Systematic experimentation — from screening to optimization, statistically grounded.
Traditional one-factor-at-a-time experimentation is slow, expensive, and blind to interactions. If a formulation depends on temperature, pH, and concentration, testing each factor independently requires many more experiments than a designed factorial approach — and completely misses interaction effects where the optimal temperature depends on the pH. In competitive markets where time-to-market is critical, the speed difference between systematic DOE and ad-hoc experimentation can be the difference between leading and following.
DOE isn't just about efficiency — it's about rigor. A properly designed experiment provides statistical confidence in its conclusions. 'Factor X has a significant effect on Response Y' is a defensible statement backed by analysis, not a gut feeling from observing a trend in three data points. For regulated industries, this statistical rigor is often required in submissions.
But DOE is underused because it's perceived as complex. Most scientists understand the concept but find the setup, execution tracking, and analysis laborious — especially when using separate statistical software, manually transferring data, and assembling results into a report. The barrier isn't intellectual; it's operational.
Athena's DOE module guides scientists through the complete experimental design workflow: define factors and levels, select a design type (full factorial, fractional factorial, response surface, screening), generate the experimental matrix, record results against each run, and analyze with built-in statistical tools — all within the project context, all linked to the same data lineage.
The analysis layer provides ANOVA, main effects plots, interaction plots, response surface visualization, and optimization — rendered as interactive charts directly in the platform. No exporting to Minitab, no separate statistical software license, no manual data transfer that introduces transcription errors.
Each DOE study is linked to its parent project, making the experimental evidence part of the project's permanent record. When a stage-gate review asks 'what data supports this formulation decision?', the designed experiment, its statistical analysis, and its conclusions are all accessible from the project workspace — not buried in someone's laptop.
DOE studies belong to Projects. Experimental runs can reference Samples. Statistical conclusions inform stage-gate decisions. Results feed Executive Dashboards showing R&D efficiency metrics.
Every module is configured to your process — your terminology, your workflows, your approval chains. Login to explore how Athena works for your organization.
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