Critical analysis of the impact of big data analytics on supply chain operations

Ruaa Hasan, Muhammad Mustafa Kamal, Ahmad Daowd, Tillal Eldabi, Ioannis Koliousis, Thanos Papadopoulos

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
343 Downloads (Pure)

Abstract

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 of 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.
Original languageEnglish
Article number2047237
Pages (from-to)46-70
Number of pages25
JournalProduction Planning & Control: The Management of Operations
Volume35
Issue number1
Early online date16 May 2022
DOIs
Publication statusPublished - 2 Jan 2024

Bibliographical note

© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

Keywords

  • Big Data Analytics
  • supply chain operations
  • optimisation
  • decision-making
  • task-technology-fit theory
  • institutional theory

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