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SOUTHEAST Session: The widening skills gap in manufacturing continues to challenge educators, employers, and policymakers striving to cultivate a future-ready workforce. This session examines how strategic partnerships between education and industry can elevate experiential learning to better align student preparation with real-world workforce demands. Through the integration of hands-on training, industry-recognized credentials, and work-based learning opportunities, these collaborations create meaningful pathways from the classroom to the production floor. Ultimately, the session highlights how experiential learning—anchored in authentic industry collaboration—serves as a powerful catalyst for bridging the manufacturing skills gap and building a sustainable talent pipeline.

SOUTHEAST Session: Artificial intelligence has the potential to transform how companies operate, but too often, those investments fall short because the underlying data isn’t ready for AI. In this session, Rob Longfellow, CTO of Juxtum, will share how Juxtum helps organizations capture, clean, and integrate their data so that it works seamlessly with the AI platforms they already use. By breaking down silos, standardizing inputs, and creating scalable data pipelines, Juxtum ensures that AI delivers accurate insights, trusted recommendations, and measurable business value. Attendees will learn why strong data practices are the real key to AI success—and how Juxtum makes that possible today.

Speaker at SOUTHEAST: Jamie Goettler, Chief Revenue Officer, BTX Precision

Speaker at SOUTHEAST: Will Healy III, Global Industry Manager - Fabricated Metals, Teradyne Robots / UR

Speaker at SOUTHEAST: Kenneth Cowan, Vice President, Paperless Parts

Speaker at SOUTHEAST: David Babcock, Director of Technical PreSales, Ashling Partners

SOUTHEAST Session:

SOUTHEAST Session: 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. 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.

Speaker at SOUTHEAST: Susan Carlock, Co-Owner & Executive Vice President, Mursix Corporation & Murray Mentor, Inc.

SOUTHEAST Session: Manufacturers performing visual inspections on their products have historically had two options: perform the inspection manually, or program a rule-based vision system. Both of these options are slow, inflexible, and costly to maintain. However, as Graphical Processing Units (GPUs) have become more powerful, a third approach has become available: deep learning. Deep learning-based vision systems learn directly from annotated images to perform inference on images. They offer numerous advantages including rapid deployment, greater detection flexibility, and lower development and acquisition costs. They are also easily retrained and reconfigured with minimal to no programming.  As such, they represent a massive opportunity to automate quality inspection for small to medium sized manufacturers. In this presentation, we will discuss the technology that enables AI-based computer vision, the advantages they provide, use cases, and how to best implement them. Attendees will walk away with a clear understanding of where deep learning fits into their operation and how to get started with the right tools and strategy.