ERP-Enabled Predictive Maintenance in Manufacturing: Leveraging Machine Learning for Fault Detection and Production Continuity
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P107Keywords:
Predictive Maintenance, Machine Learning, ERP Integration, Internet of Things (IoT), Fault DetectionAbstract
Predictive Maintenance (PdM) Enabled ERP on fault-detection and product-community of machine learning (ML) has emerged as a significant facilitator towards the establishment of machinery dependability, reduction of downtime, and optimization of operation performance in the modern world of manufacturing. PdM can capture faults at an early stage and make proactive maintenance choices, out of the scope of traditional reactive and preventive mechanisms, using ML, Internet of Things (IoT) sensors, and advanced data analytics. This review of the predictive maintenance systems that are based on ERP, with a specific emphasis on the architectural integration of ERP, MES, and SCADA platforms, with the help of digital twin technology. The paper reviews data collection on the shop floor, water, and the IoTs; data synchronization and interoperability issues; and the use of supervised, unsupervised, semi-supervised, and deep learning methods for the detection and classification of faults. Moreover, the paper compares the effects of predictive maintenance on production continuity and operational resilience, and the contribution of ERP-enabling planning and rescheduling to reduced downtime and enhanced decision-making.
References
C. Patel, “Effect of Digital Transformation on Customer Engagement in Retail Industries : A Comparative Review,” J. Technol., vol. 10, no. 1, pp. 165–174, 2022.
[2] K. Chirumalla, P. Oghazi, R. E. Nnewuku, H. Tuncay, and N. Yahyapour, “Critical factors affecting digital transformation in manufacturing companies,” Int. Entrepreneur. Manag. J., vol. 21, no. 1, p. 54, Dec. 2025, doi: 10.1007/s11365-024-01056-3.
[3] S. Dodda, N. Kamuni, P. Nutalapati, and J. R. Vummadi, “Intelligent Data Processing for IoT Real-Time Analytics and Predictive Modeling,” in International Conference on Data Science and Its Applications (ICoDSA), Jakarta, Indonesia: IEEE, 2025, pp. 649–654, July. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/11157424
[4] A. Paul and A. Odu, “Predictive Maintenance: Leveraging Machine Learning for Equipment Health Monitoring,” 2024.
[5] S. Amrale, “Anomaly Identification in Real-Time for Predictive Analytics in IoT Sensor Networks using Deep,” Int. J. Curr. Eng. Technol., vol. 14, no. 6, Nov/Dec, pp. 526–532, 2024, doi: https://doi.org/10.14741/ijcet/v.14.6.15.
[6] N. Prajapati, “The Role of Machine Learning in Big Data Analytics : Tools , Techniques , and Applications,” ESP J. Eng. Technol. Adv., vol. 5, no. 2, pp. 16–22, 2025, doi: 10.56472/25832646/JETA-V5I2P103.
[7] V. Vlachou et al., “Intelligent Fault Diagnosis of Ball Bearing Induction Motors for Predictive Maintenance Industrial Applications,” Machines, vol. 13, 2025, doi: 10.3390/machines13100902.
[8] S. Murumkar and C. Tayal, “Optimizing Big Data Workflows Using AI and Machine Learning,” in 2026 IEEE 16th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, Jan. 2026, pp. 0550-0556,. doi: 10.1109/CCWC67433.2026.11393891.
[9] H. Nozari and A. Szmelter-Jarosz, “A Predictive Maintenance Approach for Composting Plants Based on ERP and Digital Twin Integration,” Machines, vol. 13, p. 1123, 2025, doi: 10.3390/machines13121123.
[10] V. Singh, D. Pathak, and P. Gupta, “Integrating Artificial Intelligence and Machine Learning into Healthcare ERP Systems: A Framework for Oracle Cloud and Beyond,” ESP J. Eng. Technol. Adv., vol. 3, no. 2, pp. 171–178, 2023, doi: 10.56472/25832646/JETA-V3I6P114.
[11] A. N. John Wesly Sajja, “Enterprise Finance Reimagined: Harnessing ERP and Data Innovation for Next-Generation Value Creation,” Apr. 2024. doi: 10.52710/cfs. 743.
[12] “Predictive Maintenance of Flowlines Using Machine Learning and Sensor Data,” 2025.
[13] D. Olojede, S. King, and I. K. Jennions, “Application of machine learning in power grid fault detection and maintenance,” Energy Informatics, vol. 8, 2025, doi: 10.1186/s42162-025-00574-w.
[14] S. Sen, “Digital Twin and Data Governance for Mining: Architecting Resilient and Compliant Operational Frameworks,” Int. J. Eng. Res. Technol., vol. 15, no. 03, March, pp. 1–4, 2026.
[15] H. Nozari, “A Predictive Maintenance Approach for Composting Plants Based on ERP and Digital Twin Integration,” pp. 1–24, 2025.
[16] K. M. R. Seetharaman and S. Pandya, “LEVERAGING AI AND IOT TECHNOLOGIES FOR DEMAND FORECASTING IN MODERN SUPPLY CHAIN,” Int. J. Recent Technol. Sci. Manag., vol. 9, no. 6, June, pp. 66–73, 2024, [Online]. Available: https://ijrtsm.com/wp-content/uploads/2025/05/June-2024-Karthika-66-73.pdf
[17] J. Giose, “ERP, MES, AND SCADA SYSTEMS IN INDUSTRY 4.0 Architecture, Integration, and Real-World Deployment Insights,” 2025. doi: 10.13140/RG.2.2.32840.58880.
[18] S. Garg, “AI-Driven Innovations in Storage Quality Assurance and Manufacturing Optimization,” Int. J. Multidiscip. Res. Growth Eval., vol. 1, no. 1, 2020.
[19] D. Esther, “Integration of SCADA and MES for Seamless Data Flow,” 2024.
[20] S. Singh, S. A. Pahune, P. Chatterjee, and R. Sura, “Advanced Machine Learning Methods for Churn Prediction and Classification in Telecom Sector,” in 2025 IEEE 6th India Council International Subsections Conference (INDISCON), Rourkela, India: IEEE, 2025, pp. 1–7. doi: 10.1109/INDISCON66021.2025.11252233.
[21] R. Fezari and M. Fezari, “Sensors In IoT Applications,” 2025. doi: 10.13140/RG.2.2.19045.05606.
[22] S. G. Jubin Thomas, Kirti Vinod Vedi, “The Effect and Challenges of the Internet of Things (IoT) on the Management of Supply Chains,” IJRAR, vol. 8, no. 3, 2021.
[23] J. Roy, S. Shahid, M. Alam, and A. Chanda, “Application of Machine Learning in Fault Detection and Predictive Maintenance of Power Transformers,” J. Artif. Intell. Gen. Sci. ISSN3006-4023, vol. 5, pp. 581–602, 2024, doi: 10.60087/jaigs.v5i1.423.
[24] D. M. Dave and B. Mittapally, “Data Integration and Interoperability in IOT: Challenges, Strategies and Future Direction,” Int. J. Comput. Eng. Technol., vol. 15, pp. 45–60, 2024.
[25] A. R. Toorpu, S. K. Vududala, A. Nerella, and B. P. Madupati, “Hybrid AI Models for Privacy-Preserving Big Data Analytics in Distributed Environments,” in 2025 Global Conference in Emerging Technology (GINOTECH), PUNE, India: IEEE, 2025, pp. 1–8, July. doi: 10.1109/GINOTECH63460.2025.11076666.
[26] Q. Abdullah Abed, “SMART IOT SOLUTIONS: LEVERAGING MACHINE LEARNING FOR ANOMALY DETECTION AND FAULT PREDICTION,” Int. J. Appl. Math., vol. 38, pp. 991–1012, 2025, doi: 10.12732/ijam.v38i11s.1228.
[27] N. Kalibhat, K. Narang, L. Tan, H. Firooz, M. Sanjabi, and S. Feizi, “Understanding Failure Modes of Self-Supervised Learning,” 2022. doi: 10.48550/arXiv.2203.01881.
[28] R. Patel and P. Patel, “Machine Learning-Driven Predictive Maintenance for Early Fault Prediction and Detection in Smart Manufacturing Systems,” ESP J. Eng. Technol. Adv., vol. 4, no. 1, pp. 141–149, 2024, doi: 10.56472/25832646/JETA-V4I1P120.
[29] V. Prajapati, “Improving Fault Detection Accuracy in Semiconductor Manufacturing with Machine Learning Approaches,” J. Glob. Res. Electron. Commun., vol. 1, no. 1, January, pp. 20–25, 2025, doi: https://doi.org/10.5281/zenodo.14935091.
[30] Y. J. Kim, W. Nam, and J. Lee, “Multiclass anomaly detection for unsupervised and semi-supervised data based on a combination of negative selection and clonal selection algorithms,” Appl. Soft Comput., vol. 122, p. 108838, 2022.
[31] D. Jain and S. Jain, “Artificial Intelligence (AI)-Driven Network Traffic Anomaly Detection for IT Infrastructure Security,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), IEEE, 2026, pp. 1–6, Feb. [Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=R2BqyqAAAAAJ&citation_for_view=R2BqyqAAAAAJ:u5HHmVD_uO8C
[32] Y. Yuan, Y. Huang, Y. Yuan, and J. Wang, “SADDE: Semi-supervised Anomaly Detection with Dependable Explanations,” 2024. doi: 10.48550/arXiv.2411.11293.
[33] J. W. Sajja, G. B. Komarina, and N. K. R. Choppa, “The Convergence of Financial Efficiency and Sustainability in Enterprise Cloud Management,” J. Comput. Sci. Technol. Stud., vol. 7, no. 4, May, pp. 964–992, 2025, doi: 10.32996/jcsts.2025.7.4.110.
[34] B. Wijaya, P. Madiawati, and M. Pradana, “Strategy to Improve Service Continuity Performance Through
[35] Management,” Enrich. J. Multidiscip. Res. Dev., vol. 3, pp. 3262–3278, 2025, doi: 10.55324/enrichment.v3i7.413.
[36] V. Varma, “Data Analytics for Predictive Maintenance for Business Intelligence for Operational Efficiency,” Asian J. Comput. Sci. Eng., vol. 7, no. 4, pp. 1–9, 2022, doi: https://doi.org/10.22377/ajcse.v7i04.247.
[37] K. Mäkká and K. Kampová, “Cyber security and business continuity management: ensuring resilience in a digital world,” Challenges to Natl. Def. Contemp. Geopolit. Situat., vol. 1, no. 1, 2024.
[38] T. Hossain, “The Impact of Machine Learning-Based Predictive Maintenance on Downtime in Smart Manufacturing Systems in Bangladesh,” Int. J. Comput. Eng., vol. 4, pp. 10–19, 2025, doi: 10.47941/ijce. 2840.
[39] M. Celestin, “How Predictive Maintenance in Logistics Fleets Is Reducing Equipment Downtime and Operational Losses,” Brainae J. Business, Sci. Technol., vol. 7, no. 10, pp. 1023–1033, 2023.
[40] S. Thangavel, K. Narukulla, and R. Sundaram, “Edge-Enabled Distributed Computing for Low-Latency IoT Applications: Architectures, Challenges, and Future Directions,” Int. J. Emerg. Res. Eng. Technol., vol. 3, no. 1, pp. 28–41, 2022, doi: 10.63282/3050-922x.ijeret-v3i1p104.
[41] I. R. Uhlmann, S. L. T. Berger, C. A. de Souza Silva, and E. M. Frazzon, “Digital and smart production planning and control,” in Designing Smart Manufacturing Systems, Elsevier, 2023, pp. 311–343.
[42] A. Parupalli, “Business-Oriented Employee Performance Assessment via Machine Learning in ERP Systems,” Int. Res. JOURNALaINTERNATIONAL Res. J., vol. 11, no. 11, November, p. 8, 2024, [Online]. Available: https://tijer.org/tijer/papers/TIJER2411138.pdf
[43] “ERP-DRIVEN SIMULATION FOR PRODUCTION PLANNING AND CONTROL IN THE INDUSTRY 4 . 0 : A REVIEW,” vol. 24, pp. 425–436, 2025.
[44] O. Elugbadebo, “Predictive Maintenance in Software Systems: Machine Learning Approaches for Fault Detection and Prevention,” 2025.
[45] I. Torshin, “Machine Learning Approaches for Predictive Maintenance and Fault Detection in PCB Manufacturing,” 2025.
[46] Sandhya Sadashiv Mahure, A. Deharkar, and L. Yadav, “The role of machine learning in predictive maintenance for Industry 4.0,” Int. J. Sci. Res. Arch., vol. 15, no. 1, pp. 1664–1679, Apr. 2024, doi: 10.30574/ijsra.2025.15.1.1248.
[47] A. Owen, L. Axel, and J. Nelson, “Predictive Maintenance for ATMs: Leveraging IoT and Machine Learning,” 2023.
[48] K. Sheriffdeen, “AI-Enabled Anomaly Detection in Industrial Systems: A New Era in Predictive Maintenance,” 2022.
[49] T. Parmar, “Predictive Maintenance in Semiconductor Manufacturing: Leveraging IoT Sensor Data for Equipment Reliability,” INTERNATIONAL J. Sci. Res. Eng. Manag., vol. 05, pp. 1–7, 2021, doi: 10.55041/IJSREM7616.
