Analysis of AI-Agent Implementation in Industry 5.0 Production Optimization: A Systematic Literature Review
Kata Kunci:
AI Agent, Industry 5.0, Production Optimization, Smart Manufacturing, Multi-Agent System xAbstrak
Industry 5.0 represents the next evolution of manufacturing, emphasizing the integration of advanced technologies with human-centered, sustainable, and resilient production systems. This study aims to analyze the implementation of AI agents in Industry 5.0 production optimization and evaluate their contributions to manufacturing performance. A Systematic Literature Review (SLR) approach was employed to examine relevant studies published between 2020 and 2026, sourced from Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink. The selected literature was analyzed across three dimensions The findings indicate that various AI-agent technologies, including Intelligent Agents, Multi-Agent Systems (MAS), Reinforcement Learning Agents, and Generative AI Agents, are increasingly utilized in manufacturing environments. Their implementation has led to significant improvements in production scheduling through reduced scheduling conflicts and enhanced machine utilization, predictive maintenance through improved failure prediction and reduced downtime, quality control through intelligent defect detection and automated inspection, and resource allocation through optimized utilization of labor, machinery, materials, and energy. Furthermore, AI agents contribute substantially to the core pillars of Industry 5.0 by supporting human-centric manufacturing through decision assistance and workplace safety enhancement, promoting sustainability through waste reduction and energy optimization, and increasing operational resilience through adaptive responses to disruptions and demand fluctuations. Despite challenges related to data integration, implementation costs, workforce readiness, and ethical considerations, the overall findings demonstrate that AI agents are highly effective enablers of production optimization and intelligent manufacturing. Future research should focus on autonomous factory ecosystems, human–AI collaborative agents, Digital Twin–AI Agent integration, and Explainable AI frameworks to further advance Industry 5.0 adoption and manufacturing innovation.
Referensi
Bérubé, M., Giannelia, T., & Vial, G. (2021). Barriers to the Implementation of AI in Organizations: Findings from a Delphi Study.
Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11(11), 2869.
Gotzamani, K. D., & Tsiotras, G. D. (2001). An empirical study of the ISO 9000 standards’ contribution towards total quality management. International Journal of Operations & Production Management, 21(10), 1326–1342.
Kang, C. W., Ramzan, M. B., Sarkar, B., & Imran, M. (2018). Effect of inspection performance in smart manufacturing system based on human quality control system. The International Journal of Advanced Manufacturing Technology, 94(9), 4351–4364.
Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent maintenance systems and predictive manufacturing. Journal of Manufacturing Science and Engineering, 142(11), 110805.
Lee, W. W., Zankl, W., & Chang, H. (2016). An ethical approach to data privacy protection.
Leusin, M. E., Frazzon, E. M., Uriona Maldonado, M., Kück, M., & Freitag, M. (2018). Solving the job-shop scheduling problem in the industry 4.0 era. Technologies, 6(4), 107.
Marik, V., & McFarlane, D. (2005). Industrial adoption of agent-based technologies. IEEE Intelligent Systems, 20(1), 27–35.
Massaro, A. (2021). Electronics in advanced research industries: Industry 4.0 to Industry 5.0 Advances. John Wiley & Sons.
Müller, J. (2020). Enabling technologies for Industry 5.0. European Commission, 8–10.
Nahavandi, S. (2019). Industry 5.0—A human-centric solution. Sustainability, 11(16), 4371.
Ouelhadj, D., & Petrovic, S. (2009). A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling, 12(4), 417–431.
Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access, 8, 220121–220139.
Romero, D., & Stahre, J. (2021). Towards the resilient operator 5.0: the future of work in smart resilient manufacturing systems. Procedia Cirp, 104, 1089–1094.
Shahbakhsh, M., Emad, G. R., & Cahoon, S. (2021). The Asian Journal of Shipping and Logistics. Vol, 38, 10–18.
SHARKAWY, M. H. D. (2020). Potential applications of collaborative intelligence technologies in manufacturing: study of applicability of collaborative intelligence technologies in manufacturing small-and-medium enterprises, collaborative intelligence frameworks, application benefits and adoption barriers.
Shen, W., Wang, L., & Hao, Q. (2006). Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(4), 563–577.
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.
Trakadas, P., Simoens, P., Gkonis, P., Sarakis, L., Angelopoulos, A., Ramallo-González, A. P., Skarmeta, A., Trochoutsos, C., Calvο, D., & Pariente, T. (2020). An artificial intelligence-based collaboration approach in industrial iot manufacturing: Key concepts, architectural extensions and potential applications. Sensors, 20(19), 5480.
Varela, M., Machado, J., Putnik, G., Manupati, V., Rajyalakshmi, G., & Trojanowska, J. (2018). Collaborative manufacturing based on cloud, and on other I4. 0 oriented principles and technologies: a systematic literature review and reflections. Management and Production Engineering Review, 9(3).
Waffenschmidt, S., Knelangen, M., Sieben, W., Bühn, S., & Pieper, D. (2019). Single screening versus conventional double screening for study selection in systematic reviews: a methodological systematic review. BMC Medical Research Methodology, 19(1), 132.
Wan, J., Chen, B., Imran, M., Tao, F., Li, D., Liu, C., & Ahmad, S. (2018). Toward dynamic resources management for IoT-based manufacturing. IEEE Communications Magazine, 56(2), 52–59.
Yerram, S. R. (2021). Driving the shift to sustainable Industry 5.0 with green manufacturing innovations. Asia Pacific Journal of Energy and Environment, 8(2), 55–66.
Zambetti, M., Khan, M. A., Pinto, R., & Wuest, T. (2020). Enabling servitization by retrofitting legacy equipment for Industry 4.0 applications: benefits and barriers for OEMs. Procedia Manufacturing, 48, 1047–1053.
Zhu, T., Ran, Y., Zhou, X., & Wen, Y. (2019). A survey of predictive maintenance: Systems, purposes and approaches. ArXiv Preprint ArXiv:1912.07383.
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