Event Identification for Supply Chain Risk Management Through News Analysis by Using Large Language Models

Maryam Shahsavari (), Omar Khadeer Hussain, Morteza Saberi and Pankaj Sharma
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Maryam Shahsavari: UNSW Canberra
Omar Khadeer Hussain: UNSW Canberra
Morteza Saberi: School of Computer Science, UTS
Pankaj Sharma: UNSW Canberra

The Review of Socionetwork Strategies, 2024, vol. 18, issue 2, 255-278

Abstract: Abstract Event identification is important in many areas of the business world. In the supply chain risk management domain, the timely identification of risk events is vital to ensure the success of supply chain operations. One of the important sources of real-time information from across the world is news sources. However, the analysis of large amounts of daily news cannot be done manually by humans. On the other hand, extracting related news depends on the query or the keyword used in the search engine and the news content. Recent advancements in artificial intelligence have opened up opportunities to leverage intelligent techniques to automate this analysis. This paper introduces the LUEI framework, a lightweight framework that, with only the event’s name as input, can autonomously learn all the related phrases associated with that event. It then employs these phrases to search for relevant news and presents the search engine results with a label indicating their relevance. Hence, by conducting this analysis, the LUEI framework is able to identify the occurrence of the event in the real world. The framework’s novel contribution lies in its ability to identify those events (termed as the Contributing Events (CEs)) that contribute to the occurrence of a risk event, offering a proactive approach to risk management in supply chains. Pinpointing CEs from vast news data gives supply chain managers actionable insights to mitigate risks before they escalate.

Keywords: Supply chain risk management; Event identification; Risk identification; Large language models; Bayesian network; Natural language processing; Information retrieval (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12626-024-00169-z

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