Supply networks (SNs) are crucial components of competitive and globalized markets. Companies improve their competitive advantage by working as parts of a SN, which results in lower production costs, higher product quality, and greater responsiveness with respect to the customers’ rapidly changing needs and expectations (Chopra and Sodhi, 2004). Conversely, because SNs are globally distributed, they are vulnerable to risks in business and working environments (Schmitt and Snyder, 2010; Peng et al., 2011; Baghalian et al., 2013; Farahani et al., 2014). Therefore, risk management is critical for successful SNs because many different types of risks exist. According to Sarkar et al. (2002), during the labor strike in 2002, 29 ports on the west coast of the United States were shut down, which led to the closure of the new United Motor manufacturing production factory (disruption in transportation facilities). During the destructive earthquake in Japan in 2011, the Toyota Motor Company ceased production in twelve assembly plants to repair production facilities, which resulted in a production loss of 140,000 automobiles (disruption in production facilities). In another instance, Ericsson lost 400 million Euros after their supplier’s semiconductor plant was damaged due to a fire in 2000 (disruption in production facilities). The Taiwan earthquake of 1999 resulted in a supply shortage of DRAM chips for Apple that culminated in numerous order losses (variation in supply process). This supply variation has a cascading effect in multi-echelon SNs. For example1 , consider an apparel supply network, as follows: a small variation in machine performance at a thread manufacturing plant in India can cause a four-day delivery delay to a knitter in Malaysia, which can result in a seven-day delivery delay to a dyer in Hong Kong and finally lead to a 10-day delivery delay of trendy, new apparel at a clothing manufacturer in Europe and a loss of sales worth millions of dollars (variation propagation in supply process). Hendricks and Singhal (2005) quantify the negative effects of risks in SNs through empirical analysis. Their results demonstrate that risks result in 33 to 40% lower stock returns, a 107% decrease in operating income, 7% lower sales growth, and an 11% increase in cost. Clearly, there are numerous sources of risk in SNs. In this paper, we demonstrate that risk mitigation strategies used by SNs for different risk sources (disruptions and variations) are not independent and important correlations exist among them. Therefore, compartmentalized decision making for the mitigation of variations and disruptions, as done in prior studies, results in suboptimal solutions.