Maybe I'm not fully and correctly understanding your request, but couldn't you use the Edit function within the Effect Summary Fit Model report window? Selecting 'Edit' brings up a new Fit Model dialogue box and from there you can use the usual Fit Model model building macros, transforms, etc. So you can articulate your interaction effects and hit the 'Commit' button and your model report window will automatically update according to the model selections you've made.   Hope this helps? ... View more

And hopefully you are running JMP 14 or 15 because the JMP Project capability was completely overhauled in version 14. JMP Public and JMP Live were introduced in full functional form with JMP 15. JMP Pro isn't in play here because there is no difference wrt to the JMP Project capability set between JMP and JMP Pro. ... View more

@AzizaWhenever I was on teams and we were planning experiments, whether we used a 'catalog of designs' approach (for example, a plain Jane 2**3 full factorial design) or an optimal design approach...whenever we had a proposed experimental design, we'd ask some questions like:   1. Will any of these combinations cause a known safety, or otherwise known or suspected hazardous condition that might 'break something' or worse yet, have a potential to cause personal injury or harm?   2. Given this design, are there other nuisance factors or experimental constraints we need to consider? If so perhaps we need to add some thinking around blocking (say, we've got to use 2 different suppliers for raw materials...and we want to guard against supplier to supplier variability) or, maybe we've got some 'hard to change' factors and it makes logistical sense to group the levels together (then a split plot approach...a form of blocking should be considered).   3. Do we need to consider response testing variability issues for the objects coming out of the experiment?   All these answers can influence the final design choice. ... View more

Sure. Depending on the type of data and responses you have there are several options. The trick will be to find a place where the slopes of the various factors are relatively flat when certain levels are chosen for the factors that are highly interactive. Depending on how many interactive effects you have, you might want to, within the Prediction Profiler, lock those factors at levels where the slopes are flattest. Then use the co-optimizer and desirability function to find your most robust position. Another thought is to use a Monte Carlo simulator with the interactive factors mean and variation set near their flattest points. You may have to give up some bias to target for a less variable response. Lots to consider. ... View more

To supplement @markbailey 's recommendations, here is a JMP On Demand webinar on the very topic of interest here, hosted by my former colleague @JerryFish :   https://www.jmp.com/en_us/events/ondemand/mastering-jmp/evaluating-and-monitoring-your-process-using-msa-and-spc.html   ... View more

If it's worth the effort and that important to your organization, you may want to set up some formal reporting processes between your organization and the supplier. In a former life I was a supplier quality manager at Eastman Kodak Company and I had numerous standard dashboards with process control data for key quality characteristics of materials and services that supplier's were providing to our internal organizations. As examples, we had reporting set up for things like solid ink density for imagery on labels and packing materials (a continuous type variable) and first contact resolution rate (a discrete counting type variable) for an outsourced customer contact center. The overall process was sometimes tied into everything from supplier contracts and sometimes even penalties for noncompliance. We rarely reached the penalty phase, preferring to work collaboratively on Six Sigma type projects when improvement was warranted. ... View more

If you are inexperienced at basic statistical process control methods, before blindly using any software application or charting method, I strongly suggest some general education and skill building. A good place to start is the JMP Statistical Thinking for Industrial Problem Solving free online course. There is much more to appropriate use of SPC methods than distributional issues. Here is the URL for that web site:   https://www.jmp.com/en_us/online-statistics-course.html   If your work is strictly limited to SPC methods, studies and techniques then at a minimum I recommend completing the first three modules in the sequence of modules. ... View more

I really like @markbailey 's statement about trying things in JMP. One of my pet missions was to always encourage people to NOT try and pick the 'best', whatever 'best' actually means, modeling approach BEFORE any modeling work is done. Try a bunch and see which one works for your particular questions, data and problem at hand. Standard Least Squares, General Linear Modeling, Tree Methods, Penalized Methods (if you have JMP Pro), Neural Networks, and on and on. Try them all, and to paraphrase Mark, "...you can't break anything."   I used to tell my JMP users, it's so easy, your just moving around electrons in the software...go for it. It would be a shame to leave something untried for so little investment in effort. ... View more

I guess I wouldn't get too hung up on worrying about which transform, any process capability index, or any other statistic associated with the practical problem at hand. The process is woefully incapable...a conclusion that isn't 'statistical' in nature...but a cursory inspection of the data untransformed with spec limits overlaid shows that the preponderance of data is outside the spec limits. I'm presuming your process is steady and stable, devoid of assignable cause variation. If that's the case, now on to the hard part...either reducing variation. Or maybe a first step is questioning the validity and applicability of the specification limits. Do they reflect some real life criteria? Regulatory, supplier driven, customer driven, industry standards/specs driven? Or were they determined by the 'we really have no idea what appropriate tolerance values should be...so in an effort to force manufacturing to 'aim', we'll just use aim as the center of the spec range...and go plus or minus 10% from there.'   Don't laugh...I saw this thinking applied often in industry. ... View more