通过代理AI方法自动化供应链中断监控
Automating Supply Chain Disruption Monitoring via an Agentic AI Approach
AI总结：
通过代理AI方法，实现对供应链中断的自动化监控与响应，提升供应链的主动韧性和抗风险能力。
摘要：
现代供应链日益暴露于地缘政治事件、需求冲击、贸易限制和自然灾害等中断因素的影响。尽管许多这些中断起源于供应链深处，但大多数公司仍然缺乏对Tier-1供应商以上的可见性，导致上游的脆弱性在影响 cascades 下游时才被发现。为克服这一盲点并从被动恢复转向主动韧性，我们引入了一个最小监督的代理AI框架，该框架能够自主监控、分析和响应供应链扩展网络中的中断。该架构由七个专门化的代理组成，这些代理由大型语言模型和确定性工具驱动，共同从非结构化新闻中检测中断信号，将它们映射到多级供应商网络，根据网络结构评估暴露性，并推荐缓解措施，如替代采购选项。我们评估了该框架在30个合成场景中的表现，涵盖了三家汽车制造商和五种中断类别。系统在核心任务上实现了高准确率，F1分数在0.962至0.991之间，且在平均3.83分钟内完成端到端分析，每中断成本为0.0836美元。与行业基准的多日、分析师驱动的评估相比，这代表了响应时间减少超过三个数量级。2022年俄乌冲突的现实案例研究进一步证明了该方法的实用性。本工作为构建具有韧性和主动性、能够管理深层网络中断的供应链奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
