A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter. Issue 4 (3rd April 2023)

• Record Type: Journal Article
• Authors: Janjua, Naeem Khalid; Nawaz, Falak; Prior, Daniel D
• Is Part Of: Enterprise information systems. Volume 17:Issue 4(2023)
• Publication Date: 2023-04-03
• Subjects: Supply chain -- risk assessment -- social media -- fuzzy logic -- natural language processing; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2021.1959652
• Languages: English
• ISSNs: 1751-7575

Abstract

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.