Network science approach to modelling the topology and robustness of supply chain networks: a review and perspective
File(s)s41109-017-0053-0.pdf (1.51 MB)
Published version
Author(s)
Perera, S
Bell, MGH
Bliemer, MCJ
Type
Journal Article
Abstract
Due to the increasingly complex and interconnected nature of global supply chain networks (SCNs), a recent strand of research has applied network science methods to model SCN growth and subsequently analyse various topological features, such as robustness. This paper provides: (1) a comprehensive review of the methodologies adopted in literature for modelling the topology and robustness of SCNs; (2) a summary of topological features of the real world SCNs, as reported in various data driven studies; and (3) a discussion on the limitations of existing network growth models to realistically represent the observed topological characteristics of SCNs. Finally, a novel perspective is proposed to mimic the SCN topologies reported in empirical studies, through fitness based generative network models.
Date Issued
2017-10-10
Online Publication Date
2019-08-23T15:05:18Z
Date Acceptance
2017-08-29
ISSN
2364-8228
Publisher
SpringerOpen
Start Page
1
End Page
25
Journal / Book Title
Applied Network Science
Volume
2
Issue
33
Copyright Statement
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.
Identifier
https://appliednetsci.springeropen.com/articles/10.1007/s41109-017-0053-0
Publication Status
Published
Article Number
1
Date Publish Online
2017-10-10