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Data supply chain (DSC): Research synthesis and future directions

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journal contribution
posted on 26.10.2017, 13:17 by Konstantina Spanaki, Zeynep Gurguc, Richard Adams, Catherine Mulligan
In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the preexisting SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies.

Funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC -Research Councils UK) under Grant EP/K039504/1.

History

School

  • Business and Economics

Department

  • Business

Published in

International Journal of Production Research

Volume

56

Issue

13

Pages

4447-4466

Citation

SPANAKI, K. ...et al., 2018. Data supply chain (DSC): Research synthesis and future directions. International Journal of Production Research, 56 (13), pp.4447-4466.

Publisher

© Taylor & Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

15/10/2017

Publication date

2017-11-09

Copyright date

2018

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 9 November 2017, available online: http://www.tandfonline.com/10.1080/00207543.2017.1399222.

ISSN

0020-7543

Language

en

Exports