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題名 智慧製造與作業價值管理(AVM)結合對管理決策之影響
The Impacts of Integrating Intelligent Manufacturing and Activity Value Management on Decision Making作者 張家茜
Chang, Chia-Chien貢獻者 吳安妮
Wu, Anne
張家茜
Chang, Chia-Chien關鍵詞 工業4.0
智慧製造
作業價值管理
管理決策
Industry 4.0
Intelligent manufacturing,
Activity Value Management
Management decision日期 2022 上傳時間 1-Aug-2022 17:06:23 (UTC+8) 摘要 本研究之目的為探討工業4.0環境下,台灣中小型製造業進行數位轉型的具體方法以及其對工廠管理的效益。本研究採用個案研究法,以國內某端子台製造商為研究對象,透過導入智慧製造即時成本管理系統,將工業物聯網結合作業價值管理制(AVM),除了即時的工單成本資訊之外,亦提供六大管理日報表,以整合因果關係之成本資訊協助資料導向決策的執行,同時解決智慧製造的管理問題。研究結果發現:導入本系統能夠有效地管理工單成本,且透過品質及產能屬性成本之分析,進一步追蹤內部失敗作業成本及無生產力作業成本發生的原因,以便管理者採取相對應之改善措施,俾提升產品品質及工廠生產力。此外,本系統之成本資訊可作為管理者從事十項管理決策的基礎,強化決策之精準度進而提升企業經營績效。
The purpose of this study is to explore an effective method of digital transformation in Taiwan`s small and medium-sized manufacturing industry and its benefits to factory management under Industry 4.0. This research adopts the case study method and takes a connector manufacturer in Taiwan as a research object. It integrates the Industrial Internet of Things (IIoT) and Activity Value Management System (AVMS) by introducing the Intelligent Cost System (ICS). ICS provides not only real-time cost information but also insightful management reports for the managers to make better decisions and to solve all kinds of product line challenges.The research results reveal two important findings. First, the introduction of ICS manages costs effectively. Besides, the productivity cost analysis sheet enables managers to find out the possible reason for productive inefficiency, so that they might take further actions to improve product quality and factory productivity. Second, the information provided by ICS serves as the basis for strategic business decisions. Therefore, it strengthens the accuracy of decision-making and enhances business performance.參考文獻 1.中文部分:吳安妮,2001,作業制成本制度(ABC)在管理決策上之效益,會計研究月刊,第182期:59-63。吳安妮,2015,管理會計技術商品化:以ABC為核心之作業價值管理系統(AVMS)為例,會計研究月刊,第359期:20-24。吳安妮,2019,企業策略的終極答案,用「作業價值管理AVM」破除成本迷思,掌握正確因果資訊,作對決策賺到「管理財」,台北:臉譜出版。朱靜慧,2016,電子連接器產業通訊月刊,第134期:16-20。李亦晴等,2020,智慧製造與機器人應用發展趨勢,台北:財團法人資訊工業策進會產業情報研究所。林上育,2017,工業4.0與作業價值管理(AVM)之結合,國立政治大學會計學系碩士論文。洪哲倫,2020,智慧製造的關鍵角色:工業大數據分析,機械工業雜誌,第444期:40-44。莊鎮遠,2021,智慧製造與作業價值管理(AVM)的結合—以A公司為例,國立政治大學會計學系碩士論文。張曙,2014,工業4.0和智能製造,機械設計與製造工程,第43卷第8期:31-35。麥斯.貝澤曼與唐.摩爾,2019,精準決策:哈佛商學院教你繞開大腦的偏誤,不出錯的做出好判斷,洪士美譯,台北:樂金文化。楊朝旭,2006,智慧資本、價值創造與企業績效關聯性之研究,中山管理評論,第14卷第1期:43-78。羅伯特.尹,2001,個案研究法,尚榮安譯,台北:弘智文化。產業價值鏈資訊平台,2021,連接器產業鏈簡介,https://ic.tpex.org.tw/introduce.php?ic=K000&stk_code=1617,擷取日期:2022年5月3日。2.英文部分:Brynjolfsson E., L. M. Hitt , and H. H. Kim. 2011. Strength in numbers: how does data-driven decision making affect firm performance? Working paper, Massachusetts Institute of Technology(MIT) and University of Pennsylvania.Bortolini, M., E. Ferrari, M. Gamberi, F. Pilati, and M. Faccio. 2017. Assembly system design in the industry 4.0 era: A general framework. IFAC-PapersOnLine,50(1): 100-105.Bazerman, M. H. and D. A. Moore. 2012. Judgment in managerial decision making. New Jersey: John Wiley & Sons Inc.Bousdekis A., K. Lepenioti, D. Apostolou, and G. Mentzas. A review of data-driven decision-making methods for Industry 4.0 maintenance applications. Electronics, 10(7):828.Cooper, R. and R. S. Kaplan. 1991. Profit priorities from activity-based costing.Harvard Business Review, 69(3):130-135.Collins, J. C. and W.C. Lazier. 2020. Beyond entrepreneurship 2.0: Turning your business into an enduring great company. London: Penguin Books.Drucker, P. F. 1995. The information executives truly need. Harvard Business Review,73(1):54-62.Kahneman, D. 2013. Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.Lee, M. X., Y. C. Lee, and C. J. Chou. 2017. Essential implications of the digitaltransformation in industry 4.0, 76(8): 465-467.Ness, A., and T. G. Cucuzza. 1995. Tapping the full potential of ABC. HarvardBusiness Review,73(4):130-138.Obitko, M., and V. Jirkovsky. 2015. Big data semantics in industry 4.0. Ininternational conference on industrial applications of holonic and multi-agentsystems: 217-229.Posada, J. and C. Toro. 2015. Visual computing as a key enabling technology forindustry 4.0 and industrial internet. IEEE Computer Graphics and Applications,35(2): 26-40.Provost, F. and T. Fawcett. 2013. Data science and its relationship to big data anddata-driven decision making. Big Data, 1(1):51-58.Roblek, V., M. Mesko, and A. Krapez. 2016. A complex view of industry 4.0. SageOpen, 6(2) :1-11.Schiff, B. 1992. How to succeed at activity-based costing management. ManagementAccounting, 73(9):64-66.Shan, Siqing; Wen, Xin; Wei, Yigang; Wang, Zijin; Chen, Yong. 2020. Intelligentmanufacturing in industry 4.0: A case study of Sany Heavy Industry. SystemsResearch & Behavioral Science, 37(4) :679-690.Schmidt, R., Mohring, M., Harting, R. C., Reichstein, C., Neumaier, P., and Jozinovic,P. 2015. Industry 4.0— Potentials for creating smart products: empirical researchresults. In International Conference on Business Information Systems :16-27.Sanchez, M., E. Exposito, and J. Aguilar. 2020. Industry 4.0: Survey from a systemintegration perspective. International Journal of Computer IntegratedManufacturing, 33(10):17-41.Stewart, T. A. 1997. Intellectual capital: The new wealth of organizations. New York,NY: Bantam Doubleday Dell.Sibony, O. 2020. You’re about to make a terrible mistake: How biases distortdecision-making. Little, New York, NY: Little, Brown Book Group.Thames, L., and D. Schaefer. 2016. Software-defined cloud manufacturing forindustry 4.0. Procedia Cirp, 52(3):12-17.Thaler, R. H. 2015. Misbehaving: the making of behavioral economics. London:Penguin Books.Yang, Jie; Ying, Limeng; Gao, Manru. 2020. The influence of intelligent manufacturing on financial performance and innovation performance: the case of China. Enterprise Information Systems, 14(6): 812-832.Yin, R. K. 2017. Case study research: Design and methods. New York, NY:Sage Publications. 描述 碩士
國立政治大學
會計學系
109353015資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109353015 資料類型 thesis dc.contributor.advisor 吳安妮 zh_TW dc.contributor.advisor Wu, Anne en_US dc.contributor.author (Authors) 張家茜 zh_TW dc.contributor.author (Authors) Chang, Chia-Chien en_US dc.creator (作者) 張家茜 zh_TW dc.creator (作者) Chang, Chia-Chien en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:06:23 (UTC+8) - dc.date.available 1-Aug-2022 17:06:23 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:06:23 (UTC+8) - dc.identifier (Other Identifiers) G0109353015 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/140981 - 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) 109353015 zh_TW dc.description.abstract (摘要) 本研究之目的為探討工業4.0環境下,台灣中小型製造業進行數位轉型的具體方法以及其對工廠管理的效益。本研究採用個案研究法,以國內某端子台製造商為研究對象,透過導入智慧製造即時成本管理系統,將工業物聯網結合作業價值管理制(AVM),除了即時的工單成本資訊之外,亦提供六大管理日報表,以整合因果關係之成本資訊協助資料導向決策的執行,同時解決智慧製造的管理問題。研究結果發現:導入本系統能夠有效地管理工單成本,且透過品質及產能屬性成本之分析,進一步追蹤內部失敗作業成本及無生產力作業成本發生的原因,以便管理者採取相對應之改善措施,俾提升產品品質及工廠生產力。此外,本系統之成本資訊可作為管理者從事十項管理決策的基礎,強化決策之精準度進而提升企業經營績效。 zh_TW dc.description.abstract (摘要) The purpose of this study is to explore an effective method of digital transformation in Taiwan`s small and medium-sized manufacturing industry and its benefits to factory management under Industry 4.0. This research adopts the case study method and takes a connector manufacturer in Taiwan as a research object. It integrates the Industrial Internet of Things (IIoT) and Activity Value Management System (AVMS) by introducing the Intelligent Cost System (ICS). ICS provides not only real-time cost information but also insightful management reports for the managers to make better decisions and to solve all kinds of product line challenges.The research results reveal two important findings. First, the introduction of ICS manages costs effectively. Besides, the productivity cost analysis sheet enables managers to find out the possible reason for productive inefficiency, so that they might take further actions to improve product quality and factory productivity. Second, the information provided by ICS serves as the basis for strategic business decisions. Therefore, it strengthens the accuracy of decision-making and enhances business performance. en_US dc.description.tableofcontents 第壹章 緒論 1第一節 研究動機與目的 1第二節 研究問題 4第三節 論文架構 6第貳章 文獻探討 8第一節 智慧製造之相關文獻 8第二節 作業基礎成本制與作業價值管理之相關文獻 14第三節 作業價值管理與智慧製造結合之相關文獻 24第四節 研究延伸 25第參章 研究方法 29第一節 個案研究法 29第二節 研究流程 30第肆章 個案公司介紹 32第一節 個案公司所處之產業 32第二節 個案公司簡介 33第伍章 智慧製造與作業價值管理之結合 35第一節 如何結合智慧製造與作業價值管理 35第二節 結合智慧製造與作業價值管理對管理決策的影響 52第三節 結合智慧製造與作業價值管理對生產力的效益 57第陸章 結論與建議 60第一節 研究結論 60第二節 研究限制 62第三節 研究建議 63參考文獻 65 zh_TW dc.format.extent 3666590 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109353015 en_US dc.subject (關鍵詞) 工業4.0 zh_TW dc.subject (關鍵詞) 智慧製造 zh_TW dc.subject (關鍵詞) 作業價值管理 zh_TW dc.subject (關鍵詞) 管理決策 zh_TW dc.subject (關鍵詞) Industry 4.0 en_US dc.subject (關鍵詞) Intelligent manufacturing, en_US dc.subject (關鍵詞) Activity Value Management en_US dc.subject (關鍵詞) Management decision en_US dc.title (題名) 智慧製造與作業價值管理(AVM)結合對管理決策之影響 zh_TW dc.title (題名) The Impacts of Integrating Intelligent Manufacturing and Activity Value Management on Decision Making en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1.中文部分:吳安妮,2001,作業制成本制度(ABC)在管理決策上之效益,會計研究月刊,第182期:59-63。吳安妮,2015,管理會計技術商品化:以ABC為核心之作業價值管理系統(AVMS)為例,會計研究月刊,第359期:20-24。吳安妮,2019,企業策略的終極答案,用「作業價值管理AVM」破除成本迷思,掌握正確因果資訊,作對決策賺到「管理財」,台北:臉譜出版。朱靜慧,2016,電子連接器產業通訊月刊,第134期:16-20。李亦晴等,2020,智慧製造與機器人應用發展趨勢,台北:財團法人資訊工業策進會產業情報研究所。林上育,2017,工業4.0與作業價值管理(AVM)之結合,國立政治大學會計學系碩士論文。洪哲倫,2020,智慧製造的關鍵角色:工業大數據分析,機械工業雜誌,第444期:40-44。莊鎮遠,2021,智慧製造與作業價值管理(AVM)的結合—以A公司為例,國立政治大學會計學系碩士論文。張曙,2014,工業4.0和智能製造,機械設計與製造工程,第43卷第8期:31-35。麥斯.貝澤曼與唐.摩爾,2019,精準決策:哈佛商學院教你繞開大腦的偏誤,不出錯的做出好判斷,洪士美譯,台北:樂金文化。楊朝旭,2006,智慧資本、價值創造與企業績效關聯性之研究,中山管理評論,第14卷第1期:43-78。羅伯特.尹,2001,個案研究法,尚榮安譯,台北:弘智文化。產業價值鏈資訊平台,2021,連接器產業鏈簡介,https://ic.tpex.org.tw/introduce.php?ic=K000&stk_code=1617,擷取日期:2022年5月3日。2.英文部分:Brynjolfsson E., L. M. Hitt , and H. H. Kim. 2011. Strength in numbers: how does data-driven decision making affect firm performance? Working paper, Massachusetts Institute of Technology(MIT) and University of Pennsylvania.Bortolini, M., E. Ferrari, M. Gamberi, F. Pilati, and M. Faccio. 2017. Assembly system design in the industry 4.0 era: A general framework. IFAC-PapersOnLine,50(1): 100-105.Bazerman, M. H. and D. A. Moore. 2012. Judgment in managerial decision making. New Jersey: John Wiley & Sons Inc.Bousdekis A., K. Lepenioti, D. Apostolou, and G. Mentzas. A review of data-driven decision-making methods for Industry 4.0 maintenance applications. Electronics, 10(7):828.Cooper, R. and R. S. Kaplan. 1991. Profit priorities from activity-based costing.Harvard Business Review, 69(3):130-135.Collins, J. C. and W.C. Lazier. 2020. Beyond entrepreneurship 2.0: Turning your business into an enduring great company. London: Penguin Books.Drucker, P. F. 1995. The information executives truly need. Harvard Business Review,73(1):54-62.Kahneman, D. 2013. Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.Lee, M. X., Y. C. Lee, and C. J. Chou. 2017. Essential implications of the digitaltransformation in industry 4.0, 76(8): 465-467.Ness, A., and T. G. Cucuzza. 1995. Tapping the full potential of ABC. HarvardBusiness Review,73(4):130-138.Obitko, M., and V. Jirkovsky. 2015. Big data semantics in industry 4.0. Ininternational conference on industrial applications of holonic and multi-agentsystems: 217-229.Posada, J. and C. Toro. 2015. Visual computing as a key enabling technology forindustry 4.0 and industrial internet. IEEE Computer Graphics and Applications,35(2): 26-40.Provost, F. and T. Fawcett. 2013. Data science and its relationship to big data anddata-driven decision making. Big Data, 1(1):51-58.Roblek, V., M. Mesko, and A. Krapez. 2016. A complex view of industry 4.0. SageOpen, 6(2) :1-11.Schiff, B. 1992. How to succeed at activity-based costing management. ManagementAccounting, 73(9):64-66.Shan, Siqing; Wen, Xin; Wei, Yigang; Wang, Zijin; Chen, Yong. 2020. Intelligentmanufacturing in industry 4.0: A case study of Sany Heavy Industry. SystemsResearch & Behavioral Science, 37(4) :679-690.Schmidt, R., Mohring, M., Harting, R. C., Reichstein, C., Neumaier, P., and Jozinovic,P. 2015. Industry 4.0— Potentials for creating smart products: empirical researchresults. In International Conference on Business Information Systems :16-27.Sanchez, M., E. Exposito, and J. Aguilar. 2020. Industry 4.0: Survey from a systemintegration perspective. International Journal of Computer IntegratedManufacturing, 33(10):17-41.Stewart, T. A. 1997. Intellectual capital: The new wealth of organizations. New York,NY: Bantam Doubleday Dell.Sibony, O. 2020. You’re about to make a terrible mistake: How biases distortdecision-making. Little, New York, NY: Little, Brown Book Group.Thames, L., and D. Schaefer. 2016. Software-defined cloud manufacturing forindustry 4.0. Procedia Cirp, 52(3):12-17.Thaler, R. H. 2015. Misbehaving: the making of behavioral economics. London:Penguin Books.Yang, Jie; Ying, Limeng; Gao, Manru. 2020. The influence of intelligent manufacturing on financial performance and innovation performance: the case of China. Enterprise Information Systems, 14(6): 812-832.Yin, R. K. 2017. Case study research: Design and methods. New York, NY:Sage Publications. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200701 en_US