Workshop 3: AI at Work: Using Digital Tools to Troubleshoot, Optimize & Accelerate Production
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Learn how modern AI and digital systems identify bottlenecks, flag underperforming KPIs, and streamline operations on the factory floor. This workshop focuses on practical, real-world applications—not theory—to help teams boost throughput and reduce downtime.
Sainyam “Sam” Arora, QA & Systems Engineer at Johnson Matthey, will present “Connecting Legacy Systems to Smart Factories.” Arora offers a practical roadmap for integrating manual, spreadsheet-driven quality systems with emerging digital tools. Rather than replacing existing systems, this interoperability approach strengthens ISO-based workflows and builds a connected continuous improvement loop that enhances operational and quality visibility.
Bryan Bauw, COO of PICO, will present “From Standard Work to Smarter Decisions: Making AI Actually Work on the Factory Floor.” Bauw separates AI hype from practical implementation by demonstrating how data readiness enables real value in manufacturing. He will outline a roadmap that takes assembly manufacturers from standard work and operator enablement to error-proofing and data collection, ultimately supporting stronger decision-making and continuous improvement, with AI tools accelerating each stage of the journey.
Ethan Hicks, Account Executive at Practical Software Solutions, will present “AI + ERP: The Future of Predictive Enterprise Operations.” Hicks will demonstrate how ERP platforms are evolving with embedded AI capabilities that enable manufacturers to anticipate supply chain disruptions, optimize planning, and improve operational responsiveness. Through real-world examples, attendees will learn how next-generation ERP tools such as Sage X3 support proactive decision-making.
Jessica Morrison, VP of Strategic Partnerships and Growth at Golgix, will present “Human-Centric Predictive Intelligence.” Morrison explains why successful AI adoption on the plant floor requires systems designed around operators instead of dashboards. Using cross-industry case studies, she shows how AI can structure messy data, provide contextual guidance, and deliver predictive insights that improve yield, uptime, constraint detection, and loss elimination.
Dilip Shah, President of E=mc3 Solutions, will present “AI-Driven Quality & Auditing.” The session explores how machine learning and advanced analytics are transforming reactive, checklist-based audits into predictive systems that improve defect detection, strengthen compliance, enhance traceability, and reduce costly downtime, while also addressing the ethical considerations required for responsible AI implementation.
