面對國際市場環境的劇烈變化、產業技術不斷創新，加上自然環境資源限制、勞動人力不足等問題，先進國家相繼以智慧製造為主軸，推出振興產業的政策。智慧製造的概念其實就「虛實整合」，它是基於物聯網技術，建立軟硬體系統充分鏈結的智慧製造生產體系。本研究採用質化個案研究的方式，針對所挑選出來導入智慧製造有成的企業案例，透過深度訪談個案C公司內部相關部門的高階主管，探討業者面對智慧製造的新型態製造流程下的轉型過程和實際運作，以及所遭遇的戰與因應之道。
本研究分別依照策略面的導入動機、導入策略、轉型挑戰，人力資源面的組織結構與行為、工作安全、人力資源、教育訓練、職能發展，生產管理面的製造流程、作業活動、產能調整、作業環境等十二個子構面，共歸納出三十個重要發現。C公司導入智慧製造的目的，除了須要精準管理全球各地數百家的工廠，也為了打造數位化、永續發展，以及值得信賴的合作伙伴。工廠數位化轉型的關鍵包括管理、人力資源及技術等三大面向。管理面包括績效管理、除錯能力、辨識轉型必要性、員工管理等四項指標。人力資源對智慧製造轉型的影響關鍵包括員工心態、員工主動發想、員工數位化能力、團隊和人員發展。技術層面關鍵成功因素，則必須針對不同產品的所有內外部環節加以數位化連結優化。
工廠端大數據的整合則是數位轉型的一大挑戰，C公司利用Eco- structure的開放式平台進行變革管理。人員的安全對於智慧製造也相當重要，其工安特殊性體現於人機協作的SOP，而且不能用原來舊有的思維方式來設計。
當生產作業導入智慧製造，並配合人機協作，可以提升產能。另外，設備的質量管理也很重要，當設備完好，才能精準追溯、流程互鎖、防呆、線上監測和診斷、智能維護管理設備，實現預測性維護和設備優化。

In the face of drastic changes in the international market environment, continuous innovation in industrial technology, and problems such as limitations in natural environmental resources and insufficient labor, advanced countries have successively launched policies to revitalize the industry with intelligent manufacturing as the main axis. The concept of intelligent manufacturing is actually "virtual and real integration". It is based on the Internet of Things technology and establishes a smart manufacturing production system that is fully linked with software and hardware systems. This study adopts the method of qualitative case study, focusing on the selected case C company that has successfully introduced intelligent manufacturing, and through in-depth interviews with senior managers of relevant departments to explore the transformation process and actual operation of the case company in the face of new processes of intelligent manufacturing, as well as the challenges encountered and how to respond.
This study is based on the strategic aspects of introduction motivation, introduction strategies, and transformation challenges; the human resources aspect of organizational structure and behavior, job safety, human resources, education and training, and functional development; the production management aspect of manufacturing processes, operating activities, production capacity adjustment, and working environment. A total of thirty important findings were summarized in those twelve sub-aspects. Company C’s purpose of introducing intelligent manufacturing is not only to accurately manage hundreds of factories around the world, but also to create digital, sustainable, and trustworthy partners. The key to factory digital transformation includes three major aspects: management, human resources and technology. The management aspect includes four indicators: performance management, debugging ability, identifying the necessity of transformation, and employee management. The impact of human resources on the transformation of intelligent manufacturing includes employee mentality, employee proactive thinking, digital capabilities, team and personnel development. Thekey success factors of the technical aspect must be digitally linked and optimized for all internal and external links of different products.
The integration of factory-side big data is a major challenge in digital transformation. Company C uses Eco-structure's open platform for change management. The safety of personnel is also very important to intelligent manufacturing. The particularity of industrial safety is reflected in the SOP of human-machine collaboration, and it cannot be designed using the old way of thinking.
When production operations are introduced into intelligent manufacturing and combined with human-machine collaboration, production capacity can be increased. In addition, the quality management of equipment is also very important. When the equipment is in good condition, it can achieve accurate traceability, process interlocking, foolproofing, online monitoring and diagnosis, intelligent maintenance and management of equipments, and achieve predictive maintenance and equipment optimization.

1.工業局（2019），智慧機械產業推動方案進度及成果，工業局。
2.吳禹澔、黃詩文、左峻德、吳清沂、謝沛宏、陳彥豪（2016），臺灣推動智慧製造作法建議與實踐藍圖，產業管理評論， 9(1)，25-43。
3.吳琮璠（1997），資訊管理個案研究方法，資訊管理學報，4(1)，7-17。
4.尾木蔵人（2015），決定版インダストリー4.0:第4次産業革命の全貌，東京:東洋經濟新報社。
5.周黎芳（1995），企業流程再造之理論探討與規劃–以個案公司為例（未出版碩士論文），國立臺灣大學會計學研究所，台北市。
6.洪哲倫、張志宏、林宛儒（2019），工業4.0與智慧製造的關鍵技術：工業物聯網與人工智慧，科儀新知，221，19-25。
7.許南雄（2007），組織理論與管理，台中市：滄海。
8.陳建良、林建勳、陳佳雯、張昭偉（2004），企業流程再造之通用參考模式與方法初探，中原學報，32(4)，527-554。
9.陳萬淇（1992），個案研究法，台北：華泰文化。
10.楊仁壽、卓秀足、俞慧芸（2009），組織理論與管理，台北市：雙葉書廊。
11.經濟部（2016），五大產業創新研發計畫- 智慧機械產業推動方案，經濟部。
12.葉至誠、葉立誠（1999），研究方法與論文寫作，台北：商鼎文化。
13.熊治民（2019），臺灣智慧製造發展與應用的下一步，機械工業雜誌， 437，9-15。
14.潘耀徽（2007），流程改善與企業再造，品質月刊，43(11)，53-58。
15.簡禎富（2015），「物聯網帶來的危機與轉機」，哈佛商業評論全球繁體中文版，李郁怡採訪，106(6 月)，150-153。
16.簡禎富、王宏鍇、傅文翰（2018），「工業3.5」之先進智慧製造系統架構：半導體智慧製造為例，管理評論，37(3)，15-34。
17.藍天雄、羅佳蕙（2018），企業組織變革之影響以導入資訊科技部門為例，管理資訊計算，7(2)，20-30。
18.陳雅文（1995），個案研究法，台北市：圖書館學與資訊科學大辭典。2019年2月11日。取自：http://terms.naer.edu.tw/detail/1681584/
19.王文科、王智弘（2014），教育研究法（16版），台北市：五南圖書出版股份有限公司。
20.黃瑞琴（1997），質的教育研究法，臺北市：心理。
21.熊治民（2019），台灣智慧製造發展與應用的下一步，機械工業雜誌，437。
22.朱志洋（2020），助台灣邁向智慧製造 擁抱數位轉型與創新，機械工業雜誌，444。
23.熊治民（2017），工業4.0應用方案發展現況：德國、中國與台灣，機械工業雜誌，406，20-28。
24.陳政雄、李翔祖（2016），從工業1.0談到工業4.0，中華印刷科技年報，53-60。
25.郭楚其、徐作聖（2018），「工業4.0」下製造業之轉型政策，管理評論，37(3)，1-13。
26.楊錦洲（2020），工業4.0與智能製造，品質月刊，56(7)，6-9。
27.王怡惠（2015），從工業4.0看我國生產力4.0之挑戰，臺灣經濟研究月刊，38(8)，111-119。
28.樊晉源、張瑞芬（2018），智慧製造全球政策及智財解析暨台灣工業工程相關研發契機，管理與系統，25(3)，291-319。
29.溫芳宜（2015），解析中國製造2025，經濟前瞻，160(7月)，13-19。
1.Benbasat et al. (1987), “The Case Research Strategy in Studies of Information Systems”, MIS Quarterly, 11(3), 369-388.
2.Castillo, Enrique Del (2002), Statistical Process Adjustment for Quality Control. New York: John Wiley & Sons.
3.Chien, Chen-Fu, Yun-Ju Chen, and Jin-Tang Peng (2010), “Manufacturing Intelligence for Semiconductor Demand Forecast Based on Technology Diffusion and Product Life Cycle”, International Journal of Production Economics, 128(2), 496-509.
4.Daft, R. L. (1994), Management, Orlando: The Dryden Press.
5.Daft, R. L. (1995), Organization theory and design, Paul: west Pub.
6.Daft, R. L. and Steers, R. M. (1986), Organizations: A micro/macro approach, Glenview, IL: Scott Foreman & Company.
7.Damanpour, F. (1987), “The Adoption of Technological, Administrative, and Ancillary Innovations: Impact of Organizational Factors”, Journal of Management,13(4), 675–688.
8.Davenport, T. H. and J. E. Short (1990), “The New Industrial Engineering : Information Technology and Business Process Redesign”, Sloan Management Review ,31,11-27.
9.Eisenhardt, K. M. (1989), “Building Theories from Case Study Research”, Academy of Management Review, 14(4), 532-550.
10.Executive Office of the President of the United States (2014), “Accelerating U.S. Advanced Manufacturing, AMP2.0 Steering Committee Report”, research report, The President’s Council of Advisors on Science and Technology of the United States.
11.Friedlander, F. and Brown, L. D. (1974), “Organization Development”, Annual Review of Psychology, 25, 328-329.
12.Furey, T. R.(1993), “Applying Information Technology to Reengineering”, Planning Review,21, 22-26.
13.Hammer, M. and J. Champy(1993), Reengineering the Corporation-A Manifesto for Business Revolution, Harper Business.
14.Hammer, M.(1990), “Re-engineering work: don't automate, obliterate”, Harvard Business Review, 68(4),104-112.
15.Kagermann, Henning, Johannes Helbig, Ariane Hellinger, and Wolfgang Wahlster (2013), “Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry” , research report, Forschungsunion.
16.Kang H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., and Do Noh, S. (2016), “Smart Manufacturing: Past Research, Present Findings, and Future Directions”, International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111-128.
17.King, N. and Anderson, N.(1995), Innovation and change in organizations, London & New York.
18.Lee, J., Baghen B., and Kao H. A.(2015), “A Cyber-Physical System Architecture for Industry 4.0-Based Manufacturing Systems”, Manufacturing Letters, 3, 18-23.
19.Lee, J., Kao, H. A. and Yang, S. (2014), “Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment”, Procedia CIRP, 16(June), 3-8.
20.Lu, Y. (2017), “Industry 4.0: A Survey on Technologies, Applications and Open Research Issues”, Journal of Industrial Information Integration, 6, 1-10.
21.McCutcheon, D. M.,and Meredith. J. R. (1993), “Conducting case study research in operations management”, Journal of Operations Management, 11, 239-256.
22.Rűꞵmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., and Harnisch, M.(2015), “Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries”, Boston Consulting Group.
23.Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. R., and Prote, J. P. (2014), “Collaboration Mechanisms to Increase Productivity in the Context of Industrie 4.0”, Procedia CIRP,19. 51-56.
24.Wimmer, R.D., and Dominick, J.R. (1991), Mass media research: An introduction, Belmont, CA: Wadsworth.
25.Xu, Xun (2012), “From Cloud Computing to Cloud Manufacturing”, Robotics and Computer-Integrated Manufacturing, 28(1), 75-86.
26.Yin, R. K. (1994), Case Study Research: Design and Methods, 2nd Ed. Newbury Park, CA：Sage.
27.Gillham, B. (2000), Case study research methods, New York, NY: Continuum.
28.Taylor, S. E. (1984), Social Cognition, CA: Addison Wesley Publishing.
29.Vogel-Heuser, Birgit, & Hess, Dieter(2016), Guest Editorial Industry 4.0 - Prerequisites and Visions, IEEE Transactions on Automation Science and Engineering ,13, 411-13.
30.Zhong, Ray Y., Xu, Xun, Klotz, Eberhard, & Newman, Stephen T.(2017), Intelligent Manufacturing in the Context of Industry 4.0: A review, Engineering, 3, 616-630.