研究生: |
林沅霆 Lin,Yuan-Ting |
---|---|
論文名稱: |
臺灣智慧製造創新應用之研究-以AHP及DEMATEL方法分析 A Study on Taiwan's Smart Manufacturing Innovation Applications:Using of AHP and DEMATEL Methods |
指導教授: |
蘇友珊
Su, Yu-Shan |
學位類別: |
碩士 Master |
系所名稱: |
工業教育學系 Department of Industrial Education |
論文出版年: | 2020 |
畢業學年度: | 108 |
語文別: | 中文 |
論文頁數: | 108 |
中文關鍵詞: | 智慧製造 、層級分析法 、決策實驗分析法 |
英文關鍵詞: | Smart manufacturing, AHP, DEMATEL |
DOI URL: | http://doi.org/10.6345/NTNU202000393 |
論文種類: | 學術論文 |
相關次數: | 點閱:278 下載:0 |
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近年來,智慧製造受到了學術界和工業界的越來越多的關注,因為它為製造公司提供了競爭優勢,使生產變得更加高效率和可持續性。本研究分析相關智慧製造相關之文獻,歸納出智慧製造五個構面以及二十九項準則,五個智慧製造的構面分別為智慧設計、智慧生產、智慧工廠、智慧服務和工業物聯網管理。
本研究運用層級分析法(AHP)結合決策實驗室分析法(DEMATEL),兩種研究方法評估智慧製造構面與準則之間的權重與因果關係,藉由兩者結論判斷台灣優先發展智慧製造之策略。研究結果顯示五大構面中優先發展的是工業物聯網管理。而準則方面分別是:一、工業物聯網管理構面下硬體優先發展感測器、軟體著重於權限管理。而軟、硬體結合後再來做設備管理和建立標準通訊協議。二、智慧生產構面下優先發展先進製程控制,在機器學習最佳的製程之後達到機台智慧化。三、智慧工廠構面下優先發展數位轉型,再依據數位轉型的內容更新或是增加IT基礎設施。研究結果將提供台灣邁向智慧製造一個未來發展規劃。
In recent years, smart manufacturing has received more and more attention from academia and industry, because it provides manufacturing companies with a competitive advantage and makes production more efficient and sustainable. This study analyzes related literature on smart manufacturing, and summarizes five aspects of smart manufacturing and 29 criteria. The five aspects of smart manufacturing are smart design, smart production, smart factory, smart service, and industrial IoT management.
This study uses the Hierarchical Analysis Method (AHP) and the Decision Laboratory Analysis Method (DEMATEL). Two research methods are used to evaluate the weight and causality between smart manufacturing facets and guidelines. Based on the conclusions of the two, judge Taiwan's priority to develop Strategy. The research results show that the priority development of the five facets is the Industrial Internet of Things management. The criteria are as follows: First, the hardware prior to the development of sensors and software under the industrial Internet of Things management focus on rights management. After the combination of software and hardware, do device management and establish standard communication protocols. Second,The development of advanced process control is prioritized under the intelligent production structure, and the machine is intelligent after the best process of machine learning. Third, under the smart factory structure, digital transformation is prioritized, and IT infrastructure is updated or added based on the content of the digital transformation.The research results will provide Taiwan with a future development plan for smart manufacturing.
一、中文部分
工研院(2015)。我國對於中國製造2025計畫之因應策略。取自https://www.ndc.gov.tw/News_Content.aspx?n=2FE923B6D5878FBA&ms=72B16E02BCB79287&s=0AD7EE4AA7F6B300。
古亞薇、李震華、陳凱迪、陳賜賢、童啟晟、韓揚銘、魏傳虔(2016)。智慧製造產業發展趨勢與精選個案。臺北市:資策會產業情報研究所。
行政院(2016)。生產力 4.0 發展方案調整報告。取自http://class.nchu.edu.tw/bulletin/MOE/105_MoE_re_allr.pdf
汪建南、馬雲龍(2016)。工業4.0 的國際發展趨勢與台灣因應之道,國際金融參考資料,69,133-155。
韋康博(2015)。工業4.0:從製造業到「智」造業,下一波產業革命如何顛覆全世界。臺北市:商周出版。
林蒧均(2015),淺談工業 4.0 浪潮下的國際製造業政策。經濟前瞻,162,65-72。
林瑋琦(2016)。德國「工業4.0」產業趨勢與衍生商機大揭密。經貿透視雙周刊,453,68-69。
范哲偉(2019)。運用層級分析法探討台灣印刷電路板業者於中國市場導入工業4.0之關鍵成功因素—A公司為例(未出版之論文)。國立中央大學,桃園市。
陳佳安(2015)。韓國製造業創新3.0對台灣傳產的啟示。金屬中心產業評析,1-5。
張睿婕(2018)。智慧製造導入製造業經營創新發展策略之研究(未出版之博士論文)。私立義守大學,高雄市。
賀桂芬(2019)。566家企業智慧製造大盤點:誰是最佳工業4.0。天下雜誌,665。臺北市:天下雜誌。
鄧振源、曾國雄(1989)。分析層級法的內含特性與應用(下)。中國統計學報,27(7),13767-13870。
劉錦鳳、吳信宏(2019)。工業4.0對供應商管理策略因子之影響:以臺灣半導體為例。品質學報,26(4),207-227。
蔡佳憲(2017)。應用分析層級程序法於製造執行系統廠商之評選-以S公司為例(未出版之論文)。國立台灣科技大學,臺北市。
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。
Bauernhansl, T., Ten, H., & Heuser, B. (2014). Industrie 4.0 in Produktion, Automatisierung und Logistik: Anwendung, Technologien und Migration.
Bosch. (2015). Harnessing the Power of the Internet of Things - The IoT for the Extented Enterprise, research report, Bosch Software Innovations.
Chen ,Y. (2017). Integrated and Intelligent Manufacturing: Perspectives and Enablers. Engineering, 3(5), 588-595.
Chien, C., Hong, T., & Guo, H. (2017). A Conceptual Framework for “Industry 3.5” to Empower Intelligent Manufacturing and Case Studies. Procedia Manufacturing, 11, 2009-2017.
Chou,Y., Sun, C., & Yen, H. (2012). Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach. Applied Soft Computing, 12(1), 64-71.
David, R., & Francois, V. (2016). Enterprise information systems state of the art: past, present and future trends.Comput.Ind, 79, 3–13.
Esfahania, M., Niknafs, A., Kuss, D., Nilashi, M., & Afrough, S.(2019). Social media addiction: Applying the DEMATEL approach. Telematics and Informatics, 43-54.
Felice, F., Petrillo, A., & Zomparelli, F. (2018). A Bibliometric Multicriteria Model on Smart Manufacturing from 2011 to 2018. IFAC Papers OnLine, 51(11), 1643-1648.
Gabus, A., & Fontela, E. (1972). World problems, An Invitation to Further Thought within the Framework of DEMATEL.Battelle Geneva Research Centre, Geneva, Switzerland.
Gai, K., Qiu, M., Xiong, Z., & Liu, M. (2018). Privacy-preserving multi-channel communication in Edge-of-Things. Future Generation Computer Systems, 85.
Gorecky, D., Schmitt, M., Loskyll ,M., & Zuhlke, D. (2014). Human-machine-interaction in the Industry 4.0 era. International Conference on Industrial Informatics (INDIN), 12, 289–294 .
Hugh , B., Bil, H., Joe, C., & Tim, W. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in Industry, 101, 1-12.
Lu, Y., Morris, K.C., & Frechette, S. (2015). Standards landscape and directions for smart manufacturing systems. 2015 IEEE International Conference on Automation Science and Engineering (CASE), 24-28.
Ministry of Industry and Information technology of China (MIIT) and Standardiza-tion Administration of China (SAC). (2015). National Intellegent Manufacturing Standards Architecture Construction Guidance.
Philipp, O., Lukas, B., Thomas, F. (2019). The smart factory as a key construct of industry 4.0: A systematic literature review. International Journal of Production Economics.Retrived from https://www.sciencedirect.com/science/article/abs/pii/S092552731930286
Qinglin, Q., & Fei, T. (2018). Digital twin and Big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585-3593.
Qing, L., Qianlin, T., Iotong, C., Hailong, W., Yudi, P., Hongzhen J., Jun L., & Jian, Z. (2018). Smart manufacturing standardization: Architectures, reference models and standards framework. Computers in Industry,101,91-106.
Raj, A., Dwivedi, G., Sharma,A., Jabbour , A., & Rajak, S. (2019) . Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics. Retrived from https://www.sciencedirect.com/science/article/abs/pii/S092552731930372X.
Saaty, T.L. (1980). The Analytic Hierarchy Process, New York, NY: McGraw-Hill.
Shan, R., Yingfeng, Z., Yang, L., Tomohiko, S., Donald, H., & Cecilia, A. (2019). A comprehensive review of big data analytics throughout productlifecycle to support sustainable smart manufacturing: A framework、challenges and future research directions. Journal of Cleaner Production, 210.
Shuyou, Z., Jinghua, X., Huawei, G., & Jianrong, T. (2017). A Research Review on the Key Technologies of Intelligent Design for Customized Products. Engineering, 3(5),631-640.
Song, L., Fisher, R., Wang, L., & Cui, B. (2018). Environmental performance evaluation with big data: theories and methods. Annals of Operations Research, 270(1), 459–472.
Tim, S., & Günther, S. (2016). Opportunities of sustainable manufacturing in Industry 4.0. Procedia CIRP , 40, 536–541 .
Tobias, M., Martin, R., Till, G., Arno, K., Roman, D. (2018). Building blocks for planning and implementation of smart services based on existing products.Procedia CIRP, 73, 102-107.
Torbacki ,W., Kijewska, K. (2019). Identifying Key Performance Indicators to be used in Logistics 4.0 and Industry 4.0 for the needs of sustainable municipal logistics by means of the DEMATEL method. Transportation Research Procedia, 39,534-543.
Wu, W., , & Lee, T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Syst Appl, 32(2), 499-507.
Usman H. (2015). Managing Privacy in The Internet of Things, Harvard Business Review, Retrived from
https://hbr.org/2015/02/managing-privacy-in-the-internet-of-things.
Yugma, C., Jakey B., Stéphane., P, & Ali, O. (2015), Integration of Scheduling and Advanced Process Control in Semiconductor Manufacturing: Review and Outlook, Journal of Scheduling, 18(2), 195-205.
Zhong, Y., Xu, X., Klotz, E., & Newman, T. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616–630.
Zhou, J., Peigen, L.,Yanhong, Z., Baicun, W., Jiyuan, Z., & Meng, L. (2019). Toward New-Generation Intelligent Manufacturing. Engineering, 4(1),11-20.