Comparative Analysis of Real-time and Conventional Overall Equipment Effectiveness Applications in Manufacturing Industry
Abstract
Overall Equipment Effectiveness (OEE) is a comprehensive measure to identify the level of productivity and performance of machines/equipments. Conventional approaches to OEE data processing, such as using Microsoft Excel, have limitations. Consequently, the data processing process becomes less efficient and prone to human error. This study aims to examine the application of OEE in the Manufacturing Industry, measurable performance gaps between conventional and real-time approaches. The methodology used in this study was based on a synthetic literature review to evaluate the effectiveness of both OEE approaches based on existing studies. The transition of OEE from mere calculations to dynamic, real-time, and integrated systems is a direct response to the increasing complexity and competitiveness of the modern manufacturing environment. This study can be used to identify areas for future development and as a reference for further research to provide a guide to OEE practitioners in implementing improvements.
##Keywords:##
OEE Application, Conventional, Manufacturing, Overall Equipment Effectiveness, Real-time.
Published
Dec 1, 2025
How to Cite
SUSILAWATI, Anita et al.
Comparative Analysis of Real-time and Conventional Overall Equipment Effectiveness Applications in Manufacturing Industry.
Journal of Ocean, Mechanical and Aerospace -science and engineering-, [S.l.], v. 69, n. 3, p. 224-228, dec. 2025.
ISSN 2527-6085.
Available at: <https://isomase.org/Journals/index.php/jomase/article/view/570>. Date accessed: 10 may 2026.
doi: http://dx.doi.org/10.36842/jomase.v69i3.570.
References
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[21] Jwo, J.-S., Lin, C.-S., & Lee, C.-H. (2021). An Interactive Dashboard Using a Virtual Assistant for Visualizing Smart Manufacturing. Mobile Information Systems, 2021, 1-15.
[22] Stern, S., Getnet, D., Van Hoorde, E., Cucalon, J., & Cherbaka, N. (2024, May). Reducing Lead Times in Manufacturing with Real-Time Data Visualizations. In 2024 Systems and Information Engineering Design Symposium (SIEDS) (pp. 337-342). IEEE.
[23] Alsarori, A., Abujaber, A., Wang, Z., Wang, Y., Yoon, S., Schillo, C., & Jackson, A. (2023, August). Kanban Digitization for Discrete Manufacturing Systems: A Case Study. In Global Joint Conference on Industrial Engineering and Its Application Areas (pp. 171-182). Cham: Springer Nature Switzerland.
[24] Mishra, D., Priyadarshi, A., Das, S. M., Shree, S., Gupta, A., Pal, S. K., & Chakravarty, D. (2022). Industry 4.0 application in manufacturing for real-time monitoring and control. Journal of Dynamics, Monitoring and Diagnostics, 1(3), 176-187.
[2] Zubair, M., Maqsood, S., Habib, T., Usman Jan, Q. M., Nadir, U., Waseem, M., & Yaseen, Q. M. (2021). Manufacturing productivity analysis by applying overall equipment effectiveness metric in a pharmaceutical industry. Cogent Engineering, 8(1), 1953681.
[3] Yuan, M., Alghassi, A., Zhao, S. F., Wu, S. W., Muhammad, A., Cui, J., & Myo, K. S. (2021). Online overall equipment effectiveness (OEE) improvement using data analytics techniques for CNC machines. In Implementing Industry 4.0: The Model Factory as the Key Enabler for the Future of Manufacturing (pp. 201-228). Cham: Springer International Publishing.
[4] Ardi, M., Sutanto, A., & Susilawati, A. (2023). Analysis of Effectiveness of Cut Size Line Machines Based on Total Productive Maintenance (TPM) and Analytical Hierarchy Process (AHP)-A Case Study. Journal of Ocean, Mechanical and Aerospace-science and engineering-, 67(3), 109-117.
[5] Mustafa, R., Susilawati, A., & Mulyadi, I. H. (2024). Analysis of Cut Size Paper Cutting Machine Performance Using Total Productive Maintenance Concept (Case Study: PT. RST). Journal of Ocean, Mechanical and Aerospace-science and engineering-, 68(2), 64-74.
[6] Waghmare, S. N., Raut, D. N., Mahajan, S. K., & Bhamare, S. S. (2014). Failure mode effect analysis and total productive maintenance: A review. International Journal of Innovative Research in Advanced Engineering, 1(6), 183-203.
[7] Tsarouhas, P., & Varzakas, T. (2022). An analytical approach of feta-cheese production line based on overall equipment effectiveness. International Journal of Productivity and Quality Management, 36(4), 459-477.
[8] Masmoudi, E., Piétrac, L., & Durieux, S. (2023, May). A literature review on the contribution of Industry 4.0 technologies in OEE improvement. In International Conference on Decision Support System Technology (pp. 69-79). Cham: Springer Nature Switzerland.
[9] Calandreli, P. R., Valle, P. D., & Deschamps, F. (2025). Maximizing operational efficiency with Industry 4.0 technology: integrating OEE as a performance indicator. The International Journal of Advanced Manufacturing Technology, 1-18.
[10] Singh, S., Khamba, J. S., & Singh, D. (2021). Analysis and directions of OEE and its integration with different strategic tools. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 235(2), 594-605.
[11] Di Luozzo, S., Pop, G. R., & Schiraldi, M. M. (2021). The human performance impact on OEE in the adoption of new production technologies. Applied Sciences, 11(18), 8620.
[12] Šajdlerová, I., Schindlerová, V., & Kratochvíl, J. (2020). Potential and limits of OEE in the total productivity management. Advances in Science and Technology. Research Journal, 14(2).
[13] Bobeica, V., Popescu, D., Angelescu, N., & Ichim, L. (2025, August). The impact of MES on OEE on Smart Manufacturing in Industry 4.0. In 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-6). IEEE.
[14] Calandreli, P. R., Valle, P. D., & Deschamps, F. (2025). Maximizing operational efficiency with Industry 4.0 technology: integrating OEE as a performance indicator. The International Journal of Advanced Manufacturing Technology, 1-18.
[15] Dieguez, T., Malheiro, M. T., Leal, N., & Machado, J. (2025, June). Systematic Literature Review on Manufacturing Execution Systems in the Era of Industry 4.0: A Bibliometric Analysis. In International Conference Innovation in Engineering (pp. 298-310). Cham: Springer Nature Switzerland.
[16] Nakajima, S. (1988). Introduction to TPM: Total productive maintenance (Trans.). Productivity Press.
[17] Rahayu, P. C., & Wicaksono, K. A. (2024). Real Time OEE Monitoring for Intelligent Manufacture Technology. 2024 International Conference on Mechanical, Industrial and Manufacturing Technologies (ICMIMT), 1-6.
[18] Li, Y., Inoue, L. C. G. V., & Sinha, R. (2022). Real-time OEE visualisation for downtime detection. 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), 1-6.
[19] Lerch, W. G. (2022). Analytical Hierarchy Process Strategy for Assessment of Overall Equipment Effectiveness. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 315-326).
[20] Zaki, D., Ridwan, R., Himawan, H. M., & Kurniawan, A. (2024). Otomatisasi Perhitungan OEE pada Mesin Filling ILAPAK 50g Menggunakan PLC dan HMI. Elposys: Jurnal Sistem Kelistrikan, 11(3), 265-272.
[21] Jwo, J.-S., Lin, C.-S., & Lee, C.-H. (2021). An Interactive Dashboard Using a Virtual Assistant for Visualizing Smart Manufacturing. Mobile Information Systems, 2021, 1-15.
[22] Stern, S., Getnet, D., Van Hoorde, E., Cucalon, J., & Cherbaka, N. (2024, May). Reducing Lead Times in Manufacturing with Real-Time Data Visualizations. In 2024 Systems and Information Engineering Design Symposium (SIEDS) (pp. 337-342). IEEE.
[23] Alsarori, A., Abujaber, A., Wang, Z., Wang, Y., Yoon, S., Schillo, C., & Jackson, A. (2023, August). Kanban Digitization for Discrete Manufacturing Systems: A Case Study. In Global Joint Conference on Industrial Engineering and Its Application Areas (pp. 171-182). Cham: Springer Nature Switzerland.
[24] Mishra, D., Priyadarshi, A., Das, S. M., Shree, S., Gupta, A., Pal, S. K., & Chakravarty, D. (2022). Industry 4.0 application in manufacturing for real-time monitoring and control. Journal of Dynamics, Monitoring and Diagnostics, 1(3), 176-187.
