Social media data analytics to improve supply chain management in food industries

Singh, Akshit; Shukla, Nagesh; Mishra, Nishikant

Abstract

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.

Citation

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. https://doi.org/10.1016/j.tre.2017.05.008

• Journal Article Type: Article
• Acceptance Date: May 16, 2017
• Online Publication Date: Jun 9, 2017
• Publication Date: 2018-06
• Deposit Date: Jun 11, 2017
• Journal: Transportation research. Part E, Logistics and transportation review
• Electronic ISSN: 1366-5545
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 114
• Pages: 398-415
• DOI: https://doi.org/10.1016/j.tre.2017.05.008
• Keywords: Beef supply chain, Twitter data, Sentiment analysis
• Public URL: https://hull-repository.worktribe.com/output/452227
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1366554516303817
• Additional Information: This is a description of an article which has been published in: Transportation research. Part E, Logistics and transportation review, 2017
• Contract Date: Jun 11, 2017