Arunachalam, Deepak and Kumar, Niraj and Kawalek, John Paul (2018) Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114. pp. 416-436. DOI https://doi.org/10.1016/j.tre.2017.04.001
Arunachalam, Deepak and Kumar, Niraj and Kawalek, John Paul (2018) Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114. pp. 416-436. DOI https://doi.org/10.1016/j.tre.2017.04.001
Arunachalam, Deepak and Kumar, Niraj and Kawalek, John Paul (2018) Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114. pp. 416-436. DOI https://doi.org/10.1016/j.tre.2017.04.001
Abstract
In the era of Big Data, many organisations have successfully leveraged Big Data Analytics (BDA) capabilities to improve their performance. However, past literature on BDA have put limited focus on understanding the capabilities required to extract value from big data. In this context, this paper aims to provide a systematic literature review of BDA capabilities in supply chain and develop the capabilities maturity model. The paper presents the bibliometric and thematic analysis of research papers from 2008 to 2016. This paper contributes in theorizing BDA capabilities in context of supply chain, and provides future direction of research in this field.
Item Type: | Article |
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Uncontrolled Keywords: | Supply chain management; Big data analytics; Capabilities; Maturity model |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 15 Aug 2020 10:13 |
Last Modified: | 30 Oct 2024 17:16 |
URI: | http://repository.essex.ac.uk/id/eprint/28437 |
Available files
Filename: Final accepted Manuscript - TRE.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0