Design of Experiments :: Overview Click here to go to the homepage
What is it?
DOE is a systematic approach to investigation of a system or process. A series of structured tests are designed in which planned changes are made to the input variables of a process or system. The effects of these changes on a pre-defined output are then assessed. DOE was developed by Ronald Fisher in England in 1920 and used in agriculture. Melder & Mead (simplex method in the field of response surface) in the early '60s, Box-Hunter (based on ANOVA methods) in the late '70s and Genichi Taguchi (orthogonal designs) in the early '80s made further significant contributions in the arena.
Why is it important?
DOE is important as a formal way of maximizing information gained while resources required. It has more to offer than 'one change at a time' experimental methods, because it allows a judgement on the significance to the output of input variables acting alone, as well input variables acting in combination with one another.

'One change at a time' testing always carries the risk that the experimenter may find one input variable to have a significant effect on the response (output) while failing to discover that changing another variable may alter the effect of the first (i.e. some kind of dependency or interaction). This is because the temptation is to stop the test when this first significant effect has been found. In order to reveal an interaction or dependency, 'one change at a time' testing relies on the experimenter carrying the tests in the appropriate direction. However, DOE plans for all possible dependencies in the first place, and then prescribes exactly what data are needed to assess them i.e. whether input variables change the response on their own, when combined, or not at all. In terms of resource the exact length and size of the experiment are set by the design (i.e. before testing begins).
When to use it?
DOE can be used to find answers in situations such as "what is the main contributing factor to a problem?", "how well does the system/process perform in the presence of noise?", "what is the best configuration of factor values to minimize variation in a response?" etc. In general, these questions are given labels as particular types of study. In the examples given above, these are problem solving, parameter design and robustness study. In each case, DOE is used to find the answer, the only thing that marks them different is which factors would be used in the experiment.
How to use it?
The order of tasks to using this tool starts with identifying the input variables and the response (output) that is to be measured. For each input variable, a number of levels are defined that represent the range for which the effect of that variable is desired to be known. An experimental plan is produced which tells the experimenter where to set each test parameter for each run of the test. The response is then measured for each run. The method of analysis is to look for differences between response (output) readings for different groups of the input changes. These differences are then attributed to the input variables acting alone (called a single effect) or in combination with another input variable (called an interaction).

DOE is team oriented and a variety backgrounds (e.g. design, manufacturing, statistics etc.) should be involved when identifying factors and levels and developing the matrix as this is the most skilled part. Moreover, as this tool is used to answer specific questions, the team should have a clear understanding of the difference between control and noise factors.

In order to draw the maximum amount of information a full matrix is needed which contains all possible combinations of factors and levels. If this requires too many experimental runs to be practical, fractions of the matrix can be taken dependent on which effects are of particular interest. The fewer the runs in the experiment the less information is available.

   form a hypothesis and
   create an experimental design

   test the hypothesis
   verify the replicability of the

   make this proven hypothesis a
   part of standard


Food for Thought !
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