Implementation of Internet of Things for Predictive Maintenance in Manufacturing Industry

Rizky Adi Pratama; James Andrew Collins

doi:10.70716/reswara.v3i3.450

Authors

Rizky Adi Pratama Department of Mechanical Engineering, Institut Teknologi Bandung, Bandung, Indonesia Author
James Andrew Collins Department of Manufacturing and Industrial Systems Engineering, University of Manchester, Manchester, United Kingdom Author

DOI:

https://doi.org/10.70716/reswara.v3i3.450

Keywords:

internet of things, predictive maintenance, manufacturing industry, industry 4.0, machine learning

Abstract

The rapid advancement of Industry 4.0 has accelerated the adoption of Internet of Things (IoT) technologies in manufacturing systems, particularly in predictive maintenance applications. Traditional maintenance strategies, such as corrective and preventive maintenance, often result in unplanned downtime, increased operational costs, and inefficient resource utilization. This study aims to analyze and synthesize recent scientific literature on the implementation of IoT-based predictive maintenance in the manufacturing industry, focusing on system architecture, data acquisition, analytics techniques, and operational impacts. A qualitative systematic literature review method was employed, analyzing peer-reviewed journal articles, conference proceedings, and book chapters published between 2020 and 2025. The findings indicate that IoT-enabled predictive maintenance significantly improves equipment reliability, reduces downtime by up to 50%, lowers maintenance costs, and enhances production efficiency. The integration of machine learning, edge computing, and digital twin technologies further strengthens real-time decision-making and failure prediction accuracy. This study contributes by providing a comprehensive and structured understanding of IoT-driven predictive maintenance implementations and identifying research gaps related to scalability, data interoperability, and cybersecurity. The results serve as a reference for both researchers and practitioners seeking to adopt predictive maintenance solutions in smart manufacturing environments.

References

Alves, F., Badikyan, H., Moreira, H. J. A., Ferreira, L., Costa, N., & Pereira, J. (2020). Deployment of a smart and predictive maintenance system in an industrial case study. Proceedings of the IEEE International Symposium on Industrial Electronics. https://doi.org/10.1109/ISIE45063.2020.9152441

Anagnostara, I., Sagani, A., Passias, V., Chatzidakis, S., & Papageorgiou, N. (2024). Cost-oriented predictive maintenance using exponential degradation modelling: Application on manufacturing industries. Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation. https://doi.org/10.1109/ETFA61755.2024.10710954

Arowolo, M. (2025). Predictive maintenance of energy-intensive industrial equipment using IoT and machine learning technologies. IOSR Journal of Mechanical and Civil Engineering. https://doi.org/10.9790/1684-2203031426

Bristy, I. J., Tabassum, M., Islam, M. S., Rahman, M. M., & Hossain, M. S. (2025). IoT-driven predictive maintenance dashboards in industrial operations. Saudi Journal of Engineering and Technology, 10(9). https://doi.org/10.36348/sjet.2025.v10i09.009

Devare, M. (2022). Predictive maintenance in Industry 4.0. In Industry 4.0 and smart manufacturing (pp. 85–102). CRC Press. https://doi.org/10.1201/9781003323426-5

Gupta, K., & Kaur, P. (2024). Application of predictive maintenance in manufacturing with the utilization of AI and IoT tools. TechRxiv. https://doi.org/10.36227/techrxiv.173532375.50630906

Henderson, J. H., & Sanders, M. C. (2025). AI-driven predictive maintenance: Reducing downtime and enhancing productivity in manufacturing environments. Preprints. https://doi.org/10.20944/preprints202504.0602.v1

Kanna, S. K. R. (2025). AI-powered predictive maintenance in industrial IoT: A framework for reducing downtime and enhancing productivity. International Journal of Research and Innovation, 1(1). https://doi.org/10.63665/ijri.v1i1.01

Kaur, T., & Kaur, P. D. (2025). Unlocking predictive maintenance in Industry 4.0: A digital twin–IoT perspective. International Journal of Scientific and Advanced Technology, 16(2). https://doi.org/10.71097/ijsat.v16.i2.6291

Kommanaboina, K. Y. (2022). Real-time predictive maintenance in manufacturing: Leveraging IoT and big data analytics. Journal of Material Sciences & Manufacturing Research. https://doi.org/10.47363/jmsmr/2022(3)e101

Lukito, T., Herlianti, R., Mayanti, M., & Prasetyo, A. (2025). Implementation of predictive maintenance in various industry: A review. Teknosains: Jurnal Sains, Teknologi dan Informatika, 12(1). https://doi.org/10.37373/tekno.v12i1.1338

Mahesh, G. U., Mangadevi, A., & Chakravarthy, B. K. (2024). A novel advanced IoT system for predictive maintenance systems and supply chain optimization solutions for manufacturing industries. Advanced Innovations in Business, Technology and Industrial Applications, 3(1). https://doi.org/10.46610/aibtia.2024.v03i01.005

Nangia, S., Makkar, S., & Hassan, R. (2020). IoT-based predictive maintenance in manufacturing sector. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3563559

Novak, P., & Vacek, T. (2023). An IoT-driven predictive maintenance model for smart manufacturing systems. International Academic Journal of Innovative Research, 10(3). https://doi.org/10.71086/iajir/v10i3/iajir1019

Pai, S., Cheekati, V. R., Prasad, N., Reddy, S., & Kumar, A. (2024). IoT-driven predictive maintenance for energy-efficient industrial systems. Proceedings of the IEEE International Conference on Emerging Technologies. https://doi.org/10.1109/INCET61516.2024.10593017

Parmar, T. (2025). Predictive maintenance in semiconductor manufacturing: Leveraging IoT sensor data for equipment reliability. Indian Scientific Journal of Research in Engineering and Management. https://doi.org/10.55041/ijsrem7616

Rajamani, K. (2025). The integration of IoT and acoustic analysis in predictive maintenance for industrial equipment. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/cseit251112325

Rakholia, R., Suárez-Cetrulo, A. L., Singh, M., & Patel, R. (2025). Integrating AI and IoT for predictive maintenance in Industry 4.0 manufacturing environments: A practical approach. Information, 16(9), 737. https://doi.org/10.3390/info16090737

Rao, N. R. (2025). AI-driven predictive maintenance using IoT in automotive manufacturing. International Journal of Science and Research Archive, 16(2). https://doi.org/10.30574/ijsra.2025.16.2.2380

Sarjan, S., Agustiawan, D., & Supratikta, H. (2024). Optimizing the system life cycle in manufacturing industry through implementation of IoT. Journal of Industrial Development and Engineering Applications, 1(2). https://doi.org/10.70001/jidea.v1i2.224

Sheikh, A. I., Kumar, T. C. A., Gupta, A., Sharma, R., Mehta, P., & Verma, S. (2024). Using IoT and machine learning together for manufacturing predictive maintenance. In Advances in intelligent manufacturing systems. CRC Press. https://doi.org/10.1201/9781003596721-9

Singh, P., & Abhishek. (2025). Integration of IoT and machine learning for predictive maintenance in manufacturing industries. Software Development World Journal for Future Practices. https://doi.org/10.71143/sdwjfp14

Taoufyq, H., El Guemmat, K., Mansouri, K., Bensaid, A., & Rahmani, M. (2025). Predictive maintenance for Industry 4.0 using IoT and machine learning. Proceedings of the International Conference on Communication and Information Technology. https://doi.org/10.1109/ICCCIT65724.2025.11166961

Yu, W. J., Liu, Y., Dillon, T. S., & Chang, E. (2023). Edge computing-assisted IoT framework with an autoencoder for fault detection in manufacturing predictive maintenance. IEEE Transactions on Industrial Informatics, 19(4), 2641–2652. https://doi.org/10.1109/TII.2022.3178732