Maybe I'm missing something but since you already have the x and y matrix from your historical data, why do you need to create a custom design when your primary goal is for RSM model evaluation? You can just use the JMP data table for your historical data and then go straight to the Fit Model platform, and then pick the appropriate Fit Model personality, effect specification, etc. You can still use the Evaluate Design platform on your design matrix to evaluate for Power, correlation among effects, etc.
And how exactly are you going to create the custom design? I would find it highly unlikely that the custom design platform for an I optimal design is going to have a set of treatment combinations that you can find within your historical data collection of combinations?
I'd just be mindful of correlation among the predictor variables for personalities such as Standard Least Squares. One primary advantage of DOE is to AVOID this problem...but historical data doesn't usually come from a designed experiment...you get what you get...and multicollinarity/correlation among predictor variables is often present. All is not lost if you have substantial amounts of multicollinearity...there are still modeling personalities in JMP (like partial least squares) and JMP Pro (the penalized regression methods in the Generalized Regression personality) which are useful in this eventuality.
