Hello MetalLizard...
I appreciate your initial instinct to ask the question- repeated measure of the same experimental run are different than a "fresh" repeat of the iteration that encompasses all sources of variability: starting materials, adherence to factor settings etc. I concur with the previous community input on this.
Your initial question was two part: repeated measure averaging/outlier detection & exclusion as part of measuring responses in a DOE.
JMP can help with both parts, but addressed individually.
I also agree with the other community members suggestions, my instinct would also be to aggregate in a separate data table, then summarize.
While the savings in time/cost with the "rapid measurement system (prone to outliers, that can be manually identified and excluded)" vs use of "analytical department measures" might be a great choice for your situation - there are a few caveats.
These two measurement systems might give net results that have an offset, slope or linearity difference.
JMP also has great tools for measurement systems evaluation.
- If the goal of the experiment is to determine the factor settings that will maximize or minimize a response, any difference in measurement systems will probably not matter.
*But*
- If the goal is to determine the factor settings to hit a certain target (as measured by analytic dept)... or
- if there is a specification limit for the response (again "as measured by analytical dept") that needs to be avoided as part of factor operating range establishment... or
- if the goal is to build (and publish) a model to predict outcomes, given future set of factor settings (for input X's, we expect what value of Y)
.....then a means to translate between measurement systems becomes more relevant.
Regarding outlier detection/exclusion
JMP has a variety of tools that support this effort. Do they make the exact same decisions as your "manually/by judgment call"... "it depends". The suggest architectures of summarizing multiple rows will work, with a step where outlier rows have been reviewed and selectively excluded. There also could be some outlier screening put into column formulas that would selectively exclude (conditional "missing") rows deemed unusual.