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Speaker at WEST: Brian Liles, Vice President, R&D, Cost Segregation &179D, CSSI-Specialty Tax Services

WEST Session: The R&D Tax Credit is a powerful federal incentive that rewards manufacturers for innovation in product design, process improvements, and new technologies. It directly reduces tax liability or payroll tax, freeing up cash to reinvest in equipment, workforce, and growth. Manufacturers often face challenges such as rising production costs, global competition, supply chain constraints, and the need to modernize with automation, robotics, and sustainable practices. The R&D Tax Credit helps offset these pressures by turning day-to-day problem-solving—like improving tooling, enhancing production efficiency, or developing prototypes—into measurable tax savings. To qualify, activities must pass the IRS “Four-Part Test”: seeking to resolve technical uncertainty, relying on science/engineering, involving experimentation, and aiming to improve a product or process. Eligible expenses include wages, materials consumed in development, and contractor costs. Two paths provide benefits: the Standard Credit , which reduces income taxes, and the Payroll Credit , which offsets up to $500,000 annually in employer payroll taxes—especially valuable for manufacturers reinvesting in growth. The One Big Beautiful Bill Act (2025) restored immediate expensing of domestic R&D costs, eliminating the burdensome 5-year amortization. It also allows companies that capitalized expenses since 2022 to retroactively accelerate deductions. Strategies for manufacturers include building stronger documentation systems, aligning R&D tracking with engineering workflows, and leveraging tax planning to maximize credits year after year. Together, these updates give manufacturers powerful tools to manage costs, stay competitive, and invest confidently in new technologies.

Speaker at WEST: John Eide, Founder and President, Verx Corporation

Speaker at WEST: Al Youngwerth, CTO, VersaBuilt

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.

Speaker at WEST: Joe Mattekatt, CEO, Mesoware Inc.

WEST Session: Robotics is advancing at a remarkable pace. Smarter AI models and improved hardware have made robots more capable and accessible than ever before. These shifts are transforming how we can make use of automation in fundamental ways: 1. Expand use cases through adaptability. Flexible robotic deployments open up opportunities to capture value from a wider range of applications. Adaptive automation becomes a tool for navigating uncertainty rather than a rigid asset. 2. Deploy faster with simplified programming. Your team no longer needs to spend days learning how to program the robot. Instruct robots in plain English instead. 3. Scale through modularity. Ecosystems designed around modular components allow deployments to grow and evolve with your operations. The cost of integration falls as automation spreads across your workflows. 4. Empower your workforce. Automation learns and scales the manufacturing knowledge of your company, enabling your team to focus on higher-value, higher-impact work. Join us to explore how these changes are reshaping both the economics and the role of automation, and what they mean for the future of your operations.

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

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.