Experiment Planning Tool
Build a practical DoE plan with orthogonal arrays.
Choose an orthogonal array or full factorial design, set factor names and levels, randomize the run order, and download a planning sheet your team can use right away.
2 to 5 factors
L4 / L8 / L16
2-level screening
Randomized run order
CSV export
Planning Output
DoE plan overview
Use the generated run order as your experiment sheet, then capture responses back into the CSV.
Next Best Actions
Take the plan into production
Uses an orthogonal screening matrix to cut run count while keeping balanced main-effect coverage. Download the run sheet, record the response for each run, and use the Cp / Cpk calculator after the trial if you want to check capability on the improved condition.
Factor settings
- Factor 1: Low to High
- Factor 2: Low to High
- Factor 3: Low to High
How to use this plan
Run each trial in the listed order, measure the response, and keep process conditions stable outside the chosen factors.
The CSV includes both actual levels and coded levels so you can continue the analysis in Excel, Python, or other statistical tools.
Run table
| Run | Standard | Factor 1 | Factor 2 | Factor 3 | Factor 1 coded | Factor 2 coded | Factor 3 coded | Response |
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | Low | High | Low | -1 | 1 | -1 | |
| 2 | 4 | High | High | High | 1 | 1 | 1 | |
| 3 | 1 | Low | Low | High | -1 | -1 | 1 | |
| 4 | 3 | High | Low | Low | 1 | -1 | -1 |
What this DoE planner is for
This tool is built for practical experiment setup when you want a clean orthogonal array or factorial planning sheet without building the matrix manually. It is especially useful for process improvement, parameter screening, and quick shop-floor studies.
What this MVP covers
The planner now supports orthogonal array presets such as L4, L8, and L16 alongside full factorial generation for 2 to 5 factors. It does not yet calculate effects or ANOVA, but it removes the setup friction so you can get to execution faster.
How it fits with Cp / Cpk
Use Cp / Cpk to diagnose process capability, then use this DoE planner to test improvement ideas in a structured way. That makes the toolset more useful for real manufacturing work instead of stopping at diagnosis.