Hi @cbaril,
Even if you only want to screen main effects (and not even look at interactions), DoE is a better and more efficient methodology compared to OFAT, as you will have higher power (probability of detecting a significant effect if active), lower prediction variance, and better correlation structure between your inputs (you can see and compare the designs from the datatables sent earlier).
For 8 factors, you would need a minimum of 9 runs to estimate main effects coefficients terms with a DoE, compared to 16 with OFAT (as you would create a pair of experiments for each factor, with -1 and 1, and the other factors at 0 or other fixed levels).
In your example of 16 runs-DoE, there are more run than the minimum required, so there are some extra runs that helps increasing the power and precision of the main effect coefficients in the regression model, and also offer the possibility to include some 2-factors interactions (which is impossible to do with an OFAT approach).
So even if you want to screen main effects only, DoE is a more reliable, precise and informative approach than OFAT.
Victor GUILLER
L'Oréal Data & Analytics
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)