A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter

Author Identifier

Naeem Janjua

https://orcid.org/0000-0003-0483-8196

Document Type

Journal Article

Publication Title

Enterprise Information Systems

Publisher

Taylor & Francis

School

School of Science

RAS ID

36603

Comments

Janjua, N. K., Nawaz, F., & Prior, D. D. (2023). A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter. Enterprise Information Systems, 17(4), Article 1959652.

https://doi.org/10.1080/17517575.2021.1959652

Abstract

In this study, we develop a novel methodology to identify supply chain disruption events using Twitter feeds in real time. Underpinned by advances in Natural Language Processing (NLP) and machine learning, we propose an approach that includes a state-of-the-art variant of Conditional Random Field (CRF) model for event annotation, location-based clustering of the annotated events, and a fuzzy inference system to evaluate supply chain risk. We validate the new approach through a text corpus derived from a Twitter data stream, which is a popular method in NLP. The results show that the proposed model outperforms the baseline model.

DOI

10.1080/17517575.2021.1959652

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