Rmse) in Assessing Average Model\nPerformance," Climate research (30:1), pp. 79-82.\nWinters, P. R. 1960. "Forecasting Sales by Exponentially Weighted Moving\nAverages," Management science (6:3), pp. 324-342.\nZhao, X., Xie, J., and Wei, J. C. 2002. "The Impact of Forecast Errors on Early Order\nCommitment in a Supply Chain," Decision Sciences (33:2), pp. 251-280.\nZheng, H., Yuan, J., and Chen, L. 2017. "Short-Term Load Forecasting Using EmdLstm Neural Networks with a Xgboost Algorithm for Feature Importance\nEvaluation," Energies (10:8), p. 1168.\nZhu, X., Ninh, A., Zhao, H., and Liu, Z. 2021. "Demand Forecasting with Supply‐\nChain Information and Machine Learning: Evidence in the Pharmaceutical\nIndustry," Production and Operations Management (30:9), pp. 3231-3252. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202201211 en_US
