Create Taguchi Design

A Taguchi design is a designed experiment that lets you choose a product or process that functions more consistently in the operating environment. Taguchi designs recognize that not all factors that cause variability can be controlled. These uncontrollable factors are called noise factors. Taguchi designs try to identify controllable factors (control factors) that minimize the effect of the noise factors. During experimentation, you manipulate noise factors to force variability to occur and then determine optimal control factor settings that make the process or product robust, or resistant to variation from the noise factors. A process designed with this goal will produce more consistent output. A product designed with this goal will deliver more consistent performance regardless of the environment in which it is used.

A well-known example of Taguchi designs is from the Ina Tile Company of Japan in the 1950s. The company was manufacturing too many tiles outside specified dimensions. A quality team discovered that the temperature in the kiln used to bake the tiles varied, causing nonuniform tile dimension. They could not eliminate the temperature variation because building a new kiln was too costly. Thus, temperature was a noise factor. Using Taguchi designed experiments, the team found that by increasing the clay's lime content, a control factor, the tiles became more resistant, or robust, to the temperature variation in the kiln, letting them manufacture more uniform tiles.

Perform the analysis

Complete the following steps to specify the design.

Enter the name of your response variable: The worksheet includes a column with this name where you enter the data from the experiment.
Number of levels: Select the number of factors with 2 levels and the number of factors with 3 levels. Levels are the number of settings to study in the experiment. For example, a marketing specialist conducts an experiment that includes a factor for the type of content on a website for the display of an advertisement. The specialist decides to include a basketball website, an international news website, and a celebrity news website in the study. These types of content form a 3-level factor.
Table of factors: Under Name, enter a descriptive name for each factor.; In the table, 2-level factors are first and 3-level factors are second. Under Level Values, enter the level values for each factor. For example, an engineer wants to test two different temperatures and enters 150 300. If you have a text factor and the levels have no natural order, you can specify the levels in any order.
Number of runs: Adding runs can help increase the precision of your model and increase the power to detect effects. To determine how many runs to include in your design, consider the available resources and the purpose of your design. For example, in an early design phase you could begin with the minimum number of runs and then consider whether to add runs after you analyze the data.

Example

An engineer for a golf equipment manufacturer wants to design a new golf ball that has better flight distance. The engineer has identified four control factors (core material, core diameter, number of dimples, and cover thickness) and one noise factor (type of golf club). Each control factor has 2 levels. Because there is no signal factor, the engineer creates a static Taguchi design.

Choose Stat > DOE > Quick Designs.
Select Select a Five-Factor Design.
Select Create an experiment with five categorical factors. Select OK.
Select Find the optimal factor settings to achieve robustness to uncontrollable noise. Select OK.
In the new dialog, in Number of levels, select Five 2-level factors.

Complete the table with the following settings:

Name	Levels
Core material	Polybutadiene	Polyurethane
Core diameter	40.6	41.9
Number of dimples	300	500
Cover thickness	1.6	2.1
Type of golf club	Iron	Driver

In Number of runs, select 8. Select OK.

The design summary table shows that the design has 8 runs. The worksheet contains the 8 runs.