| 研究生: |
陳品媜 Pin-Chen Chen |
|---|---|
| 論文名稱: |
台灣地區工業4.0成熟度現況之探討 |
| 指導教授: |
呂俊德
Jun-Der Leu |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 企業管理學系 Department of Business Administration |
| 論文出版年: | 2020 |
| 畢業學年度: | 108 |
| 語文別: | 中文 |
| 論文頁數: | 92 |
| 中文關鍵詞: | 工業4.0 、數位轉型 、成熟度 、財務績效 |
| 外文關鍵詞: | Industry 40., digital transformation, maturity level, financial performance |
| 相關次數: | 點閱:7 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
德國為下個世代建立產業發展政策,為此建立工業4.0的開端。然而,朝向工業4.0的路徑中,必須歷經數位轉型。但企業朝向數位轉型遇到的困境,如公司對工業4.0缺乏方向,最重要的是企業無法評估自身公司的能力。在此困境下,企業須先對自身評估企業的工業4.0的成熟度,再透過與目標相比,明確了解公司目前身處何種程度以及需加強哪些構面。本研究沿用Puchan , Krischke 與Jun-Der Leu於2019年01月16天下雜誌665期,台灣地區製造業的工業4.0現況調查之問卷,工業4.0的成熟度分為五個構面﹕策略、組織、數位化聯結、數位化工廠、數位化服務,其中策略與組織視為數位化管理構面,數位化聯結、數位化工廠、數位化服務為數位化工程構面。財務績效構面下包括毛利率、資本生產力及人均產值。財務績效是一個反應企業是否達到經濟目標的指標,在新技術的導入下,加上前述工業4.0本身的特性,進行生產優化的結果是否能真實提升財務績效,為多數企業關注的課題。本研究透過結構方程模型(Structural equation modeling, SEM),對其分析數位化管理面與數位化工程如何影響財務績效,以及兩者如何相輔相成。本研究提出三個假設,假設一為數位化管理構面對財務績效有正向影響。假設二為數位化工程主構面對財務績效有顯著正向影響,假設三為數位化管理主構面對數位化工程構面有顯著正向影響。由經濟意涵而言,若持續精機器設備至最佳,將無法有效提升財務績效,必須考量策略及組織構面如抗拒變革、員工參與等因素,搭配數位化管理面向才能使財務績效提升。若是企業內部數位化管理構面越成熟,數位化工程構面越成熟,即考慮管理面向越全面,員工抗拒變革程度越小,將更容易導入新的機器設備。
To develop the policy of economics for the next generation, Germany government published the industry 4.0 guild with enterprise and non-governmental organization. To the path to industry 4.0, company needed to perform digital transformation. The dilemmas were company didn’t know the goal or the strategy were correct or not. Even if company wasn’t know the benefit and the expense. Most important was company can’t measure the capability of industry 4.0 by itself. Therefore, company need to measure the capability of industry 4.0 by itself and then compare the current situation with goal that company know which dimensions should be improved. This study is based on the results of a study conducted by Puchan, Krischke and Jun-Der Leu on 01/01/2019. The questionnaire of the survey on the current status of Industry 4.0 in Taiwan's regional manufacturing industry was published in Magazine, Issue 665, September 16, 2012. The maturity level of industry 4.0 were combined with five dimensions:strategy, organization, digital connection, digital service and digital factory. Our study defined strategy and organization as digital management aspect and defined digital connection, digital service and digital factory as digital engineering aspect. The financial performance aspect included gross margin, capital productivity and output per capital. Three assumptions are made in this study. The study used the structural equation model (SEM) method to analyze how the digital management aspect and digital engineering aspect affect financial performance aspect. There are three Hypothesis in this study. The results of the study show that Hypothesis 1 and 3 are valid. Assumption 2 does not hold. If company always improved the machine or software in the factory that can’t be build up the financial performance, company should be considered the strategy and organization such as reinforce or participation.
Aaltonen, I., Salmi, T., & Marstio, I. (2018). Refining levels of collaboration to support the design and evaluation of human-robot interaction in the manufacturing industry. Procedia Cirp, 72, 93-98.
Bowersox, D. J., Closs, D. J., & Drayer, R. W. (2005). The digital transformation: technology and beyond. Supply Chain Management Review, 9(1), 22-29.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588.
Buer, S.-V., Strandhagen, J. O., & Chan, F. T. (2018). The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda. International Journal of Production Research, 56(8), 2924-2940.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5): Prentice hall Upper Saddle River, NJ.
Heavin, C., & Power, D. J. (2018). Challenges for digital transformation–towards a conceptual decision support guide for managers. Journal of Decision Systems, 27(sup1), 38-45.
Kiel, D., Arnold, C., & Voigt, K.-I. (2017). The influence of the Industrial Internet of Things on business models of established manufacturing companies–A business level perspective. Technovation, 68, 4-19.
Lichtblau, K., Goericke, D., & Stich, V. (2015). Industrie 4.0-Readiness-Check. Retrieved 24 May 2020, from https://www.industrie40-readiness.de/?lang=en
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological bulletin, 105(3), 430.
Pini, F., Leali, F., & Ansaloni, M. (2015). A systematic approach to the engineering design of a HRC workcell for bio-medical product assembly. Paper presented at the 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA).
Proença, D., & Borbinha, J. (2016). Maturity models for information systems-A state of the art. Procedia Computer Science, 100, 1042-1049.
Puchan, J., Krischke, A., & Jun-Der, Leu. (2019年1月)。全台首次566家公司智慧製造大調查:誰是最佳工業4.0企業﹖天下雜誌,665,72-86。
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group, 9(1), 54-89.
Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2020). Impacts of Industry 4.0 technologies on Lean principles. International Journal of Production Research, 58(6), 1644-1661.
Scheer, A.-W., Abolhassan, F., Wolfram, J., Kirchmer, M., Scheer, A., Abolhassan, F., & Jost, W. (2002). Business process excellence: Springer.
Schumacher, A., Erol, S., & Sihn, W. (2016). A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia Cirp, 52, 161-166. doi:10.1016/j.procir.2016.07.040
Schumacher, A., Nemeth, T., & Sihn, W. (2019). Roadmapping towards industrial digitalization based on an Industry 4.0 maturity model for manufacturing enterprises. Procedia Cirp, 79, 409-414.
Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling: psychology press.
Senvar, O., & Sennaroglu, B. (2016). Comparing performances of clements, box-cox, Johnson methods with weibull distributions for assessing process capability. Journal of Industrial Engineering and Management, 9(3), 634-656.
Shi, J., Jimmerson, G., Pearson, T., & Menassa, R. (2012). Levels of human and robot collaboration for automotive manufacturing. Paper presented at the Proceedings of the Workshop on Performance Metrics for Intelligent Systems.
Tay, S., Lee, T., Hamid, N., & Ahmad, A. (2018). An overview of industry 4.0: Definition, components, and government initiatives. Journal of Advanced Research in Dynamical and Control Systems, 10(14), 1379-1387.
Ustundag, A., & Cevikcan, E. (2017). Industry 4.0: managing the digital transformation: Springer.
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems.
Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing Smart Factory of Industrie 4.0: An Outlook. International Journal of Distributed Sensor Networks, 12(1), 3159805. doi:10.1155/2016/3159805
Westerman, G., Calméjane, C., Bonnet, D., Ferraris, P., & McAfee, A. (2011). Digital Transformation: A roadmap for billion-dollar organizations. MIT Center for Digital Business and Capgemini Consulting, 1, 1-68.
Winkelhake, U., Winkelhake, & Schilgerius. (2018). Digital Transformation of the Automotive Industry: Springer.
Yuan, X.-M. (2020). Impact of Industry 4.0 on Inventory Systems and Optimization Industry 4.0-Impact on Intelligent Logistics and Manufacturing: IntechOpen.