工具機又稱為工作母機，為製造機械的機械，工具機產業競爭力更是衡量國家工業實力的重要指標，台灣工具機產業每年產值高達千億全球排名第七，出口值全球排名第五為台灣重點產業之一，台灣工具機產業競爭力立基於將水平分工發揮到極致的工具機產業聚落，聚落約九成皆位於大台中地區，素有機械黃金縱谷之稱。近年遭遇美中貿易戰和新冠疫情等不利因素，又面臨台灣少子化和機械產業人才斷層危機，綜觀而言台灣工具機不論中高階或泛用型產品皆與亞洲鄰近國家如日本、韓國、中國的兢爭都越趨激烈，如何藉由智慧製造策略找出工具機產業的轉型施力點，提升工具機產業競爭力是一個相當重要的課題。
本研究以紫式決策分析架構為基礎，結合PDCCCR製造策略和工業3.5參考應用架構，建構舉目為綱的工具機智慧製造策略方案。從根本目標層級架構與定義評估指標為核心，完整建構相關工具目標與技術內容，提供給工具機產業決策者見樹又見林的轉型決策藍圖，運用AHP層級分析法擬定公司智慧製造執行方案之優先順序，凝聚內部共識一步步藉智慧製造逐步提升公司競爭力。
以台灣某工具機公司為實證對象，運用所提出的工具機智慧製造策略方案，針對公司高階經營層做調查，分析三大根本目標與評估屬性之重要性，歸納該公司智慧製造策略之群體共識，經分析討論後得到產能規劃最佳化為該公司首要之根本目標，並針對此目標之評估屬性排程規劃效能與生產資源調度，各別展開APS排程專案與生產資訊可視化專案，即時掌握產線生產狀況與優化生產排程並提前分析產線人力缺口，有效提升產線資源調度的決策品質，已驗證本研究策略方法之效度與可行性。
關鍵字：工具機、智慧製造、紫式決策架構、AHP層級分析法

Machine tool is the foundation of machinery manufacturing, and the competitiveness of machine tool industry is the key point of measuring a country’s industrial strength. The output value of machine tool industry in Taiwan reaches billion dollars per year, ranking 7th, and the value of export is ranking 5th. The competitiveness of machine tool in Taiwan is established by developing of industrial cluster, which applies horizontal division of labor. In addition, 90% of this cluster is located at Taichung area which is known as Golden Valley of machinery manufacturing. In recent years, due to the international situation such as U.S.-China trade war and COVID-19, and talent shortage problems in Taiwan, the competition between Taiwan and other nearby countries such as Japan, Korea and China is much fiercer. Therefore, it is very important to apply smart manufacturing strategy, not only maintain but also enhance the competitiveness of machine tool industry.
This research is based on UNISON framework and refers to PDCCCR manufacturing strategy and industrial 3.5 application framework. In addition, takes fundamental-objective hierarchy and define evaluation attribute as the core to build means objectives and techniques completely, then go a step further to provide decision makers blueprint for the transformation in smart manufacturing. By adopting Analytic Hierarchy Process to assign the priority of company’s smart manufacturing implementation, meanwhile, to build interior consensus and boost company’s competitiveness.
In the research, take one machine tool company in Taiwan as an empirical object. Applying the above-mentioned machine tool smart manufacturing strategy to conduct a survey of the company’s top executives, then analyze the significance of top three fundamental objectives and their evaluated properties, finally conclude the company’s group consciousness of smart manufacturing. As a conclusion, the capacity planning optimization is the priority target. Moreover, in order to enhance the abilities of understanding the real-time status of production line, optimize production planning and analyze the problem of short manpower in advance, we implement APS scheduling project and production status visualization project. Through this procedure, which can improve decision quality of enterprise’s resource utilization and verify validity and feasibility of this research.
Keywords: Machine Tool, Smart Manufacturing, UNISON, AHP

目錄 i
第一章 緒論 1
1.1 研究背景、動機與重要性 1
1.2 研究目的 4
1.3 論文結構 5
第二章 文獻回顧 6
2.1 紫式決策架構與AHP層級分析法 6
2.2 工業3.5智慧製造與參考模型應用架構 9
第三章 研究架構 13
3.1 瞭解及定義問題(Understand and Define the right problem) 14
3.2 利基發掘(Niche Exploration) 16
3.2.1 PDCCCR製造策略 16
3.2.2 決策目標定義 17
3.3 架構影響關係(Influence relationships structuring) 17
3.4 客觀敘述(Sense and describe the results) 20
3.5 綜合判斷與權衡(Overall judgments for subjective assessment) 21
3.6 最適決策與執行(Make appropriate decision) 21
第四章 實證研究 22
4.1 實證研究問題之背景與情境說明 22
4.2 工具機廠智慧製造實證研究 24
4.2.1 瞭解及定義問題 24
4.2.2 利基發掘 25
4.2.3 架構影響關係 28
4.2.4 客觀敘述 30
4.2.5 綜合判斷與權衡 31
4.2.6 最適決策與執行 32
4.3 工具機廠智慧製造實證研究 33
4.3.1 資訊系統規劃與基礎建設 33
4.3.2 組裝產線MES系統 37
4.3.3 APS排程系統 39
第五章 結論 43
5.1 研究貢獻和限制 43
5.2 未來研究方向 44
參考文獻 45
附註一、數位轉型與智慧製造調查問卷 47

林繼勇，簡禎富，胡志翰（2008），紫式決策分析以建構液晶原料廠製程確效評估模式，品質學報，第15卷5期，頁385-398。
周明璇，陳文智，簡禎富（2013），建構半導體廠跨廠績效評估以提昇資源分配決策品質之研究，品質學報，第20卷4期，頁427-444。
周哲維，簡禎富（2016），需求不確定性之產能擴充決策分析－TFT-LCD產業之個案研究，管理與系統，第23卷1期，頁1-29。
洪建中（2021），工業3.5智慧工廠方案，清華大學智慧製造高階主管碩士學程。
簡禎富，游智閔，徐紹鐘（2009），紫式決策分析以建構半導體晶圓廠人力規劃決策模型，管理與系統，第16卷2期，頁157-180。
簡禎富，胡志翰（2011），全面資源管理架構：以晶圓廠為實證，品質學報，第18卷6期，頁581-607。
簡禎富，陳勁甫，林國義（2013），在新竹科學工業園區及週邊規劃研究園區之研究，管理與系統，第20卷2期，頁227-255。
簡禎富，侯建良，吳建瑋，林國義，胡益芬， 朱珮君（2017），推動高等教育校務經營全面品質管理之研究－以國立清華大學為實證，管理與系統，第24卷4期，頁591-614。
簡禎富，林國義（2016），建構半導體晶圓廠整廠績效指標架構與全面資源管理，管理與系統，第23卷4期，頁451-473。，
簡禎富（2017），決策分析與管理(二版)，雙葉書廊，台北。
簡禎富（2019），工業3.5，天下雜誌，台北。
簡禎富（2022），藍湖策略，天下雜誌，台北。
勤業眾信（2020），台灣智慧製造關鍵能力調查。

Chien, C.-F., Wang, H.-J., Wang, M.(2007), “A UNISON framework for analyzing alternative strategies of IC final testing for enhancing overall operational effectiveness,” International Journal of Production Economics, Vol.107, No1, 20-30.
Chien C.-F., Pérès S.-D., Huh W.-T., Jang Y.-J., Morrison J.-R(2020), “Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies,” International Journal of Production Research, Vol.58, No.9, pp.2730–2731.
Chien C.-F., Hong T.-Y., and Guo H.-Z.(2017), “A Conceptual Framework for Industry 3.5 to Empower Intelligent Manufacturing and Case Studies,” Procedia Manufacturing, Vol.11, pp.2009-2017.
Chien, C.-F., Chen, H.-K., Wu, J.-Z. and Hu, C.-H.(2007), “Constructing the OGE for promoting tool group productivity in semiconductor manufacturing,” International Journal of Production Research, Vol.45, No.3, pp.509-524.
Chien C.-F., Huynh N.-T.(2018), “An Integrated Approach for IC Design R&D
Portfolio Decision and Project Scheduling and a Case Study,” IEEE
TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, Vol.31,
No.1, pp.76-86.
Chien C.-F., Hsu C.-Y.(2011) , “UNISON analysis to model and reduce step-and-scan overlay errors for semiconductor manufacturing,” Journal of Intelligent Manufacturing, Vol 22, No.3, pp.399-412, doi.org/10.1007/s10845-009-0298-2.
Chien C.-F., Hu C.-H., Lin C.-Y.(2008), “Analysing inspection frequency for wafer bumping process and an empirical study of UNISON decision framework,” International Journal of Manufacturing Technology and Management,Vol.14, No.1-2, pp.130-144.
Ku C.-C., Chien C.-F., Ma K.-T.(2020), “Digital transformation to empower smart production for Industry 3.5 and an empirical study for textile dyeing,” Computers & Industrial Engineering, Vol.142, No.106297.