許多國家都試著協助產業制定工業4.0的技術路徑，以幫助公司提昇競爭力，而推動智慧工廠是實現此目標的方法之一。然而，許多行業並不了解智慧工廠的核心概念或關鍵成分，亦不明白將傳統工廠轉型為智慧工廠的過程中所需滿足之最低標準。因此，本研究開發了一個多屬性決策分析模型，以瞭解並幫助印尼或其他國家的食品飲料產業評估其準備情況，並依此制定推動智慧工廠的適當策略。
基於專家意見和文獻回顧，本研究建立了一個由四個維度和八個準則組成而成的模型，以評估智慧工廠導入之準備程度。四個維度分別是實體資源、數據、資通訊技術與終端界面，八個準則分別是製造單元、生產線重置、數據採集設備、數據建模技術、數據使用、網絡技術、雲端平台和移動設備。此外，為了評估不同構面與準則的相對重要性與瞭解不同維度之間的相互依存關係，本研究採決策實驗室分析法（Decision Making Trial and Evaluation Laboratory，DEMATEL）和網路分析程序法（analytic network process，ANP）來確認具有高影響力的維度和重要的準則。研究發現強化製造單元是促成智慧工廠的最重要因素。因此，在制定智慧工廠實施略時，企業管理者需要優先考慮實體資源，尤其要注重加強製造單元，以實現轉型。

Nowadays, many countries try helping their industries to architect their Industry 4.0 roadmap to utilize new technology to compete with companies around the world in the new business environment. One possible approach for achieving this goal is to propel a manufacturing firm to run a smart factory. However, many industries in Indonesia do not understand the concept or key components of the smart factory, so guidelines on how to assess industry readiness for the smart factory in Indonesia and on what minimum criteria need to be fulfilled are needed. Therefore, this research developed a model to help food and beverage industry in Indonesia evaluate their readiness and develop appropriate strategies for implementing smart factory.
Based on expert opinions and literate review, a model consists of four dimensions and eight criteria was developed for accessing readiness of the smart factory. The four dimensions are physical resource, data, IT, and terminal interface, while the eight criteria are manufacturing units, reconfigured production lines, data acquisition equipment, digital modelling, data usage, network technology, cloud platforms and mobile devices. Furthermore, for better understanding the relative importance of different criteria and formulating the inter-dependence between different dimensions, Decision Making Trial and Evaluation Laboratory (DEMATEL) and analytic network process (ANP) methods were implemented to identify influential dimensions and important criteria. This study shows that the most important factor that must be considered to reach a smart factory is to strengthen the manufacturing unit. Thus, when developing smart factory implementation strategy, company managers need to prioritize physical resources and focus especially on strengthening manufacturing units to enable the transformation.

Alaei, A. A. R., & Alroaia, Y. V. (2016). Identifying and prioritizing the factors affecting and affected by the performance of small and medium enterprises by using DEMATEL technique (The case study of a province in Iran). Indian Journal of Science and Technology, 9(6).
Almada-Lobo, F. (2015). The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES). Journal of innovation management, 3(4), 16-21.
Bacudio, L. R., Benjamin, M. F. D., Eusebio, R. C. P., Holaysan, S. A. K., Promentilla, M. A. B., Yu, K. D. S., & Aviso, K. B. (2016). Analyzing barriers to implementing industrial symbiosis networks using DEMATEL. Sustainable Production and Consumption, 7, 57-65.
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., & Yin, B. (2017). Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access, 6, 6505-6519.
Ciulli, E. (2019). Tribology and Industry: from the Origins to 4.0. Frontiers in Mechanical Engineering, 5, 55.
Drath, R., & Horch, A. (2014). Industrie 4.0: Hit or hype?[industry forum]. IEEE industrial electronics magazine, 8(2), 56-58.
Fatorachian, H., & Kazemi, H. (2020). Impact of Industry 4.0 on supply chain performance. Production Planning & Control, 1-19.
Hermann, M., Pentek, T., & Otto, B. (2016, January). Design principles for industrie 4.0 scenarios. In 2016 49th Hawaii international conference on system sciences (HICSS) (pp. 3928-3937). IEEE.
Huang, C. Y., Shyu, Z., & Tzeng, G. H. (2007). Reconfiguring the Innovation Policy Portfolios for Taiwan's SIP. In Kuo, Yi-Wen (2000) Institutional Design of Deregulation for the Route Franchise of Local Motor Carriers, Thesis, Institute of Traffic and Transportation, Chiao-Tung University, Taiwan (ROC).
Kementerian Perindustrian (2018): Analisis Kerja Industri 2018, Kemenperin, 1–18.
Kearney, (2017): Roadmap Implementasi Industri 4.0.

Lin, C. L., & Tzeng, G. H. (2009). A value-created system of science (technology) park by using DEMATEL. Expert systems with applications, 36(6), 9683-9697.
Lin, G. T. R., and Sun, C.-C. (2010): Driving industrial clusters to be nationally competitive, Technology Analysis & Strategic Management, 22(1), 81–97.
Lucke, D., Constantinescu, C., & Westkämper, E. (2008). Smart factory-a step towards the next generation of manufacturing. In Manufacturing systems and technologies for the new frontier (pp. 115-118). Springer, London.
Malekzadeh, G., Kazemi, M., Lagzian, M., & Mortazavi, S. (2016). Modeling organizational intelligence using DEMATEL method in Iranian public universities. Journal of Modelling in Management.
Morgan, D. L. (1996). Focus groups as qualitative research (Vol. 16). Sage publications.
Qin, J., Liu, Y., & Grosvenor, R. (2016). A categorical framework of manufacturing for industry 4.0 and beyond. Procedia Cirp, 52, 173-178.
Radziwon, A., Bilberg, A., Bogers, M., and Madsen, E. S. (2014): The smart factory: Exploring adaptive and flexible manufacturing solutions, Procedia Engineering, 69(June 2015), 1184–1190.
Rojko, A. (2017). Industry 4.0 concept: background and overview. International Journal of Interactive Mobile Technologies (iJIM), 11(5), 77-90.
Santos, R. C., & Martinho, J. L. (2019). An Industry 4.0 maturity model proposal. Journal of Manufacturing Technology Management.
Si, S. L., You, X. Y., Liu, H. C., & Zhang, P. (2018). DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications. Mathematical Problems in Engineering, 2018.
Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
Wan, J., Tang, S., Yan, H., Li, D., Wang, S., & Vasilakos, A. V. (2016). Cloud robotics: Current status and open issues. IEEE Access, 4, 2797-2807.
Wang, S., Wan, J., Li, D., and Zhang, C. (2016): Implementing Smart Factory of Industrie 4.0: An Outlook, International Journal of Distributed Sensor Networks, 2016.
Zakoldaev, D. A., Shukalov, A. V., Zharinov, I. O., and Zharinov, O. O. (2019): Modernization stages of the Industry 3.0 company and projection route for the Industry 4.0 virtual factory, IOP Conference Series: Materials Science and Engineering, 537(3), 0–6.