DoE Software: Plan, Analyze, and Optimize Experiments Online

Design of Experiments (DoE) software helps you learn more from fewer, well-planned experimental runs. Instead of changing one factor at a time, you can vary several factors systematically, estimate their effects, identify interactions, and optimize a response such as yield, strength, cycle time, or product quality.

numiqo provides a browser-based DoE workflow for creating test plans, entering measured responses, analyzing results, and exploring better factor settings. It covers the designs used in many product development, process improvement, and Six Sigma projects without requiring a local software installation.

What Should DoE Software Help You Do?

Useful DoE software should support the complete experimental workflow. Creating a design matrix is important, but it is only the first step. A practical tool should also help you select an appropriate design, randomize the run order, export the test plan, analyze the measured responses, and interpret the model.

With numiqo, you can use one workflow to:

• define numeric or categorical factors and their levels,
• choose a suitable experimental design,
• add replicates, center points, and blocks where applicable,
• randomize the experimental run order,
• export the test plan to Excel,
• enter the response values after completing the runs,
• analyze main effects and interactions, and
• use the fitted model for process optimization.

The interface groups these tasks into Create DoE, Analyse, and Optimization (Beta). This makes it easier to move from planning an experiment to evaluating its results without switching between separate tools.

Experimental Designs Available in numiqo

The right design depends on your goal. Early in a project, you may want to screen many possible factors with a limited number of runs. Later, you may want to model curvature and find settings that optimize the response. numiqo supports the main design families for these stages.

Factorial Designs

A full factorial design tests all selected combinations of factor levels. This provides a clear basis for studying main effects and interactions. If a full factorial experiment would require too many runs, a fractional factorial design uses a carefully selected subset of combinations. numiqo shows the number of runs and the resolution of the fractional design so you can judge the tradeoff between effort and confounding.

Screening Designs

When you have many potential factors and need to identify the most influential ones, screening designs can reduce the required experimental effort. Plackett-Burman designs are available for efficient screening before you investigate a smaller set of factors in more detail.

Response Surface Designs

Once the important factors are known, response surface methods can help you model curvature and optimize the response. numiqo supports Box-Behnken designs and central composite designs. These designs are useful when the best settings may lie between the low and high factor levels used in an initial experiment.

Mixture and D-Optimal Designs

In a mixture experiment, the factors are proportions that add up to a fixed total, such as the ingredients in a formulation. numiqo supports simplex-centroid, simplex-lattice, and extreme-vertices mixture designs.

For experiments with constraints or a custom set of possible combinations, you can create a D-optimal design from a candidate set. This is useful when a standard factorial design would include combinations that are impractical, unsafe, or impossible to run.

Analyze Experimental Results Online

After carrying out the test plan, enter the measured response values into numiqo and analyze the experiment online. The analysis helps you evaluate which factors matter, whether interactions are relevant, and how well the fitted model describes the response.

Effect estimates, regression results, ANOVA, and visualizations provide complementary views of the experiment. For example, a main effect can show that increasing a temperature improves yield on average, while an interaction can show that this effect changes depending on the pressure setting. Looking at both is one of the major advantages of a structured DoE over changing one factor at a time.

Browser-Based DoE Software and Data Privacy

numiqo runs directly in your browser, so you do not need to install a desktop application before creating a design. The statistical calculations run locally on your computer. Your experiment data stays private and is not uploaded for the calculation.

This is useful when several people need access to a DoE tool, when you work on different computers, or when experimental data should remain on the device being used for the analysis.

When Is numiqo a Good Fit?

numiqo is a practical choice when you want an accessible DoE software tool for screening, factorial experiments, response surface methods, mixture experiments, or constrained D-optimal designs. It is especially useful when you want to create a randomized test plan quickly, export it to Excel, and analyze the measured responses in the same browser-based workflow.

No single DoE tool is the right choice for every experiment. Some projects require specialized methods such as split-plot designs, definitive screening designs, or Taguchi designs. If these are central to your project, compare the available methods before selecting software. For a more detailed discussion of these differences, see numiqo vs. Minitab for Design of Experiments.

How to Choose a DoE Tool

Before selecting software, write down your experimental goal, factors, levels, constraints, expected interactions, and maximum number of runs. Then choose the simplest design that can answer the question reliably. A screening design may be the right starting point when many factors could matter. A response surface design is often more suitable when you already know the important factors and want to optimize them.

If you are new to the topic, start with the Design of Experiments tutorial. To create a test plan directly, open the numiqo Design of Experiments calculator.