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The greatest achievements of Taguchi were:
- developing a methodology for experimental design that could be used by people who were not statisticians
- raising awareness of the importance of minimizing process and product variation
- identifying the importance of robust design; designing products and processes that are as tolerant as possible to 'noise' factors
The methodology of Taguchi is often criticized as being statistically unsound, or at least open to question. For example, it is not hard to demonstrate that the Signal to Noise approach does not necessarily give the best solution.
The dangers of ignoring interactions, or using arrays with confounded columns are troubling. Taguchi does stress the importance of carrying out conformation experiments at the selected settings, and so this can be regarded as a calculated risk.
A summary of some of the differences between the classical and Taguchi approaches:
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Classical
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Taguchi
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| Mainly Resolution IV or V and all potential interactions considered |
Mainly Resolution III (screening designs), and only consider selected 2 way interactions |
| Use ANOVA analysis and hypothesis testing to create a regression equation |
Use mainly graphical methods, and base the analysis on the largest S/N value |
| Randomization considered important |
Randomization not important, replaced by the 'outer array' |
| Confirmation runs not considered essential |
Confirmation runs stressed |
| Little attention paid to dispersion |
Dispersion is the key consideration |
| Emphasis on complying with assumptions, particularly the normality assumption |
Less emphasis on the finer points, the S/N ratio is less sensitive to the normality assumption |
The logic behind using the Signal to Noise ratio to minimize or maximize the response make sense, although probably don't affect the outcome in most cases. The idea of identifying 'control factors' when trying to achieve a specific average response is important.
The outer array concept is effective if a small number of noise factors that have a significant effect on the response can be identified, and if the 'common cause variation' is relatively small. This is often the case when a product is being designed, the conditions under which the product will be used can usually be identified and simulated.
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