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題名 半導體關鍵零組件預測錯誤之實證分析
An Empirical Analysis of Semiconductor Key Component Forecast Errors作者 洛昀
Lo, Yun貢獻者 莊皓鈞
Chuang, Hao-Chun
洛昀
Lo, Yun關鍵詞 企業決策
需求預測
迴歸分析
半導體產業
Semiconductor industry
Business decision-making
Demand forecasting
Regression analysis日期 2024 上傳時間 5-八月-2024 12:10:57 (UTC+8) 摘要 由於半導體庫存資訊不對稱的關係，需求預測對於半導體各大廠商來說都是非常重要的決策依據。需求預測的錯誤往往會伴隨庫存管理不良、產能過剩等後果。為了有效降低需求預測的錯誤，本研究旨在分析導致需求預測錯誤的相關因素，期望能夠找到預測錯誤產生的慣性，提供企業經理人在制定需求預測時的一些不同見解。 本研究透過分析Intel公司於2019年提供的187週、五個不同銷售地區（ALPHA、BETA、GAMMA、DELTA 和 EPSILON，以下簡稱配送中心A、B、G、D 和 E）的微處理器銷售和需求預測數據，並且在原有數據資料的基礎下，創造了表示產品銷售成長幅度(累積銷售總量)、產品近期銷售狀況(產品近四周的銷售總量)、產品生命週期(產品已上市週數) 、下一代產品的世代交替(距離下一代產品上市週數)、替代品與現有產品的共存與銷售策略(下一代的產品的同期銷售量)的相關變項，並以預測錯誤作為目標變數，運用回歸分析挖掘預測錯誤產生的潛在因素。 研究指出，市場趨勢評估、多世代產品策略及企業內部資訊整合程度均對需求預測的準確性有重要影響。經理人在進行需求預測時，不僅需要依賴產品相關的歷史數據，還需考慮市場的新興趨勢、產品生命週期、競爭者動態以及企業內部資訊的有效整合等潛在因素。期望透過本研究能為企業經理人提供在面對需求預測優化時的全面思考方式，減少決策錯誤的發生，進一步打造更完善的需求預測報表。
Due to the asymmetry of information in the semiconductor industry, demand forecasting is a crucial decision-making tool for major semiconductor manufacturers. In order to effectively reduce forecast errors, this study aims to analyze the factors contributing to inaccuracies in demand forecasting. By identifying the inherent biases in forecasting, the research seeks to provide business managers with new perspectives when formulating demand forecasts. This study analyzes the microprocessor sales and demand forecasting data provided by Intel in 2019 with over 187 weeks of data across five different sales regions (ALPHA, BETA, GAMMA, DELTA, and EPSILON, referred to as distribution centers A, B, G, D and E). Based on the original data, we created variables representing product sales growth (cumulative sales), recent sales performance (sales over the past four weeks), product life cycle (weeks since product launch), next-generation product transition (weeks until the next-generation product launch), and the coexistence sales strategy of substitutes and existing products (concurrent sales of next-generation products). Using forecasting errors as the target variable, regression analysis was employed to uncover potential factors leading to forecasting errors. This study points out that market trend assessment, multi-generation product strategies, and the degree of internal information integration within the company all significantly affect the accuracy of demand forecasts. Managers should not only rely on historical data when making forecasts but also consider emerging market trends, product life cycles, competitor dynamics, and the effective integration of internal information. It is hoped that this study can provide managers with a comprehensive approach to optimizing demand forecasts, reducing decision errors, achieving knowledge externalization, and further creating more accurate demand forecast reports.參考文獻 丁光偉，(2008)。建構半導體零組件採購策略選擇準則。國立中央大學工業管理研究所碩士論文。 李佳蓁、王宣智與江柏風等，(2023)。2023半導體產業年鑑。工研院產科國際所。搜尋日期：2024年 5 月 13 日。 李宗翰，(2014)。運用 ARIMA 與向量自我回歸模式探討新竹科學園區半導體產值預測。國立交通大學管理學院管理科學學程碩士論文。 程凱裕，(2022)。工具機零組件之需求預測－以 A 公司為例。國立中興大學企業管理學系碩士論文。 黃錫鴻，(2009)。應用灰色理論預測半導體設備消耗性零件需求量。國立中央大學工業管理研究所碩士論文。 黃義翔、聶喬齡，(2012) 。不同本土化版本的教練領導行為量表的實用性研究。臺灣體育學術研究，第五十三期：43-64。 劉家宏，(2023)。公允價值會計對分析師行為之影響 。國立臺北大學會計學系碩士論文。 蕭博修，(2016)。電子零件通路商在非直販模式下的訂單決策研究－以 ABC 公司為例。國立政治大學資訊管理學系碩士論文。 蘇國章，(2008)。創新力對公司績效影響：美國與台灣半導體產業實證分析。國立交通大學科技管理學系碩士論文。 Aliyeva, K. (2017). Demand forecasting for manufacturing under Z-Information. Procedia Computer Science, 120, 509-514. Chen, Y., Kang, Y., Chen, Y., & Wang, Z. (2020). Probabilistic forecasting with temporal convolutional neural network. Neurocomputing, 399, 491-501. Davis, T. (1993), Effective supply chain management. Sloan management review, 34, 35-35. Draganska, M., & Jain, D. (2005). Product‐line length as a competitive tool. Journal of Economics and Management Strategy, 14(1), 1-28. de Brentani, U., & Ragot, E. (1996). Diffusion of innovation in service industries. Service Industries Journal, 16(4), 441-464. Guo, J. (2022). Market sales forecasting related to the semiconductor manufacturing industry. Proceedings of the International Conference on Big Data Economy and Digital Management, 94-99. Habla, C., Drießel, R., Mönch, L., Ponsignon, T., & Ehm, H. (2007). A short-term forecast method for demand quantities in semiconductor manufacturing. 2007 IEEE International Conference on Automation Science and Engineering, 94-99. Hilbe, J. M. (2009). Data analysis using regression and multilevel/hierarchical models. Journal of Statistical Software, 18(1), 1-22. Hsu, L.-C. (2003). Applying the grey prediction model to the global integrated circuit industry. Technological Forecasting and Social Change, 70(6), 563-574. Jaipuria, S., & Mahapatra, S. (2014). An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, 41(5), 2395-2408. Jian-hua, R. (2007). Application of ARIMA model in semiconductor demand forecasting. Semiconductor Technology, 32(4), 45-50. Karthikeyan, K., & Karthika, D. (2020). A recent review article on demand forecasting. Journal of Xi'an University of Architecture & Technology, 12(3), 123-130. Kim, J. H. (2019). Multicollinearity and misleading statistical results. Korean Journal of Anesthesiology, 72(6), 558-569. Koca, E., Souza, G. C., & Druehl, C. T. (n.d.). Managing product rollovers. In Robert H. Smith School of Business, University of Maryland; Kelley School of Business, Indiana University; The School of Management, George Mason University. Manary, M. P., & Willems, S. P. (2019). Data Set: 187 Weeks of Customer Forecasts and Orders for Microprocessors from Intel Corporation. Manufacturing & Service Operations Management, 21(1), 171-176. Meeran, S., Jahanbin, S., Goodwin, P., & Frota Neto, J. Q. (2017). When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable? European Journal of Operational Research, 258(2), 512-524. Nagashima, M., Wehrle, F. T., Kerbache, L., & Lassagne, M. (2015). Impacts of adaptive collaboration on demand forecasting accuracy of different product categories throughout the product life cycle. Supply Chain Management: An International Journal, 20(4), 415-433. Norton, J. A., & Bass, F. M. (1987). A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products. Management Science, 33(9), 1069-1086. Prak, D., & Teunter, R. (2019). A general method for addressing forecasting uncertainty in inventory models. Journal of Economic Dynamics and Control, 100, 288-296. Schober, P., Boer, C., & Schwarte, L. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. Schrieber, J. (2005). Demand visibility improves demand forecasts. The Journal of Business Forecasting, 24(3), 32. Van Donselaar, K. H., Peters, J., De Jong, A., & Broekmeulen, R. A. C. M. (2016). Analysis and forecasting of demand during promotions for perishable items. International Journal of Production Economics, 172, 65-75. Vilko, J., Ritala, P., & Edelmann, J. (2014). On uncertainty in supply chain risk management, Lappeenranta: Lappeenranta University of Technology. Wilcox, R. 2005. Trimming and winsorization. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics: 2768-2771. Zimmermann, H.-J. (2000). An application-oriented view of modeling uncertainty. European Journal of Operational Research, 122(2), 190-198 CompaniesMarketCap.com. (2024). Largest semiconductor companies by market cap. Retrieved May 31, 2024, from https://companiesmarketcap.com/semiconductors/largest-semiconductor-companies-by-market-cap/ 描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
111363031資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111363031 資料類型 thesis dc.contributor.advisor 莊皓鈞 zh_TW dc.contributor.advisor Chuang, Hao-Chun en_US dc.contributor.author (作者) 洛昀 zh_TW dc.contributor.author (作者) Lo, Yun en_US dc.creator (作者) 洛昀 zh_TW dc.creator (作者) Lo, Yun en_US dc.date (日期) 2024 en_US dc.date.accessioned 5-八月-2024 12:10:57 (UTC+8) - dc.date.available 5-八月-2024 12:10:57 (UTC+8) - dc.date.issued (上傳時間) 5-八月-2024 12:10:57 (UTC+8) - dc.identifier (其他 識別碼) G0111363031 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152430 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 企業管理研究所(MBA學位學程) zh_TW dc.description (描述) 111363031 zh_TW dc.description.abstract (摘要) 由於半導體庫存資訊不對稱的關係，需求預測對於半導體各大廠商來說都是非常重要的決策依據。需求預測的錯誤往往會伴隨庫存管理不良、產能過剩等後果。為了有效降低需求預測的錯誤，本研究旨在分析導致需求預測錯誤的相關因素，期望能夠找到預測錯誤產生的慣性，提供企業經理人在制定需求預測時的一些不同見解。 本研究透過分析Intel公司於2019年提供的187週、五個不同銷售地區（ALPHA、BETA、GAMMA、DELTA 和 EPSILON，以下簡稱配送中心A、B、G、D 和 E）的微處理器銷售和需求預測數據，並且在原有數據資料的基礎下，創造了表示產品銷售成長幅度(累積銷售總量)、產品近期銷售狀況(產品近四周的銷售總量)、產品生命週期(產品已上市週數) 、下一代產品的世代交替(距離下一代產品上市週數)、替代品與現有產品的共存與銷售策略(下一代的產品的同期銷售量)的相關變項，並以預測錯誤作為目標變數，運用回歸分析挖掘預測錯誤產生的潛在因素。 研究指出，市場趨勢評估、多世代產品策略及企業內部資訊整合程度均對需求預測的準確性有重要影響。經理人在進行需求預測時，不僅需要依賴產品相關的歷史數據，還需考慮市場的新興趨勢、產品生命週期、競爭者動態以及企業內部資訊的有效整合等潛在因素。期望透過本研究能為企業經理人提供在面對需求預測優化時的全面思考方式，減少決策錯誤的發生，進一步打造更完善的需求預測報表。 zh_TW dc.description.abstract (摘要) Due to the asymmetry of information in the semiconductor industry, demand forecasting is a crucial decision-making tool for major semiconductor manufacturers. In order to effectively reduce forecast errors, this study aims to analyze the factors contributing to inaccuracies in demand forecasting. By identifying the inherent biases in forecasting, the research seeks to provide business managers with new perspectives when formulating demand forecasts. This study analyzes the microprocessor sales and demand forecasting data provided by Intel in 2019 with over 187 weeks of data across five different sales regions (ALPHA, BETA, GAMMA, DELTA, and EPSILON, referred to as distribution centers A, B, G, D and E). Based on the original data, we created variables representing product sales growth (cumulative sales), recent sales performance (sales over the past four weeks), product life cycle (weeks since product launch), next-generation product transition (weeks until the next-generation product launch), and the coexistence sales strategy of substitutes and existing products (concurrent sales of next-generation products). Using forecasting errors as the target variable, regression analysis was employed to uncover potential factors leading to forecasting errors. This study points out that market trend assessment, multi-generation product strategies, and the degree of internal information integration within the company all significantly affect the accuracy of demand forecasts. Managers should not only rely on historical data when making forecasts but also consider emerging market trends, product life cycles, competitor dynamics, and the effective integration of internal information. It is hoped that this study can provide managers with a comprehensive approach to optimizing demand forecasts, reducing decision errors, achieving knowledge externalization, and further creating more accurate demand forecast reports. en_US dc.description.tableofcontents 第壹章 緒論 1 第一節 研究背景與流程 1 第二節 全球半導體產業背景 4 第貳章 文獻探討 5 第參章 數據來源與變數 7 第一節 數據來源 7 第二節 變數解釋 8 第三節 敘述統計量與相關係數圖表 13 第肆章 實證研究方法與結果 15 第一節 變異膨脹因子(VARIANCE INFLATION FACTOR, VIF) 15 第二節 迴歸分析模型 17 第伍章 結論與建議 23 第一節 研究結論 23 第二節 研究建議 25 參考文獻 26 zh_TW dc.format.extent 3792264 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111363031 en_US dc.subject (關鍵詞) 企業決策 zh_TW dc.subject (關鍵詞) 需求預測 zh_TW dc.subject (關鍵詞) 迴歸分析 zh_TW dc.subject (關鍵詞) 半導體產業 zh_TW dc.subject (關鍵詞) Semiconductor industry en_US dc.subject (關鍵詞) Business decision-making en_US dc.subject (關鍵詞) Demand forecasting en_US dc.subject (關鍵詞) Regression analysis en_US dc.title (題名) 半導體關鍵零組件預測錯誤之實證分析 zh_TW dc.title (題名) An Empirical Analysis of Semiconductor Key Component Forecast Errors en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 丁光偉，(2008)。建構半導體零組件採購策略選擇準則。國立中央大學工業管理研究所碩士論文。 李佳蓁、王宣智與江柏風等，(2023)。2023半導體產業年鑑。工研院產科國際所。搜尋日期：2024年 5 月 13 日。 李宗翰，(2014)。運用 ARIMA 與向量自我回歸模式探討新竹科學園區半導體產值預測。國立交通大學管理學院管理科學學程碩士論文。 程凱裕，(2022)。工具機零組件之需求預測－以 A 公司為例。國立中興大學企業管理學系碩士論文。 黃錫鴻，(2009)。應用灰色理論預測半導體設備消耗性零件需求量。國立中央大學工業管理研究所碩士論文。 黃義翔、聶喬齡，(2012) 。不同本土化版本的教練領導行為量表的實用性研究。臺灣體育學術研究，第五十三期：43-64。 劉家宏，(2023)。公允價值會計對分析師行為之影響 。國立臺北大學會計學系碩士論文。 蕭博修，(2016)。電子零件通路商在非直販模式下的訂單決策研究－以 ABC 公司為例。國立政治大學資訊管理學系碩士論文。 蘇國章，(2008)。創新力對公司績效影響：美國與台灣半導體產業實證分析。國立交通大學科技管理學系碩士論文。 Aliyeva, K. (2017). Demand forecasting for manufacturing under Z-Information. Procedia Computer Science, 120, 509-514. Chen, Y., Kang, Y., Chen, Y., & Wang, Z. (2020). Probabilistic forecasting with temporal convolutional neural network. Neurocomputing, 399, 491-501. Davis, T. (1993), Effective supply chain management. Sloan management review, 34, 35-35. Draganska, M., & Jain, D. (2005). Product‐line length as a competitive tool. Journal of Economics and Management Strategy, 14(1), 1-28. de Brentani, U., & Ragot, E. (1996). Diffusion of innovation in service industries. Service Industries Journal, 16(4), 441-464. Guo, J. (2022). Market sales forecasting related to the semiconductor manufacturing industry. Proceedings of the International Conference on Big Data Economy and Digital Management, 94-99. Habla, C., Drießel, R., Mönch, L., Ponsignon, T., & Ehm, H. (2007). A short-term forecast method for demand quantities in semiconductor manufacturing. 2007 IEEE International Conference on Automation Science and Engineering, 94-99. Hilbe, J. M. (2009). Data analysis using regression and multilevel/hierarchical models. Journal of Statistical Software, 18(1), 1-22. Hsu, L.-C. (2003). Applying the grey prediction model to the global integrated circuit industry. Technological Forecasting and Social Change, 70(6), 563-574. Jaipuria, S., & Mahapatra, S. (2014). An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, 41(5), 2395-2408. Jian-hua, R. (2007). Application of ARIMA model in semiconductor demand forecasting. Semiconductor Technology, 32(4), 45-50. Karthikeyan, K., & Karthika, D. (2020). A recent review article on demand forecasting. Journal of Xi'an University of Architecture & Technology, 12(3), 123-130. Kim, J. H. (2019). Multicollinearity and misleading statistical results. Korean Journal of Anesthesiology, 72(6), 558-569. Koca, E., Souza, G. C., & Druehl, C. T. (n.d.). Managing product rollovers. In Robert H. Smith School of Business, University of Maryland; Kelley School of Business, Indiana University; The School of Management, George Mason University. Manary, M. P., & Willems, S. P. (2019). Data Set: 187 Weeks of Customer Forecasts and Orders for Microprocessors from Intel Corporation. Manufacturing & Service Operations Management, 21(1), 171-176. Meeran, S., Jahanbin, S., Goodwin, P., & Frota Neto, J. Q. (2017). When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable? European Journal of Operational Research, 258(2), 512-524. Nagashima, M., Wehrle, F. T., Kerbache, L., & Lassagne, M. (2015). Impacts of adaptive collaboration on demand forecasting accuracy of different product categories throughout the product life cycle. Supply Chain Management: An International Journal, 20(4), 415-433. Norton, J. A., & Bass, F. M. (1987). A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products. Management Science, 33(9), 1069-1086. Prak, D., & Teunter, R. (2019). A general method for addressing forecasting uncertainty in inventory models. Journal of Economic Dynamics and Control, 100, 288-296. Schober, P., Boer, C., & Schwarte, L. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. Schrieber, J. (2005). Demand visibility improves demand forecasts. The Journal of Business Forecasting, 24(3), 32. Van Donselaar, K. H., Peters, J., De Jong, A., & Broekmeulen, R. A. C. M. (2016). Analysis and forecasting of demand during promotions for perishable items. International Journal of Production Economics, 172, 65-75. Vilko, J., Ritala, P., & Edelmann, J. (2014). On uncertainty in supply chain risk management, Lappeenranta: Lappeenranta University of Technology. Wilcox, R. 2005. Trimming and winsorization. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics: 2768-2771. Zimmermann, H.-J. (2000). An application-oriented view of modeling uncertainty. European Journal of Operational Research, 122(2), 190-198 CompaniesMarketCap.com. (2024). Largest semiconductor companies by market cap. Retrieved May 31, 2024, from https://companiesmarketcap.com/semiconductors/largest-semiconductor-companies-by-market-cap/ zh_TW
