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Ontology-Based Approach to Supplier Risk Management Using Large Language Models

Zuha Shahid, Arnold Beckmann Orcid Logo, Abdourahim Sylla, Cinzia Giannetti Orcid Logo, Gülgün Alpan

IFAC-PapersOnLine, Volume: 59, Issue: 10, Pages: 2826 - 2831

Swansea University Authors: Arnold Beckmann Orcid Logo, Cinzia Giannetti Orcid Logo

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DOI (Published version): 10.1016/j.ifacol.2025.09.475

Abstract

Suppliers play a critical role in the efficient functioning of supply chains, and any risks associatedwith them can significantly impact supply chain performance. While numerous studies have developed ontologies for various supplier-related areas, there is a lack of focus on ontologies specifically...

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Published in: IFAC-PapersOnLine
ISSN: 2405-8963
Published: Elsevier BV 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa69399
Abstract: Suppliers play a critical role in the efficient functioning of supply chains, and any risks associatedwith them can significantly impact supply chain performance. While numerous studies have developed ontologies for various supplier-related areas, there is a lack of focus on ontologies specifically addressing supplier risk management. In addition, the construction of ontologies has mainly relied on approaches which are time-consuming and resource-intensive. This paper bridges this gap with two major contributions: (i) A new methodology for ontology development that combines a Large Language Model (LLM) and a human expert to efficiently extract and organize domain knowledge from academic literature and (ii) A new supplier risk management ontology that formalizes knowledge related to supplier risk management. To evaluate its effectiveness, the proposed ontology is compared with one developed by a human expert to assess its completeness and accuracy.
Keywords: Supplier risk; Supplier Selection; Ontology; Knowledge management; Large language models; LLM
College: Faculty of Science and Engineering
Funders: This work has been partially supported by the MIAI Multidisciplinary AI Institute at the Univ. Grenoble Alpes: (MIAI@Grenoble Alpes - ANR-19-P3IA-0003)
Issue: 10
Start Page: 2826
End Page: 2831

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