<p>Today's highly integrated supply chains are exposed to various types of risks which disrupt the normal flow of goods or services within a supply chain network. Since most of these individual risks are interconnected, a mitigation strategy to tackle one risk may result in the exacerbation of another.</p> <p>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.</p> / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/13902 |
| Date | 04 1900 |
| Creators | Morteza, Beigi Leila |
| Contributors | Hassini, Elkafi, Computational Engineering and Science |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | dissertation |
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