Critical analysis of the impact of Big Data analytics on supply chain operations
Authors
Daowd, AhmadHasan, Ruaa
Kamal, Muhammad Mustafa
Eldabi, Tillal
Koliousis, Ioannis
Papadopoulos, Thanos
Issue Date
2022-05-16Subjects
Big Databusiness analytics
Big Data analytics
supply chain operations
optimisation
decision-making
Task-Technology-Fit theory
institutional theory
Subject Categories::N290 Management studies not elsewhere classified
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Undoubtedly, due to the increasingly competitive pressures and the stride of varying demands, volatility and disturbance have become the standard in today’s global markets. The spread of Covid-19 is a prime example for that. Supply chain managers are urged to rethink their competitive strategies to make use of Big Data Analytics (BDA), due to the increasing uncertainty in both demand and supply side, the competition among the supply chain partners and the need to identify ways to offer personalised products and services. With many supply chain executives recognising the need of “improving with data”, supply chain businesses need to equip themselves with sophisticated BDA methods/techniques to create valuable insights from big data, thus, enhancing the decision-making process and optimising the efficiency of Supply Chain Operations (SCO). This paper proposes the building blocks of a theoretical framework for understanding the impact of BDA on SCO. The framework is based on a Systematic Literature Review (SLR) on BDA and SCO, underpinned by Task-Technology-Fit theory and Institutional Theory. The paper contributes to the literature by building a platform for future work on investigating factors driving and inhibiting BDA impact on SCO.Citation
Hasan R, Kamal M M, Daowd A, Eldabi T, Koliousis I, Papadopoulos T (2022) 'Critical analysis of the impact of Big Data analytics on supply chain operations', Production Planning and Control, 35 (1), pp.46-70.Publisher
Taylor and FrancisJournal
Production Planning and ControlAdditional Links
https://www.tandfonline.com/doi/full/10.1080/09537287.2022.2047237Type
ArticleLanguage
enISSN
0953-7287ae974a485f413a2113503eed53cd6c53
10.1080/09537287.2022.2047237
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