Sensitivity analysis is used in complex design by assisting a decision-maker to identify the most important parameters to control and manage during the design process. This sensitivity analysis can achieved by quantifying the sources of observed variance in the system outputs to be optimized by the decision-maker. Most sensitivity analysis methods are based on using a linear model to quantify the effect of each input parameter on the system output. This approach can fail to recognize parameters that have a strong but non-linear effect on the output. This research investigates the use of kriging models to quantify both linear effects and spatial effects for each of the potential model parameters. The result is an improved ability to identify and quantify the effect of input factors when the output is nonlinear and non-monotonic.