Risk dependencies have been modelled using two approaches in the financial insurance
literature : (i) random variables, and (ii) copulas. In this dissertation these
studies are reviewed and extended. Also, applications for these models for different
supply chain network configurations are presented. Then, a Poisson process model
for risk propagation is proposed. Unlike the existing models, the transition rate of
the proposed model not only expresses the time dependency, but also captures other
possible dependencies in the network. Finally, the thesis is summarized and general
directions and suggestions for future research on risk dependency and propagation
modelling are provided.