Developing an automated approach of risk analysis in supply chains using AI

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

Abstract

This paper aimed to identify existing approaches of automated risk management procedures for companies in their supply chains and to present an approach for automated risk analysis based on textual news using artificial intelligence. Methodologically, a structured literature review was carried out in the databases "Web of Science" and "Scopus", in which the relevant results were categorized by the aim of the presented approaches on the basis of the records' abstracts. Four approaches were categorized as “Textual risk evaluation using AI/LLM” and were presented in process flow diagrams. Including learnings from these approaches, a new framework for an automated risk analysis was developed. Further research efforts are recommended for optimization in the area of process models and prompt engineering for automated risk analysis.

Details

Original languageEnglish
Title of host publicationProceedings of the 39th ECMS International Conference on Modelling and Simulation, ECMS 2025
EditorsMarco Scarpa, Salvatore Cavalieri, Salvatore Serrano, Fabrizio De Vita
PublisherEuropean Council for Modelling and Simulation
Pages412-418
Number of pages7
ISBN (electronic)978-3-937436-86-9, 978-3-937436-85-2
Publication statusPublished - 2025
Peer-reviewedYes

Publication series

SeriesProceedings - European Council for Modelling and Simulation, ECMS
Volume2025-June
ISSN2522-2414

Conference

Title39th ECMS International Conference on Modelling and Simulation
Abbreviated titleECMS 2025
Conference number39
Duration24 - 27 June 2025
Website
LocationUniversità degli Studi di Catania
CityCatania
CountryItaly

External IDs

ORCID /0000-0003-1862-181X/work/208075037

Keywords

ASJC Scopus subject areas

Keywords

  • artificial intelligence, automation, disruptions, early warning systems, large language models, risk management, Supply chain resilience, textual analysis