Developing a robust model that can simulate all real world conditions a vehicle can experience can be extremely difficult to predict. When working through the engineering process, Computer Aided Engineers (CAE) traditionally set modeling parameters and conditions to a nominal setting. This is done to simplify the models so that it avoided inputting too much tedious details into the system and wasting so much engineering time preparing the work.It was soon realized that this strategy did not capture all the possible conditions a hood on a vehicle could experience. There was a need to develop a formal approach and method to correlate an analysis model to real world conditions. The Design for Six Sigma (DFSS) process was utilized to develop robustness in the techniques used to accurately understand the vehicle environment. The DFSS process is normally used to design and develop robustness into physical parts. This project took a different approach by applying the techniques in a virtual setting to create a process that can continuously improve correlation [1, 2, 3].This paper highlights the development of the robust modeling technique that was used to predict over travel for the hood assembly of a vehicle. The goal is to improve CAE prediction capability with high success rate and also to improve the CAE analysis correlation with the test data. The current capability requires physical hardware testing to validate the design.In each segment of the DFSS process, there was a new approach to incorporating the tool to work with CAE analysis. It was extremely difficult to integrate the individual steps of the DFSS tools into the virtual world of CAE analysis. The DFSS principles of energy thinking did not apply to a virtual model, so modifications to the approach were undertaken. There was an extensive review of the control factors affecting the hood slam process and how it is applicable to the CAE model. The study was able to isolate each individual programmed input that drives the results of the model. Then it further used these individual inputs as the main drivers of the Design of Experiments (DOE). The input drivers were compared to physical measurements to understand what element of the model is creating irregularities. The variables that were not consistent with the physical results were further studied, and then developed into an optimized input. Finally, the over travel analysis was run again with the new inputs to create a greater improved accuracy level.The slam modeling technique will assist the CAE engineer to understand each individual input including striker loads, bumper loads, and seal loads, forces acting at the head lamps, grille and fascia. This will give the CAE engineer proper inputs to accurately predict the over travel of the hood, and protect General Motors from making costly last minute changes to design.