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題名 工業4.0及AVM : 數位轉型之路
Industry 4.0 and Activity Value Management: The road of Digital Transformation作者 黃崑瑋
Huang, Kun-Wei貢獻者 吳安妮
WU, AN-NI
黃崑瑋
Huang, Kun-Wei關鍵詞 工業4.0
智慧製造
即時機台管理
作業價值管理
Industry 4.0
Smart manufacturing
Real-time machine management
Activity value management日期 2023 上傳時間 9-Mar-2023 18:50:39 (UTC+8) 摘要 企業的生產方式與商業模式隨著工業 4.0 的發展將有很大的轉變，其中大數據分析與虛實整合尤為重要，因其決定了企業彈性決策能力的上限。台灣製造業在過去累積了無數人才與成功經驗，如何運用物聯網、雲端、人工智慧以及5G 等相關技術，將生產和商業模式數位化及建立相關決策架構，確保企業在智慧製造的時代仍保有一席之地，為台灣製造業現階段最重要的課題。 因此，本研究結合工業 4.0 、作業價值管理系統、即時機台管理系統之概念，提供台灣企業在轉型時參考之架構。從智慧製造轉型各階段應達成之目標，透過作業價值管理系統作為企業溝通平台，並以即時機台管理系統收集原因型資料且即時呈現，透過「軟體+硬體+管理制度」，使企業生產和管理策略都能同時升級，最終在工業 4.0 的時代取得一席之地。
With the development of Industry 4.0, enterprises` production methods and business models will undergo great changes, among which big data analysis and virtual-real integration are particularly important, as they determine the upper limit of enterprises` flexible decision-making ability. Taiwan`s manufacturing industry has accumulated numerous talents and successful experiences in the past. How to utilize the Internet of Things, cloud, artificial intelligence, and 5G technologies to digitize production and business models and establish relevant decision-making structures to ensure that enterprises remain in the era of smart manufacturing is the most important issue for Taiwan`s manufacturing industry at this stage. Therefore, this study combines the concepts of Industry 4.0, Operational Value Management System, and Real-Time Machine Management System to provide a framework for Taiwan enterprises to refer to when transforming. From the objectives that should be achieved at each stage of smart manufacturing transformation, we use the operation value management system as a communication platform for enterprises, and use the real-time machine management system to collect and present the cause data in real time.參考文獻 中文部分 1. 吳安妮，2001，作業制成本制(ABC)在管理決策上之效益，會計研究月刊， 第182期 : 59-63。 2. 吳安妮，2015，管理會計技術商品化:以ABC為核心之作業價值管理系統 (AVMS)為例，會計研究月刊，第359期(10月):20-24。 3. 吳安妮，2019，企業策略的終極答案：用「作業價值管理AVM」破除成本迷思，掌握正確因果資訊，做對決策賺到「管理財」，臉譜出版社。 4. 吳安妮、黃政仁、蔡瑞煌、林怡玲、李蔡彥、羅崇銘、左瑞麟、曾一凡、陳春龍、陳俊龍、羅明琇、唐揆，2022，智慧製造，新陸書局股份有限公司發行 5. 簡禎富，2019，工業3.5台灣企業邁向智慧造與數位決策的戰略，天下雜誌出版。 6. 簡禎富，2022，藍湖策略，發展智慧化管理科技與數位決策，超越藍海紅海循環宿命，天下雜誌出版。 7. 林上育，2017，工業 4.0 與作業價值管理(AVM)之結合，國立政治大學會 計學系碩士論文。 8. 莊鎮遠，2021，智慧製造與作業價值管理系統(AVM)的結合 – 以A公司為例，國立政治大學會計學系碩士論文。 9. 魏傳虔，2021，解密2021台灣智慧製造發展現況與投資需求，產業情報研究所 10. 產業價值鏈資訊平台，2021，連接器產業鏈簡介，擷取日期 : 2022/12/10，檢自，https://ic.tpex.org.tw/introduce.php?ic=K000 。 11. 經濟部，2022，中小企業白皮書，擷取日期 : 2023/1/3，檢自， https://book.moeasmea.gov.tw/book/doc_detail.jsp?pub_SerialNo=2022A01686&click=2022A01686。 12. 勤業眾信，2018，智慧製造大解讀，擷取日期 : 2022/12/15，檢自， https://www2.deloitte.com/tw/tc/pages/consumer-industrial-products/topics/smart-manufacturing.html。 13. 勤業眾信，2018，工業4.0新戰略與發展路徑，擷取日期 : 2022/12/15，檢自，https://www2.deloitte.com/tw/tc/pages/consumer-industrial-products/topics/smart-manufacturing.html。 14. 台灣積體電路製造股份有限公司，2022，擷取日期 : 2022/12/11，檢自https://www.tsmc.com/chinese/dedicatedFoundry/manufacturing/intelligent_operations 。 英文部分 1. Arnold,C., D. Kiel, and K. Voigt, 2016. How Industry 4.0 Changes Business Models in Different Manufacturing Industries. The International Society for Professional Innovation Management : 1-20. 2. Bazerman, M. H and D. A. Moore. 2012. Judgment in Managerial Decision Making. New Jersey : John Wiley & Sons Inc. 3. Bortolini, M., E. Ferrari, M. Gamberi, F. Pilati, and M. Faccio. 2017. Assembly System Design in the Industry 4.0 Era: a General Framework. IFAC-PapersOnLine 50 (1): 5700-5705. 4. Bryndin, E. 2018. Directions of Development of Industry 4.0, Digital Technology and Social Economy. American Journal of Information Science and Technology 2 (1): 9-17. 5. Büchi, G., C. Monica, and C. Rebecca. 2018. Smart Factory Performance and Industry 4.0. Technological Forecasting and Social Change 150 (119790): 1-10. 6. Chromjakova, F. 2016. Flexible Man-man Motivation Performance Management System for Industry 4.0. International Journal of Management Excellence 7 (2): 829-840. 7. Chukalov, K. 2017. Horizontal and Vertical Integration, as a Requirement for Cyber-physical Systems in the Context of Industry 4.0. International Scientific Journal 2 (4): 155-157. 8. Çınar, Z. M., A. A. Nuhu, Q. Zeeshan, O. Korhan, M. Asmael, and B. Safaei. 2020. Machine Learning in Predictive Maintenance Towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability 12 (19): 1-42. 9. Dalenogare, L. S., G. B. Beniteza, N. F. Ayalab, and A. G. Franka. 2018. The ExpectedContribution of Industry 4.0 Technologies for Industrial Performance. International Journal of Production Economics 204: 383-394. 10. Franka, A. G., G. H. S. Mendes, N. F. Ayalac, and A. Ghezzid. 2019. Servitization and Industry 4.0 Convergence in the Digital Transformation of Product Firms: a Business Model Innovation Perspective. Technological Forecasting & Social Change 141: 341-351. 11. Ibarra, D., J. Ganzarain, and J. I. Igartua. 2018. Business Model Innovation Through Industry 4.0: a Review. Procedia Manufacturing 22: 4-10. 12. Lee, M. X., Y. C. Lee, and C. J. Chou. 2017. Essential Implications of the Digital Transformation in Industry 4.0. Journal of Scientific & Industrial Research 76 (8): 465-467. 13. Liao, Y., Deschamps, F., Loures, E. D. F. R. & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. International journal of production research, 55(12), 3609-3629. 14. Qi, Q. and F. Tao. 2018. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access 6: 3585-3593. 15. Reischauer, G. (2018). Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing. Technological Forecasting and Social Change, 132, 26-33. 16. Sanchez, M., E. Exposito, and J. Aguilar. 2020. Industry 4.0: Survey from a System Integration Perspective. International Journal of Computer Integrated Manufacturing 33 (10-11): 1017-1041. 17. Sarkar, M. and B. Sarkar. 2019. Optimization of Safety Stock Under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management. Energies 12 (2059): 1-16. 18. Siemens,(2022,December,23)https://www.plm.automation.siemens.com/global/en/our-story/glossary/industry-4-0/29278 19. Vaidya, S., P. Ambad, and S. Bhosle. 2018. Industry 4.0 – a Glimpse. Procedia Manufacturing 20: 233-238. 20. Wang, S., J. Wan, D. Zhang, D. Li, and C. Zhang. 2016. Towards Smart Factoryfor Industry 4.0: a Self-organized Multi-agent System with Big Data Base Feedback and Coordination. Computer Networks 101: 158-168. 21. Yan, J., Y. Meng, L. Lu, and L. Li. 2017. Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance. IEEE Access 5: 23484-23491. 22. Yin, R. K. (1994). Case study research: Design and methods, applied social research. Methods series, 5. 23. Yin, Y. (2018). Fund Portfolio Holdings and Affiliated Analyst`s Objectivity: Do Equity and Employment Relationship Matter?. Journal of Behavioral Finance, 19(1), 16-29. 24. Zhuang, C., J. Liu, and H. Xiong. 2018. Digital Twin-based Smart ProductionManagement and Control Framework for the Complex Product Assembly Shop-floor. The International Journal of Advanced Manufacturing Technology 96 (1): 1149-1163. 描述 碩士
國立政治大學
會計學系
109353039資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109353039 資料類型 thesis dc.contributor.advisor 吳安妮 zh_TW dc.contributor.advisor WU, AN-NI en_US dc.contributor.author (Authors) 黃崑瑋 zh_TW dc.contributor.author (Authors) Huang, Kun-Wei en_US dc.creator (作者) 黃崑瑋 zh_TW dc.creator (作者) Huang, Kun-Wei en_US dc.date (日期) 2023 en_US dc.date.accessioned 9-Mar-2023 18:50:39 (UTC+8) - dc.date.available 9-Mar-2023 18:50:39 (UTC+8) - dc.date.issued (上傳時間) 9-Mar-2023 18:50:39 (UTC+8) - dc.identifier (Other Identifiers) G0109353039 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/143887 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 會計學系 zh_TW dc.description (描述) 109353039 zh_TW dc.description.abstract (摘要) 企業的生產方式與商業模式隨著工業 4.0 的發展將有很大的轉變，其中大數據分析與虛實整合尤為重要，因其決定了企業彈性決策能力的上限。台灣製造業在過去累積了無數人才與成功經驗，如何運用物聯網、雲端、人工智慧以及5G 等相關技術，將生產和商業模式數位化及建立相關決策架構，確保企業在智慧製造的時代仍保有一席之地，為台灣製造業現階段最重要的課題。 因此，本研究結合工業 4.0 、作業價值管理系統、即時機台管理系統之概念，提供台灣企業在轉型時參考之架構。從智慧製造轉型各階段應達成之目標，透過作業價值管理系統作為企業溝通平台，並以即時機台管理系統收集原因型資料且即時呈現，透過「軟體+硬體+管理制度」，使企業生產和管理策略都能同時升級，最終在工業 4.0 的時代取得一席之地。 zh_TW dc.description.abstract (摘要) With the development of Industry 4.0, enterprises` production methods and business models will undergo great changes, among which big data analysis and virtual-real integration are particularly important, as they determine the upper limit of enterprises` flexible decision-making ability. Taiwan`s manufacturing industry has accumulated numerous talents and successful experiences in the past. How to utilize the Internet of Things, cloud, artificial intelligence, and 5G technologies to digitize production and business models and establish relevant decision-making structures to ensure that enterprises remain in the era of smart manufacturing is the most important issue for Taiwan`s manufacturing industry at this stage. Therefore, this study combines the concepts of Industry 4.0, Operational Value Management System, and Real-Time Machine Management System to provide a framework for Taiwan enterprises to refer to when transforming. From the objectives that should be achieved at each stage of smart manufacturing transformation, we use the operation value management system as a communication platform for enterprises, and use the real-time machine management system to collect and present the cause data in real time. en_US dc.description.tableofcontents 第壹章 緒論 1 第一節 研究動機與目的 1 第二節 研究問題 2 第三節 研究架構 4 第貳章 文獻探討 6 第一節 工業 4.0 發展現況及發展方向 6 第貳節 工業 4.0 數位轉型之機器設備管理與決策 16 第參節 工業 4.0 與AVM和數位轉型之連結 23 第四節 研究延伸 28 第參章 研究方法 33 第一節 個案研究法 33 第二節 研究流程 34 第肆章 個案公司介紹 36 第一節 個案公司所處產業概況 36 第二節 個案公司介紹 39 第伍章 透過工業 4.0、AVM 與即時設備管理系統數位轉型 41 第一節 工業 4.0、AVM與即時設備管理系統數位轉型之現況探討 41 第二節 如何透過工業 4.0、AVM與即時設備管理系統進行數位轉型 46 第三節 工業 4.0、AVM與即時設備管理系統數位轉型後之效益 64 第陸章 結論與建議 68 第一節 研究結論 68 第二節 研究限制 71 第三節 研究建議 72 參考文獻 74 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109353039 en_US dc.subject (關鍵詞) 工業4.0 zh_TW dc.subject (關鍵詞) 智慧製造 zh_TW dc.subject (關鍵詞) 即時機台管理 zh_TW dc.subject (關鍵詞) 作業價值管理 zh_TW dc.subject (關鍵詞) Industry 4.0 en_US dc.subject (關鍵詞) Smart manufacturing en_US dc.subject (關鍵詞) Real-time machine management en_US dc.subject (關鍵詞) Activity value management en_US dc.title (題名) 工業4.0及AVM : 數位轉型之路 zh_TW dc.title (題名) Industry 4.0 and Activity Value Management: The road of Digital Transformation en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文部分 1. 吳安妮，2001，作業制成本制(ABC)在管理決策上之效益，會計研究月刊， 第182期 : 59-63。 2. 吳安妮，2015，管理會計技術商品化:以ABC為核心之作業價值管理系統 (AVMS)為例，會計研究月刊，第359期(10月):20-24。 3. 吳安妮，2019，企業策略的終極答案：用「作業價值管理AVM」破除成本迷思，掌握正確因果資訊，做對決策賺到「管理財」，臉譜出版社。 4. 吳安妮、黃政仁、蔡瑞煌、林怡玲、李蔡彥、羅崇銘、左瑞麟、曾一凡、陳春龍、陳俊龍、羅明琇、唐揆，2022，智慧製造，新陸書局股份有限公司發行 5. 簡禎富，2019，工業3.5台灣企業邁向智慧造與數位決策的戰略，天下雜誌出版。 6. 簡禎富，2022，藍湖策略，發展智慧化管理科技與數位決策，超越藍海紅海循環宿命，天下雜誌出版。 7. 林上育，2017，工業 4.0 與作業價值管理(AVM)之結合，國立政治大學會 計學系碩士論文。 8. 莊鎮遠，2021，智慧製造與作業價值管理系統(AVM)的結合 – 以A公司為例，國立政治大學會計學系碩士論文。 9. 魏傳虔，2021，解密2021台灣智慧製造發展現況與投資需求，產業情報研究所 10. 產業價值鏈資訊平台，2021，連接器產業鏈簡介，擷取日期 : 2022/12/10，檢自，https://ic.tpex.org.tw/introduce.php?ic=K000 。 11. 經濟部，2022，中小企業白皮書，擷取日期 : 2023/1/3，檢自， https://book.moeasmea.gov.tw/book/doc_detail.jsp?pub_SerialNo=2022A01686&click=2022A01686。 12. 勤業眾信，2018，智慧製造大解讀，擷取日期 : 2022/12/15，檢自， https://www2.deloitte.com/tw/tc/pages/consumer-industrial-products/topics/smart-manufacturing.html。 13. 勤業眾信，2018，工業4.0新戰略與發展路徑，擷取日期 : 2022/12/15，檢自，https://www2.deloitte.com/tw/tc/pages/consumer-industrial-products/topics/smart-manufacturing.html。 14. 台灣積體電路製造股份有限公司，2022，擷取日期 : 2022/12/11，檢自https://www.tsmc.com/chinese/dedicatedFoundry/manufacturing/intelligent_operations 。 英文部分 1. Arnold,C., D. Kiel, and K. Voigt, 2016. How Industry 4.0 Changes Business Models in Different Manufacturing Industries. The International Society for Professional Innovation Management : 1-20. 2. Bazerman, M. H and D. A. Moore. 2012. Judgment in Managerial Decision Making. New Jersey : John Wiley & Sons Inc. 3. Bortolini, M., E. Ferrari, M. Gamberi, F. Pilati, and M. Faccio. 2017. Assembly System Design in the Industry 4.0 Era: a General Framework. IFAC-PapersOnLine 50 (1): 5700-5705. 4. Bryndin, E. 2018. Directions of Development of Industry 4.0, Digital Technology and Social Economy. American Journal of Information Science and Technology 2 (1): 9-17. 5. Büchi, G., C. Monica, and C. Rebecca. 2018. Smart Factory Performance and Industry 4.0. Technological Forecasting and Social Change 150 (119790): 1-10. 6. Chromjakova, F. 2016. Flexible Man-man Motivation Performance Management System for Industry 4.0. International Journal of Management Excellence 7 (2): 829-840. 7. Chukalov, K. 2017. Horizontal and Vertical Integration, as a Requirement for Cyber-physical Systems in the Context of Industry 4.0. International Scientific Journal 2 (4): 155-157. 8. Çınar, Z. M., A. A. Nuhu, Q. Zeeshan, O. Korhan, M. Asmael, and B. Safaei. 2020. Machine Learning in Predictive Maintenance Towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability 12 (19): 1-42. 9. Dalenogare, L. S., G. B. Beniteza, N. F. Ayalab, and A. G. Franka. 2018. The ExpectedContribution of Industry 4.0 Technologies for Industrial Performance. International Journal of Production Economics 204: 383-394. 10. Franka, A. G., G. H. S. Mendes, N. F. Ayalac, and A. Ghezzid. 2019. Servitization and Industry 4.0 Convergence in the Digital Transformation of Product Firms: a Business Model Innovation Perspective. Technological Forecasting & Social Change 141: 341-351. 11. Ibarra, D., J. Ganzarain, and J. I. Igartua. 2018. Business Model Innovation Through Industry 4.0: a Review. Procedia Manufacturing 22: 4-10. 12. Lee, M. X., Y. C. Lee, and C. J. Chou. 2017. Essential Implications of the Digital Transformation in Industry 4.0. Journal of Scientific & Industrial Research 76 (8): 465-467. 13. Liao, Y., Deschamps, F., Loures, E. D. F. R. & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. International journal of production research, 55(12), 3609-3629. 14. Qi, Q. and F. Tao. 2018. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access 6: 3585-3593. 15. Reischauer, G. (2018). Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing. Technological Forecasting and Social Change, 132, 26-33. 16. Sanchez, M., E. Exposito, and J. Aguilar. 2020. Industry 4.0: Survey from a System Integration Perspective. International Journal of Computer Integrated Manufacturing 33 (10-11): 1017-1041. 17. Sarkar, M. and B. Sarkar. 2019. Optimization of Safety Stock Under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management. Energies 12 (2059): 1-16. 18. Siemens,(2022,December,23)https://www.plm.automation.siemens.com/global/en/our-story/glossary/industry-4-0/29278 19. Vaidya, S., P. Ambad, and S. Bhosle. 2018. Industry 4.0 – a Glimpse. Procedia Manufacturing 20: 233-238. 20. Wang, S., J. Wan, D. Zhang, D. Li, and C. Zhang. 2016. Towards Smart Factoryfor Industry 4.0: a Self-organized Multi-agent System with Big Data Base Feedback and Coordination. Computer Networks 101: 158-168. 21. Yan, J., Y. Meng, L. Lu, and L. Li. 2017. Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance. IEEE Access 5: 23484-23491. 22. Yin, R. K. (1994). Case study research: Design and methods, applied social research. Methods series, 5. 23. Yin, Y. (2018). Fund Portfolio Holdings and Affiliated Analyst`s Objectivity: Do Equity and Employment Relationship Matter?. Journal of Behavioral Finance, 19(1), 16-29. 24. Zhuang, C., J. Liu, and H. Xiong. 2018. Digital Twin-based Smart ProductionManagement and Control Framework for the Complex Product Assembly Shop-floor. The International Journal of Advanced Manufacturing Technology 96 (1): 1149-1163. zh_TW
