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WEST Session: This session explores how connected manufacturing processes can unify production, inventory, scheduling, and financial data into a single, real-time source of truth. By integrating critical business functions—from sales orders to production planning, quality control, and shipping—manufacturers can break down silos, eliminate redundant processes, and improve responsiveness to market changes. With access to accurate and timely information, decision-makers gain the visibility needed to identify bottlenecks, reduce errors, and optimize resource allocation. Attendees will learn how adopting a connected approach not only increases efficiency and collaboration but also positions manufacturers to adapt quickly to evolving customer demands and market conditions, ensuring long-term competitiveness and growth.

Speaker at WEST: George Barnych, Vice President and Chief Technology Officer, National Center for Defense Manufacturing and Machining (NCDMM)

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.

WEST Session: In this presentation, we’ll explore how GibbsCAM empowers modern machine shops to overcome complex manufacturing challenges through advanced, yet intuitive, CAM technology. We’ll walk through real-world part examples that demonstrate how GibbsCAM streamlines programming for Milling, Turning, and Multi-Task Machines. Attendees will learn how to reduce cycle times, improve toolpath quality, and eliminate redundant operations using intelligent automation, toolpath optimization, and post processor customization. We’ll highlight strategies like adaptive roughing, simultaneous machining, and sync management for multi-channel machines—all designed to help manufacturers maximize spindle uptime and shorten setup times. We'll also showcase how GibbsCAM’s associative modeling, geometry creation tools, and integrated simulation reduce scrap and improve confidence before the part hits the machine. This session will provide actionable insights to improve programming workflow. By combining powerful functionality with a user-friendly interface, GibbsCAM gives you the control and flexibility needed to stay competitive in today’s fast-paced manufacturing world. Join us to see how GibbsCAM can help you do more with your machines.

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

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Speaker at WEST: Guillermo Peregrino, Manufacturing Engineering Manager, Aero Bending Company

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WEST 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.

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.