Cloud AI-Powered Predictive Maintenance for SAP Supply Chains: Integrating Machine Learning, Big Data, Image Denoising, and Automation
DOI:
https://doi.org/10.15662/kzva9h32Keywords:
Predictive Maintenance, Cloud Computing, Artificial Intelligence, Machine Learning, Big Data Analytics, Image Denoising, Automation, SAP Supply Chain Management, Industrial IoT, Asset Reliability, Smart Manufacturing, Data-Driven Decision MakingAbstract
The increasing complexity of modern supply chains demands proactive strategies to ensure asset reliability and operational efficiency. This paper proposes a cloud AI-powered predictive maintenance framework for SAP supply chains, integrating machine learning, big data analytics, image denoising, and automation. By leveraging real-time sensor data and historical maintenance records, the system predicts potential equipment failures, reduces downtime, and optimizes maintenance schedules. Image denoising techniques enhance the quality of visual data for accurate fault detection, while cloud computing ensures scalable and secure data processing. The framework demonstrates improved asset performance, cost efficiency, and decision-making capabilities in complex industrial environments, providing a robust approach to next-generation smart supply chain management.
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