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EAST Session:

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.

EAST Session: Moderated by: Mark Michalski Join us for "Empowering Regional Manufacturing Hubs: Strategies for Growth and Competitiveness," a dynamic panel discussion focused on the Northeast’s evolving manufacturing landscape. This session brings together industry leaders, policymakers, and tech innovators to explore how collaboration, workforce development, cutting-edge technologies, and sustainable practices are shaping the future of regional manufacturing. Whether you're a startup, established manufacturer, or economic development advocate, walk away with actionable insights to strengthen your organization and help drive a more competitive, connected, and resilient regional ecosystem. Don't miss this opportunity to be part of the conversation—and the solution.

Speaker at EAST: Michael Tamasi, Owner and CEO | Co-Chair | Chair, Board Of Directors, AccuRounds | Advanced Manufacturing Collaborative | GBMP

Speaker at EAST: Ron Angelo, President and Chief Executive Officer, Connecticut Center for Advanced Technology Inc

Speaker at EAST: Tom Connell, Vice President of Business Development for the Americas, Magic Software

EAST Session: Moderated by: Ryan Cahalane. In this dynamic session, experts from Axiom Systems will present innovative strategies to optimize operations and drive competitive advantages for mid-market manufacturers. From leveraging technology to implementing lean principles, this session will explore real-world solutions for achieving operational excellence in today’s ever-changing industrial landscape.

Speaker at EAST: Ryan Cahalane, Managing Partner, Axiom Systems

EAST Session: Abstract : Manufacturers have more customer data than ever before, yet many still struggle to turn that data into real insights. Too often, sales teams rely on gut instinct, outdated reports, or incomplete CRM entries, leading to missed opportunities and inefficient processes. AI changes the game by analyzing patterns humans can’t see, uncovering hidden sales opportunities, and predicting customer needs before they arise. In this session, we’ll explore how AI can transform the way manufacturers understand and engage with customers—without requiring a complete digital overhaul. We’ll discuss real-world applications of AI in sales and customer relationships, including proactive recommendations, automated data capture, and predictive insights. You’ll leave with a clear understanding of how AI can help you move beyond guesswork, make data-driven decisions, and build stronger, more profitable customer relationships. Significance/Importance : Manufacturers have long relied on relationships and gut instinct to drive sales, but today’s competitive landscape demands more. Traditional CRMs were meant to help but became data-entry burdens, leading to poor adoption and missed opportunities. AI is changing the game by turning raw data into actionable insights—automating manual processes, predicting customer needs, and uncovering hidden sales opportunities. Companies that embrace AI gain a competitive edge, while those that don’t risk falling behind. This session will show how AI helps manufacturers move beyond guesswork, make smarter decisions, and build stronger customer relationships with less effort.

EAST Session: Effective data collection is critical for optimizing production lines, yet traditional methods such as manual recording and PLC-coded data collection are fraught with inefficiencies and inaccuracies. Manual data entry often misses short downtime events and is subject to operator bias, while PLC-based systems suffer from inconsistencies, excessive costs, and revalidation challenges. The future of data collection lies in automation, modular modeling, and intelligent data processing, providing a foundation for digital transformation and sustainable manufacturing excellence. This session will explore the following concepts: · Advanced data collection goes beyond monitoring bottleneck operations, incorporating machine-level insights across all assets. · A multi-layered approach – integrating real-time signal processing, logic engines, and high-speed data acquisition – enhances fidelity, reduces integration costs, and improves root cause analysis. · Additionally, Aa Fault Learning approach dynamically identifies and ranks faults, leading to better diagnostics and predictive maintenance. · By leveraging digital twins, synchronizing multiple data streams, and enabling fast data validation, companies can significantly improve operational efficiency. · A robust data collection strategy supports MES, OEE, and AI/ML applications, ensuring accurate modeling, predictive analytics, and enterprise-wide standardization.