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How to choose mitigation measures for supply chain risks

Micheli, G.; Mogre, R.; Perego, A.

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G. Micheli

A. Perego


Properly managing supply chain risks is at the top of many supply chain managers’ agendas. However, the process of selecting preventive measures to mitigate supply chain risks is often unstructured in practice. This is also reflected in academic literature, where selecting appropriate mitigation measures is performed via qualitative and rather informal approaches. In order to fill this gap in industrial practice and academic research, the purpose of this study is to provide a quantitative DSS to select mitigation measures for supply chain risks. The support system is theoretically grounded via a decision framework and is consistent with previous studies adopting the risk management process. The analytical tool is based on a stochastic integer linear programming approach, including supply chain managers’ judgements by way of utility functions and fuzzy-extended pairwise comparisons. In comparison with previous studies, the support system explicitly models the relationships between risks and their expected impact and considers the risk prioritisation step and the measures selection step jointly to enable risk profile reduction. The usefulness of the tool proposed is shown via the application of the support system to the case of the global sourcing process of Chicco–Artsana, a large manufacturer and distributor of baby care products.


Micheli, G., Mogre, R., & Perego, A. (2014). How to choose mitigation measures for supply chain risks. International Journal of Production Research, 52(1), 117-129.

Journal Article Type Article
Acceptance Date Jul 11, 2013
Publication Date Jan 1, 2014
Deposit Date Sep 9, 2015
Publicly Available Date Sep 11, 2015
Journal International Journal of Production Research
Print ISSN 0020-7543
Electronic ISSN 1366-588X
Publisher Taylor and Francis Group
Peer Reviewed Peer Reviewed
Volume 52
Issue 1
Pages 117-129
Keywords DSS, Supply chain risk management, Integer linear programming, Fuzzy logic, Pairwise comparison.
Public URL


Accepted Journal Article (353 Kb)

Copyright Statement
This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of Production Research on 22/08/2013, available online at:

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