Modeling drivers to big data analytics in supply chains

Siddique, MNA; Hasan, KW; Ali, SM; Moktadir, MA; Paul, S; Kabir, G

Modeling drivers to big data analytics in supply chains

Siddique, MNA Hasan, KW Ali, SM Moktadir, MA Paul, S

Kabir, G

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Publisher:: ImperialOpen
Publication Type:: Journal Article
Citation:: Journal of Production Systems and Manufacturing Science, 2021, 2, (1), pp. 4-25
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Siddique, MNA
dc.contributor.author	Hasan, KW
dc.contributor.author	Ali, SM
dc.contributor.author	Moktadir, MA
dc.contributor.author	Paul, S https://orcid.org/0000-0001-9523-179X
dc.contributor.author	Kabir, G
dc.date.accessioned	2021-12-21T22:01:47Z
dc.date.available	2020-09-17
dc.date.available	2021-12-21T22:01:47Z
dc.date.issued	2021
dc.identifier.citation	Journal of Production Systems and Manufacturing Science, 2021, 2, (1), pp. 4-25
dc.identifier.issn	2634-4572
dc.identifier.uri	http://hdl.handle.net/10453/152449
dc.description.abstract	The recent emergence of data-driven business markets and the ineligibility of traditional data management systems to trace them have fostered the application of Big Data Analytics (BDA) in supply chains of the present decade. Literature reviews reveal that the successful implication of BDA in a supply chain mainly depends on some key drivers considering the size and operations of an organization. However, collective analysis of all these drivers is still neglected in the existing research field. Therefore, the purpose of this research is to identify and prioritize the most significant drivers of BDA in the supply chains. To this aim, a novel Best-worst method (BWM) based framework has been proposed, which has successfully identified and sequenced the twelve most significant drivers with the help of previous literature and experts’ opinions. Theoretically, this study contributes to the BDA literature by offering some unique drivers to BDA in supply chains. The findings show that ‘sophisticated structure of information technology’ and ‘group collaboration among business partners’ are the top most significant drivers. ‘Digitization of society’ is identified as the least significant driver of BDA in this study. The outcome of this study is expected to assist the industry managers to find out the most and least preferable drivers in their supply chains and then take initiatives to improve the overall efficiency of their organizations accordingly.
dc.language	en
dc.publisher	ImperialOpen
dc.relation.ispartof	Journal of Production Systems and Manufacturing Science
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Modeling drivers to big data analytics in supply chains
dc.type	Journal Article
utslib.citation.volume	2
utslib.location.activity	United Kingdom
utslib.for	1503 Business and Management
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Management Discipline
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2021-12-21T22:01:46Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	2
utslib.citation.issue	1

Abstract:

The recent emergence of data-driven business markets and the ineligibility of traditional data management systems to trace them have fostered the application of Big Data Analytics (BDA) in supply chains of the present decade. Literature reviews reveal that the successful implication of BDA in a supply chain mainly depends on some key drivers considering the size and operations of an organization. However, collective analysis of all these drivers is still neglected in the existing research field. Therefore, the purpose of this research is to identify and prioritize the most significant drivers of BDA in the supply chains. To this aim, a novel Best-worst method (BWM) based framework has been proposed, which has successfully identified and sequenced the twelve most significant drivers with the help of previous literature and experts’ opinions. Theoretically, this study contributes to the BDA literature by offering some unique drivers to BDA in supply chains. The findings show that ‘sophisticated structure of information technology’ and ‘group collaboration among business partners’ are the top most significant drivers. ‘Digitization of society’ is identified as the least significant driver of BDA in this study. The outcome of this study is expected to assist the industry managers to find out the most and least preferable drivers in their supply chains and then take initiatives to improve the overall efficiency of their organizations accordingly.

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http://hdl.handle.net/10453/152449