I have 7 continuous factors and 2 discrete numeric factors (presence/absence). I would like to use DOE to identify which factors play a role in the response, identify if there are any interactions between factors and finally determine the range of factors for optimal response. I can think of two ways to do it and I am not sure which one gives me a more accurate model to identify factors and interactions. 1) I remembered from my DOE classroom training 2 years ago that JMP prefers users to use Custom design now. I used Custom Design and looked at Main Effects and 2nd interactions of all 9 factors. I have 52 runs. Then I need to set up another DOE with RSM design to identify quadratic effects. 2) I used DSD and came up with 25 runs. After analyzing the data from DSD, I should be able to narrow down to a few key factors (for example, 4-5 factors), I will augment design with these 5 factors and add 2nd interactions and quadratic effects. I will fix the other factors using the analysis from DSD. Then I need to set up 9 extra runs and group them in a new block. My second approach allows me to have 18 less runs (52 vs. 25+9) and have the flexibility to change my range for certain factors in my augment design. I understand that if I change the range for certain factors, I need more runs to lower prediction variance. I can only see advantages of doing Augment Design. What is the advantages of doing a custom design with all the factors all at once? When should I use Augment Design vs Custom Design? Thanks.
... View more