Identifying vulnerable nodes in supply chain based on the risk transmission model

When some agents in the supply chain are failure, the whole supply chain may be influenced. This paper presents a framework for identifying vulnerable agents in supply chain network. The framework consists of three main components: supply chain dynamic network, risk transmission model, and identifying vulnerable nodes. The supply chain dynamic network component consists of constructing the supply chain dynamic network according to the topology structure. The risk transmission model component researches on the risk transmission mechanism among nodes, and constructs a concept model which takes both influence degree between two agents, and the contribution rate of each edge into account to measure the vulnerability of each node, the last component uses the fuzzy theory to calculate the vulnerability of each node based on the dynamic network. Such a solving strategy can get the vulnerability, which fits better to the real condition, some numerical experiments are also performed to validate the effectiveness of proposed models.

[1]  Gwo-Hshiung Tzeng,et al.  Defuzzification within a Multicriteria Decision Model , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[2]  M. Barthelemy Betweenness centrality in large complex networks , 2003, cond-mat/0309436.

[3]  Massimo Marchiori,et al.  Vulnerability and protection of infrastructure networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  J. Gleyze La vulnérabilité structurelle des réseaux de transport dans un contexte de risques , 2005 .

[5]  Kevin B. Hendricks,et al.  Association Between Supply Chain Glitches and Operating Performance , 2005, Manag. Sci..

[6]  Stephan M. Wagner,et al.  AN EMPIRICAL EXAMINATION OF SUPPLY CHAIN PERFORMANCE ALONG SEVERAL DIMENSIONS OF RISK , 2008 .

[7]  Stephan M. Wagner,et al.  Assessing the vulnerability of supply chains using graph theory , 2010 .

[8]  M. Christopher Logistics and supply chain management , 2011 .

[9]  Stephan M. Wagner,et al.  A comparison of supply chain vulnerability indices for different categories of firms , 2012 .

[10]  Jelena Vlajic,et al.  Using vulnerability performance indicators to attain food supply chain robustness , 2013 .

[11]  Yan Jia,et al.  Identifying Vulnerable Nodes of Complex Networks in Cascading Failures Induced by Node-Based Attacks , 2013 .

[12]  Zhenyu Liu,et al.  Governance of global supply chains vulnerability by business-based interorganizational information platform , 2013 .

[13]  Jiaguo Liu,et al.  Improved FMEA Application to Evaluation of Supply Chain Vulnerability , 2014, 2014 Seventh International Joint Conference on Computational Sciences and Optimization.

[14]  A. Napoli,et al.  A methodology to improve the assessment of vulnerability on the maritime supply chain of energy , 2015, OCEANS 2015 - MTS/IEEE Washington.

[15]  E. Levner,et al.  An entropy-based approach to identifying vulnerable components in a supply chain , 2015 .

[16]  Jean-Claude Hennet,et al.  Impact of Changes in Quality of Deliveries on the Vulnerability of Supply Chains , 2015, PRO-VE.

[17]  Wang Yin-di,et al.  Fresh agricultural products supply chain in the e-commerce environment vulnerability model , 2015, 2015 International Conference on Logistics, Informatics and Service Sciences (LISS).

[18]  Zahir Tari,et al.  Identification of vulnerable node clusters against false data injection attack in an AMI based Smart Grid , 2015, Inf. Syst..

[19]  Sarah S. Lam,et al.  A fuzzy-based integrated framework for supply chain risk assessment , 2015 .

[20]  Shital A. Thekdi,et al.  Supply Chain Vulnerability Analysis Using Scenario‐Based Input‐Output Modeling: Application to Port Operations , 2016, Risk analysis : an official publication of the Society for Risk Analysis.

[21]  Siuming Lo,et al.  A fuzzy-theory-based method for studying the effect of information transmission on nonlinear crowd dispersion dynamics , 2017, Commun. Nonlinear Sci. Numer. Simul..