Top 7 Common Mistakes Engineers Make with DOE (and How to Avoid Them)

) Skipping randomization (and letting time drift fool you)

What goes wrong: Machine warm-up, tool wear, or material lot changes masquerade as “factor effects.”
Fix it: Randomize run order; if some factors are hard to change, use a split-plot design and still randomize within whole plots.

2) Choosing the wrong design resolution

What goes wrong: In fractional factorials, aliasing hides interactions or confuses main effects with them.
Fix it: For screening, pick Resolution IV (protects main effects). If interactions matter, use Resolution V or a full factorial. Add foldover to break aliases if needed.

3) Forgetting center points / curvature checks

What goes wrong: You fit a straight line to a curved response and miss the optimal region.
Fix it: Add center points to two-level designs; if curvature appears, augment to RSM (e.g., central composite or Box-Behnken).

4) Treating hard-to-change factors like easy ones

What goes wrong: Extra setup changes inflate cost and inject bias; the ANOVA is wrong if you ignore the structure.
Fix it: Use split-plot DOE: whole-plot factors (setups) vs sub-plot factors (easy toggles). Analyze with mixed-effects models.

5) Weak measurement systems (MSA neglected)

What goes wrong: Gauge noise buries real effects; you “optimize” random error.
Fix it: Run MSA (GR&R) first. If %GRR is high, fix the measurement before running DOE or increase replication.

6) Overfitting and under-validating the model

What goes wrong: Too many terms “explain” noise; effects don’t replicate on confirmation runs.
Fix it: Use hierarchical models, Pareto of effects, and lack-of-fit tests. Keep a confirmation set (or do post-DOE confirmation runs).

7) Poor factor/spec definition

What goes wrong: Levels outside safe operating limits, factors you can’t truly control, or responses that aren’t tied to the CTQ.
Fix it: Bound factor ranges with process experts, define CTQs up front, and ensure each factor is measurable & adjustable at the cadence of your runs.

Quick pre-flight checklist

  • Randomized run order?

  • Correct design type & resolution for your goals?

  • Center points included (if two-level)?

  • Split-plot planned for hard-to-change factors?

  • MSA acceptable? (%GRR in bounds)

  • Model hierarchy respected + confirmation runs planned?

  • Factors/levels/CTQs validated with SMEs?

Want a guided path that bakes these guardrails in? Try the Excedify DOE Training — screening → modeling → optimization, with assignments and a free preview to see if the format fits:
https://www.excedify.com/