Big data analytics and application for logistics and supply chain management

Govindan, K; Cheng, TCE; Mishra, N; Shukla, N

Big data analytics and application for logistics and supply chain management

Govindan, K Cheng, TCE Mishra, N Shukla, N

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Publication Type:: Journal Article
Citation:: Transportation Research Part E: Logistics and Transportation Review, 2018, 114 pp. 343 - 349
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Govindan, K	en_US
dc.contributor.author	Cheng, TCE	en_US
dc.contributor.author	Mishra, N	en_US
dc.contributor.author	Shukla, N	en_US
dc.date.available	2021-08-12T19:00:37Z
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	Transportation Research Part E: Logistics and Transportation Review, 2018, 114 pp. 343 - 349	en_US
dc.identifier.issn	1366-5545	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124828
dc.description.abstract	© 2018 Elsevier Ltd This special issue explores big data analytics and applications for logistics and supply chain management by examining novel methods, practices, and opportunities. The articles present and analyse a variety of opportunities to improve big data analytics and applications for logistics and supply chain management, such as those through exploring technology-driven tracking strategies, financial performance relations with data driven supply chains, and implementation issues and supply chain capability maturity with big data. This editorial note summarizes the discussions on the big data attributes, on effective practices for implementation, and on evaluation and implementation methods.	en_US
dc.relation.ispartof	Transportation Research Part E: Logistics and Transportation Review	en_US
dc.relation.isbasedon	10.1016/j.tre.2018.03.011	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Logistics & Transportation	en_US
dc.title	Big data analytics and application for logistics and supply chain management	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:

© 2018 Elsevier Ltd This special issue explores big data analytics and applications for logistics and supply chain management by examining novel methods, practices, and opportunities. The articles present and analyse a variety of opportunities to improve big data analytics and applications for logistics and supply chain management, such as those through exploring technology-driven tracking strategies, financial performance relations with data driven supply chains, and implementation issues and supply chain capability maturity with big data. This editorial note summarizes the discussions on the big data attributes, on effective practices for implementation, and on evaluation and implementation methods.

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

http://hdl.handle.net/10453/124828