How the Taguchi Method Simplifies Experimental Design and Analysis
Published: October 7, 2024 by Ken Feldman
- 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.
Example 1 of using Taguchi
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.
Example 2 of using Taguchi
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 .
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.
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Introduction to the Taguchi Method
- First Online: 25 July 2018
Cite this chapter
- Alessandro Freddi 3 &
- Mario Salmon 4
Part of the book series: Springer Tracts in Mechanical Engineering ((STME))
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We now introduce a further powerful engineering tool: the Robustness concept. It is inextricably linked to the name of Genichi Taguchi, a Japanese engineer. Although his approach is based on what was outlined in the previous chapter on Design of Experiment, it contains strong innovation of those concepts. Significant improvements are proposed for three design phases: system design, parameters design and tolerance design. It is not possible to draw a comprehensive picture of the method here. Only the main concepts are introduced, leaving any more complex treatment of the method to specialized books. This method is a real improvement of the application of statistical methods to design applications. This chapter describes the basic concepts, among which the Quality Loss Function that better than other interpretations clarifies the term, often ambiguous, of quality. In this way, the link between robustness, reliability and design of quality becomes obvious. We tried to give emphasis to ideas more than to technicality, but a limited mathematical formalism is necessary.
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Obviously, tighter tolerances require additional costs, which in Japanese philosophy are precisely relegated to the role of extreme necessity and should be avoided whenever this is possible. Tolerance is to be understood in the general sense: even the introduction of more valuable materials, more expensive machines and equipment must be interpreted as a tolerance reduction [ 15 ].
\(\sum _{i=1}^n {x_{ik} \cdot x_{il}=0}\) .
The last one when all interactions are assumed to be zero.
Factors can be arbitrarily assigned to columns, (remember that, without confusion, factors are indicated with the same letters as the effects).
In the Table, again, letters are used to represent factors and effects.
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Freddi, A., Salmon, M. (2019). Introduction to the Taguchi Method. In: Design Principles and Methodologies. Springer Tracts in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-95342-7_7
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The Taguchi method involves reducing the variation in a process through robust design of experiments. The overall objective of the method is to produce high quality product at low cost to the manufacturer.
Taguchi methods (Japanese: タグチメソッド) are statistical methods, sometimes called robust design methods, developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to engineering, [1] biotechnology, [2][3] marketing and …
Taguchi refers to experimental design as "off-line quality control" because it is a method of ensuring good performance in the design stage of products or processes. Some experimental designs, however, such as when used in evolutionary operation, can be used on-line while the process is running.
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.
The Taguchi approach to experimental design can be implemented in the lab or the field, with a systematic three-step process. Before beginning this process, researchers will identify their dependent variable(s) of interest along with a potentially large set of independent variables and their manipulation levels by reviewing relevant theory and ...
There are options for creating Taguchi arrays for the design of experiments, depending on how many times you choose to test each level of each parameter. For example, consider an experiment with 3 parameters and 3 levels of each parameter (P = 3 and L = 3), as discussed in a previous learning module. We showed two Taguchi arrays for this case:
Genichi Taguchi applied the statistical methods and the design of experiments concepts, (that he used in communication theory and practiced in department of a Japanese company and in laboratory of electrical communications and telegraphs), to the product and process design.
The Taguchi method is an experimental design approach used for process optimization studies and the design of high-quality systems. Such a design can minimize the total number of experiments while providing accuracy and consistency to the process.
• Compared to the general experimental design methods, the Taguchi approach has the following features and merits [11, 14, 16]: – It uses factorial as well as fractional experimental design plans. – It does not require the response surface representation nor a …
Tutorial on Taguchi Design of Experiments. Includes design examples, as well as software to implement the analysis in Excel.