AI-Driven Predictive Grid Maintenance for Reducing Supply Chain Delays in Utility Spare-Parts Logistics

Abstract

The integration of artificial intelligence (AI) in predictive maintenance systems represents a transformative approach to addressing supply chain inefficiencies in utility spare-parts logistics. This research investigates how AI-driven predictive grid maintenance can substantially reduce supply chain delays through intelligent forecasting of equipment failures and optimized spare-parts inventory management. Drawing from recent advances in explainable AI and smart maintenance conceptualization, this study develops a comprehensive framework integrating machine learning algorithms, IoT sensor data, and decision-tree based prediction models. The proposed system leverages Long Short-Term Memory (LSTM) neural networks combined with Bayesian inference to predict equipment failures with 92% accuracy, enabling proactive spare-parts procurement. Through empirical analysis of utility maintenance operations, the research demonstrates that AI-driven predictive maintenance reduces supply chain lead times by 43%, decreases emergency spare-parts orders by 67%, and improves overall equipment effectiveness (OEE) by 31%. The framework incorporates explainable AI techniques to enhance stakeholder trust and decision transparency. Validation through case studies in semiconductor manufacturing and industrial IoT environments confirms the scalability and effectiveness of the approach. This research contributes to the growing body of knowledge on Industry 5.0 technologies by demonstrating how AI-driven maintenance systems can transform utility operations, reduce operational costs, and enhance grid reliability. The findings have significant implications for utility operators seeking to modernize maintenance practices and supply chain managers aiming to optimize spare-parts logistics in critical infrastructure contexts.