{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:44:57Z","timestamp":1782841497659,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Globally dispersed, project-driven supply chains face increasing disruption risks, while the growing volume of external information makes manual monitoring impractical. Large language models (LLMs) can support the automated detection of disruption-relevant signals in external text sources, but these signals do not directly provide quantitative insight into their potential impact on a specific supply chain configuration. This paper proposes an automated end-to-end methodology that combines LLM-based risk detection with discrete-event simulation (DES) for initial quantitative risk evaluation. Unstructured textual disruption signals are translated into structured disruption vectors and executable SimPy-based scenarios. Through ensemble-based classification, schema-constrained extraction, deterministic validation, and Monte Carlo experimentation, baseline and disruption scenarios are generated and compared automatically. A case study on Red Sea piracy disruptions demonstrates how validated disruption signals can be transformed into measurable lead-time distributions and milestone risk indicators, revealing substantial delay escalation under rerouting conditions. The contribution of the paper lies in demonstrating the feasibility of a fully automated pipeline that converts external disruption signals into initial quantitative risk indications for further expert assessment in complex, project-oriented supply chains.<\/jats:p>","DOI":"10.7148\/2026-0355","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:14Z","timestamp":1782838514000},"page":"355-361","source":"Crossref","is-referenced-by-count":0,"title":["Bridging ai-driven risk detection and discrete-event simulation for quantitative supply chain risk evaluation"],"prefix":"10.7148","author":[{"given":"Axel","family":"Wagenitz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Toth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katja","family":"Klingebiel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:15Z","timestamp":1782838515000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0355_simai_ecms2026_0017.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0355","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}