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Big data analytics in supply chain management: A state-of-the-art literature review

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen, Truong, Zhou, Li ORCID: 0000-0001-7132-5935, Spiegler, Virginia, Ieromonachou, Petros ORCID: 0000-0002-5842-9585 and Lin, Yong ORCID: 0000-0001-7118-2946 (2017) Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98. pp. 254-264. ISSN 0305-0548 (doi:https://doi.org/10.1016/j.cor.2017.07.004)

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Abstract

The rapid growing interest from both academics and practitioners towards the application of Big Data Analytics (BDA) in Supply Chain Management (SCM) has urged the need of review up-to-date research development in order to develop new agenda. This review responds to this call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structured based on the content analysis method of Mayring (2008), addressing four research questions on (1) what areas of SCM that BDA is being applied, (2) what level of analytics is BDA used in these application areas, (3) what types of BDA models are used, and finally (4) what BDA techniques are employed to develop these models. The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

Item Type: Article
Additional Information: ABS 3*; SJR 2.33 - 4*
Uncontrolled Keywords: Literature review; Big data; Big data analytics; Supply chain management; Research directions
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group
Last Modified: 11 Jun 2019 15:15
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 6
URI: http://gala.gre.ac.uk/id/eprint/17484

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