Most people are
familiar with Six Sigma. This includes the goals of Six Sigma, the typical
methods used and the culture that goes with it. If not, I am not going to cover
these here, but there are plenty of websites that do. One of the key
characteristics of Six Sigma is the frequent use of experimental methods. You
will note this in most technical texts on Six Sigma. I believe this is a result
of the experimental approaches that dominated the quality revolution some
decades ago. These approaches originated with quality control methods, which
typically were best suited to a statistical approach. Statistics usually
requires data, which means an experiment is also required. Taguchi then further
reinforced the experimental approach in the minds of quality practitioners when
he introduced DOE for robustification. This focus on the experimental approach
has resulted in a neglect of the analytical approach. Certainly, there are some
out there who appreciate the value the analytical approach and take full
advantage of that value. However, there is a stronger focus on the experimental
approach in Six Sigma, and this raises the question: can Six Sigma (and Design
for Six Sigma) be improved with probabilistic/analytical methods?
Well I certainly think
so and I think the greatest benefit is evident when probabilistic methods are
applied to Design For Six Sigma (DFSS). DFSS is basically robustification so
it’s obvious that probabilistic methods are of value: but why DFSS especially?
DFSS is used when a new system is being developed. Under these situations it is
much harder, at times impractical, to gain access to the kind of data that is
required for an experimental or statistical approach. Because the system being
developed is new it has not yet been properly implemented, and there is no
quality data (on either system performance or component characteristics) that
can be analyzed. Therefore, the experimental approach is of limited usefulness
when developing a new system. This could be a new product or a new business
venture or a one off engineering system like a dedicated machine tool or a
large piece of construction like a bridge. In such cases, the analytical
approach is really the only option because it is the only way we can gain any
insight into the effects of random variability upon system performance. So how
should the probabilistic approach be combined with Six Sigma?
A full probabilistic
analysis of a system can be very intellectually demanding, and thus seem very
daunting. Therefore, I think an approach that only relies on the simplest and
easiest of methods at the early stages is required. The more demanding methods
can be saved for when the final design is selected, and the extra effort
becomes worthwhile. If we assume that the Define step of DMAIC has already been
taken and that there is little need to Measure (because there is currently
nothing to measure), then we can look at the Analyze stage:
- The earliest, and possibly most valuable, step would be a simple sensitivity analysis. I spoke about this in an earlier entry. This does of course require an analytical model of the system, and this was covered earlier too. Such a model at this stage might be a simple approximation, which provides a general ‘feel’, so that less effort is required.
- If a preliminary sensitivity analysis does not rule a design out, then it is time to develop a more comprehensive model. This model can then be combined with Monte Carlo, error propagation or FORM (depending upon the model and your preferences) to make a more accurate prediction of quality.
- Now that it is possible to predict the quality, the next step is to optimize the design. This type of optimization is robustification. This is essentially the Improve stage.
- Once the optimized design has been developed, it might be necessary to test the accuracy of the model with a physical model. Depending upon how the model was developed, this step might not be required.
- The final probabilistic step is to use the model and the optimized design to ascertain the system parameters that most affect the quality. With this information one can determine where the
quality control efforts can be most efficiently directed. Thus we have the Control stage.
The above approach allows you to most effectively utilize probabilistic methods with DFSS. Hopefully, you are able to use it as a guide so that you can develop high quality, high reliability systems with a short development period.
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