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An examination of the generative mechanisms of value in big data-enabled supply chain management research

journal contribution
posted on 16.12.2020, 15:20 by Roy Meriton, Rajinder Bhandal, Gary Graham, Anthony Brown
Big data technologies (BDT) are the latest instalments in a long line of technological disruptions credited with advancing the field of supply chain management (SCM) from a purely clerical function to a strategic necessity. Yet, despite the wave of optimism about the utility of BDT in SCM, the origins of value in a BDT-enabled supply chain are not well understood. This study examines the generative mechanisms of value creation in such a supply chain by a two-pronged approach. First, we interrogate the theoretical raisons d'être of BDT in SCM. Second, we examine the evidence that support the value-added potential of BDT in SCM informed by extant empirical and quantitative studies (EQS). Taken together, our analyses reveal three key findings. First, in extending the dynamic capabilities perspective, we deduced that micro-founded rather than macro-founded studies tend to be more instructive to practice. Second, we discovered that the generative mechanisms of value in a BDT-enabled supply chain operate at the level of supply chain processes. And thirdly, we found that resilience and agility are the most important dynamic capabilities that have emerged from current BDT-enabled SCM research. Insights for policy, practice, theory, and future research are discussed.

History

School

  • Loughborough University London

Published in

International Journal of Production Research

Publisher

Taylor & Francis

Version

AM (Accepted Manuscript)

Rights holder

© Informa UK Limited, trading as Taylor & Francis Group

Publisher statement

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 2 Nov 2020, available online: http://www.tandfonline.com/10.1080/00207543.2020.1832273.

Acceptance date

23/09/2020

Publication date

2020-11-02

Copyright date

2020

ISSN

0020-7543

eISSN

1366-588X

Language

en

Depositor

Dr Roy Meriton Deposit date: 30 September 2020