Work

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

Year: 2021

Type: article

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.

Source: Enterprise Information Systems

DOI: 10.1080/17517575.2021.1959652

Authors: Naeem Khalid Janjua, Falak Nawaz, Daniel D Prior

Institutions: Edith Cowan University, Australian National University, National Computational Infrastructure, UNSW Canberra

Language: English

Topic: Supply Chain Resilience and Risk Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W3191502413

FWCI: 3.211

Cites: 46

Cited by: 37

Related to: 10