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Speaker at WEST: Jeffrey Fiala, President, UnDesked

WEST Session:

WEST Session: This presentation challenges the prevailing narrative that manufacturing must conform to AI, proposing instead that AI must evolve to meet manufacturing's unique demands. After over a decade of attempting to transplant cloud-designed AI models into manufacturing environments, it has become clear that this approach is not practical. Rather than continuing to quotes percentages of failures, we advocate for a fundamental shift in perspective. If AI's core strength lies in pattern recognition and learning, why not leverage this capability to make AI itself more adaptable to manufacturing contexts? This talk demonstrates how AI can be redesigned to thrive in manufacturing environments through concrete examples that accelerate the development of robust, continuously learning models. We examine three critical assumptions that, when reconsidered, significantly enhance AI adoption and scalability in manufacturing. First, we start with quantifying success. Time invested in understanding and quantifying the trade-offs that matter to a production line is invariably worthwhile. Consider quality control as an example: should you prioritize developing a model that catches every defect, or one that minimizes false positives by avoiding the misclassification of good products as defective? Like human decision-making, AI systems will inevitably make errors—the key is to design systems that account for and manage these errors rather than pretending they won't occur. Second, we tackle data strategy. Manufacturing data represents valuable intellectual property that demands strategic handling. Contrary to popular belief, more data doesn't always yield better results. Our experience shows that indiscriminate data usage often produces sluggish, costly models that are challenging to troubleshoot and maintain. Hence, data selection strategies play a crucial role in the long-term success of a solution. Finally, we emphasize AI's inherently non-deterministic nature. Treating AI as a deterministic tool fundamentally limits its adaptive potential. Instead of rebuilding AI systems with every product change, we propose designing solutions that inherently evolve with environmental shifts—both incremental and substantial. This approach positions AI as a dynamic partner in manufacturing, capable of continuous learning and adaptation rather than a rigid tool requiring costly reconfiguration.

WEST Session: This presentation challenges the prevailing narrative that manufacturing must conform to AI, proposing instead that AI must evolve to meet manufacturing's unique demands. After over a decade of attempting to transplant cloud-designed AI models into manufacturing environments, it has become clear that this approach is not practical. Rather than continuing to quotes percentages of failures, we advocate for a fundamental shift in perspective. If AI's core strength lies in pattern recognition and learning, why not leverage this capability to make AI itself more adaptable to manufacturing contexts? This talk demonstrates how AI can be redesigned to thrive in manufacturing environments through concrete examples that accelerate the development of robust, continuously learning models. We examine three critical assumptions that, when reconsidered, significantly enhance AI adoption and scalability in manufacturing. First, we start with quantifying success. Time invested in understanding and quantifying the trade-offs that matter to a production line is invariably worthwhile. Consider quality control as an example: should you prioritize developing a model that catches every defect, or one that minimizes false positives by avoiding the misclassification of good products as defective? Like human decision-making, AI systems will inevitably make errors—the key is to design systems that account for and manage these errors rather than pretending they won't occur. Second, we tackle data strategy. Manufacturing data represents valuable intellectual property that demands strategic handling. Contrary to popular belief, more data doesn't always yield better results. Our experience shows that indiscriminate data usage often produces sluggish, costly models that are challenging to troubleshoot and maintain. Hence, data selection strategies play a crucial role in the long-term success of a solution. Finally, we emphasize AI's inherently non-deterministic nature. Treating AI as a deterministic tool fundamentally limits its adaptive potential. Instead of rebuilding AI systems with every product change, we propose designing solutions that inherently evolve with environmental shifts—both incremental and substantial. This approach positions AI as a dynamic partner in manufacturing, capable of continuous learning and adaptation rather than a rigid tool requiring costly reconfiguration.

Speaker at WEST: Matthew Dainko, Director of Business Development, Complete

WEST Session:

Speaker at WEST: Rob Sims, Founder & CTO, Alchemi Data Management, Inc.

Speaker at WEST: Kevin Kerston, Senior Account Executive, SugarCRM

WEST Session: As manufacturers strive to improve machine utilization, reduce downtime, and address workforce shortages, robotic CNC machine tending has become a critical automation strategy. Advances in robotic platforms and machine-tending technologies now enable shops of all sizes to increase throughput while maintaining consistent quality in competitive markets. In this session, Verx Corporation—a FANUC Robotics Authorized System Integrator and full-service distributor for VersaBuilt —will introduce the fundamentals of robotic CNC machine tending. Attendees will gain a framework for understanding how system components—robots, grippers, workholding, and operator interfaces—work together in collaborative applications. Following this introduction, VersaBuilt will highlight key challenges and solutions specific to CNC machine tending. Topics will include gripping strategies for varied parts and high-mix applications, simplified integration methods that reduce operator complexity while improving reliability, and how VersaBuilt’s MultiGrip, DuoGrip, and Zero Point (ZPS) Automation Systems leverage FANUC CRX robots to deliver scalable automation to a broader range of manufacturers.   The session will conclude with a live Q&A, giving participants the opportunity to connect these insights with their own manufacturing challenges. Attendees will leave with both a strong conceptual foundation and practical examples for addressing common barriers in robotic machine tending.

WEST Session: Sponsored by: UiPath Moderated by: Paul Boris This panel brings together different perspectives, from a small, family-owned manufacturing company to one of the largest digital solutions providers, to discuss the deployment of advanced technologies and how the landscape continues to change rapidly.  Using AI to improve efficiency, automate tasks and analyze data allows manufacturers enhanced decision making and is even transforming entire industries.  AI is disrupting traditional thinking at an accelerated pace.  This panel will cover organizational factors/business considerations, technical challenges, skills/labor gaps, ethical considerations and other.