科技與技術進步速度越來越快，產品週期與客戶需求變化越來越短與快速，非常考驗工廠客製化以及小量訂單的生產機動性，進而製造工廠需要將進入工業4.0的時代，實現自動化與高度客製化的生產，但在3.0進入4.0時有極大的困難與門檻，公司可以先行進入工業3.5過度後再進入4.0。
工廠生產規劃總是需要人工計算材料、工時、人力才能規劃出排程，並且依照生產規劃回覆訂單交期，當有人機料法環各種因素的變動將導致排程重新規劃並討論生產順序與交期，時間的即時性以及準確率將會有所風險以及疑慮，公司為了加速排程規劃以及減少判斷失誤的風險，開發智能製造系統並且導入至工廠，提升規劃及生產效率，並且將縮短人員作業時間，提升各部門工作效率。
本研究方法以D公司電子零組件事業群為例，研究即將導入的智慧製造系統系統效益，以及使用者操作上的優勢與困境，給予個案公司未來改善方向與建議。

　Technological advancements and the rapid pace of progress have resulted in shorter product cycles and faster changes in customer demands. This poses a significant challenge for manufacturing factories in terms of customization and handling small orders, necessitating their transition into the era of Industry 4.0 to achieve automation and highly personalized production. However, the transition from Industry 3.0 to 4.0 presents considerable difficulties and barriers. As a solution, companies can first transition into Industry 3.5 before entering 4.0.
　　Factory production planning traditionally relies on manual calculations of materials, labor hours, and manpower to determine scheduling and provide order delivery estimates. When various factors such as human resources, machinery, materials, and regulations change, scheduling needs to be re-planned, and discussions regarding production sequences and delivery times arise. The real-time nature and accuracy of such planning pose risks and concerns. To expedite scheduling and minimize the risk of judgment errors, companies are developing intelligent manufacturing systems and implementing them in their factories to enhance planning and production efficiency. This, in turn, reduces personnel working hours and improves the overall effectiveness of each department.
　　In this research, the case study focuses on the Electronic Component Business Group of Company D to examine the benefits of the upcoming smart manufacturing system and analyze its advantages and challenges in terms of user operations. The findings will provide the case company with future improvement directions and recommendations.

中文參考文獻
1. 簡禎富，台企新五四運動，《工業3.5》，天下雜誌，2019
2. 簡禎富，工業3.5：台灣企業邁向智慧製造與數位決策的戰略，天下雜誌發行，台北，2019
3. 王俊中，呂俊德，許秉瑜，先進規劃應用於供應鏈產銷計畫之研究，電子商務研究期刊，237-259，2009
4. 王俊中 、許秉瑜、 呂俊德、 蕭文龍、 謝淑慈 (2010), "全球供應鏈產銷規劃之決策支援架構: 決策模型與演算法", 台大管理論叢, 20(2), pp.135-172, 2010
5. 周哲維，以決策為基礎之智能製造系統架構 A Framework for Decision-based Intelligent Manufacturing System，博士論文，國立清華大學工業工程與工程管理學系，2016
6. 許嘉裕，決策策分析與管理：紫式決策分析以全面提升決策品質，第二版，台北：雙葉書廊， 2014
英文參考文獻
1. Kagermann, H., J. Helbig, A. Hellinger, and W. Wahlster ,Recommendations for Implementing the Strategic Initiative Industrie 4.0: Final Report of the Industrie 4.0 Working Group, Berlin, Germany: Federal Ministry of Education and Research. (2013a)
2. Adhesive tapes market: global industry trends, share, size, growth, opportunity and forecast 2022-2027 , Research & Markets,2022
3. Kuo, T. C., Chen, K. J., Shiang, W. J., Huang, P. B., Otieno, W., & Chiu, M. C.. A collaborative data-driven analytics of material resource management in smart supply chain by using a hybrid Industry 3.5 strategy. Resources, Conservation and Recycling, 164, 105160. 2021
4. Chien, C. F., Hong, T. Y., & Guo, H. Z.. A conceptual framework for “Industry 3.5” to empower intelligent manufacturing and case studies. Procedia Manufacturing, 11, 2009-2017. 2017
5. Chien, C. F., Hsu, C. Y., & Chang, K. H. (2013). Overall wafer effectiveness (OWE): A novel industry standard for semiconductor ecosystem as a whole. Computers & Industrial Engineering, 65(1), 117-127.