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

Akshit Singh, Nagesh Shukla and Nishikant Mishra

Transportation Research Part E: Logistics and Transportation Review, 2018, vol. 114, issue C, 398-415

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.

Keywords: Beef supply chain; Twitter data; Sentiment analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (31)

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DOI: 10.1016/j.tre.2017.05.008

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