[Paper] Automating Supply Chain Disruption Monitoring via an Agentic AI Approach

Source: arXiv - 2601.09680v1

Overview

Supply‑chain managers are still largely “blind” to risks that originate beyond their immediate Tier‑1 suppliers. The paper “Automating Supply Chain Disruption Monitoring via an Agentic AI Approach” proposes a minimally supervised, multi‑agent system built on large language models (LLMs) that can continuously scan news, map disruption signals onto deep‑tier supplier networks, assess exposure, and suggest mitigation actions—all without human analysts in the loop. The authors demonstrate that the system can cut response times from days to minutes, opening the door to truly proactive resilience.

Key Contributions

• Agentic AI Architecture: Introduces a seven‑agent framework (signal detection, entity extraction, network mapping, exposure scoring, impact simulation, mitigation recommendation, and orchestration) that combines LLM reasoning with deterministic tools.
• End‑to‑End Automation: Enables the full pipeline—from raw, unstructured news to actionable sourcing alternatives—without manual labeling or supervision.
• High‑Performance Benchmarks: Achieves F1 scores of 0.962–0.991 on core tasks across 30 synthetic scenarios covering three automotive OEMs and five disruption classes.
• Speed & Cost Efficiency: Completes a full analysis in ≈3.8 minutes at ≈$0.08 per disruption, a > 1,000× speedup over traditional analyst‑driven assessments.
• Real‑World Validation: Applies the system to the 2022 Russia‑Ukraine conflict, showing practical feasibility on a live, high‑impact event.

Methodology

1. Signal Ingestion: A News‑Watcher agent continuously pulls headlines and articles from public feeds.
2. LLM‑Powered Extraction: Using prompting techniques, the Disruption‑Detector LLM classifies whether a piece of text signals a supply‑chain event (e.g., factory shutdown, sanctions).
3. Entity & Relationship Mapping: A Network‑Builder agent extracts company names, locations, and product links, then stitches them onto a pre‑existing multi‑tier supplier graph supplied by the enterprise.
4. Exposure Scoring: Deterministic graph algorithms compute metrics such as shortest‑path distance, centrality, and redundancy to quantify how “close” the disruption is to the OEM’s critical components.
5. Impact Simulation & Mitigation: A Planner agent runs scenario simulations (e.g., loss of a Tier‑3 supplier) and queries the LLM for alternative sourcing options, cost estimates, and lead‑time impacts.
6. Orchestration & Reporting: The Coordinator agent aggregates results, formats a concise risk report, and triggers alerts via existing ERP or messaging systems.

All agents communicate through a lightweight message bus, allowing the system to scale horizontally and to swap out individual components (e.g., replace the LLM with a newer model) without redesigning the whole pipeline.

Results & Findings

• Speed: Average end‑to‑end runtime = 3.83 minutes per disruption.
• Cost: $0.0836 per incident (LLM inference + compute).
• Comparison: Traditional analyst teams need 2–3 days per incident, costing $200–$500 in labor.

The case study on the Russia‑Ukraine war showed the system correctly flagged the loss of Russian steel producers, propagated the impact through Tier‑2 and Tier‑3 automotive parts suppliers, and suggested viable European and Asian alternatives within minutes.

Practical Implications

• Real‑Time Risk Dashboards: Companies can embed the agentic pipeline into existing supply‑chain control towers, delivering live alerts and “what‑if” analyses to procurement officers.
• Cost‑Effective Resilience Planning: Small‑to‑mid‑size manufacturers, which cannot afford dedicated analyst teams, can now run continuous disruption monitoring for a fraction of the cost.
• Automated Contingency Generation: The mitigation agent can auto‑populate sourcing contracts or trigger pre‑approved purchase orders, shortening the time to switch suppliers.
• Regulatory & ESG Reporting: Transparent, auditable logs of disruption detection and response can satisfy compliance requirements (e.g., EU CSRD) and support sustainability disclosures.
• Extensibility: The modular agent design lets firms plug in domain‑specific tools—such as customs‑data APIs or IoT sensor feeds—to enrich the risk picture beyond news.

Limitations & Future Work

• Synthetic Evaluation Bias: Most benchmark scenarios were generated synthetically; real‑world noise (mis‑attributed news, language nuances) may degrade performance.
• Dependence on LLM Prompt Quality: The system’s accuracy hinges on well‑crafted prompts; prompt engineering remains a manual bottleneck.
• Network Data Completeness: Accurate tier mapping assumes the enterprise already maintains a reasonably complete supplier graph—many firms still lack deep‑tier visibility.
• Scalability to Global Enterprises: While the prototype runs efficiently on a single GPU, scaling to thousands of concurrent news streams will require distributed orchestration and cost‑optimisation.

Future research directions include: (1) integrating structured data sources (customs, shipping manifests) to corroborate news signals, (2) employing reinforcement learning for the agents to self‑improve their prompting strategies, and (3) extending the framework to other risk domains such as cyber‑security or financial supply‑chain exposures.

Authors

• Sara AlMahri
• Liming Xu
• Alexandra Brintrup

Paper Information

• arXiv ID: 2601.09680v1
• Categories: cs.AI
• Published: January 14, 2026
• PDF: Download PDF