ABSTRACT Personal empirical experience when lecturing and consulting shows that not only students, but also experienced engineers familiar with DOE, show much more interest in the modeling of a process than to statistical inference, neglecting attention to “boundary conditions” of the process. But exactly the observation of ancillary boundary conditions of experiments, such as minimizing Beta-risk and noise, is determinant for the efficient execution of an experimental design and the effective application of DOE derived models. This essay focuses attention to the must-dos in the DOE statistics approach in order to avoid research pitfalls by presenting a fail-proof 14-step approach when applying DOE modeling.
Cite this paper
Rüttimann, B. and Wegener, K. (2015) The Power of DOE: How to Increase Experimental Design Success and Avoid Pitfalls. Journal of Service Science and Management, 8, 250-258. doi: 10.4236/jssm.2015.82028.
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