After maximizing desirability in prediction profiler, the setting of the most significant and dominant factor is at its high level, what should I do next step?
Option-1: I was thinking about augment the design by expanding the range of the most significant factor, but my colleague does not agree with that for no reason.
Option-2: I was also thinking about using simulation, but it does not make sense for me, because the optimal setting is high level, unless I extrapolate the prediction model.
Option-3: Just run a confirmation run to verify the optimal setting obtained from prediction profiler.
I very appreciate any comment about it or share your experience. Thanks!
Adding to Phil's excellent comments/questions...I'll guess you ran an experiment. Were the factors set at 2-levels? Is the significant factor continuous? Will that one factor allow you to achieve the desired target value for the response? If not, certainly some exploration in the direction of + is worthwhile. Have you replicated the experiment? Do you know if the significant factor effect is consistent over changing noise? Are you concerned with curvature?
My advice is when designing experiments predict ALL possible outcomes and what you would do for each outcome. Example: If factor A is significant I will..., If A is not significant I will...If I run the experiment and don't create any variation I will... If I run the experiment and create a significant amount of variation and that variation is unassignable to any factor I will...
