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

Author

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  • 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.

Suggested Citation

  • Singh, Akshit & Shukla, Nagesh & Mishra, Nishikant, 2018. "Social media data analytics to improve supply chain management in food industries," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 398-415.
    • Handle: RePEc:eee:transe:v:114:y:2018:i:c:p:398-415
    DOI: 10.1016/j.tre.2017.05.008
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    References listed on IDEAS

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