• What data foundations need to be fixed before AI can deliver meaningful value and enable decisions?
  • How can organisations improve confidence in data without creating additional governance and bureaucracy?
  • What practical steps have the greatest impact on data quality and consistency?

Discussion points:

• How can manufacturers avoid supplier engagement becoming a reporting exercise rather than a route to measurable emissions reduction?
   
• When does supplier engagement need to move from encouragement and collaboration to clearer expectations, consequences and commercial pressure?
   
• How can procurement teams identify the “sweet spot” where carbon reduction, cost reduction, resilience and risk management all align?

Discussion points:

• What prevents operational data from being used beyond the control room, and how do we unlock it at the enterprise layer?

• How can businesses blend raw OT data with wider enterprise sources (like SAP, workforce, and external data) to drive better decision-making
• How do we handle naturally "imperfect" OT data—with its noise, gaps, and latency—so it is actually trusted by the business?

• Identify which of your products are the strongest candidates for take-back. Test your portfolio against the conditions that make BIC's model work: knowing your materials, being able to disassemble what you make, and having customer touchpoints already in place.
• Decide which barrier to tackle first. Work through the practical blockers with peers, from collection costs and logistics to mixed-brand returns and customer behaviour, and prioritise the one that would unlock progress fastest.
• Determine whether to go it alone or build partnerships. Assess whether you could run a scheme on your own, as BIC does today, or would need recovery, logistics or EPR partners to make the economics work.
  
  

• Diagnose why successful pilots lose momentum when they move across sites, teams or business units.
• Determine which processes should be standardised and where local adaptation is essential.
• Create a repeatable rollout model with clear ownership, resource commitments and benefits tracking.

Discussion points:

• Identify where legacy technology is genuinely constraining performance and where the underlying problem lies elsewhere.
• Explore practical ways to improve connectivity, data access and automation while protecting uptime and security.
• Build a phased modernisation roadmap tied to operational value rather than a costly rip-and-replace programme.

• Identify the largest sources of margin leakage across yield loss, rework, scrap and material variance.
• Connect production, quality and process data to uncover the root causes of recurring waste.
• Prioritise improvements that raise first-pass yield, reduce material consumption and deliver measurable savings.

• Turn competing initiatives into explicit choices based on value, urgency, resource requirements and execution risk.
• Align leadership attention, ownership and operational capacity behind the priorities that matter most.
• Decide which projects to accelerate, sequence, pause or stop before resources are spread too thinly.

• Identify which growth investments remain commercially defensible when demand, input costs and policy are uncertain.
• Balance short-term margin protection with longer-term investment in capacity, technology, skills and resilience.
• Use scenarios, milestones and clear investment criteria to sequence strategic bets and limit downside risk.