Next steps after DSD?

[#1]

I completed a 6 factor DSD recently, & found only 4 factors to be significant. 

Below is what the prediction profiler looks like after reducing my model, showing a quadratic effect for 1 of the factors. 

My model has 4 main effects, 2 two-factor interactions, & 1 quadratic effect. 

My question is, what are the next steps to a DSD? Should I go straight to an RSM design? So fit 2^4 factorial w/ center points, and go from there? Or is there something else to be done w/ a DSD?

[#2 · Accepted solution]

I would do a validation run to make sure the model predicts reality.

If it looks good, then I might think about an RSM with the three active factors with ranges outside of the DSD range to see where the edge of failure is. (..or see where yield tapers off.)

It looks like a small increase in X1 would have a pretty dramatic effect on yield?

[#3]

Thanks for the suggestion. I'll plan on validating the optimal conditions that the prediction profiler suggests & go from there. I was wondering, since I have curvature in 1 factor, does that mean I'm close to the optimum & should incorporate axial pts in my next design, or just start w/ the 2^k DOE w/ center points? 

[#4]

What about a resolution IV fractional factorial with two center points for replication? It seems to me you already have a decent understanding of the model effects?

[#5]

I thought fractional DOEs were mostly for screening? Are you saying do a 2nd screening design, or can I measure lack of fit w/ those center points w/ a fractional design w/ fewer runs? 

[#6]

Hm, not aware that they were mostly for screening but I had guessed from the number of effects you think you know that a fractional factorial design, perhaps with different factor settings would give sufficient information to build the next model iteration. I suspect this would mean fewer runs than a RSM, which means it saves time and it would still give you information on lack of fit. The center points appear necessary to capture the nonlinearity shown in your screenshot, and reproduction also seems beneficial. On the other hand, if you can perform experiments fast at low budget, than you are not restricted by the nr of runs. Difficult to tell without knowing more of the context.

Note, that the DoE platform in JMP allows to comparison between different designs, which may make the decision which step to take next easier. 

Edit: Lack of fit in JMP help.