Data-Driven AI-ML Framework for Predictive Maintenance in Supply Chains
DOI:
https://doi.org/10.46610/JBADV.2025.v06i02.001Keywords:
Artificial intelligence, Machine learning, Predictive maintenance, Supply chain, SustainabilityAbstract
This paper examines the role of Predictive Maintenance (PM) in enhancing supply chain resilience through the integration of Machine Learning (ML), Artificial Intelligence (AI), and the Internet of Things (IoT). PM leverages these technologies to predict equipment failures before they occur, offering a more proactive approach compared to traditional maintenance methods. By utilizing real-time data and advanced analytics, PM minimizes downtime, reduces maintenance costs, and improves overall asset reliability, resulting in optimized supply chain operations. The paper also presents a dashboard for real-time monitoring and ensuring consistent supply of tools based on predictive maintenance. This dashboard integrates real-time data visualization, predictive insights, and performance metrics to facilitate informed decision-making.
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