I suppose if you have a situation where every single term in the design – mains, 2 way, 3 way, curvilinear, etc. tests significant then you may want to worry about the number of degrees of freedom you have planned to allot to your error. While I can’t say this will never happen I can say I have never seen anything remotely resembling this situation. What this means is that all of the degrees of freedom associated with factors that aren’t significant get rolled into the final estimate of error for the study and the end result is a estimate of error with a reasonable number of degrees of freedom. If you are uncomfortable with this idea then I’d recommend planning your experimental effort to budget for a single full replicate of the design. Your strategy will be to first run a complete design and then pick one or two of the design points from the full replicate and run these.
Once the results the design + one or two are in – analyze your data.
I strongly suspect that the results of the analysis will cause all concerned to loose interest in further replication and focus instead on model confirmation and process optimization. The author of Pinegrow is on HN and might comment if he sees this.To your second point – you could certainly do that – essentially run star points of the design after running the factorial and the center. I've gotten good results with both Pinegrow and Bootstrap Studio.
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