IoT and OEE Integration for Improving Rewinder Performance in Paper Industry: A Systematic Review
Abstract
The rewinder is a critical unit in the paper production process because it determines operational stability, final product quality, and the overall effectiveness of production flow. However, this unit frequently experiences operational disturbances such as downtime, speed loss, and quality deviations, which directly contribute to reductions in Overall Equipment Effectiveness (OEE). With the advancement of Industry 4.0 technologies, the integration of the Internet of Things (IoT) with OEE monitoring systems offers new opportunities to improve machine efficiency through real-time monitoring, early fault detection, and data-driven performance analysis. This study conducts a systematic literature review of 37 scientific publications related to IoT implementation, predictive maintenance, and OEE improvement in manufacturing environments. The reviewed literature was analyzed using the PRISMA framework and categorized into four major themes: IoT–OEE integration, OEE improvement strategies, predictive maintenance and smart manufacturing, and downtime reduction and availability improvement. The findings indicate that IoT-based predictive maintenance systems can increase equipment availability by approximately 25–30%, reduce downtime by 30–40%, and improve fault detection accuracy through machine learning models reaching up to 92%. In addition, IoT-enabled monitoring systems enhance data visibility and improve the accuracy of OEE measurement by approximately 15%, thereby strengthening operational decision-making. Future research should validate IoT–OEE integration through industrial case studies, given its potential to improve rewinder performance and reliability in paper machines.
##Keywords:##
IoT, OEE, Paper Machine, Paper Industry, Rewinder.
Published
Mar 30, 2026
How to Cite
SHOLIHIN, Muhammad; AGUSTINA, Dinni.
IoT and OEE Integration for Improving Rewinder Performance in Paper Industry: A Systematic Review.
Journal of Ocean, Mechanical and Aerospace -science and engineering-, [S.l.], v. 70, n. 1, p. 55-63, mar. 2026.
ISSN 2527-6085.
Available at: <https://www.isomase.org/Journals/index.php/jomase/article/view/574>. Date accessed: 29 may 2026.
doi: http://dx.doi.org/10.36842/jomase.v70i1.574.
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[3] Llopis J., Lacasa A., Montés N., & Garcia E. (2024). I3oT tool for continuous improvement in production line efficiency. Machines, 12(11), 760. https://doi.org/10.3390/machines12110760.
[4] Becker J. A., & Sanders J. P. M. (2016). The role of IoT in manufacturing: A systematic review. International Journal of Engineering Research and Applications, 6(5), 36–44.
[5] Mikhailov A. B. et al. (2021). Development of the production monitoring system based on the Industrial Internet of Things platform, Journal of Physics: Conference Series, 1753, 012033. https://doi.org/10.1088/1742-6596/1753/1/012033
[6] Ooi B. Y., et al. (2021). A flexible and reliable IoT solution for real-time production tracking. IEEE Industrial Electronics and Applications Conference, 270–275. https://doi.org/10.1109/IEACON51066.2021.9654672
[7] Cabrini F. H. et al. (2021). Enabling the Industrial Internet of Things to cloud continuum in a real city environment. Sensors, 21(22), 7707. https://doi.org/10.3390/s21227707
[8] Sharma D. K., Bhargava S., & Singhal K. (2020). IoT applications in pharmaceutical industry, in An Industrial IoT Approach for Pharmaceutical Industry Growth. Elsevier, 153–190.
[9] Sama M., Saini A., & Singh R. (2023). Automated data acquisition system for industrial applications using IoT. International Journal of Engineering Research & Technology, 12(5).
[10] Seetharaman A. et al. (2019). Customer expectation from IIoT. Journal of Manufacturing Technology Management, 30(8), 1161–1178. https://doi.org/10.1108/JMTM-08-2018-0278
[11] Ginting P., et al. (2025). Application of Internet of Things (IoT) technology in predictive maintenance of industrial machines. International Journal of Technology & Energy, 1(2), 42–52.
[12] Fakinafiddin A. N. P., Muslimin M., & Imaduddin B. E. (2025). Analisis downtime mesin mixer dengan basis IoT terhadap efektivitas produksi di PT SIGK. Jurnal Riset Multidisiplin Edukasi, 2(8), 343–354. https://doi.org/10.59653/jrmed.v2i8.1065
[13] Bimo A. E. A. & Sari A. Y. (2021). Data collection system using Internet of Things support for daily operations and machine performance in the refrigeration engineering laboratory. National Conference on Applied Machine Learning (NCAML), 1–8.
[14] Gonzales-Vera R., et al. (2025). Improvement model of OEE in the production process of cardboard boxes through SMED, TPM, automation, and IoT. SSRG International Journal of Mechanical Engineering, 12(6), 34–51. https://doi.org/10.14445/23488360/IJME-V12I6P104
[15] Ayvaz S. & Alpay K. (2021). IoT-based predictive maintenance for industrial applications. Journal of Manufacturing Systems, 60, 639–649. https://doi.org/10.1016/j.jmsy.2021.07.001
[16] Ghosh A., Edwards D. J., & Hosseini M. R. (2021). Patterns and trends in Internet of Things (IoT) research. Engineering, Construction and Architectural Management, 28(10), 3171–3200. https://doi.org/10.1108/ECAM-04-2020-0271
[17] Durocher D. B., & Sprinkle L. (2021). The experiences of a global electrical manufacturing enterprise: The journey to become Industry 4.0 ready. IEEE Industry Applications Magazine, 27(2), 67–75. https://doi.org/10.1109/MIAS.2020.3024476
[18] Compare M., Baraldi P., & Zio E. (2019). A four-layer architecture for predictive maintenance in Industry 4.0. IEEE Transactions on Industrial Informatics, 15(12), 6715–6724. https://doi.org/10.1109/TII.2019.2936724
[19] Zhang Y. & L. Wang. (2022). An IoT-based decision support system for predictive maintenance in smart manufacturing. Journal of Manufacturing Systems, 62, 1–12. https://doi.org/10.1016/j.jmsy.2021.11.001
[20] Braglia M., Frosolini M., & Zammori F. (2009). Overall equipment effectiveness of a manufacturing line (OEEML): An integrated approach to assess systems performance. Journal of Manufacturing Technology Management, 20(1), 8–29. https://doi.org/10.1108/17410380910925389
[21] Nor Aziana A. A., et al. (2025). Lean manufacturing and Industry 4.0. Jurnal Kejuruteraan, 37(1), 151–165. https://doi.org/10.17576/jkukm-2025-37(1)-10
[22] Peko I., et al. (2022). Production key figures and novel quality metrics for production quality. Procedia Computer Science, 200, 740–748,. https://doi.org/10.1016/j.procs.2022.01.283
[23] Novochadlo M., Kaczmarek M., & Kaczmarek A. (2024). Real-time OEE indicator for manufacturing processes using IoT. Sensors, 24(5), 1600. https://doi.org/10.3390/s24051600
[24] Civerchia F., Bocchino S., Salvadori C., Rossi E. & De Leo G. (2017). An IoT-based predictive maintenance system for industrial machinery. IEEE International Conference on Industrial Technology (ICIT), 1374–1379. https://doi.org/10.1109/ICIT.2017.7915570
[25] Christou I. T., Zalonis A., Kefalakis N., & Soldatos J. (2020). Predictive and explainable machine learning for Industrial Internet of Things applications. IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), 213–220. https://doi.org/10.1109/DCOSS49796.2020.00043
[26] Teoh S. S., Tan C. S., & Lim K. H. (2021). Fog-computing based predictive maintenance for industrial IoT. IEEE IEEM, 1022–1026. https://doi.org/10.1109/IEEM50594.2021.9672935
[27] Kamgba S. (2024). Predictive maintenance of industrial machinery using machine learning: A review. Journal of Engineering Research and Applications, (14)1, 1–10.
[28] Mendes D., Gaspar P. D., Charrua-Santos F., and Navas H. (2023). Enhanced real-time maintenance management model. Electronics, 12(18), 3872. https://doi.org/10.3390/electronics12183872
[29] Setiawan A., Aprilia D. N. & Nelfiyanti N. (2025). Implementation of OEE as a strategy to increase Kanzaki shaving machine productivity. ICECREAM 2025 Proceedings, 1–12.
[30] Badiea S. M., Al-Habaibeh A. & Al-Mawali A. (2023). Total Productive Maintenance (TPM) implementation in manufacturing industries: A systematic review. Journal of Quality in Maintenance Engineering, 29(2), 305–328. https://doi.org/10.1108/JQME-03-2022-0023.
