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

Singh, A; Shukla, N; Mishra, N

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

Singh, A Shukla, N Mishra, N

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Publication Type:: Journal Article
Citation:: Transportation Research Part E: Logistics and Transportation Review, 2018, 114 pp. 398 - 415
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Singh, A	en_US
dc.contributor.author	Shukla, N	en_US
dc.contributor.author	Mishra, N	en_US
dc.date.available	2021-08-12T19:01:08Z
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	Transportation Research Part E: Logistics and Transportation Review, 2018, 114 pp. 398 - 415	en_US
dc.identifier.issn	1366-5545	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136424
dc.description.abstract	© 2017 Elsevier Ltd 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.	en_US
dc.relation.ispartof	Transportation Research Part E: Logistics and Transportation Review	en_US
dc.relation.isbasedon	10.1016/j.tre.2017.05.008	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Logistics & Transportation	en_US
dc.title	Social media data analytics to improve supply chain management in food industries	en_US
dc.type	Journal Article
utslib.citation.volume	114	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	1507 Transportation and Freight Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	114	en_US

Abstract:

© 2017 Elsevier Ltd 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/136424