Key Points
- The Taguchi Method is a way of designing experiments to see how they affect the variance of a process.
- Main effects and two-way interactions are the only ones considered in the Taguchi method.
- The Taguchi Method ignores noise when considering the signal-to-noise ratio.
How does the Taguchi Method benefit your organization?
Design of Experiments (DOE) full or fractional factorial designs find the optimal combination of factors and levels for your process. But, it can take time and resources. The Taguchi Method is a more efficient way to accomplish the same desired outcomes.
Overview: What Is the Taguchi Method?
The Taguchi Method is a statistical method, sometimes referred to as a robust design method, developed by the Japanese engineer and statistician Dr. Genichi Taguchi.
Taguchi developed his method for designing experiments to investigate how different process factors will affect the mean and variance of a process performance characteristic.
The experimental design proposed by Taguchi involves using orthogonal arrays. These organize the factors affecting the process and their ideal levels. Instead of having to test all possible combinations like the factorial design, the Taguchi method tests pairs of combinations.
This method allows for a reduced number of experimental runs to identify which factors are significant. This reduces the time and resources needed to identify the most optimal factors and levels.
The method is best when there is a reasonable number of factors (3 to 50), a few lower-order interactions between factors, and when there are only a few statistically significant factors.
Here is a comparison of the Taguchi method and traditional DOE:
- Only the main effects and two-way interactions are considered in the Taguchi method. Higher-order interactions are not considered.
- With Taguchi, possible significant interactions must be identified before experimenting using one’s best judgment.
- The Taguchi orthogonal arrays are based on judgment sampling. They are not randomly generated as with runs for a traditional DOE.
- With a traditional DOE, noise is treated as a nuisance variable and should be blocked out. Taguchi treats noise as a major focus of analysis.
Why It Matters
When you’re designing any experiment, you’re going to want to account for a plethora of variables. The Taguchi Method is a deliberate approach to handling experimental design, focusing on stated values to see how they will impact your production. As such, it is a useful thing to keep in your back pocket when doing any sort of experiment.
An Industry Example
Here are two examples showing the application of Taguchi to two situations.
An agricultural engineer studies the effect of five factors on the growth of basil plants. The engineer designs a 2-level Taguchi experiment to determine which factor settings increase the plant’s rate of growth without increasing the variability in growth.
The engineer also manipulates two noise factors. These help to determine which settings for the five factors increase plant growth across the true range of temperature and humidity conditions.
An engineer for a golf equipment manufacturer wants to design a new golf ball to maximize ball 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. The noise factor is two types of golf clubs: a driver and a 5-iron. The engineer measures flight distance for each club type and records the data in two noise factor columns in the worksheet.
Other Useful Tools and Concepts
While we’ve focused on how the Taguchi Method lets you hone in on a process, we haven’t covered some other aspects of robust design methods. Understanding how the value index works in your process design can help you line up areas that need focus now.
Additionally, you might want to consider reading some case studies on the Taguchi Method. As a tried and tested method of design, there is certainly no shortage of success stories. You can read all about it in our comprehensive guide.
Conclusion
The Taguchi method is a cornerstone of robust design processes. Learning how to best utilize the method during your design of experiments takes some of the legwork out of optimizing your processes. As with any tool, there is a time and resource cost to consider, but it is very much worth it.
