A Novel Advanced IoT System for Predictive Maintenance Systems and Supply Chain Optimization Solutions for Manufacturing Industries

Abstract

This paper presents a pioneering approach to revolutionize manufacturing industries through the development of a novel advanced Internet of Things (IoT) system tailored for predictive maintenance and supply chain optimization. Leveraging the vast array of data generated within manufacturing environments, the proposed system integrates cutting-edge IoT technologies with advanced predictive analytics to enable proactive identification of equipment failures and optimization of supply chain operations. The research encompasses a comprehensive dataset comprising sensor data, maintenance records, and supply chain information sourced from industrial pumps, facilitating the creation of robust predictive maintenance models and insightful supply chain optimization strategies. Experimental procedures involve data pre-processing, feature engineering, and model development, followed by rigorous evaluation against ground truth data. The results showcase significant enhancements in equipment up-time; maintenance cost reduction, and supply chain responsiveness, demonstrating the efficacy and scalability of the IoT-based solutions. Through this innovative approach, manufacturing industries can achieve unprecedented levels of operational efficiency, cost savings, and competitive advantage, paving the way for transformative advancements in predictive maintenance and supply chain management paradigms.