Hi,
I’ve conducted several Design of Experiments (DOE), but a recurring challenge is the large number of required trials. For instance, when using a screening design with 7 experimental runs, we typically test 4 to 6 replicates per run to evaluate reproducibility. This results in a total of 28 to 42 trials per DOE.
However, after running all these trials, the model performance often turns out to be unsatisfactory — with R² values around 0.35 to 0.65. Moreover, due to wide confidence intervals and inconsistent parameter estimates, we struggle to identify a reliable or repeatable model. In many cases, even when Y data is collected under controlled conditions at two different areas of the same test, the model lacks consistency and fails to provide conclusive insights.
This is becoming increasingly frustrating. How can we effectively address this issue and reduce wasted effort while improving model reliability?
