A delayed shipment can affect production schedules and customer deliveries across a supply chain. Understanding what caused the delay, where its effects could spread and how to respond takes time that teams may not have.

Causal AI could help organizations investigate these connections quickly enough to act. Making that possible requires data foundations that capture how the supply chain fits together. Graph databases map relationships between suppliers, manufacturing sites and deliveries, helping teams trace the movement of goods and understand dependencies.

This webcast will draw on our data and analytics work with a global pharmaceutical manufacturer to explore the foundations for these capabilities. We’ll then outline our proposed approach to combining graph databases with AI agents to investigate root causes and assess downstream effects. Finally, learn what organizations can do now and the longer-term opportunity to connect intelligence across the supply chain.

We will cover:

• How connected supply chain pressures complicate decisions
• Why having the data is not the same as having answers in time to act
• How a phased approach to data and analytics lays the groundwork for AI
• How graph databases help teams understand supply chain relationships
• The causal AI opportunity for investigating problems and evaluating possible responses

Is this for you?

• Are you responsible for supply chain, logistics, data, analytics or AI?
• Are disruptions, cost pressures and uncertain demand making decisions harder?
• Does it take too long to answer urgent questions
• Are you looking to introduce AI into your existing ecosystem?