Social media data analytics to improve supply chain management in food industries
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Shukla, Nagesh
Mishra, Nishikant
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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.
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Transportation Research Part E: Logistics and Transportation Review
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114
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© 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
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Applied mathematics
Numerical and computational mathematics
Transportation, logistics and supply chains
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Singh, A; Shukla, N; Mishra, N, Social media data analytics to improve supply chain management in food industries, Transportation Research Part E: Logistics and Transportation Review, 2018, 114, pp. 398-415