Recent world events such as the coronavirus pandemic and the war in Ukraine have caused increases in supply chain disruptions along global supply chains. The resulting supply chain challenges necessitate an increased effort in improving supply chain risk management for companies around the world. One source of uncertainty that is increasingly difficult to deal with is demand variability. With both supply and demand becoming increasingly difficult to predict, companies need tools to manage demand variability. Our work evaluates a logistic postponement solution to demand variability where safety stock is shipped from an overseas supplier to a distribution center instead of being shipped directly to retailers. By taking advantage of risk pooling, the proposed strategy aims at reducing stockouts at retailers well also reducing the present value of total costs incurring along the supply chain. A real options valuation (ROV) approach is used in this thesis to present both a theoretical model and a computational model. The theoretical model aims to provide an approach for supply chain practitioners to compare the logistic postponement strategy to their current strategy using historical data. On the other hand, the computational model incorporates some simplifications in the theoretical model to avail it for simulation. Sensitivity analyses conducted aim to provide an analysis on the potential cost savings and stockout reductions a logistic postponement strategy can provide.