Design of Experiments (DOE) lets you change several factors at once and untangle their individual and joint effects with far fewer runs than one-factor-at-a-time testing. This guide covers the design choices that matter most in practice.
Choosing a design
- Full factorial — every combination of every factor at every level. Best for small factor counts (≤ 4).
- Fractional factorial — a carefully chosen subset that preserves main-effect estimates and key interactions. Look at the resolution (III, IV, V) to know what gets aliased.
- Split-plot — when one factor is hard to change between runs (oven temperature, batch material). Two error strata; ANOVA must respect them.
- Completely Randomised Design (CRD) — when all factors can be randomised freely.
Analysis essentials
- Run order randomisation — protects against time-correlated noise.
- ANOVA with % contribution — quantifies how much of the response variance each effect explains, not just whether it is statistically significant.
- Residual diagnostics — residuals vs fitted, residuals vs run order, normal Q-Q. Patterns reveal missed effects, non-constant variance, or order effects.
- Main effects and interaction plots — translate the ANOVA into pictures the team can act on.
- Normal Probability Plot of effects — separates active effects from noise without an ANOVA F-test, especially useful for unreplicated designs.
Bayesian optimisation
Once a screening DOE identifies the active factors, Bayesian optimisation iteratively suggests the next experimental point that best balances exploration of unknown regions with exploitation of promising ones. It is well suited to expensive experiments where each run costs hours or days.
How Ops Excellence handles this
The DOE tool generates the design and run order, accepts response data, runs ANOVA with % contribution, plots residual diagnostics, main effects, interactions, and a Normal Probability Plot, and includes a Bayesian optimisation engine for follow-up runs. Build your first design.