AI-Driven Predictive Maintenance Models in ERP Systems for Critical Infrastructure and National Defense Logistics
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I1P111Keywords:
Artificial Intelligence, Predictive Maintenance, ERP Systems, Defense Logistics, Critical Infrastructure, Deep Learning, Failure PredictionAbstract
Predictive Maintenance (PdM) has taken a pivotal role in the current Enterprise Resource Planning (ERP) systems, particularly in application to main infrastructure and national defense logistics. The advent of Artificial Intelligence (AI) has increased the ability of PdM greatly, providing real-time diagnostics, failure predictions, and decision-making on the strategic level in the most sensitive areas. The current paper focuses on combining AI-based PdM models with the ERP systems that focus on the critical infrastructure and defense logistics. We present a detailed discussion of artificial intelligence algorithms, including deep learning, reinforcement learning, and Bayesian networks, in the context of fault detection, and apply them to be used in defense logistics cases. The paper also explores the role of AI-based PdM in increasing the reliability of equipment, resource utilization, the combat-readiness of equipment, and security in operations. The thorough literature review reveals the development of predictive maintenance and the integration of ERP. Following this, an architecture containing the proposed AI-based PdM in defence is proposed. The model is tested using both real-world data and simulations. Based on experimental findings, there is a considerable increase in the accuracy of fault prediction, maintenance planning and general performance of the logistics. The paper then concludes with a discussion on the difficulties, constraints, and prospects of AI in mission-critical logistics scenarios
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