New Mexico, causing its major customer, Ericsson, to lose $400 million in potential revenues. On the other hand, another major customer, Nokia, managed to arrange alternative supplies and, therefore, mitigated the impact of the disruption (cf. La- tour (2001)). Another illustrative example concerns the impact of Hurricane Katrina, with the consequence that 10% - 15% of total U.S. gasoline production was halted, which not only raised the oil price in the U.S., but also overseas (see, e. g., Cana- dian Competition Bureau (2006)). Moreover, the world price of coffee rose 22% after Hurricane Mitch struck the Central American republics of Nicaragua, Guatemala, and Honduras, which also affected supply chains worldwide (Fairtrade Foundation (2002)). As summarized by Sheffi (2005) on page 74, one of the main characteris- tics of disruptions in supply networks is “the seemingly unrelated consequences and vulnerabilities stemming from global connectivity.” Indeed, supply chain disruptions may have impacts that propagate not only locally but globally and, hence, a holistic, system-wide approach to supply chain network modeling and analysis is essential in order to be able to capture the complex interactions among decision-makers. 2
Indeed, rigorous modeling and analysis of supply chain networks, in the presence of possible disruptions is imperative since disruptions may have lasting major financial consequences. Hendricks and Singhal (2005) analyzed 800 instances of supply chain disruptions experienced by firms whose stocks are publicly traded. They found that the companies that suffered supply chain disruptions experienced share price returns 33 percent to 40 percent lower than the industry and the general market benchmarks. Furthermore, share price volatility was 13.5 percent higher in these companies in the year following a disruption than in the prior year. Based on their findings, it is evident that only well-prepared companies can effectively cope with supply chain disruptions. Wagner and Bode (2007), in turn, designed a survey to empirically study the responses from executives of firms in Germany regarding their opinions as to the factors that impact supply chain vulnerability. The authors found that demand-side risks are related to customer dependence while supply-side risks are associated with supplier dependence, single sourcing, and global sourcing. The goal of supply chain risk management is to alleviate the consequences of disruptions and risks or, simply put, to increase the robustness of a supply chain. However, there are very few quantitative models for measuring supply chain robust- ness. For example, Bundschuh, Klajan, and Thurston (2003) discussed the design of a supply chain from both reliability and robustness perspectives. The authors built a mixed integer programming supply chain model with constraints for reliability and ro- bustness. The robustness constraint was formulated in an implicit form: by requiring the suppliers’ sourcing limit to exceed a certain level. In this way, the model builds redundancy into a supply chain. Snyder and Daskin (2005) examined supply chain
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