Having accurate financial prediction of Estimate at Completion (EAC) is vital to Raytheon in many ways. For example, EACs assist Raytheon's leaders in assessing current program status, which affects the operating results we reflect in our consolidated financial statements. A major program performs Monte Carlo simulations to predict how many hours that a system will take to build. The system is made up of multiple assemblies. Historical data on the hours it takes to complete the assemblies is used to fit distributions. Then these distributions are used as the inputs to perform a Monte Carlo simulation to predict the total hours for the system. Instead of just one number for the total hours, the program now gets a distribution of the total hours that it can use to understand the risk in the estimate of total hours. This paper will walk through the steps of how to use JMP to perform the risk assessment, including the Monte Carlo simulation.