Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7427629 | Transportation Research Part E: Logistics and Transportation Review | 2018 | 18 Pages |
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
Related Topics
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Business and International Management
Authors
Akshit Singh, Nagesh Shukla, Nishikant Mishra,