posted on 2017-10-26, 13:17authored byKonstantina 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.
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
2017-10-15
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.