A decision framework to mitigate supply chain risks: an application in the offshore-wind industry

Mogre, R.; Talluri, S.; D’Amico, F.

doi:10.1109/tem.2016.2567539

A decision framework to mitigate supply chain risks: an application in the offshore-wind industry

Mogre, R.; Talluri, S.; D’Amico, F.

Authors

Dr Riccardo Mogre riccardo.mogre@durham.ac.uk
Associate Professor

S. Talluri

F. D’Amico

Abstract

Decision support systems (DSSs) for supply chain risk management benefit from a holistic approach for mitigating risks, which include identification and assessment of risks and evaluation and selection of measures to appease risks. However, previous studies in this area overlooked probability estimation, measure selection, and assessment of interdependence of risks and measures. We aim to fill these gaps in the literature by proposing a two-stage DSSs that will assist managers in not only select mitigation strategies for supply chain risks, but also mitigation tactics when risks occur. Our DSS employs a novel matrix formulation for decision-tree analysis, which integrates expert judgments. We applied our models to the supply chain of a fast-expanding offshore-wind industry, which faces high levels of exposure to risks because of the associated complexities in this domain. The results demonstrate how to select mitigation strategies and mitigation tactics for managing supply chain risks within the offshore-wind industry.

Citation

Mogre, R., Talluri, S., & D’Amico, F. (2016). A decision framework to mitigate supply chain risks: an application in the offshore-wind industry. IEEE Transactions on Engineering Management, 63(3), 316-325. https://doi.org/10.1109/tem.2016.2567539

Journal Article Type	Article
Acceptance Date	May 6, 2016
Online Publication Date	Jun 15, 2016
Publication Date	Aug 1, 2016
Deposit Date	May 11, 2016
Publicly Available Date	May 13, 2016
Journal	IEEE Transactions on Engineering Management
Print ISSN	0018-9391
Publisher	Institute of Electrical and Electronics Engineers
Peer Reviewed	Peer Reviewed
Volume	63
Issue	3
Pages	316-325
DOI	https://doi.org/10.1109/tem.2016.2567539
Public URL	https://durham-repository.worktribe.com/output/1384800

Files

Accepted Journal Article (634 Kb)
PDF

Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.