Learning Models (DMAIC versus DMADV)

Learning models

Knowledge is hierarchical, meaning that some ideas have more impact
than others because they are more fundamental. Six Sigma tends to take a
very ‘‘practical view’’ of the world, but this perspective is dangerous if its
context isn’t well understood. True, the focus is on doing. But how do we
know that what we are doing is correct? If we are wrong, then our actions
may make matters worse instead of better. The question of how we know
that we know is a philosophical one, not a technical one. Technical tools,
such as statistical methods, can be used to help us answer this question,
but unless we have a deep understanding of the philosophy that underlies
the use of the tools we won’t really know how to interpret the results
obtained.

Learning is the acquisition of new knowledge about the way the world
works. Both DMAIC and DMADV are learning frameworks. Learning
must occur if the project deliverable is to provide the intended benefit.
Without learning to guide process change activity it’s just hit-and-miss, and
Murphy’s Law assures that our efforts will miss the mark more often than not. There is a long and proud history of learning models that reflect the
thinking of some of the greatest minds in twentieth century business, such as
Drs. Shewhart, Deming and Juran. There are new learning models that incorporate
recent discoveries in the fields of chaos theory and complexity theory.
The new models, Select-Experiment-Learn (SEL) and Select-Experiment-
Adapt (SEA), apply to systems in dynamic, far from equilibrium environments
where the traditional models break down.