EAST Session: Abstract : In today's highly competitive manufacturing landscape, operational efficiency is more critical than ever. Yet, excessive energy consumption and unplanned downtime remain major challenges, significantly impacting productivity and costs. Traditional maintenance strategies often fail to address the root causes of inefficiencies, leading to unnecessary energy waste and unexpected failures. This session explores Energy-Centered Maintenance (ECM)—a data-driven, AI-powered approach that goes beyond conventional reliability-centered maintenance by integrating energy efficiency as a key decision-making factor. By leveraging advanced IoT sensors, AI-driven analytics, and real-time machine health monitoring, manufacturers can proactively detect faults, minimize energy loss, and extend asset life. Through real-world case studies and industry insights, attendees will learn how ECM enables manufacturers to reduce operational expenses, prevent unplanned downtime, and achieve sustainability goals—all without compromising productivity. The session will also highlight how machine learning and AI-driven predictive analytics help manufacturers make smarter maintenance decisions, optimizing energy use while ensuring equipment reliability. Whether you're looking to cut energy costs, enhance machine uptime, or align with Industry 4.0 and sustainability initiatives, this session will provide practical takeaways to help you transform your maintenance strategy. Significance/Importance : Learning Objectives Understand the limitations of traditional maintenance strategies and how excessive energy waste and unexpected downtime impact manufacturing costs and efficiency. Explore the principles of Energy-Centered Maintenance (ECM) and how AI-driven predictive analytics can optimize machine performance, reduce energy waste, and prevent costly breakdowns.
