基於 iBench 評量模式探討大型製造業智慧製造發展：以個案公司為例

Abstract

中文摘要 本研究以大型跨國製造企業「鴻海科技集團」為案例，探討在全球產業競爭加劇、客製化需求提高與供應鏈管理挑戰日益嚴峻的背景下，導入智慧製造的發展歷程與實際影響。為解決傳統製造在效率、品質、成本與彈性上的限制，企業逐步透過自動化設備、ERP、MES、工業物聯網、AI 視覺檢測、大數據分析與智慧倉儲物流等技術，推動生產流程由人工經驗轉向即時化、數據化與智慧化管理，以期建立更具韌性的生產模式。 此外，企業在數位轉型與智慧化升級的過程中往往缺乏客觀的評估指標。因此，本研究採用「iBench 生產力再造成熟度評量模式」，從組織規劃力、智慧製造力、智慧驅動力與創新價值力四個構面，客觀分析其發展現況。評量結果顯示，鴻海科技集團的生產力 4.0 成熟度參考指數為 3.365，整體已達中高程度，在自動化生產、數位系統建置、全球供應鏈整合與新興科技應用方面已有顯著成果，尤其在組織規劃力與創新價值力表現較佳；而在智慧驅動力方面，未來仍可持續強化跨系統資料整合、即時分析與供應鏈資訊共享。 綜合研究結果，未來企業可持續深化數位孿生、AI 輔助決策、綠色智慧製造與智慧供應鏈整合，使智慧製造不僅能提升生產效率與品質穩定性，更能強化永續發展與全球營運彈性。本研究之客觀指標評估與個案探討，期能作為其他製造業者推動數位轉型與生產力再造之實務參考。

Abstract This study uses Hon Hai Precision Industry Co., Ltd., a large-scale cross-national manufacturing enterprise, as a case study to explore the development process and actual impact of implementing smart manufacturing amidst intensifying global industry competition, increasing customization demands, and growing challenges in global supply chain management. To address the limitations of traditional manufacturing in efficiency, quality, cost, and flexibility, the enterprise has gradually introduced technologies such as automation equipment, ERP, MES, Industrial Internet of Things (IIoT), AI visual inspection, big data analysis, and smart warehousing and logistics. These technologies facilitate the transition of production processes from manual experience-based management to real-time, data-driven, and intelligent management, aiming to build a more resilient production model. Furthermore, enterprises often lack objective evaluation indicators during the digital transformation and smart upgrading process. Therefore, this study adopts the "iBench Productivity Reengineering Maturity Assessment Model" to objectively analyze the current development status from four dimensions: organizational planning capability, smart manufacturing capability, smart driving capability, and innovation value capability. The assessment results show that Hon Hai's Productivity 4.0 maturity reference index is 3.365, indicating a medium-high level of overall maturity. The company has achieved significant results in automated production, digital system construction, global supply chain integration, and emerging technology applications (performing particularly well in organizational planning and innovation value capabilities). However, in terms of smart driving capability, there is still room to strengthen cross-system data integration, real-time analysis, and supply chain information sharing. In conclusion, the enterprise can continue to deepen the development of digital twins, AI-assisted decision-making, green smart manufacturing, and smart supply chain integration in the future. These advancements will not only improve production efficiency and quality stability but also enhance sustainable development and global operational flexibility. The objective indicator assessment and case study findings of this research can serve as a practical reference model for other manufacturing companies promoting digital transformation and productivity reengineering.