Statistical Design of Experiments (“DoE”)
Systematic Design of Experiments (DoE) instead of Trial and Error
In order to identify optimal settings, so that the response variable (e.g. yield) in fact reaches the maximum, it is indispensable to run experiments under controlled conditions. If these experiments are performed in a systematic way, varying potential influence factors simultaneously, much information can be gained from a surprisingly low number of experiments. This is what statistical Design of Experiments (DoE) is about.
[More: The main principles of statistical Design of Experiments (DoE)]
What are the advantages of statistical Design of Experiment(DoE)?
The most important point is that the systematic approach allows estimating exactly what information can be obtained at which step. The various aspects are presented subsequently.
And how can statistical Design of Experiments (DoE) be applied in practice?
The main difficulty is clarifying which questions are to be investigated in the DoE project. Then the remaining steps are more or less straightforward.
Use cases from many different areas of application can be found in our publications data base. We however present some success stories subsequently in order to give an impression how to apply statistical Design of Experiments (DoE) and STAVEX in practice.
But I know experienced colleagues who do not really appreciate statistical Design of Experiments...
In discussions often the same reservations with respect to statistical Design of Experiments (DoE) are mentioned: too many experiments needed, too complicated, personal expertise replaced by black box procedure. [More... ]
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