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題名 工業4.0下製造管理議題及相關的決策資訊系統
Manufacturing Management and Related Decision Support Systems Under Industry 4.0作者 林青平
Lin, Ching-Ping貢獻者 吳安妮
Wu, Anne
林青平
Lin, Ching-Ping關鍵詞 工業4.0
製造管理議題
決策資訊系統
Industry 4.0
Manufacturing management
Decision support system日期 2018/06/22 上傳時間 11-Jul-2018 17:22:32 (UTC+8) 摘要 隨著客製化需求提升，企業希望以合理之成本，提升整體反應速度及生產靈活性，保持其競爭力，故工業4.0已成為當前製造業之趨勢。工業4.0並非突然出現之產業革命，而是隨著科技進步，而逐漸成熟之概念。然而，工業4.0尚未有明確之定義，各公司導入工業4.0之目的及程度皆不同。 工業4.0之導入不僅需要龐大之資金投入，也需要與以往全然不同之組織思維。導入工業4.0將會帶來與以往完全不同之製造管理議題，產業界也希望透過新科技，如決策資訊系統，達到更快速且高品質之決策。目前之研究都只有分別針對工業4.0之特色、製造管理議題及與其相關之決策資訊系統；且目前之研究在實務方面數量有限，多侷限於理論層面。 因此，本研究將以一家個案公司導入工業4.0作為探討標的，並深入分析其所帶來之不同傳統之製造管理議題及運用管理決策資訊系統輔助之。
With the increase in customization requirements, companies hope to maintain their competitiveness by increase the speed of response and production flexibility at a reasonable cost. As a result, Industry 4.0 has become the trend in the current manufacturing industry. Industry 4.0 is not an industrial revolution that suddenly emerged, but a concept of gradual maturity as technology advances. However, Industry 4.0 has not yet been clearly defined. The purpose and degree of each company`s introduction into Industry 4.0 are different. The introduction of Industry 4.0 not only requires huge capital investment, but also requires organizational mindset that is completely different from the past. The introduction of Industry 4.0 will bring about completely different manufacturing management issues than before. The industry also hopes to achieve faster and higher-quality decisions through new technologies such as decision supporting information systems. All the current researches have only focused on the characteristics of Industry 4.0, manufacturing management issues, and the decision support information systems. Moreover, the current research has a limited number of practices and is limited to the theoretical level. Therefore, this study will introduce a case company as the subject of the study, and further analyze the different traditional manufacturing management issues brought by it and use the management decision support system to assist it.參考文獻 中文部分 呂正華，2017，產業競爭力提升與生產力4.0，台北：國家圖書館。 李傑，2016，工業大數據：工業4.0時代的智慧轉型與價值創新。台北：天下雜誌。 林上育，2017，工業4.0與作業價值管理(AVM)之結合，國立政治大學會計研究所碩士論文。 研華科技，2017，智慧工廠核心靈魂在哪裡？──研華林口互聯網體驗園區，網址：https://buzzorange.com/techorange/2017/01/12/advantech-factory/，搜尋日期：2018年4月26日。 韋康博，2016，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？台北：商周出版。 許家愷，2016，工業4.0對製造業的影響分析－以元件製造商為例，淡江大學管理科學學系企業經營碩士在職專班碩士論文。 陳端武，2018，機器人是帶來巨變的顛覆性技術，網址：https://www.digitimes.com.tw/iot/article.asp?cat=158&cat1=20&cat2=90&id=0000524399_VML2Y8P6LVMERD8S99VG0，搜尋日期：2018年4月23日。 廖家宜，2018，智慧製造大趨勢：經驗傳承、資訊創新應用是關鍵，網址：https://www.digitimes.com.tw/tech/dt/most.asp?pack=12880&cnlid=1&cat=10，搜尋日期：2018年4月23日。 籃貫銘，2018，2018年台灣工業電腦的出貨總值將成長12.2%。網址：https://www.ctimes.com.tw/DispNews-tw.asp?O=HK234CT2U2KSAA00NJ，搜尋日期：2018年4月23日 經濟部統計局，2018年，當前經濟情勢概況：機械產業生產力（4月） 經濟部工業局，2016年，五大產業創新研發計畫智慧機械產業推動方案（7月） 英文部分 Almada-Lobo, F. 2015. The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES). Journal of Innovation Management 3: 16-21. Bordeleau, F. E., E. Mosconi, and L. Antonio Santa-Eulalia. 2018. Business Intelligence in Industry 4.0: State of the art and research opportunities. Paper presented at 51st Hawaii International Conference on System Sciences, Waikoloa, HI. Brüggemann, H., and P. Bremer. 2015. Grundlagen Qualitätsmanagement. Wiesbaden: Springer. Cao, H., P. Folan, J. Mascolo, and J. Browne. 2009. RFID in product lifecycle management: a case in the automotive industry. International Journal of Computer Integrated Manufacturing 22.(DOI: https://doi.org/10.1080/09511920701522981) Carroll, S. T., T. A. Mahoney, T. H. Jerdee. 1963. The Job(s) of Management. Industrial Relations. 4(2), (p.97-110) (DOI: https://doi.org/10.1111/j.1468-232X.1965.tb00922.x) Küpper, D., A. Heidemann, J. Ströhle, D. Spindelndreier, and C. Knizek. 2017. When Lean Meets Industry 4.0. Available at: https://www.bcg.com/publications/2017/lean-meets-industry-4.0.aspx#9-11110-1. Accessed: May 8, 2018. Eckerson, W. W. 2015. Performance dashboards: measuring, monitoring, and managing your business. John Wiley & Sons. (DOI: 10.1002/9781119199984) Elena, C. 2011. Business intelligence. Journal of knowledge management, economics and information technology, Romania. Fink, L., N. Yogev, and A. Even. 2017. Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management (DOI: https://doi.org/10.1016/j.im.2016.03.009) Foidl, H., and M. Felderer 2016. Research Challenges of Industry 4.0 for Quality Management. Paper presented at Innovations in Enterprise Information Systems Management and Engineering, Germany. Goel, D. 2017. What Is Industry 4.0 And How It Increases Machine Efficiency? Available at: https://thingtrax.com/2017/10/05/industry-4-0-increases-machine-efficiency/. Accessed: May 8, 2018. Hehenberger, P. 2011. Computerunterstützte Fertigung: Eine kompakte Einführung. Berlin: Springer-Verlag. Hozdić, E. 2015. Smart factory for industry 4.0: A review. Paper presented at 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Malaysia. (DOI: 10.1109/IEEM.2014.7058728). British Standards Institution. 2005. Quality management systems : fundamentals and vocabulary.No.3. England: British Standards Institution. Koch, V., S. Kuge, R. Geissbauer, and S. Schrauf. 2015. Industry 4.0: Opportunities and challenges of the industrial internet. Available at: https://www.strategyand.pwc.com/reports/industrial-internet. Accessed: May 5, 2018. Lasi, H., P. Fettke, H.-G. Kemper, T. Feld, and M. Hoffmann. 2014. Industry 4.0. Business & Information Systems Engineering 6(4): 239-242. Li, B. H., B. C. Hou, W. T. Yu, X. B. Lu, and C. W. Yang. 2018. Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1): 86-96. (DOI:10.1631/fitee.1601885) Mittelstädt, V., P. Brauner, M. Blum, and M. Ziefle. 2015. On the visual design of erp systems the–role of information complexity, presentation and human factors. Procedia Manufacturing 3: 448-455. (DOI: https://doi.org/10.1016/j.promfg.2015.07.207) Nikolic, B., J. Ignjatic, N. Suzic, B. Stevanov, and A. Rikalovic. 2017. Predictive manufacturing systems in industry 4.0: trends, benefits and challenges. Paper presented at 28TH DAAAM international symposium on intelligent, Croatia. (DOI: 10.2507/28th.daaam.proceedings.112) Posada, J., C. Toro, I. Barandiaran, D. Oyarzun, D. Stricker, R. de Amicis, E. Pinto, E. Peter, J. Döllner and I. Vallarino. 2015. Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE computer graphics and applications 2: 26-40. Putnik, G. D., M. Varela, R. Leonilde, C. Carvalho, C. Alves, V. Shah, H. Castro, and P. Ávila. 2015. Smart objects embedded production and quality management functions. International Journal for Quality Research (March). Schuh, G., T. Potente, C. Wesch-Potente, A. R. Weber, and J. P. Prote. 2014. Collaboration Mechanisms to Increase Productivity in the Context of Industrie 4.0. Procedia CIRP, 19, 51-56. (DOI: https://doi.org/10.1016/j.procir.2014.05.016) Singh, M., I. Khan, and S. Grover. 2012. Tools and techniques for quality management in manufacturing industries. Paper presented at the National Conference on Trends and Advances in Mechanical Engineering, Haryana. Valdeza, A. C., P. Braunera, A. K. Schaara, A. Holzingerb, and M. Zieflea. 2015. Reducing complexity with simplicity-usability methods for industry 4.0. Paper presented at the Proceedings 19th triennial congress of the IEA, Australia (DOI: DOI: 10.13140/RG.2.1.4253.6809) Yin, R. K. 1994. Case study research: Design and Methods. No.5. London: Sage Publications. 描述 碩士
國立政治大學
會計學系
105353027資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105353027 資料類型 thesis dc.contributor.advisor 吳安妮 zh_TW dc.contributor.advisor Wu, Anne en_US dc.contributor.author (Authors) 林青平 zh_TW dc.contributor.author (Authors) Lin, Ching-Ping en_US dc.creator (作者) 林青平 zh_TW dc.creator (作者) Lin, Ching-Ping en_US dc.date (日期) 2018/06/22 en_US dc.date.accessioned 11-Jul-2018 17:22:32 (UTC+8) - dc.date.available 11-Jul-2018 17:22:32 (UTC+8) - dc.date.issued (上傳時間) 11-Jul-2018 17:22:32 (UTC+8) - dc.identifier (Other Identifiers) G0105353027 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118592 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 會計學系 zh_TW dc.description (描述) 105353027 zh_TW dc.description.abstract (摘要) 隨著客製化需求提升，企業希望以合理之成本，提升整體反應速度及生產靈活性，保持其競爭力，故工業4.0已成為當前製造業之趨勢。工業4.0並非突然出現之產業革命，而是隨著科技進步，而逐漸成熟之概念。然而，工業4.0尚未有明確之定義，各公司導入工業4.0之目的及程度皆不同。 工業4.0之導入不僅需要龐大之資金投入，也需要與以往全然不同之組織思維。導入工業4.0將會帶來與以往完全不同之製造管理議題，產業界也希望透過新科技，如決策資訊系統，達到更快速且高品質之決策。目前之研究都只有分別針對工業4.0之特色、製造管理議題及與其相關之決策資訊系統；且目前之研究在實務方面數量有限，多侷限於理論層面。 因此，本研究將以一家個案公司導入工業4.0作為探討標的，並深入分析其所帶來之不同傳統之製造管理議題及運用管理決策資訊系統輔助之。 zh_TW dc.description.abstract (摘要) With the increase in customization requirements, companies hope to maintain their competitiveness by increase the speed of response and production flexibility at a reasonable cost. As a result, Industry 4.0 has become the trend in the current manufacturing industry. Industry 4.0 is not an industrial revolution that suddenly emerged, but a concept of gradual maturity as technology advances. However, Industry 4.0 has not yet been clearly defined. The purpose and degree of each company`s introduction into Industry 4.0 are different. The introduction of Industry 4.0 not only requires huge capital investment, but also requires organizational mindset that is completely different from the past. The introduction of Industry 4.0 will bring about completely different manufacturing management issues than before. The industry also hopes to achieve faster and higher-quality decisions through new technologies such as decision supporting information systems. All the current researches have only focused on the characteristics of Industry 4.0, manufacturing management issues, and the decision support information systems. Moreover, the current research has a limited number of practices and is limited to the theoretical level. Therefore, this study will introduce a case company as the subject of the study, and further analyze the different traditional manufacturing management issues brought by it and use the management decision support system to assist it. en_US dc.description.tableofcontents 第壹章、緒論 3 第一節、研究動機與目的 3 第二節、研究問題 3 第三節、研究架構 5 第貳章、文獻探討 7 第一節、工業4.0之製造特色 7 第二節、工業4.0下之製造管理議題 18 第三節、工業4.0下之管理決策資訊系統 31 第四節、本研究之延伸 36 第參章、研究方法 40 第一節、個案研究法 40 第二節、研究流程 41 第肆章、個案公司介紹 44 第一節、產業介紹 44 第二節、個案公司介紹 52 第伍章、個案分析－個案公司之工業4.0製造管理議題及管理決策資訊系統 58 第一節、個案公司工業4.0之執行方向及內容 58 第二節、工業4.0下之製造管理議題 63 第三節、工業4.0下不同管理階層之決策資訊系統 66 第陸章、結論與建議 72 第一節、 研究結論 72 第二節、研究限制 74 第三節、研究建議 75 參考文獻 77 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105353027 en_US dc.subject (關鍵詞) 工業4.0 zh_TW dc.subject (關鍵詞) 製造管理議題 zh_TW dc.subject (關鍵詞) 決策資訊系統 zh_TW dc.subject (關鍵詞) Industry 4.0 en_US dc.subject (關鍵詞) Manufacturing management en_US dc.subject (關鍵詞) Decision support system en_US dc.title (題名) 工業4.0下製造管理議題及相關的決策資訊系統 zh_TW dc.title (題名) Manufacturing Management and Related Decision Support Systems Under Industry 4.0 en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文部分 呂正華，2017，產業競爭力提升與生產力4.0，台北：國家圖書館。 李傑，2016，工業大數據：工業4.0時代的智慧轉型與價值創新。台北：天下雜誌。 林上育，2017，工業4.0與作業價值管理(AVM)之結合，國立政治大學會計研究所碩士論文。 研華科技，2017，智慧工廠核心靈魂在哪裡？──研華林口互聯網體驗園區，網址：https://buzzorange.com/techorange/2017/01/12/advantech-factory/，搜尋日期：2018年4月26日。 韋康博，2016，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？台北：商周出版。 許家愷，2016，工業4.0對製造業的影響分析－以元件製造商為例，淡江大學管理科學學系企業經營碩士在職專班碩士論文。 陳端武，2018，機器人是帶來巨變的顛覆性技術，網址：https://www.digitimes.com.tw/iot/article.asp?cat=158&cat1=20&cat2=90&id=0000524399_VML2Y8P6LVMERD8S99VG0，搜尋日期：2018年4月23日。 廖家宜，2018，智慧製造大趨勢：經驗傳承、資訊創新應用是關鍵，網址：https://www.digitimes.com.tw/tech/dt/most.asp?pack=12880&cnlid=1&cat=10，搜尋日期：2018年4月23日。 籃貫銘，2018，2018年台灣工業電腦的出貨總值將成長12.2%。網址：https://www.ctimes.com.tw/DispNews-tw.asp?O=HK234CT2U2KSAA00NJ，搜尋日期：2018年4月23日 經濟部統計局，2018年，當前經濟情勢概況：機械產業生產力（4月） 經濟部工業局，2016年，五大產業創新研發計畫智慧機械產業推動方案（7月） 英文部分 Almada-Lobo, F. 2015. The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES). Journal of Innovation Management 3: 16-21. Bordeleau, F. E., E. Mosconi, and L. Antonio Santa-Eulalia. 2018. Business Intelligence in Industry 4.0: State of the art and research opportunities. Paper presented at 51st Hawaii International Conference on System Sciences, Waikoloa, HI. Brüggemann, H., and P. Bremer. 2015. Grundlagen Qualitätsmanagement. Wiesbaden: Springer. Cao, H., P. Folan, J. Mascolo, and J. Browne. 2009. RFID in product lifecycle management: a case in the automotive industry. International Journal of Computer Integrated Manufacturing 22.(DOI: https://doi.org/10.1080/09511920701522981) Carroll, S. T., T. A. Mahoney, T. H. Jerdee. 1963. The Job(s) of Management. Industrial Relations. 4(2), (p.97-110) (DOI: https://doi.org/10.1111/j.1468-232X.1965.tb00922.x) Küpper, D., A. Heidemann, J. Ströhle, D. Spindelndreier, and C. Knizek. 2017. When Lean Meets Industry 4.0. Available at: https://www.bcg.com/publications/2017/lean-meets-industry-4.0.aspx#9-11110-1. Accessed: May 8, 2018. Eckerson, W. W. 2015. Performance dashboards: measuring, monitoring, and managing your business. John Wiley & Sons. (DOI: 10.1002/9781119199984) Elena, C. 2011. Business intelligence. Journal of knowledge management, economics and information technology, Romania. Fink, L., N. Yogev, and A. Even. 2017. Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management (DOI: https://doi.org/10.1016/j.im.2016.03.009) Foidl, H., and M. Felderer 2016. Research Challenges of Industry 4.0 for Quality Management. Paper presented at Innovations in Enterprise Information Systems Management and Engineering, Germany. Goel, D. 2017. What Is Industry 4.0 And How It Increases Machine Efficiency? Available at: https://thingtrax.com/2017/10/05/industry-4-0-increases-machine-efficiency/. Accessed: May 8, 2018. Hehenberger, P. 2011. Computerunterstützte Fertigung: Eine kompakte Einführung. Berlin: Springer-Verlag. Hozdić, E. 2015. Smart factory for industry 4.0: A review. Paper presented at 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Malaysia. (DOI: 10.1109/IEEM.2014.7058728). British Standards Institution. 2005. Quality management systems : fundamentals and vocabulary.No.3. England: British Standards Institution. Koch, V., S. Kuge, R. Geissbauer, and S. Schrauf. 2015. Industry 4.0: Opportunities and challenges of the industrial internet. Available at: https://www.strategyand.pwc.com/reports/industrial-internet. Accessed: May 5, 2018. Lasi, H., P. Fettke, H.-G. Kemper, T. Feld, and M. Hoffmann. 2014. Industry 4.0. Business & Information Systems Engineering 6(4): 239-242. Li, B. H., B. C. Hou, W. T. Yu, X. B. Lu, and C. W. Yang. 2018. Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1): 86-96. (DOI:10.1631/fitee.1601885) Mittelstädt, V., P. Brauner, M. Blum, and M. Ziefle. 2015. On the visual design of erp systems the–role of information complexity, presentation and human factors. Procedia Manufacturing 3: 448-455. (DOI: https://doi.org/10.1016/j.promfg.2015.07.207) Nikolic, B., J. Ignjatic, N. Suzic, B. Stevanov, and A. Rikalovic. 2017. Predictive manufacturing systems in industry 4.0: trends, benefits and challenges. Paper presented at 28TH DAAAM international symposium on intelligent, Croatia. (DOI: 10.2507/28th.daaam.proceedings.112) Posada, J., C. Toro, I. Barandiaran, D. Oyarzun, D. Stricker, R. de Amicis, E. Pinto, E. Peter, J. Döllner and I. Vallarino. 2015. Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE computer graphics and applications 2: 26-40. Putnik, G. D., M. Varela, R. Leonilde, C. Carvalho, C. Alves, V. Shah, H. Castro, and P. Ávila. 2015. Smart objects embedded production and quality management functions. International Journal for Quality Research (March). Schuh, G., T. Potente, C. Wesch-Potente, A. R. Weber, and J. P. Prote. 2014. Collaboration Mechanisms to Increase Productivity in the Context of Industrie 4.0. Procedia CIRP, 19, 51-56. (DOI: https://doi.org/10.1016/j.procir.2014.05.016) Singh, M., I. Khan, and S. Grover. 2012. Tools and techniques for quality management in manufacturing industries. Paper presented at the National Conference on Trends and Advances in Mechanical Engineering, Haryana. Valdeza, A. C., P. Braunera, A. K. Schaara, A. Holzingerb, and M. Zieflea. 2015. Reducing complexity with simplicity-usability methods for industry 4.0. Paper presented at the Proceedings 19th triennial congress of the IEA, Australia (DOI: DOI: 10.13140/RG.2.1.4253.6809) Yin, R. K. 1994. Case study research: Design and Methods. No.5. London: Sage Publications. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.ACCT.020.2018.F07 -
