Formulation and process · Development
“We have too many factors and too little material, and nobody on the team is sure which design fits.”
Design of experiments (DoE): from design to analysis
A well-chosen design saves experiments; a poorly chosen one produces data you cannot learn anything from. CovaSyn picks the right design, checks up front whether the number of runs is enough for your question, and analyses the results statistically. You receive design and analysis as a report that your expert reviews and signs off.
Starting point
When you need this
- A method or process has many possible factors, and the budget only covers a few runs.
- A screening is done, and now the optimum should be found with a response surface (RSM).
- Data from an existing DoE is available, but the model fits poorly or the residuals look odd.
- Before running anything, you want to know whether the planned runs can detect a relevant effect at all.
Result
What you get
Design with justification
A screening or RSM design that fits your factors and material, such as fractional factorial, definitive screening, D-optimal, central composite or Box-Behnken, with its alias structure.
Power analysis before the experiment
How large an effect has to be for the design to detect it at your variability, and whether extra runs are needed.
Statistical analysis
Significant factors and interactions, model selection, residual diagnostics and response surfaces with the optimum.
Next step
Whether a screening can be augmented to an RSM design, which runs are missing and where the design space lies.
Three ways
Yourself, together, or done for you
Run it yourself
Draw the molecule in the sketcher or use the tools through the CovaSyn MCP directly in Claude, ChatGPT or Cursor.
Go to MCPLearn it together
In a workshop your team learns how to answer questions like this with AI, cleanly and traceably.
See workshopsDone for you
We run the evaluation with our tools and deliver the finished report. Your expert reviews and signs off.
See Done-for-You
CovaSyn
The tools behind it
Deterministic calculations instead of guessed answers: the same tools we use for Done-for-You work are available to you directly.
Covadoe
Design suggestion and creation, augmentation of existing designs, design comparison, power analysis, alias structure, analysis, RSM, residual diagnostics, optimisation and design space.
Covaopt
Recommendation for the next design, models from existing process data and multi-objective optimisation when several responses matter at once.
Audience
Who it is for
For method development, process development and CMC teams at contract labs, CDMOs, generics manufacturers and specialty chemicals companies who need sound conclusions from few experiments.
What the computation does not replace
- Statistics do not replace process knowledge. You define which factors and ranges make sense; we check and calculate.
- A model only holds within the studied region. A predicted optimum should be checked with confirmation runs in the lab.
Insights
Read on
Screening Designs Compared: Fractional, PB and DSD
Screening design comparison: fractional factorial vs Plackett-Burman vs definitive screening, with run counts, resolution and power from real CovaDOE runs.
Read articleResponse Surface Methodology, End to End
A response surface methodology tutorial with real CovaDOE output: 18-run CCD, quadratic fit R2 0.9988, optimum with CIs, design space and residual.
Read articleDoE Power Analysis Before You Run the Experiment
DoE power analysis in plain terms: why a 13-run screening design catches a 1 sigma effect 82% of the time but a 0.5 sigma effect only 31%, and how factor.
Read articleAugment a Screening DoE into a Response Surface
How to augment a DoE design to an RSM: turn a 13-run screening design into a 21-run CCD with 8 new runs instead of starting over with 17.
Read articleDoE Residual Diagnostics: Shapiro-Wilk and Cook's D
DoE residual diagnostics explained: what Shapiro-Wilk, Cook's distance and leverage actually tell you about an RSM fit, with real CovaDOE tool output.
Read article
Questions
Common questions
Which experimental design fits my question?
For many factors and the question of which ones matter at all, screening designs such as fractional factorials or definitive screening fit. For the optimum of a few factors, RSM designs such as central composite or Box-Behnken fit. CovaSyn compares the options for your factors and material and justifies the choice.
How many runs do I need for a DoE?
It depends on how large an effect you want to detect and how much your measurement varies. A power analysis before the experiment shows whether the design is enough or whether replicates or extra runs are needed. CovaSyn calculates this for your specific design.
Who can analyse our DoE data?
CovaSyn analyses existing designs: effects and interactions, model selection, residual diagnostics and response surfaces. If the model fits poorly, we show whether outliers, missing curvature or a transformation is the cause.
What to do once the screening is finished?
Usually the screening can be augmented with axial and centre points into an RSM design instead of starting over. CovaSyn checks which runs are missing and then calculates the optimum and design space.
Who can create and analyse an experimental design for us?
CovaSyn does this as Done-for-You: you describe goal, factors and constraints in the intro call, we agree on data and scope, and you typically receive the design or analysis within 5 working days once we have your data. Handled confidentially from the start.
Can I plan a DoE myself with AI?
Yes. The DoE tools are available through the CovaSyn MCP directly in your AI assistant, for example in Claude, ChatGPT or Cursor. For teams introducing this in the lab, workshops are available.
Formulation and process
Related problems we solve
Solve a solubility problem before you run out of material
Our API won't dissolve properly, and we have been trying solvents for weeks.
See solutionCrystallization process design: cooling or antisolvent
Our crystallization yield is too low, and we don't know whether to cool or use an antisolvent.
See solutionProcess optimization and process capability (Cpk, Ppk)
Our Cpk is too tight, and we don't know which parameter drives the variation.
See solution
Next step
Tell us about your experimental design.
30 minutes on Microsoft Teams with the CovaSyn team. If it is not a fit, we will say so.