Global supply chains have become increasingly complex, interconnected, and exposed to disruption. Although vast amounts of data are now available, this information remains fragmented, siloed, and dispersed across organisations, systems, and formats. In its raw form, information offers limited value. It becomes useful only when it is structured into knowledge that reveals how supply-chain networks are organised and how dependencies propagate across tiers. From this knowledge, intelligence can be derived to support timely and informed decisions. This talk explores whether recent advances in Large Language Models and agentic AI systems can support this transformation in supply-chain environments. Focusing on visibility and disruption management, it examines how unstructured external information can be converted into structured network knowledge, and how agentic systems can use that knowledge to detect disruptions, analyse their propagation, and support more effective responses, autonomously.
About the speaker
Sara AlMahri is a final-year PhD candidate at the Supply Chain AI Lab (SCAIL), University of Cambridge. Her research focuses on using Large Language Models and agentic AI systems to map complex, multi-tier supply chain networks, detect disruptions, and support network-level decision-making for more resilient and sustainable operations. Before starting her PhD, Sara worked as a Senior Researcher at the Technology Innovation Institute (TII) in Abu Dhabi and completed international training programmes with Boeing (USA) and Mitsubishi Heavy Industries (Japan). Her entrepreneurial and research work has been recognised with multiple awards, including the Parmee Prize and the Cape Acorn Postgraduate Research Award from the University of Cambridge, as well as the e& Prize for Entrepreneurship in Dubai.
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