Integration of Predictive Maintenance and Supply Chain Optimization in Smart Manufacturing Systems
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Abstract
This study talks about the supply chain optimization and predictive maintenance integration that’s becoming more and more important in smart production systems. Predictive maintenance reduces unscheduled downtime by using machine learning and real-time data to foresee equipment breakdowns. This integration lowers inventory costs, increases overall production efficiency, and guarantees immediate availability of spare parts when combined with supply chain optimization. In order to facilitate data-driven choices in dynamic manufacturing contexts, this article investigates a unified framework that integrates intelligent logistics planning with predictive analytics. Manufacturers can increase system responsiveness, cost-effectiveness, and reliability by coordinating maintenance plans with supply chain activities. The paper highlights how digital twins, industrial IoT, and cyber-physical systems help to make this integration possible. The concrete advantages of the suggested strategy, such as better asset utilization, shorter lead times, and increased resilience of supply chains under fluctuating operational uncertainty, are illustrated through a case study in a smart industrial environment.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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