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Speaker at EAST: Brian Richmond, Chief Revenue Officer, Nanoprecise SCI Corp

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: 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 Too often, sales teams rely on gut instinct, outdated reports, or incomplete CRM entries, leading to missed opportunities and inefficient processes.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.

Speaker at EAST: Nat Frampton, Co-Founder, LECS Energy, LLC

Speaker at EAST: John Carpenter, Founder, Owner, President, Excellerant

Speaker at EAST: Eric Swezey, Senior Enterprise Account Executive, Zoovu

EAST Session: Abstract : Enterprise-scale manufacturers continue to expand the use of precise 3D data and connected annotations, called Model-Based Definition (MBD), in place of traditional engineering drawings. The extent to which downstream suppliers are able to respond effectively to this ongoing, cross-industry change will be a significant determining factor on the structure of the manufacturing supply change in future decades. Guidelines from the United States Department of Defense (DoD) are major agents for change in this process. The DoD recognizes that MBD's capacity to support interoperable reuse of data across multiple production systems can accelerate engineering and manufacturing, improve quality, and reduce costs. When major private sector institutions like Deloitte produce findings showing how larger enterprises can gain efficiencies through these practices, expectations grow for the downstream suppliers to align themselves to these changes. For example: Lockheed has already made public that it expects its suppliers to be able to provide inspection data generated in downstream processes to be returned to them, a level of data exchange — the Digital Thread — only possible through integrated MBD processes. Understanding the factors that are currently limiting the expansion of MBD practices, and how technologies are being deployed to overcome those limits, gives perspective to today's manufacturing supplier on how they can prepare for the most imminent developments likely to arise. Significance/Importance : Industry advancement towards model-based definition (MBD) grows with each passing day in many key industries; leading the way are aerospace and defense. Major OEM manufacturers are deeply invested in this process evolution, and there are few if any market pressures influencing factors towards any other direction. Only inertia and cost of entry are acting to constrain this fundamental change.

EAST Session: Abstract : As factories and job shops move towards Industry 4.0, machine tools are becoming more connected, opening up new possibilities for productivity and efficiency. However, this connectivity also brings new cybersecurity risks. Many manufacturers think cybersecurity is just an IT issue, but the reality is that the shop floor is now a prime target for cyberattacks, ransomware, and system breaches that can halt production and damage equipment. In this session, we'll dive into practical, actionable strategies to boost cybersecurity specifically for machine tools and manufacturing systems. You'll learn why modern CNC machines, robots, and connected equipment are at risk, discover common vulnerabilities in connected manufacturing environments, and explore cost-effective cybersecurity best practices tailored for small and medium manufacturers. We'll also discuss how to collaborate with machine builders, software providers, and integrators to build resilient systems, share real-world case studies of attacks and lessons learned, and outline key steps you can take today to reduce risk without slowing down production. Whether you're running a high-mix job shop or a high-volume plant, this session will help you understand cybersecurity from the perspectives of operators, plant managers, and owners, and provide you with a roadmap to protect your business as you modernize operations. Significance/Importance : This presentation is crucial because it equips manufacturers with practical strategies to protect their connected systems from cyber threats, ensuring productivity and safety as they embrace Industry 4.0. Additionally, it will help the shop owner to be compliant with the cybersecurity requirement from the U.S. Deportment of Department of Defense (DoD) and European regulations.

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.