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題名 以系統動力學觀點分析疫情對半導體產業供應鏈的衝擊
A System Dynamics View of the Impact of the Pandemic on the Semiconductor Supply Chain作者 邱晨瑄
Khoo, Chern-Shiuan貢獻者 鄭至甫
Jeng, Jyh-Fu
邱晨瑄
Khoo, Chern-Shiuan關鍵詞 供應鏈管理
長鞭效應
系統動力學
Supply Chain Management
Bullwhip Effect
System Dynamics日期 2024 上傳時間 6-八月-2024 14:14:09 (UTC+8) 摘要 新冠疫情為近年全球最具代表性的黑天鵝事件，除了對民生帶來嚴重影響外，也對科技產業造成了嚴重衝擊。其影響甚深，即便已時過半載，許多科技公司仍然未完全走出陰霾，也因此迫使各大廠商開始 重新檢視了自身供應鏈的運作效率，其中受到最嚴重衝擊的莫過於車用半導體產業。由於半導體為汽車製造的關鍵原料，在疫情期間，因為車用半導體的短缺，使得許多汽車公司以及系統廠面臨被迫停工的 窘境，導致了交車交期不斷延後。 對於疫情的衝擊，過去的研究主要集中在整體產業的供應鏈中斷，往往忽略了公司內部的反應機制以及策略。本論文針對該學術缺口，結合系統動力學的方法，研究了半導體行業內部供應鏈管理。除了文獻回顧外，本文也透過訪談的方式，取得半導體公司的供應鏈運作模 式以及對本次長鞭效應的應對方法，以描繪整體產業的運作面貌。 本研究發現，本次疫情的影響使得半導體產業在經濟層面受益，但也揭露出了內部許多資訊流的斷點，因此本論文也提出了幾項減緩供應鏈衝擊的方法，像是建立供應鏈控制塔，提升對於供應鏈資訊的掌 握度，又或是採納更多維度的資訊，以獲得下游狀況的真實面貌，減少供應鏈在資訊不對稱下造成的經濟損失。
The COVID-19 pandemic has had a profound impact on the tech industry, emphasizing the importance of robust supply chain management. As a major global event, the pandemic disrupted supply chains worldwide, causing significant delays and shortages, especially in the automotive semiconductor industry. This has forced companies to re-evaluate and reassess their supply chain strategies to ensure resilience and flexibility in the face of such disruptions. The critical role of semiconductors in various high-tech applications further highlights the necessity for efficient supply chain management to maintain industry competitiveness and meet market demand. Previous research on supply chain disruptions primarily focused on external industry conditions, often neglecting the internal response mechanisms within companies. This thesis addresses this gap by investigating the internal dynamics of supply chain management in the automotive semiconductor industry using system dynamics methodology. By conducting a comprehensive case study on a automotive semiconductor company, the research examines internal supply chain management practices and the dynamic relationships within the company's supply chain. The study utilizes system dynamics diagrams to illustrate the impact of the pandemic on the company's supply chain and the recovery of automotive chip demand. The findings of this research reveal several strategies to mitigate the bullwhip effect and enhance supply chain resilience. Implementing flowcasting, establishing supply chain control towers, and incorporating customer order behavior as a reference indicator for order forecasting are key recommendations. These strategies aim to improve demand forecasting accuracy, optimize inventory management, and enhance supply chain coordination and transparency. By adopting these measures, the semiconductor industry can better manage future disruptions, ensuring stability and sustainability in its supply chain operations.參考文獻 Acar, A. Z., & Uzunlar, M. B. (2014). The effects of process development and information technology on time-based supply chain performance. Procedia-Social and Behavioral Sciences, 150, 744-753. Aizcorbe, A., Oliner, S. D., & Sichel, D. E. (2008). Shifting trends in semiconductor prices and the pace of technological progress: is there a link? How close?. Business Economics, 43, 23-39. Bitran, G. R., Gurumurthi, S., & Sam, S. L. (2007). The need for third-party coordination in supply chain governance. MIT Sloan Management Review. Blattberg, R. C., & Neslin, S. A. (1993). Sales promotion: Concepts, Methods, and Strategies. Prentice Hall. Carbone, J. (1999). CM buyers look to reduce TOTAL COST. Purchasing, 126(16), 51-51. Cakanyildirim, M., Roundy, R. O., & Wood, S. C. (2002). Optimal capacity expansion and contraction under demand uncertainty. Cornell University Operations Research and Industrial Engineering. Christopher, M. (2016). Logistics and Supply Chain Management: Logistics & Supply Chain Management. Pearson UK. Ding, L., Ball, A., Matthews, J., McMahon, C. A., & Patel, M. (2007). Product representation in lightweight formats for product lifecycle management (PLM). In 4th nternational conference on digital enterprise technology. Fildes, R., Goodwin, P., & Lawrence, M. (2006). The design features of forecasting support systems and their effectiveness. Decision Support Systems, 42(1), 351-361. Forrester, J. W. (1994). System dynamics, systems thinking, and soft OR. System dynamics review, 10(23), 245-256. Holweg, M., Disney, S., Holmström, J., & Småros, J. (2005). Supply chain collaboration: Making sense of the strategy continuum. European management journal, 23(2), 170- 181. Huang, S. H., Sheoran, S. K., & Keskar, H. (2005). Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model. Computers & industrial engineering, 48(2), 377-394. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. Ingle, C., Bakliwal, D., Jain, J., Singh, P., Kale, P., & Chhajed, V. (2021, July). Demand forecasting: Literature review on various methodologies. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. Kotler, P., & Keller, K. L. (2006). Marketing management 12e. New Jersey. La Londe, B. J. (1997). Supply Chain Management: Myth or Reality?. Supply Chain Management Review, 1, 6-7. Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management science, 43(4), 546-558. Li, L., Su, Q., & Chen, X. (2011). Ensuring supply chain quality performance through applying the SCOR model. International Journal of Production Research, 49(1), 33- 57. Luna Reyes, L. F., & Andersen, D. L. (2003). Collecting and analyzing qualitative data for system dynamics: methods and models. System Dynamics Review: The Journal of the System Dynamics Society, 19(4), 271-296. Marinov, M. A., & Marinova, S. T. (2021). COVID-19 and International Business. Change of era. Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business logistics, 22(2), 1-25. Mentzer, J. T., & Moon, M. A. (2004). Sales Forecasting Management: a Demand Management Approach. Sage Publications. Miles, R. E., & Snow, C. C. (2007). Organization theory and supply chain management: An evolving research perspective. Journal of Operations Management, 25(2), 459- 463. Mönch, L., Uzsoy, R., & Fowler, J. W. (2018). A survey of semiconductor supply chain models part I: semiconductor supply chains, strategic network design, and supply chain simulation. International Journal of Production Research, 56(13), 4524-4545. Monks, J.G. (1987). Operations Management. 3rd Edition, Theory and Problems. McGraw- Hill Book Co., New York. Potthast, J. M., Gärtner, H., & Hertrampf, F. (2010). Allocation for manufacturing companies. LogForum, 6(2), 3. Ratusny, M., Schiffer, M., & Ehm, H. (2022). Customer order behavior classification via convolutional neural networks in the semiconductor industry. IEEE Transactions on Semiconductor Manufacturing, 35(3), 470-477. Ross, D. F., & Ross, D. F. (2015). Introduction to supply chain management. Distribution planning and control: managing in the era of supply chain management, 3-43. Senge, P. M., & Sterman, J. D. (1992). Systems thinking and organizational learning: Acting locally and thinking globally in the organization of the future. European journal of operational research, 59(1), 137-150. Sharma, A. (2023). Digital transformation in supply chain management: A review. Journal of Supply Chain Management, 59(1), 23-40. Sheldon, D. H. (2006). World Class Sales & Operations Planning: a Guide to Successful Implementation and Robust Execution. J. Ross Publishing. Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-commerce: an exploration of its antecedents and consequences. Journal of retailing, 78(1), 41-50. Stank, T. P., Keller, S. B., & Daugherty, P. J. (2001). Supply chain collaboration and logistical service performance. Journal of Business logistics, 22(1), 29-48. Stadtler, H., Kilger, C., & Meyr, H. (2015). Supply chain management and advanced planning: concepts, models, software, and case studies. Springer. Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Irwin/McGraw-Hill. Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1-26. Taylor, F. W. (2004). Scientific Management. Routledge. Vollmann, T., Berry, W., Whybark, D. C., & Jacobs, F. R. (2004). Manufacturing planning and control systems for supply chain management: the definitive guide for professionals. McGraw-Hill Professional. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business logistics, 34(2), 77-84. Anjani Trivedi. (2021, February 15). Toyota broke its just-in-time rule just in time for the chip shortage. Bloomberg. Retrieved from https://www.bloomberg.com/opinion/ articles/2021-02-15/toyota-broke-its-just-in-time-rule-just-in-time-for-the-chi p-shortage?sref=Z208C5oo DIGITIMES Research. (2023). New US$12 billion factory for SMIC 7nm and other sub- 14nm wafers. Retrieved from https://www.bnext.com.tw/article/71953/foundry -cost Dobler, S., & Berking, J. (2023). This is how to prevent future semiconductor shortages. Oliver Wyman. Retrieved from https://www.oliverwyman.com/our-expertise/ins ights/2023/jul/this-is-how-to-prevent-future-semiconductor-shortages.html Gartner. (2023). Gartner forecasts worldwide semiconductor revenue to grow 17% in 2024. Retrieved from https://www.gartner.com/en/newsroom/press-releases/20 23-12-04-gartner-forecasts-worldwide-semiconductor-revenue-to-grow-17-perce nt-in-2024 KPMG. (2023). Global semiconductor industry outlook for 2023. Retrieved from https: //kpmg.com/kpmg-us/content/dam/kpmg/pdf/2024/global-semiconductor-ind ustry-outlook.pdf Leswing, K. (2021, May 14). Chip shortage expected to cost auto industry $110 billion in 2021. CNBC. Retrieved from https://www.cnbc.com/2021/05/14/chip-shortag e-expected-to-cost-auto-industry-110-billion-in-2021.html McKinsey & Company. (2021). Advanced driver-assistance systems: Challenges and opportunities ahead. Retrieved from https://www.mckinsey.com/industries/auto motive-and-assembly/our-insights/advanced-driver-assistance-systems-challen ges-and-opportunities-ahead McKinsey & Company. (2022). Navigating the semiconductor chip shortage: A controltower case study. Retrieved from https://www.mckinsey.com/capabilities/operat ions/our-insights/navigating-the-semiconductor-chip-shortage-a-control-tower -case-study PwC. (2021). Industry trends 2021. Retrieved from https://www.pwc.tw/zh/topics/t rends/industry-trends-20211224.html Regions. (2023). The semiconductor shortage. Retrieved from https://www.regions.co m/-/media/pdfs/wealth-management/The-Semiconductor-Shortage.pdf?revisi on=ac343b9e-5b42-4b20-8711-714b5b17d7c5 Semicon Society. (n.d.). Semiconductor manufacturing process explained. Retrieved from https://semiconsociety.com/semiconductor-manufacturing-process-expla ined/ TTIA. (n.d.). 半導體短缺對全球供應鏈的影響. 台灣智慧自動化與機器人協會. Retrieved from https://www.ttia-tw.org/letters.php?wshop=ttia&Opt=detaile d&tp=Letters&lang=zh-tw&letters_id=2265886 WSTS. (2021). WSTS Semiconductor Market Forecast. Retrieved from https://www. wsts.org/esraCMS/extension/media/f/WST/5145/WSTS_nr-2021_08.pdf#:~: text=URL%3A%20https%3A%2F%2Fwww.wsts.org%2FesraCMS%2Fextensi on%2Fmedia%2Ff%2FWST%2F5145%2FWSTS_nr 描述 碩士
國立政治大學
科技管理與智慧財產研究所
111364128資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111364128 資料類型 thesis dc.contributor.advisor 鄭至甫 zh_TW dc.contributor.advisor Jeng, Jyh-Fu en_US dc.contributor.author (作者) 邱晨瑄 zh_TW dc.contributor.author (作者) Khoo, Chern-Shiuan en_US dc.creator (作者) 邱晨瑄 zh_TW dc.creator (作者) Khoo, Chern-Shiuan en_US dc.date (日期) 2024 en_US dc.date.accessioned 6-八月-2024 14:14:09 (UTC+8) - dc.date.available 6-八月-2024 14:14:09 (UTC+8) - dc.date.issued (上傳時間) 6-八月-2024 14:14:09 (UTC+8) - dc.identifier (其他 識別碼) G0111364128 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152968 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 科技管理與智慧財產研究所 zh_TW dc.description (描述) 111364128 zh_TW dc.description.abstract (摘要) 新冠疫情為近年全球最具代表性的黑天鵝事件，除了對民生帶來嚴重影響外，也對科技產業造成了嚴重衝擊。其影響甚深，即便已時過半載，許多科技公司仍然未完全走出陰霾，也因此迫使各大廠商開始 重新檢視了自身供應鏈的運作效率，其中受到最嚴重衝擊的莫過於車用半導體產業。由於半導體為汽車製造的關鍵原料，在疫情期間，因為車用半導體的短缺，使得許多汽車公司以及系統廠面臨被迫停工的 窘境，導致了交車交期不斷延後。 對於疫情的衝擊，過去的研究主要集中在整體產業的供應鏈中斷，往往忽略了公司內部的反應機制以及策略。本論文針對該學術缺口，結合系統動力學的方法，研究了半導體行業內部供應鏈管理。除了文獻回顧外，本文也透過訪談的方式，取得半導體公司的供應鏈運作模 式以及對本次長鞭效應的應對方法，以描繪整體產業的運作面貌。 本研究發現，本次疫情的影響使得半導體產業在經濟層面受益，但也揭露出了內部許多資訊流的斷點，因此本論文也提出了幾項減緩供應鏈衝擊的方法，像是建立供應鏈控制塔，提升對於供應鏈資訊的掌 握度，又或是採納更多維度的資訊，以獲得下游狀況的真實面貌，減少供應鏈在資訊不對稱下造成的經濟損失。 zh_TW dc.description.abstract (摘要) The COVID-19 pandemic has had a profound impact on the tech industry, emphasizing the importance of robust supply chain management. As a major global event, the pandemic disrupted supply chains worldwide, causing significant delays and shortages, especially in the automotive semiconductor industry. This has forced companies to re-evaluate and reassess their supply chain strategies to ensure resilience and flexibility in the face of such disruptions. The critical role of semiconductors in various high-tech applications further highlights the necessity for efficient supply chain management to maintain industry competitiveness and meet market demand. Previous research on supply chain disruptions primarily focused on external industry conditions, often neglecting the internal response mechanisms within companies. This thesis addresses this gap by investigating the internal dynamics of supply chain management in the automotive semiconductor industry using system dynamics methodology. By conducting a comprehensive case study on a automotive semiconductor company, the research examines internal supply chain management practices and the dynamic relationships within the company's supply chain. The study utilizes system dynamics diagrams to illustrate the impact of the pandemic on the company's supply chain and the recovery of automotive chip demand. The findings of this research reveal several strategies to mitigate the bullwhip effect and enhance supply chain resilience. Implementing flowcasting, establishing supply chain control towers, and incorporating customer order behavior as a reference indicator for order forecasting are key recommendations. These strategies aim to improve demand forecasting accuracy, optimize inventory management, and enhance supply chain coordination and transparency. By adopting these measures, the semiconductor industry can better manage future disruptions, ensuring stability and sustainability in its supply chain operations. en_US dc.description.tableofcontents 1. 緒論 1 2. 文獻回顧 5 3. 研究方法 41 4. 研究分析 45 5. 結論與建議 57 參考文獻 65 zh_TW dc.format.extent 2710419 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111364128 en_US dc.subject (關鍵詞) 供應鏈管理 zh_TW dc.subject (關鍵詞) 長鞭效應 zh_TW dc.subject (關鍵詞) 系統動力學 zh_TW dc.subject (關鍵詞) Supply Chain Management en_US dc.subject (關鍵詞) Bullwhip Effect en_US dc.subject (關鍵詞) System Dynamics en_US dc.title (題名) 以系統動力學觀點分析疫情對半導體產業供應鏈的衝擊 zh_TW dc.title (題名) A System Dynamics View of the Impact of the Pandemic on the Semiconductor Supply Chain en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Acar, A. Z., & Uzunlar, M. B. (2014). The effects of process development and information technology on time-based supply chain performance. Procedia-Social and Behavioral Sciences, 150, 744-753. Aizcorbe, A., Oliner, S. D., & Sichel, D. E. (2008). Shifting trends in semiconductor prices and the pace of technological progress: is there a link? How close?. Business Economics, 43, 23-39. Bitran, G. R., Gurumurthi, S., & Sam, S. L. (2007). The need for third-party coordination in supply chain governance. MIT Sloan Management Review. Blattberg, R. C., & Neslin, S. A. (1993). Sales promotion: Concepts, Methods, and Strategies. Prentice Hall. Carbone, J. (1999). CM buyers look to reduce TOTAL COST. Purchasing, 126(16), 51-51. Cakanyildirim, M., Roundy, R. O., & Wood, S. C. (2002). Optimal capacity expansion and contraction under demand uncertainty. Cornell University Operations Research and Industrial Engineering. Christopher, M. (2016). Logistics and Supply Chain Management: Logistics & Supply Chain Management. Pearson UK. Ding, L., Ball, A., Matthews, J., McMahon, C. A., & Patel, M. (2007). Product representation in lightweight formats for product lifecycle management (PLM). In 4th nternational conference on digital enterprise technology. Fildes, R., Goodwin, P., & Lawrence, M. (2006). The design features of forecasting support systems and their effectiveness. Decision Support Systems, 42(1), 351-361. Forrester, J. W. (1994). System dynamics, systems thinking, and soft OR. System dynamics review, 10(23), 245-256. Holweg, M., Disney, S., Holmström, J., & Småros, J. (2005). Supply chain collaboration: Making sense of the strategy continuum. European management journal, 23(2), 170- 181. Huang, S. H., Sheoran, S. K., & Keskar, H. (2005). Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model. Computers & industrial engineering, 48(2), 377-394. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. Ingle, C., Bakliwal, D., Jain, J., Singh, P., Kale, P., & Chhajed, V. (2021, July). Demand forecasting: Literature review on various methodologies. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. Kotler, P., & Keller, K. L. (2006). Marketing management 12e. New Jersey. La Londe, B. J. (1997). Supply Chain Management: Myth or Reality?. Supply Chain Management Review, 1, 6-7. Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management science, 43(4), 546-558. Li, L., Su, Q., & Chen, X. (2011). Ensuring supply chain quality performance through applying the SCOR model. International Journal of Production Research, 49(1), 33- 57. Luna Reyes, L. F., & Andersen, D. L. (2003). Collecting and analyzing qualitative data for system dynamics: methods and models. System Dynamics Review: The Journal of the System Dynamics Society, 19(4), 271-296. Marinov, M. A., & Marinova, S. T. (2021). COVID-19 and International Business. Change of era. Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business logistics, 22(2), 1-25. Mentzer, J. T., & Moon, M. A. (2004). Sales Forecasting Management: a Demand Management Approach. Sage Publications. Miles, R. E., & Snow, C. C. (2007). Organization theory and supply chain management: An evolving research perspective. Journal of Operations Management, 25(2), 459- 463. Mönch, L., Uzsoy, R., & Fowler, J. W. (2018). A survey of semiconductor supply chain models part I: semiconductor supply chains, strategic network design, and supply chain simulation. International Journal of Production Research, 56(13), 4524-4545. Monks, J.G. (1987). Operations Management. 3rd Edition, Theory and Problems. McGraw- Hill Book Co., New York. Potthast, J. M., Gärtner, H., & Hertrampf, F. (2010). Allocation for manufacturing companies. LogForum, 6(2), 3. Ratusny, M., Schiffer, M., & Ehm, H. (2022). Customer order behavior classification via convolutional neural networks in the semiconductor industry. IEEE Transactions on Semiconductor Manufacturing, 35(3), 470-477. Ross, D. F., & Ross, D. F. (2015). Introduction to supply chain management. Distribution planning and control: managing in the era of supply chain management, 3-43. Senge, P. M., & Sterman, J. D. (1992). Systems thinking and organizational learning: Acting locally and thinking globally in the organization of the future. European journal of operational research, 59(1), 137-150. Sharma, A. (2023). Digital transformation in supply chain management: A review. Journal of Supply Chain Management, 59(1), 23-40. Sheldon, D. H. (2006). World Class Sales & Operations Planning: a Guide to Successful Implementation and Robust Execution. J. Ross Publishing. Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-commerce: an exploration of its antecedents and consequences. Journal of retailing, 78(1), 41-50. Stank, T. P., Keller, S. B., & Daugherty, P. J. (2001). Supply chain collaboration and logistical service performance. Journal of Business logistics, 22(1), 29-48. Stadtler, H., Kilger, C., & Meyr, H. (2015). Supply chain management and advanced planning: concepts, models, software, and case studies. Springer. Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Irwin/McGraw-Hill. Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1-26. Taylor, F. W. (2004). Scientific Management. Routledge. Vollmann, T., Berry, W., Whybark, D. C., & Jacobs, F. R. (2004). Manufacturing planning and control systems for supply chain management: the definitive guide for professionals. McGraw-Hill Professional. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business logistics, 34(2), 77-84. Anjani Trivedi. (2021, February 15). Toyota broke its just-in-time rule just in time for the chip shortage. Bloomberg. Retrieved from https://www.bloomberg.com/opinion/ articles/2021-02-15/toyota-broke-its-just-in-time-rule-just-in-time-for-the-chi p-shortage?sref=Z208C5oo DIGITIMES Research. (2023). New US$12 billion factory for SMIC 7nm and other sub- 14nm wafers. Retrieved from https://www.bnext.com.tw/article/71953/foundry -cost Dobler, S., & Berking, J. (2023). This is how to prevent future semiconductor shortages. Oliver Wyman. Retrieved from https://www.oliverwyman.com/our-expertise/ins ights/2023/jul/this-is-how-to-prevent-future-semiconductor-shortages.html Gartner. (2023). Gartner forecasts worldwide semiconductor revenue to grow 17% in 2024. 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