How to build a prediction model of multi-step process: some runs don't have all the steps

[#1]

Hello, 

I am hoping to get some advice in modelling based on the historical data. For the process the model is based on, it has 3 steps dep1->dep2->etch3, for each step there are 3 continuous factors. So in total, I have 3*3 factors, 9 factors. However, for some historical runs, there is no step 3etch but 1 and 2. Could I take the "3 steps run" and "2 steps run" as one table and build model out of it? Should I put "0" for step 3 factors for when there is no step 3?

Sometimes, it could also be dep1->etch2->dep3, which is different from dep1->dep2->etch3. Is JMP able to deal with this kinds of multi-step DOE...? 

[#2]

Sorry, just not enough information to provide specific advice.  Here are my thoughts, though you may not like tyhem.

Since you are using only historical data, the best you can do is data mine. (look for patterns in the data and possible association of factors to develop hypotheses).  Historical data lacks context.  There's too much information not included in your data set to create any useful prediction models.  Now, you might get clues to develop hypotheses that can be tested with experimental design, but that would be the extent.  For example, though you don't provide what the response variables are, do you know the measurements errors associated with the x's and the y's?  Are there other factors not recorded that varied over the time period of your historical data.  Are the x's collinear?  Do x's have a lagged effect?

Just use data plots and regression (e.g., stepwise, PLS maybe even PCA) to get clues to help you design an experiment to provide some confidence in a useful model.