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WEST Exhibitor: WALTER USA, LLC is global leader in the metalworking industry for over 100 years and offers a wide range of precision tools for milling, turning, drilling and threading applications. The company helps customers in the aviation and aerospace industries, automotive, energy, and general industry improve process reliability and increase metal cutting productivity. As an innovative partner capable of creating digital process solutions for optimal efficiency, Walter is pioneering Industry 4.0 throughout the machining industry. With Regional Headquarters in the Greenville, SC area, Walter markets its competence brands Walter, Walter Titex, Walter Prototyp and Walter Multiply through a network of channel partners and field engineers across the USA, Canada, Mexico and Brazil Americas Regional Headquarters and Technology Center 1510 S. Batesville Rd, Greer, SC 29650 United States.

WEST Exhibitor: Whitney Tool is a leading manufacturer of milling cutters, including Woodruff Slotting and Keyseat cutters, as well as Counterbore, T-Slot, Dovetail and other specialty milling cutters. Additionally, we manufacture the Burr-Zit™ and Handi-Burr™ de-burring tools, and a complete line of Tap and Drill Extensions. We are the industry leader in custom shank and arbor type milling cutters in high speed steel, cobalt, carbide tipped and solid carbide. Contact Whitney Tool: 906 R St., Bedford, IN 47421 812-275-4491 www.whitneytool.com

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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: If you’re in the manufacturing field, then you must be passionate about how things are made. Manufacturing matters, and it’s crucial that leaders step out of their buildings and focus on the business, rather than just working in the business. Most small to mid-sized manufacturers don’t realize there is much power in utilizing peer networks, communities, regions, and states to look from the outside in. We want to leave our businesses in a better condition than when we arrived. We want more youth in manufacturing, we want better data about our manufacturing operations, and we want to understand how to incorporate the newest technology into our operations. Fourteen years in the family’s manufacturing business has allowed me to inform myself and bring solutions to our business and remain competitive. Additionally, amid exploring opportunities, I have discovered the importance of the critical need to bridge the skills gap and the tacit knowledge that is being lost as our skilled workforce retires. The impact that harnessing and leveraging this knowledge will help alleviate the existential crisis that threatens us all.

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