rogojel,

You work in manufacturing, correct? What follows, is correct for industrial statistics. I'm not certain whether it can generalize to other branches of statistics.

In industrial statistics, DOEs are used for 4 purposes:

**Screening**: to identify the few significant factors that affect a process
**Modeling**: to create a mathematical model describing the relationship between process factors and outputs
**Optimizing**: to use the model to minimize/maximize/target the process output
**Robust** **optimizing **(desensitizing): to make the process output less sensitive to variation in the factors

In both Screening and Robust Optimizing, you are typically looking for moderate to large effects and the errors in measurements (EIM) issue is too small to worry about. If they were large enough to impact the results and decisions made, you have other problems to worry about and the effects are too small to be of practical value.

For Modeling and Optimizing, the EIM issue

*might *cause a practical problem if your model needs to be very precise. In my personal experience, this issue was never a problem in the manufacturing environment. However, I have seen several product design applications where this could have caused some practical issues. These were situations where the model needed to be very precise and was used by the product's control software.