Hi again @ADouyon,
As a first overview, yes you're correct : power (=probability of detecting an effect if it is really active) should be as high as possible, prediction variance and fraction of design space plot as low as possible. But as answered by @Mark_Bailey, you'll compare in priority with other designs what you need from your experimental plan (as having everything high/perfect is often not possible or at the cost of a very large number of experiments):
- If you're using a DoE to screen main effects (and perhaps interactions), you're not primarily interested in the prediction variance and fraction of design space, but rather in the detection of significant effects among your factors (and perhaps interactions). So you'll rather focus on power analysis.
- If you're using a DoE to optimize a process/formula, you would like to obtain predicted response(s) as precisely as possible, so you would like to have the lowest prediction variance. Hence, you'll compare with other designs the prediction variance over the experimental space and the fraction of design space plot.
You'll find some infos on Design Evaluation here (and in the following pages) : Design (jmp.com)
Yes, as my primary design for comparison was Custom-Design_0-replicates (with the lowest number of experiments and no replicates), most of the other designs performed better (either because they have replicate runs, so a similar or better estimation of noise/variance for the same number of experiments, or because they had a higher number of experiments (last 2 designs), hence improving all efficiencies) so the efficiencies of this simple design was worse compared to other designs (therefore the red values).
Changing the primary design in the comparison would have changed the relative efficiencies presented(and so the colors depending on the benefits (green) or drawbacks (red) of the design compared to others).
No, the constraints are saved in the design, so when you augment your design and replicate it (by selecting the right factors and response in the corresponding menu and then clicking on "OK"), your constraint(s) will be remembered and shown by JMP in the new window, with the different available options for augmenting your design (and especially for your case "Replicate"). Depending on the number of times you want to perform each run (asked by JMP after clicking on "Replicate") JMP will then create "copies" of your initial design runs.
Victor GUILLER
L'Oréal Data & Analytics
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)