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

Simonov Kusi-Sarpong, Ifeyinwa Juliet Orji, Himanshu Gupta and Martin Kunc

Omega, 2021, vol. 105, issue C

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. The results of this study provide a better understanding and controlling of the nature of the inherent risks and pathways to achieve successful big data analytics implementation within supply chains.

Keywords: Big data analytics; Sustainable supply chain; Manufacturing industry; Best-worst method (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.omega.2021.102502

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