9 September 2025 Block 1 Seminars

• Exploring why AI should go beyond data visualization to actively optimize manufacturing processes in real-time
• Introducing how AI agents can autonomously monitor, diagnose, and correct production processes without human intervention
• Challenging the assumption that AI is only for insights and showing how AI agents can directly drive efficiency, quality, and competitiveness in automotive production
• Addressing the challenges of AI adoption in manufacturing
• Exploring how manufacturing’s physical constraints create a unique opportunity for AI to drive continuous improvements

• Rolling out the Digital Manufacturing Platform across the plant floor to enable real-time insights
• Capturing early savings and performance improvements through data-driven decision-making
• Enhancing OEE, condition monitoring, and production transparency for long-term impact
• Adapting to fluctuating demand and supply chain disruptions with cloud-based data availability
• Lessons learned from the implementation process and key takeaways for manufacturers

• Root Causes and Solutions: Insights into the sources of machine degradation and the actionable opportunities for improvement, emphasizing the importance of predictive
maintenance practices
• The Right Data: Strategies for collecting comprehensive, high-fidelity data necessary for accurate diagnostics and analysis, including the need for frequent data sampling,
intermittent sampling, and a single pane of glass of 3rd party and legacy sensors
• The Right Analysis: Techniques for simplifying and organizing data into actionable insights using AI and machine learning; automating the identification, diagnosis, and
resolution of issues through prioritization and workflows
• The Right Action: Ensuring that insights are delivered to the right person, at the right time, and in the right context to empower workers to take effective action; improving
maintenance quality, optimizing machine health, and eliminating unplanned events