Design Of
Experiments

Run targeted preliminary experiments to separate the critical few factors from the trivial many, drastically reducing experimental scope.

Leverage Fractional Factorial, Plackett-Burman, or Definitive Screening Designs to evaluate dozens of potential variables in a fraction of the runs a full factorial would require. The deliverable is a ranked Pareto chart of factor significance, enabling your team to focus resources on the variables that truly drive performance.

Construct a rigorous mathematical model — typically a polynomial regression — that quantifies how significant factors and their interactions drive your response variable.

Apply Full Factorial or Response Surface designs to capture main effects, two-way interactions, and curvature. The resulting model is validated with ANOVA, residual diagnostics, and R-squared metrics to ensure it reliably predicts system behaviour across the design space.

Fine-tune factor settings using response surface methodology to pinpoint the operating conditions that maximise yield, quality, or efficiency.

Deploy Central Composite or Box-Behnken Designs to map the response surface and locate global optima, even in the presence of complex factor interactions. Multi-objective optimisation techniques such as desirability functions are applied when balancing competing performance targets like cost versus quality.

Execute confirmation runs under the predicted optimal conditions to validate that the model accurately reflects real-world process behaviour.

Compare observed responses against model predictions using confidence and prediction intervals to assess agreement. If deviations exceed acceptable thresholds, the model and optimisation parameters are iteratively refined until robust, repeatable performance is confirmed.

Carry out each experimental run according to the randomised design matrix, maintaining strict control over environmental conditions and procedural consistency.

All relevant process parameters, environmental readings, and response measurements are recorded in structured data logs with full traceability. Randomisation and blocking strategies are enforced to guard against systematic bias, ensuring the collected dataset is statistically sound.

Apply statistical analysis — including ANOVA, effect plots, and contour maps — to extract meaningful patterns, interactions, and correlations from the experimental data.

Beyond classical statistics, advanced predictive models are built using neural networks (SANN), machine learning algorithms, and AI-driven pattern recognition. These complementary techniques uncover non-linear relationships that traditional regression may miss, delivering deeper process understanding.