Free DOE Generator - Design of Experiments Online

Full factorial, fractional, Plackett-Burman, CCD, Box-Behnken, DSD
How to use: set your factors and design type, generate the experiment matrix, then review and export the run table. Follow the numbered steps above.
Open DOE Pro
This generator stays free, forever Pro adds more

The locks below mark what DOE Pro adds at each step: 11 bioprocess presets, D-optimal designs, power & sample size, hard-to-change factors, byproduct ceilings, influence diagnostics, a prediction profiler, publication figures and a GMP / QbD report. Bayesian optimisation and design augmentation are coming to Pro+.

See what DOE Pro does
1Define Objective (what to measure & optimise)
Response Unit Goal Target Cap
2Bioprocess Preset
2Factors (2-8 supported)
Name Low (-1) High (+1) Unit HTC
3Design Type
Center Points
Randomize
4Run trials
3Design Summary
3Visual Design Matrix
High (+1) Low (−1) Center (0) Axial (+α) Axial (−α)
3Factor Levels Across Runs
3Design Matrix Heatmap
4Run Table
Quick fill:
5Analyze Interactions
Response:
How to read this panel: Each bar in the Pareto chart shows how strongly that factor (or interaction) affects your response. Bars to the right of the dashed red line are statistically significant (p<0.05) — those are the levers worth tuning. Bar colours indicate term type: teal = main effect, blue = 2-factor interaction, purple = quadratic curvature. The Effects Table below gives precise numbers and the R² tells you how well the model fits (closer to 100% = better).
6Identify Optimum
How to read this panel:
  • Best Measured Run — the actual run from your data with the best score. Use these settings to reproduce a result you've already seen.
  • Predicted Optimum — where the fitted model says the true peak is. This may not be one of your runs — the model interpolates between data points. To validate, run 2–3 confirmation experiments at the predicted settings.
  • Multi-Response Best Compromise — when you have multiple responses with different goals (e.g. maximise titre AND minimise HCP), this finds the single set of factor values that scores well on all of them simultaneously (Derringer-Suich desirability).

Design of Experiments in Bioprocess Development

Design of Experiments is a cornerstone of Quality by Design (QbD) approaches mandated by ICH Q8 for pharmaceutical development. In bioprocess engineering, DOE is used at every stage: screening critical process parameters (CPPs) during early development, optimising conditions during process characterisation, and defining the design space for regulatory filings. A well-designed experiment can save weeks of bench time compared to one-factor-at-a-time approaches while providing far richer information about factor interactions and process robustness.

Common bioprocess DOE applications include CHO fed-batch optimisation (pH, temperature, dissolved oxygen, feed rate, osmolality), E. coli expression screening (inducer concentration, induction temperature, induction OD, post-induction time), downstream purification optimisation (loading density, flow rate, buffer pH, salt concentration), and media formulation studies. This tool now handles the full pipeline from design to predicted optimum in your browser — you can still export the run table to external statistical software if your workflow prefers it.

How to Use the DOE Experiment Generator

This free Design of Experiments (DOE) generator is a complete platform — design, analyse, and identify the optimum in one tool, without paying for JMP, Minitab, or Design-Expert. The workflow follows the standard 6-step DOE process visible in the stepper at the top of the page:

  1. Define objective — name the responses you'll measure (titre, viability, productivity, HCP, etc.) and choose a goal for each: maximise, minimise, or hit a target value. Up to four responses can be optimised simultaneously.
  2. Choose factors & ranges — pick a bioprocess preset (CHO Fed-Batch, E. coli Expression, Pichia, Chromatography, Cell Culture Media, UF/DF, AAV Production) or define your own 2–8 factors with low/high levels and units.
  3. Design experiment — pick one of six design types. Each tab shows the live run count for your current factor set so you can balance information against lab effort: Full Factorial (2k), Fractional Factorial (2k-p), Plackett-Burman, Central Composite (CCD), Box-Behnken, or Definitive Screening (DSD).
  4. Run trials — export the randomised run sheet to CSV or print it. Record measured responses directly in the Run Table (cells are editable), paste a column from Excel, or click Demo data to see how the analysis works before you have real data.
  5. Analyze interactions — fit an OLS regression model, see a plain-language summary, the Effects Pareto with significance threshold, an Effects Table with p-values and star ratings, residual diagnostics (residual-vs-fitted + Q-Q plot), and full ANOVA with lack-of-fit decomposition when replicates exist.
  6. Identify optimum — see which actual run scored best, the analytically-solved per-response optima, the multi-response best compromise via Derringer-Suich desirability, and a contour plot of any two factors with the optimum marked. Add 3 confirmation runs at the predicted settings with a single click to validate the prediction in the lab.

Built-in design types: Full Factorial for complete characterisation of 2-4 factors, Fractional Factorial for efficient screening of 4-8 factors, Plackett-Burman for main-effect screening, Central Composite Design (CCD) for response surface modelling, Box-Behnken for 3-level designs that avoid extreme corners, and Definitive Screening Design (DSD) for combined screening + RSM in minimal runs.

All analysis runs client-side in your browser — no data leaves your machine. CSV export is still available at any stage so you can also feed the run table to R, Python (statsmodels), JMP, Minitab, or Design-Expert if you prefer.

DOE Articles

Every design of experiments guide on the site, grouped by what you need next.

Start here: DOE fundamentals

Where Do I Even Start?

A plain-language, no-maths DOE starter for biologists. No coding and no statistics background needed.

DOE vs OFAT

Why one-factor-at-a-time wastes runs and misses interactions, shown side by side.

DOE for Bioprocess Optimization

The step-by-step pillar guide, with worked upstream and downstream examples.

Which DOE Design Should I Use?

A decision guide covering factorial, fractional, Plackett-Burman, CCD, Box-Behnken and DSD.

How Many Experiments Does a DOE Need?

Run counts for every common design, plus how power, effect size and replication decide the total.

How to Read Your DOE Results

The effects Pareto, ANOVA, R², lack-of-fit and residuals, without p-hacking.

Choosing a design type

Full Factorial Design

What a 2^k design is, when to use it, and how many runs it needs for complete characterisation.

Fractional Factorial & Aliasing

How to cut a 32-run study to 16 or 8, and what resolution and aliasing actually mean.

Plackett-Burman Designs

Screen 5 to 11 factors down to the critical few in as few as 12 runs.

Definitive Screening Designs (DSD)

Main, quadratic and key interaction effects in roughly 2k+1 runs.

Response Surface Methodology

Map the surface and find the optimum with central composite and Box-Behnken designs.

Mixture Design DOE

For when your factors are ingredients that must sum to 100 percent, such as media and buffers.

Applications, regulatory & software

DOE for Cell Culture & Fermentation

Does DOE work in biology? Handling variability and hard-to-change setpoints.

Media Optimization with DOE

From component screening through to mixture and response-surface designs.

ICH Q8 DOE & Design Space

How DOE delivers the QbD design space and links critical process parameters to quality attributes.

ICH Q2 DOE for Analytical Methods

Robustness testing and factor screening for analytical method and assay optimization.

CPP/CQA Mapping & Design Space

The QbD groundwork that decides which factors deserve a place in your DOE.

Free Alternatives to JMP & Minitab

A vendor-neutral comparison of DOE software, open source options and browser tools.

Frequently Asked Questions

Resources