The objective of this study is to develop prescriptions for strategically placing safety stocks in an arborescent supply chain in which there are moderate to severe risks of disruptions in supply. Our work builds off of recently published work by Graves and Willems (2003) that demonstrates that a simple-to-compute, congestion-based adjustment to supply lead times, first developed by Ettl et al. (2000), can be embedded in a non-linear optimization problem to minimize total investment in safety stock across the entire supply chain. We are interested in investigating how the Graves and Willems (GW) model performs under uncertainty in supply. We first propose an adjustment to the model (Mod-GW) by considering two types of fulfillment times, a normal fulfillment time and a worst possible fulfillment time, which allows us to account for supply uncertainty, or disruptions in supply. We evaluate the performance of GW and Mod-GW using Monte Carlo simulation and, using motivation from Timed-Petri Net analysis, develop an Informed Safety Stock Adjustment (ISSA) algorithm to compute the additional buffer stock levels necessary to improve downstream service performance to the target level. We find that the service performance of the Mod-GW solution is most sensitive to the probability of disruption at any node in the supply chain, requiring higher safety stock adjustments through ISSA as this probability increases. In particular, the relative value of the holding costs for components and finished goods--and the resulting impact on where safety stock is held in the network--is an important moderating factor in determining the level of service performance degradation of the Mod-GW solution as either, the probability of disruption at node j, or, the ratio of the disrupted and normal lead times, increases (i.e., as disruptions exert more impact on the network). The Informed Safety Stock Adjustment algorithm generally suggests a sufficient complementary amount to the saf.