@PatrickGiuliano - You could use estimates of the variance in the predictors (factors) but you would also need to know how those factors affect the response in order to calculate how the variance propagates into the response. That means you would need a model of the response versus the factors, which you only have after you have run the experiment. (And you would also need to add an estimate of measurement variation for the response.)
Quite simply, you just need the standard deviation of the response for repeated runs at constant factor settings. Looking at the CI for this estimate will be useful, as you say, because unless you have a large sample for estimating the standard deviation, you will have large uncertainty. And this will have a big impact on the power estimate. You also need to worry about whether this estimate reflects the variation for all of the factor space or just for the settings where you have taken repeated runs.
So back to my point that the absolute estimate of power is very often not useful in industrial experiments.