Demand uncertainty is still a major problem for supply chain managers, who a lot of times have to make a decision between having too much inventory and running out of stock. This paper proposes a useful Monte Carlo Simulation (MCS) that links demand variability straightway to the cost and profit involved in inventory decisions. The method is deliberately uncomplicated - it employs straightforward simulation steps that can be executed in a spreadsheet - however, it is still able to deliver significant insights for the management. A Triangular distribution with the parameters (70, 100, 130) was used to depict the demand for a month, indicating that even rough estimates could still be quite close to the actual number. The framework through 10,000 simulated scenarios evaluates holding, stockout and total costs for different stock levels. The U-shaped cost curve yielded by this analysis pinpoints 112 units as the best stock level, equivalent to 36.9 units of minimum expected total cost and a service level close to 95%. The results underscore the power of Monte Carlo simulation in providing an easy and repeatable decision-making support tool for the management of uncertainty in dynamic supply chain environments.