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

Nguyen, Truong, Zhou, Li, Spiegler, Virginia, Ieromonachou, Petros, Lin, Yong (2018) 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:10.1016/j.cor.2017.07.004) (KAR id:62271)

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http://doi.org/10.1016/j.cor.2017.07.004

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
DOI/Identification number: 10.1016/j.cor.2017.07.004
Uncontrolled keywords: Literature review; Big data; Big data analytics; Supply chain management; Research directions
Divisions: Faculties > Social Sciences > Kent Business School > Management Science
Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User: Virginia Spiegler
Date Deposited: 11 Jul 2017 16:59 UTC
Last Modified: 01 Aug 2019 10:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/62271 (The current URI for this page, for reference purposes)
Spiegler, Virginia: https://orcid.org/0000-0002-7130-3151
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