Automating Supply Chain Disruption Monitoring via an Agentic AI Approach

TL;DR

This paper presents an agentic AI framework that autonomously monitors, analyzes, and responds to supply chain disruptions across multi-tier networks, significantly reducing response time and enabling proactive resilience.

Contribution

The paper introduces a minimally supervised, multi-agent AI system using large language models to detect, analyze, and mitigate supply chain disruptions across extended networks.

Findings

Achieves high accuracy with F1 scores between 0.962 and 0.991.

Reduces response time from days to under 4 minutes.

Demonstrates operational effectiveness through a real-world case study.

Abstract

Modern supply chains are increasingly exposed to disruptions from geopolitical events, demand shocks, trade restrictions, to natural disasters. While many of these disruptions originate deep in the supply network, most companies still lack visibility beyond Tier-1 suppliers, leaving upstream vulnerabilities undetected until the impact cascades downstream. To overcome this blind-spot and move from reactive recovery to proactive resilience, we introduce a minimally supervised agentic AI framework that autonomously monitors, analyses, and responds to disruptions across extended supply networks. The architecture comprises seven specialised agents powered by large language models and deterministic tools that jointly detect disruption signals from unstructured news, map them to multi-tier supplier networks, evaluate exposure based on network structure, and recommend mitigations such as alternative sourcing options. \rev{We evaluate the framework across 30 synthesised scenarios covering three automotive manufacturers and five disruption classes. The system achieves high accuracy across core tasks, with F1 scores between 0.962 and 0.991, and performs full end-to-end analyses in a mean of 3.83 minutes at a cost of \$0.0836 per disruption. Relative to industry benchmarks of multi-day, analyst-driven assessments, this represents a reduction of more than three orders of magnitude in response time. A real-world case study of the 2022 Russia-Ukraine conflict further demonstrates operational applicability. This work establishes a foundational step toward building resilient, proactive, and autonomous supply chains capable of managing disruptions across deep-tier networks.