Big data optimisation and management in supply chain management: a systematic literature review

Alsolbi, I; Shavaki, FH; Agarwal, R; Bharathy, GK; Prakash, S; Prasad, M

Big data optimisation and management in supply chain management: a systematic literature review

Alsolbi, I

Shavaki, FH Agarwal, R Bharathy, GK Prakash, S Prasad, M

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Artificial Intelligence Review

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Field	Value	Language
dc.contributor.author	Alsolbi, I https://orcid.org/0000-0002-2957-402X
dc.contributor.author	Shavaki, FH
dc.contributor.author	Agarwal, R
dc.contributor.author	Bharathy, GK
dc.contributor.author	Prakash, S
dc.contributor.author	Prasad, M
dc.date.accessioned	2023-06-30T06:48:41Z
dc.date.available	2023-06-30T06:48:41Z
dc.identifier.citation	Artificial Intelligence Review
dc.identifier.issn	0269-2821
dc.identifier.issn	1573-7462
dc.identifier.uri	http://hdl.handle.net/10453/171043
dc.description.abstract	<jats:title>Abstract</jats:title><jats:p>The increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.</jats:p>
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Artificial Intelligence Review
dc.relation.isbasedon	10.1007/s10462-023-10505-4
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Big data optimisation and management in supply chain management: a systematic literature review
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
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 Computer Science
utslib.copyright.status	in_progress	*
dc.date.updated	2023-06-30T06:48:40Z
pubs.publication-status	Published online

Abstract:

AbstractThe increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.

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http://hdl.handle.net/10453/171043