Analysis of Indonesian Industry Readiness for the Adoption of Autonomous Manufacturing Systems in the Era of Industry 4.0
Keywords:
Autonomous Manufacturing System, Industry 4.0, Manufacturing Readiness, Smart Factory, Digital TransformationAbstract
The rapid advancement of Industry 4.0 technologies has accelerated the transition from conventional manufacturing systems toward Autonomous Manufacturing Systems (AMS), which integrate Artificial Intelligence (AI), Industrial Internet of Things (IIoT), robotics, cyber-physical systems, digital twins, and big data analytics to enable intelligent, self-optimizing, and highly efficient production processes. Given the increasing importance of autonomous manufacturing for enhancing industrial competitiveness, this study aims to assess the readiness of Indonesian manufacturing industries to adopt Autonomous Manufacturing Systems. A mixed-methods approach was employed, combining quantitative and qualitative techniques. Data were collected through questionnaires, interviews, observations, and secondary sources involving respondents from the automotive, electronics, food and beverage, and textile sectors. The collected data were analyzed using descriptive statistics, readiness index calculations, gap analysis, Structural Equation Modeling (SEM), thematic analysis, and content analysis. The results indicate that the overall readiness level of Indonesian manufacturing industries is moderate. However, human resource readiness and cybersecurity readiness remain significant challenges due to shortages of specialized talent, limited AI-related competencies, insufficient workforce training, and varying levels of cybersecurity preparedness. The study also found substantial disparities between large enterprises and SMEs in terms of technology adoption and resource availability. The study concludes that while Indonesian manufacturing industries have established a foundation for autonomous manufacturing adoption, further improvements in workforce development, technology integration, cybersecurity infrastructure, and policy support are required. To accelerate the transition toward autonomous manufacturing, collaborative efforts among industry, government, and educational institutions are essential to strengthen technological capabilities, develop skilled human resources, and create a supportive innovation ecosystem.
References
Abd Jamil, A., Sarijari, M., A Rashid, R., Pusppanathan, J., Ismail, K., & Shamsir, M. S. (2024). Technology readiness level: advancing locally made unmanned vehicles. Malaysian Journal of Computing (MJoC), 9(2), 1824–1837.
Abdullah, R., & Fakieh, B. (2020). Health care employees’ perceptions of the use of artificial intelligence applications: survey study. Journal of Medical Internet Research, 22(5), e17620.
Andronie, M., Lăzăroiu, G., Iatagan, M., Uță, C., Ștefănescu, R., & Cocoșatu, M. (2021). Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 10(20), 2497.
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26.
Georgiadou, A., Mouzakitis, S., Bounas, K., & Askounis, D. (2022). A cyber-security culture framework for assessing organization readiness. Journal of Computer Information Systems, 62(3), 452–462.
Ghobakhloo, M. (2020). Determinants of information and digital technology implementation for smart manufacturing. International Journal of Production Research, 58(8), 2384–2405.
Glenn Richey Jr, R., & Autry, C. W. (2009). Assessing interfirm collaboration/technology investment tradeoffs: the effects of technological readiness and organizational learning. The International Journal of Logistics Management, 20(1), 30–56.
González, C. (2011). Extending research on ‘conceptions of teaching’: commonalities and differences in recent investigations. Teaching in Higher Education, 16(1), 65–80.
Holt, D. T., Armenakis, A. A., Feild, H. S., & Harris, S. G. (2007). Readiness for organizational change: The systematic development of a scale. The Journal of Applied Behavioral Science, 43(2), 232–255.
Lin, T.-C., Wang, K. J., & Sheng, M. L. (2020). To assess smart manufacturing readiness by maturity model: A case study on Taiwan enterprises. International Journal of Computer Integrated Manufacturing, 33(1), 102–115.
Manyika, J., Sinclair, J., Dobbs, R., Strube, G., Rassey, L., Mischke, J., Remes, J., Roxburgh, C., George, K., & O’Halloran, D. (2012). Manufacturing the future: The next era of global growth and innovation (Vol. 41). McKinsey Global Institute New York.
Nahavandi, S. (2019). Industry 5.0—A human-centric solution. Sustainability, 11(16), 4371.
Nawanir, G., Kong Teong, L., & Norezam Othman, S. (2013). Impact of lean practices on operations performance and business performance: some evidence from Indonesian manufacturing companies. Journal of Manufacturing Technology Management, 24(7), 1019–1050.
Pirola, F., Cimini, C., & Pinto, R. (2020). Digital readiness assessment of Italian SMEs: a case-study research. Journal of Manufacturing Technology Management, 31(5), 1045–1083.
Prakosa, B. G., Guritno, D. C., Anindita, T., Kurniawan, M., & Nugroho, A. C. (2024). Correlation among components of the Indonesian industry readiness index 4.0 and its implementation on socioeconomic along with the demographic aspects. Digital Transformation and Society, 3(3), 296–309.
Rezqianita, B. L., & Ardi, R. (2020). Drivers and barriers of industry 4.0 adoption in Indonesian manufacturing industry. Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering, 123–128.
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.
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.
Siegel, D. S., Waldman, D. A., & Youngdahl, W. E. (1997). The adoption of advanced manufacturing technologies: Human resource management implications. IEEE Transactions on Engineering Management, 44(3), 288–298.
Singh, H. (2020). Artificial intelligence and robotics transforming industries with intelligent automation solutions. Available at SSRN 5267868.
Sjöberg, Ö., & Sjöholm, F. (2004). Trade liberalization and the geography of production: Agglomeration, concentration, and dispersal in Indonesia’s manufacturing industry. Economic Geography, 80(3), 287–310.
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653–661.
Vrchota, J., Mařiková, M., Řehoř, P., Rolínek, L., & Toušek, R. (2019). Human resources readiness for industry 4.0. Journal of Open Innovation: Technology, Market, and Complexity, 6(1), 3.
Zhen, Z., Yousaf, Z., Radulescu, M., & Yasir, M. (2021). Nexus of digital organizational culture, capabilities, organizational readiness, and innovation: Investigation of SMEs operating in the digital economy. Sustainability, 13(2), 720.
Zhou, K., Liu, T., & Zhou, L. (2015). Industry 4.0: Towards future industrial opportunities and challenges. 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2147–2152.
Zohrabi, M. (2013). Mixed method research: Instruments, validity, reliability and reporting findings. Theory and Practice in Language Studies, 3(2), 254–262.
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