This paper demonstrates the power of big data analysis of social media for the identification and prioritization of supply chain risks in clothing industry. Due to their high volume, data from social media can be considered a good source for obtaining customer feedback. On the other hand, the high volume of social media data could be efficiently processed solely by special tools for big data analysis. Results of such an analysis include clusters of words, topics and sentiments which allow decision makers to gather negative customer feedback and to manage risks in the distribution of products. A case study in the segment of footwear supply chain was analyzed, where one month of data from Twitter was used. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.