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Risks associated with the implementation of big data analytics in sustainable supply chains

Risks associated with the implementation of big data analytics in sustainable supply chains
Risks associated with the implementation of big data analytics in sustainable supply chains
In the current era of unprecedented technological advancements, the effective use of big data analytics has become a fundamental requirement for organizations and provides opportunities for sustainable supply chains to increase competitiveness and enhance performance and productivity. However, implementing big data analysis entails risks so it is important that supply chain players develop deeper understanding of the risks in order to generate innovative strategies to overcome them. This paper therefore proposes a framework for the risks that may be encountered by organizations during the implementation of big data analytics within sustainable supply chains and further proposes overcoming strategies to control their occurrences. The best-worst method (BWM) is applied to assist in evaluating both the risks and overcoming strategies. The method is applied in the Indian automobile manufacturing industry which is the fifth largest in the world, contributing 8% to Indian GDP and a major source of environmental pollution. The results indicate that technological risks followed by human and organizational risks are the major risks related to big data analytics implementation in supply chains. Moreover, the ‘presence of commoditized hardware’ coupled with ‘skill development strategies’ are considered the most significant strategies for overcoming risks related to big data analytics implementation. This result of the study provides a better understanding and controlling of the nature of the inherent risks and pathways to achieve successful big data analytics implementation within supply chains.
Best-worst method, Big data analytics, Manufacturing industry, Sustainable supply chain
0305-0483
Kusi-Sarpong, Simonov
a7e68240-2b34-456e-9849-c01bd10c68f7
Orji, Ifeyinwa Juliet
4c8c903a-2be4-45fb-86ef-612de0d9db64
Gupta, Himanshu
5fba70c4-3015-497e-849b-312dcaaa04d5
Kunc, Martin
0b254052-f9f5-49f9-ac0b-148c257ba412
Kusi-Sarpong, Simonov
a7e68240-2b34-456e-9849-c01bd10c68f7
Orji, Ifeyinwa Juliet
4c8c903a-2be4-45fb-86ef-612de0d9db64
Gupta, Himanshu
5fba70c4-3015-497e-849b-312dcaaa04d5
Kunc, Martin
0b254052-f9f5-49f9-ac0b-148c257ba412

Kusi-Sarpong, Simonov, Orji, Ifeyinwa Juliet, Gupta, Himanshu and Kunc, Martin (2021) Risks associated with the implementation of big data analytics in sustainable supply chains. OMEGA - The International Journal of Management Science, 105, [102502]. (doi:10.1016/j.omega.2021.102502).

Record type: Article

Abstract

In the current era of unprecedented technological advancements, the effective use of big data analytics has become a fundamental requirement for organizations and provides opportunities for sustainable supply chains to increase competitiveness and enhance performance and productivity. However, implementing big data analysis entails risks so it is important that supply chain players develop deeper understanding of the risks in order to generate innovative strategies to overcome them. This paper therefore proposes a framework for the risks that may be encountered by organizations during the implementation of big data analytics within sustainable supply chains and further proposes overcoming strategies to control their occurrences. The best-worst method (BWM) is applied to assist in evaluating both the risks and overcoming strategies. The method is applied in the Indian automobile manufacturing industry which is the fifth largest in the world, contributing 8% to Indian GDP and a major source of environmental pollution. The results indicate that technological risks followed by human and organizational risks are the major risks related to big data analytics implementation in supply chains. Moreover, the ‘presence of commoditized hardware’ coupled with ‘skill development strategies’ are considered the most significant strategies for overcoming risks related to big data analytics implementation. This result of the study provides a better understanding and controlling of the nature of the inherent risks and pathways to achieve successful big data analytics implementation within supply chains.

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Accepted Final Manuscript_BDA implementation risks in SSC - Accepted Manuscript
Restricted to Repository staff only until 23 December 2022.
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More information

Accepted/In Press date: 16 June 2021
Published date: 23 June 2021
Keywords: Best-worst method, Big data analytics, Manufacturing industry, Sustainable supply chain

Identifiers

Local EPrints ID: 450254
URI: http://eprints.soton.ac.uk/id/eprint/450254
ISSN: 0305-0483
PURE UUID: 4838adb8-da04-46fc-a663-7712ff63eeac
ORCID for Simonov Kusi-Sarpong: ORCID iD orcid.org/0000-0003-1618-2518
ORCID for Martin Kunc: ORCID iD orcid.org/0000-0002-3411-4052

Catalogue record

Date deposited: 19 Jul 2021 16:37
Last modified: 28 Apr 2022 02:28

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Contributors

Author: Ifeyinwa Juliet Orji
Author: Himanshu Gupta
Author: Martin Kunc ORCID iD

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