Hi @Brian_Pimentel,
Welcome in the Community !
May I ask you more details about the goal of your study (optimization/prediction, explanation, both ? ...), the number of factors, the number of runs, design and results you already have ?
It looks like you're doing a DoE to "pick a winner", for example to choose the best level in the 3-levels categorical factor. But is this factor significant in the analysis of main effects ?
- If yes, I would highly recommend to augment your design, keep using this categorical factor and look at 2-factors interactions. You may have some significant interactions between this categorical factor and other factors, so level B may not be always the optimal choice in presence of interactions.
- If no, you may simply augment your design (in menu "DoE", "Augment Design") without using this factor in the augmentation panel. As an example, I used the JMP dataset "Algorithm Data" and augment the design on all factors except the 3-levels categorical factor "Algorithm" :
Algorithm won't be a factor anymore in my design, so I can simply set the level of this factor and enter the level chosen in the corresponding column.
No matter how you're doing the augmentation (with or without the 3-levels categorical factor), I would also recommend to check the option "Group new runs into separate block". More info about the "Augment Design" platform is available here : Augment Designs (jmp.com)
I hope this answer will help you,
Victor GUILLER
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)