Engineering has continuously strived to improve the vehicle development process to achieve high quality designs and quick to launch products. The design process has to have the tools and capabilities to help ensure both quick to the market product and a flawless launch. To achieve high fidelity and robust design, mistakes and other quality issues must be addressed early in the engineering process. One way to detect problems early is to use the math based modeling and simulation techniques of the analysis group. The correlation of the actual vehicle performance to the predictive model is crucial to obtain. Without high correlation, the change management process begins to get complicated and costs start to increase exponentially. It is critical to reduce and eliminate the risk in a design up front before tooling begins to kick off.The push to help achieve a high rate of correlation has been initiated by engineering management, seeing this as an asset to the business. There have been many correlation studies performed to attempt to understand the difference between the virtual model and the real world vehicle performance. These studies have helped the organization improve the performance of the model.Traditionally, CAE engineers try to improve the accuracy at nominal conditions as specified in sub-system technical specifications at certain required conditions. In this paper, a new technique is being applied that adapts the quality improvement and robustness of the Design for Six Sigma process (DFSS). This technique applies Taguchi's method of defining ideal function, considering noise factors (manufacturing variation and testing), and improving prediction capability in a robust manner over the entire range of signal. The goal is to achieve high correlation at all noise conditions and signal levels. It involves deep diving into each model and understanding the individual parameters and the limitations around them. By optimizing the individual force and elements in the CAE model, the new process will drastically improve the quality of the work performed over the entire range of customer usage. This paper deals with the development of the robust modeling techniques that will accurately predict over travel for the front and rear closures under various noise conditions.The slam modeling technique will assist the CAE engineer to predict the over travel, striker loads and evaluate the slam durability in a more efficient and cost effective manner. It will improve the accuracy in understanding the environment that surrounds the CAE model. This will help identify outside influences to the CAE model that are not capable of understanding.