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L
Liming Xu
University of Cambridge
A
Alexandra Brintrup
University of Cambridge / The Alan Turing Institute
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Founder's Pitch
"Revolutionizing supply chain resilience with agentic AI for autonomous disruption monitoring and mitigation."
Commercial Viability Breakdown
Breakdown pending for this paper.
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Why It Matters
The paper addresses the challenge of supply chain disruptions that originate beyond direct suppliers, which are often missed by traditional monitoring systems. The approach could transform how companies prepare for and respond to disruptions, leading to more resilient supply chains.
Product Angle
To productize, develop a cloud-based service with user-friendly dashboards and integration APIs for existing supply chain management systems, initially targeting industries heavily reliant on complex supply chains like automotive or electronics.
Disruption
This approach could replace traditional, manual supply chain monitoring systems that are slow and limited to Tier-1 suppliers with an automated, multi-tier monitoring system.
Product Opportunity
With global supply chain disruptions costing billions annually, there is a significant market for tools that can proactively manage these risks. OEMs, suppliers, and logistics companies would be key users, paying for reduced risk and improved operational continuity.
Use Case Idea
A SaaS platform for manufacturers that offers real-time, autonomous monitoring of supply chain disruptions, helping them mitigate risks from unforeseen geopolitical events or natural disasters.
Science
The paper presents a framework utilizing agentic AI, powered by large language models, to autonomously detect and respond to supply chain disruptions. It combines natural language processing with deterministic tools to gather and analyze unstructured data, map disruptions across multi-tier networks, and suggest mitigations.
Method & Eval
The framework was tested on 30 synthetic scenarios and a real-world case study, demonstrating high accuracy in identifying disruptions and faster response times compared to industry benchmarks, reducing assessment time from days to minutes.
Caveats
The system relies on the accuracy of the initial data inputs and might not fully account for novel, unseen types of disruptions. Human oversight is necessary to mitigate potential errors from AI "hallucinations."
Author Intelligence
Sara AlMahri
LEAD University of Cambridge
sa2104@cam.ac.uk
Liming Xu
University of Cambridge
Alexandra Brintrup
University of Cambridge / The Alan Turing Institute