Abstract
Information flow is an important task in a supply chain network. Disruptive events often impede this flow due to confounding factors, which may not be identified immediately. The objective of this study is to assess supply chain risks by detecting significant risks, examining risk variations across different time phases and establishing risk sentiment relationships utilizing textual data. We examined two disruptive events—coronavirus disease 2019 (Omicron phase) and the Ukraine–Russia war—between November 2021 and April 2022. Data sources included news media and Twitter. The Latent Dirichlet Allocation algorithm was applied to the textual data to extract potential text-generated risks in the form of “topics.” A proportion of these risks were analyzed to assess their time-varying nature. Natural language processing-based sentiment analysis was applied to these risks to infer the sentiment coming from the media using the ordered probit model. The results identify various unnoticed risks, for example: logistics tension, supply chain resiliency, ripple effect, regional supply chain, etc. that may adversely affect supply chain operations if not considered. The outcomes also indicate that textual data sources are capable of capturing risks before the events actually occur. The outcomes further suggest that text data could be valuable for strategic decision making and improving supply chain visibility.
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