Applying Text-mining Techniques to Global Supply Chain Region Selection: Considering Regional Differences

• Published in: Procedia manufacturing 2019, Vol.39, p.1691-1698
• Main Authors: Chu, Chih-Yuan, Park, Kijung, Kremer, Gül E.
• Format: Article
• Language: English
• Subjects: Global supply chain; Risk analysis; Supplier region selection; Text mining
• ISSN: 2351-9789; 2351-9789
• DOI: 10.1016/j.promfg.2020.01.271

The concept of the global supply chain is built upon the mindset of competitive advantages, and it results in several benefits to the manufacturing process, such as increasing flexibility and operational cost-down. Nevertheless, there are still plenty of risks differing among different regions or nations under a global supply chain, and this increases the uncertainty of the supply chain and the possibility of disruption. This paper tackles this issue through a text-mining based global supply chain regional risk identification framework that analyzes literature to identify potential regionalglobal supply chain risks in a supplier region selection process. Text mining was implemented to collect and analyze the existing previous studies related to regional-related global supply chain risks. The results of text mining are represented in a document-term matrix that can further calculate the term-frequency (TF) and term frequency-inverse document frequency (TF-IDF) for the correlation analysis of terms related to supply chain risks. Those indicators provided information about whether the term-related content is vital in the studied literature, and further identified potential regional risk factors. A total of eight regional risk factors were extracted and organized. The proposed framework can objectively analyze geographical differences and provide enterprises with a global supply chain with intuitive guidance on region selection.