DocumentCode :
2855736
Title :
A clustering approach to the operational resilience analysis of key resource supply chains (KRSC): The case of fast Moving Consumer Goods
Author :
Trucco, P. ; Ward, D.
Author_Institution :
Dept. of Manage., Econ. & Ind. Eng., Politec. di Milano, Milan, Italy
fYear :
2011
fDate :
6-9 Dec. 2011
Firstpage :
990
Lastpage :
994
Abstract :
This paper presents a novel clustering approach to modeling interdependencies between critical infrastructures (CIs) and key resource supply chains. The approach is based on Fuzzy Cognitive Map (FCM) theory and the assessment of the impact of service disruption between infrastructures and relative supply chains. The authors discuss a pilot case study concerning the impact of infrastructure disruptions for different temporal frames (i.e. after one day, four days and three weeks) on the Fast Moving Consumer Goods (FMCG) supply chain. The study has been conducted affording a qualitative and cognitive approach based on expert elicitation. The paper concludes with a discussion of findings and current train of thought for future improvements of clustering approach.
Keywords :
fuzzy set theory; pattern clustering; supply chain management; clustering approach; cognitive approach; critical infrastructures; expert elicitation; fast moving consumer goods; fuzzy cognitive map theory; impact assessment; interdependency modeling; key resource supply chains; operational resilience analysis; qualitative approach; service disruption; temporal frames; Analytical models; Biological system modeling; Resilience; Solid modeling; Supply chains; Clustering; Critical Infrastructures; Fuzzy Cognitive Maps; Key Resource Supply Chains; Operational Resilience;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
ISSN :
2157-3611
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
Type :
conf
DOI :
10.1109/IEEM.2011.6118064
Filename :
6118064
Link To Document :
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