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All Journal Jurnal Energi Dan Manufaktur Jurnal Teknik Mesin Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Turbo : Jurnal Program Studi Teknik Mesin Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) Prosiding SNTTM
Agustina, Dinni
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Decision Sciences, Operations Research & Management Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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IoT and OEE Integration for Improving Rewinder Performance in Paper Industry: A Systematic Review Sholihin, Muhammad; Agustina, Dinni
Journal of Ocean, Mechanical and Aerospace -science and engineering- Vol 70 No 1 (2026): Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse)
Publisher : International Society of Ocean, Mechanical and Aerospace -scientists and engineers- (ISOMAse)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36842/jomase.v70i1.574

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.
Co-Authors Adriyandy, Warits Anita Susilawati Asral Aulia Ramadhan Awaluddin Martin Awaludin Martin Cahyati, Wulan Damanik, Reinhard Parulian Deviawan, Damar Agung Dodi Sofyan Arief, Dodi Sofyan Farida Isroani Jheri Hermanto Khotimah, Nur Martunis Martunis Mitha Tri Indraswari Muhammad Sholihin Ratna Sary Ratna Sary Roe Dwi Dhewaji Sibuea, Edwin Reinaldy Sunny Ineza Putri Surianto, Muhammad Akbar