AI Powered KPI Optimization: Bridging the Gap between Industry Data and Operational Excellence

Abstract:

Enhancing manufacturing efficiency increasingly relies on integrating Overall Equipment Effectiveness (OEE), Industry 4.0 technologies, and Artificial Intelligence (AI) with Machine Learning (ML). Traditionally, OEE aggregates Availability, Performance, and Quality, but in Industry 4.0 it can evolve into a real-time decision-support tool enriched with predictive analytics. This paper provides a conceptual systematic synthesis of the interplay between OEE and AI-driven methodologies, emphasizing the role of fuzzy logic and hybrid models in managing uncertainty. AI/ML applications—predictive maintenance, quality assurance, and process optimization—reduce downtime, minimize scrap, and increase productivity by improving OEE components through pattern recognition and forecasting. A further research focus is domain shift and transfer learning, which impact the scalability and robustness of industrial AI systems across changing equipment and factories. Transfer learning approaches such as fine-tuning, feature alignment, and adversarial adaptation can reduce retraining effort while maintaining performance. Finally, integrating AI with OEE supports sustainable manufacturing by improving energy efficiency and reducing environmental impacts, contributing to the long-term vision of self-optimizing digital factories capable of responding dynamically to market and environmental changes.

• Periodical: Advances in Science and Technology (Volume 181)
• Pages: 459-463
• DOI: https://doi.org/10.4028/p-7gswhF
• Online since: August 2026
• Authors: Péter Madarasi*, József Menyhárt
• Keywords: Artificial Intelligence, Fuzzy Logic, Industry 4.0, Machine Learning, OEE, Predictive Maintenance, Sustainable Manufacturing

References

[1] R. Iannone and M. Elena, "Managing OEE to Optimize Factory Performance," Operations Management, 2013.

DOI: 10.5772/55322

[2] T. Chen et al., "Machine Learning in Manufacturing towards Industry 4.0: From 'For Now' to 'Four-Know,'" Applied Sciences, vol. 13, no. 3, 2023.

DOI: 10.3390/APP13031903

[3] R. X. Gao, J. Krüger, M. Merklein, H. C. Möhring, and J. Váncza, "Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions," CIRP Annals, vol. 73, no. 2, p.723–749, 2024.

DOI: 10.1016/J.CIRP.2024.04.101

[4] A. Wasnik, "A Review on Total Productive Maintenance (TPM)," Interantional Journal of Scientific Research in Engineering and Management, vol. 08, no. 05, p.1–5, 2024.

DOI: 10.55041/IJSREM33856

[5] S. S. Kamble, A. Gunasekaran, and S. A. Gawankar, "Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives," Process Safety and Environmental Protection, vol. 117, p.408–425, 2018.

DOI: 10.1016/J.PSEP.2018.05.009

[6] R. X. Gao, J. Krüger, M. Merklein, H. C. Möhring, and J. Váncza, "Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions," CIRP Annals, vol. 73, no. 2, p.723–749, 2024.

DOI: 10.1016/J.CIRP.2024.04.101

[7] P. Dobra and J. Josvai, "Predicting the impact of type changes on Overall Equipment Effectiveness (OEE) through machine learning," 2022 IEEE 1st International Conference on Internet of Digital Reality (IoD), p.11–16, 2022.

DOI: 10.1109/IOD55468.2022.9986645

[8] A. K. Kausik, A. bin Rashid, R. F. Baki, and M. M. Jannat Maktum, "Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications," Array, vol. 26, p.100393, Jul. 2025.

DOI: 10.1016/J.ARRAY.2025.100393

[9] H. Moradizadeh and R. v Mayorga, "Overall Equipment Effectiveness and Overall Line Efficiency Measurement using Fuzzy Inference Systems".

DOI: 10.5220/0005155101990204

[10] L. Foulloy, V. Clivillé, and L. Berrah, "A fuzzy temporal approach to the Overall Equipment Effectiveness measurement," Computers and Industrial Engineering, vol. 127, p.103–115, 2019.

DOI: 10.1016/J.CIE.2018.11.043

[11] S. Thiede, "Advanced energy data analytics to predict machine overall equipment effectiveness (OEE): a synergetic approach to foster sustainable manufacturing," Procedia CIRP, vol. 116, p.438–443, 2023.

DOI: 10.1016/J.PROCIR.2023.02.074