Social media data analytics to improve supply chain management in food industries
This paper proposes a big-data analytics-based approach that considers social media (Twitter) data for the identification of supply chain management issues in food industries. In particular, the proposed approach includes text analysis using a support vector machine (SVM) and hierarchical clustering with multiscale bootstrap resampling. The result of this approach included a cluster of words which could inform supply-chain (SC) decision makers about customer feedback and issues in the flow/quality of food products. A case study in the beef supply chain was analysed using the proposed approach, where three weeks of data from Twitter were used.
Singh, A., Shukla, N. & Mishra, N. (2018). Social media data analytics to improve supply chain management in food industries. Transportation Research Part E: Logistics and Transportation Review, 114 398-415.