A systematic review of text mining analytics for supply chain risk management using online data
Year: 2025
Type: review
Study design: Systematic Review
Abstract: Global supply chains are increasingly complex and vulnerable, requiring new approaches for detecting and managing risks. Text mining, a branch of natural language processing, can extract insights from unstructured online data such as news, reports, and social media. This paper presents a systematic review of 33 peer-reviewed studies on text mining in supply chain risk management (SCRM). The review addresses four research questions: (i) which types of online data are used and how their characteristics affect reliability and timeliness, (ii) which techniques are applied and with what trade-offs, (iii) how text mining contributes to risk identification, prediction, and mitigation, and (iv) what gaps and opportunities remain for future research. A bibliometric analysis is also conducted to highlight publication trends, contributors, and thematic clusters. The findings reveal that Twitter and news sources dominate, while methods range from sentiment analysis and topic modeling to advanced neural models such as BERT. Applications emphasize risk identification and visibility, with emerging work in predictive analytics and decision support. A conceptual framework is proposed linking unstructured data to risk management decisions. This review contributes to the literature by underscoring the value of real-time textual for improving visibility, agility, and resilience in complex supply chains. • Examine thirty-three studies on text mining analytics in supply chain risk management using online data sources. • Analyze real-time online data from social media and news to enhance supply chain risk visibility. • Identify key text mining techniques, including sentiment analysis, topic modeling, and classification models. • Propose a conceptual framework linking unstructured data analytics to supply chain risk management decisions. • Highlight research gaps and suggest future directions for predictive analytics in supply chain risk management.
Source: Supply Chain Analytics
Institution: International Hellenic University
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,050
OpenAlex ID: W4414567804