Late Morning Sessions Seminars

• Identifying AI applications that drive measurable operational improvements in manufacturing environments
• Moving beyond experimental AI implementations to strategic, scalable solutions that deliver concrete business value
• Exploring advanced AI methodologies for predictive maintenance, quality control and resource optimization
• Developing comprehensive frameworks for quantifying AI investment returns across production, waste reduction and decision-making processes
• Defining (KPIs) to track and validate AI-driven operational enhancements

Developing effective cost management strategies such as strategic sourcing and bulk purchasing.
Discussing how fostering partnerships can lead to better pricing and service terms.
Emphasizing the importance of balancing cost constraints with the need for high-quality materials to ensure lab efficiency and compliance.

• What is the reality vs the hype of AI?
• What is available vs what is possible
• How to build an AI strategy for long term success
• AI rollout for construction companies

• What is the reality vs the hype of AI?
• What is available vs what is possible
• How to build an AI strategy for long term success
• AI rollout for construction companies

• Evolution of lab automation: From isolated instruments to fully integrated, digitally connected systems
• Advancements in machine learning for experimental design and hypothesis generation
• Future directions and the rise of the self-driving HT robotic laboratory: Autonomous experimentation and decision-making

• Evolution of lab automation: From isolated instruments to fully integrated, digitally connected systems
• Advancements in machine learning for experimental design and hypothesis generation
• Future directions and the rise of the self-driving HT robotic laboratory: Autonomous experimentation and decision-making

• Understanding the challenge of tracking quality in automotive manufacturing due to process complexity
• Applying the Complexity Process Framework to uncover inefficiencies and pinpoint root causes of quality issues
• Collaborating with OEMs to enhance supply chain performance and resolve production bottlenecks
• Leveraging AI and knowledge networks to improve machine learning model adaptability and optimise industrial processes
• Shaping the future of automotive manufacturing through data-driven solutions and continuous process improvement

• Managing the transition from ICE to EV production while working within legacy manufacturing environments
• Developing scalable data infrastructures to support new manufacturing requirements without disrupting existing operations
• Leveraging AI and automation to optimise EV production processes and improve operational efficiency
• Addressing supply chain challenges linked to electrification, including battery component traceability and supplier integration
• Creating a long-term digital transformation strategy to future-proof manufacturing operations against evolving market demands and regulations

• Managing the transition from ICE to EV production while working within legacy manufacturing environments
• Developing scalable data infrastructures to support new manufacturing requirements without disrupting existing operations
• Leveraging AI and automation to optimise EV production processes and improve operational efficiency
• Addressing supply chain challenges linked to electrification, including battery component traceability and supplier integration
• Creating a long-term digital transformation strategy to future-proof manufacturing operations against evolving market demands and regulations

• Understanding the challenge of tracking quality in automotive manufacturing due to process complexity
• Applying the Complexity Process Framework to uncover inefficiencies and pinpoint root causes of quality issues
• Collaborating with OEMs to enhance supply chain performance and resolve production bottlenecks
• Leveraging AI and knowledge networks to improve machine learning model adaptability and optimise industrial processes
• Shaping the future of automotive manufacturing through data-driven solutions and continuous process improvement

• Managing the transition from ICE to EV production while working within legacy manufacturing environments
• Developing scalable data infrastructures to support new manufacturing requirements without disrupting existing operations
• Leveraging AI and automation to optimise EV production processes and improve operational efficiency
• Addressing supply chain challenges linked to electrification, including battery component traceability and supplier integration
• Creating a long-term digital transformation strategy to future-proof manufacturing operations against evolving market demands and regulations

• Managing the transition from ICE to EV production while working within legacy manufacturing environments
• Developing scalable data infrastructures to support new manufacturing requirements without disrupting existing operations
• Leveraging AI and automation to optimise EV production processes and improve operational efficiency
• Addressing supply chain challenges linked to electrification, including battery component traceability and supplier integration
• Creating a long-term digital transformation strategy to future-proof manufacturing operations against evolving market demands and regulations