Risk assessment for global supplier selection using text mining. (May 2018)

• Record Type: Journal Article
• Authors: Su, Chuan-Jun; Chen, Yin-An
• Is Part Of: Computers & electrical engineering. Volume 68(2018)
• Page Start: 140
• Page End: 155
• Publication Date: 2018-05
• Subjects: Global supplier selection -- Text mining -- Social media -- Twitter Enabled Supplier Status Assessment (TESSA) tool; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.03.042
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Adequate global sourcing makes a strategic difference to an organization's ability to reduce cost and improve the quality of its product. However, the global sourcing makes the process of supplier selection riskier and more complex. The data required for confronting the criteria needs to be specific and up-to-date. Now that we have entered the Big Data era with the prominence of social media and social networks such as Twitter, Facebook, LinkedIn; we now have access to global insights and knowledge regarding international suppliers. In this research, we propose a Twitter Enabled Supplier Status Assessment (TESSA) tool that can assist companies in their global supplier selection process. TESSA firstly retrieves a target supplier's related information from the most popular microblog Twitter and then obtains potential risk and uncertainty regarding the supplier through text mining. The discovered risks and uncertainties for companies making better decision on their global supplier selection process.