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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.

WEST Session: This presentation will explore how modern manufacturers can eliminate delays and inefficiencies in MRO purchasing through a smarter, more connected procurement experience. Traditional indirect materials procurement is often siloed, manual, and reactive—creating friction across sourcing, approvals, and delivery. Vroozi’s SpendTech® platform changes that by offering an intuitive, self-service marketplace that automates requisitions, ensures contract compliance, and increases spend under management. Attendees will see how smart manufacturing systems can trigger AI-driven purchase requests, enabling real-time MRO fulfillment to keep operations running. We’ll walk through a real-world example of how a leading manufacturer accelerated response time and reduced downtime by digitizing and automating their indirect procurement workflows.

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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: 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: 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: Join us for an insightful and action-packed session that reveals how smart process optimization is transforming financial, logistical, and ecommerce operations. Whether you're looking to streamline workflows, boost accuracy, or scale your business, this presentation delivers practical strategies. Takeaways and What You'll Learn The latest improvements in shipping, fulfillment, and order flow management that are elevating logistics and customer satisfaction. Sail seamlessly! Learn from two real-world business ecommerce customer integration examples—Shopify and BigCommerce—demonstrating how seamless connections with platforms like Acumatica, SAP Business One, and an AI Pricing Agent can streamline operations and support scalable growth.  Spotlight Feature: Pricing Automation Tool -  Say goodbye to manual pricing guesswork! Discover how this: Optimizes margins in real time Syncs across multiple channels Executes hands-free updates Keeps your pricing competitive—automatically Whether you're in finance, logistics, or ecommerce, this session is will provide insights to help you work smarter, not harder. Don’t miss your chance to see how innovation is driving scalable growth and operational excellence.

WEST Session: Scaling Hardware Without the Headaches: The Critical Need for Automated Procurement Technology Hardware manufacturing teams face unprecedented challenges in scaling procurement operations while maintaining speed, compliance, and cost efficiency. Electronic components represent 70% of the bill of materials in mission-critical industries, yet traditional procurement systems rely on manual processes that create bottlenecks and force engineering teams to spend valuable time on logistics rather than core product development. The hardware industry urgently needs technological solutions that transform procurement from a laborious, error-prone process into automated workflows that scale efficiently without proportional increases in headcount. Modern procurement platforms must integrate advanced technologies to address the unique complexities of electronic component sourcing. Critical Technology Requirements: AI-Powered Process Automation - Advanced systems that can automatically parse unstructured data from supplier communications, emails, and attachments, converting them into structured data for ERP integration. Machine learning algorithms should track complete bills of materials across hundreds of suppliers, providing predictive insights and automated exception reporting to prevent supply disruptions. Intelligent Supplier Network Management - Technology platforms that leverage large databases of pre-vetted suppliers, using automated matching algorithms to connect procurement teams with suppliers based on technical specifications extracted from 2D/3D drawings and process documentation. This eliminates the time-consuming manual vetting process that often takes weeks. Real-Time Supply Chain Visibility - Integrated systems that provide instant access to inventory levels, bill of materials analysis, and global supply intelligence, with predictive algorithms that alert teams to compliance issues, supply disruptions, and component lifecycle changes weeks before they impact production. Research indicates that organizations implementing automated procurement technologies achieve 20% efficiency improvements while maintaining compliance with strict industry regulations. The future of hardware manufacturing depends on developing and adopting these technological solutions to eliminate traditional procurement bottlenecks and enable innovation in aerospace, defense, robotics, and other critical industries.

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