Taguchi DOE Calculator & Orthogonal Array Generator

Plan and analyze robust experiments using Orthogonal Arrays (L9, L16, L27) to optimize product and process quality. Signal-to-Noise Ratio Calculator

What is the Taguchi Method?

The Taguchi Method, developed by Japanese engineer and statistician Genichi Taguchi, is a systematic approach to improving product and process quality through robust design. Unlike traditional experimental design methods that focus primarily on optimizing mean performance, the Taguchi Method emphasizes minimizing the effects of uncontrollable factors (noise) to create products and processes that perform consistently under varying conditions.

The core principles of the Taguchi Method include:

Robust Design: Designing products and processes that are insensitive to variations.
Quality Loss Function: Quantifying the economic loss when a product deviates from its target.
Orthogonal Arrays: Using specially designed fractional factorial experiments to test multiple factors with minimal runs.
Signal-to-Noise (S/N) Ratios: Metrics that consider both mean performance and variability.

Toolbar — Button Reference

The toolbar at the top of the page contains all the actions you need to manage your experiment:

New Clears all data and starts a blank experiment.
Load Opens a previously saved .json file to restore your experiment data and results.
Load Example Loads a pre-filled process optimization dataset so you can explore the tool and see how the Analysis & Report works.
Analyze Runs the full Taguchi calculations (S/N Ratios, Main Effects, ANOVA) and generates the detailed report.
Save Downloads the complete experiment as a .json file to reload or share later.
Export PDF Generates and downloads a fully formatted PDF report containing all tables, charts, and analysis results.

How to Use This Tool — Step by Step

Step 1 — Setup Experiment: Go to the Setup Experiment tab. Give your experiment a name and objective.
Step 2 — Define Factors and Levels: Add factors (variables) and their levels (settings). Choose the appropriate Signal-to-Noise Ratio Type.
Step 3 — Generate Design: Click "Generate Taguchi Design". This will automatically select an Orthogonal Array and build the table in the Design & Data tab.
Step 4 — Enter Data: Execute the experiment runs and type your measured results in the rightmost column of the table.
Step 5 — Analyze Results: Click "Analyze" in the top toolbar to calculate everything and jump to the Analysis & Report tab.
Step 6 — Review Report: Interpret the executive summary, Pareto chart, Main Effects plots, and ANOVA table to discover your optimal settings.
Step 7 — Export your work: Click "Export PDF" to save a complete formal report of your findings.

Experiment Setup – Define Factors & Levels

Experiment Name:

Experiment Objective:

Factors and Levels

Response Variable:

Select Taguchi Array

Signal-to-Noise Ratio Type

Smaller is better Larger is better Nominal is best

Orthogonal Array Design & Response Data

Enter your measured results in the rightmost column.

S/N Ratio Analysis & ANOVA Report

No analysis generated yet. Please enter data in the "Design & Data" tab, then click "Analyze" above to calculate.

Taguchi S/N Ratio Formulas & Equations

Signal-to-Noise Ratio (Smaller is Better)

$$ S/N = -10 \log_{10} \left( \frac{1}{n} \sum_{i=1}^{n} y_i^2 \right) $$

Where $ y_i $ are the measured responses and $ n $ is the number of observations.

Signal-to-Noise Ratio (Larger is Better)

$$ S/N = -10 \log_{10} \left( \frac{1}{n} \sum_{i=1}^{n} \frac{1}{y_i^2} \right) $$

Where $ y_i $ are the measured responses and $ n $ is the number of observations.

Signal-to-Noise Ratio (Nominal is Best)

$$ S/N = -10 \log_{10} \left( \frac{1}{n} \sum_{i=1}^{n} (y_i - T)^2 \right) $$

Where $ T $ is the target value, $ y_i $ are the measured responses, and $ n $ is the number of observations.

Mean Response

$$ \bar{y} = \frac{1}{n} \sum_{i=1}^{n} y_i $$

Where $ y_i $ are the measured responses and $ n $ is the number of observations.

Main Effect

$$ ME = \frac{\sum \text{Responses at Level } i}{\text{Number of observations at Level } i} $$

Where $ i $ represents a specific factor level.

ANOVA Sum of Squares

$$ SS = \sum_{i=1}^{k} n_i (\bar{y}_i - \bar{y})^2 $$

Where $ n_i $ is the number of observations at level $ i $, $ \bar{y}_i $ is the mean at level $ i $, and $ \bar{y} $ is the overall mean.