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Articles in this section describe the learning activities, knowledge capture and Status Events that take place inside a Learning Cycle.
Even the best of us will devolve into Build-Test-Fix cycles when there is no structure to the learning phases of our projects. It's our default way of working when we're trying to bring something new into existence. We give the idea a try, and then we start refining it until it works.
But it's not a very efficient way to learn, because we're doggedly pursuing one path instead of looking for alternatives. . .
I've written many Knowledge Briefs at this point about how to structure the Rapid Learning Cycles framework and run RLC Events. But this week, we go inside the Learning Cycle.
That's where we do the work of Rapid Learning. That's where we observe, ask questions, build models, make prototypes and run experiments. It's where we diverge to find alternatives, and then generate the knowledge about those alternatives. . .
Set-Based Concurrent Engineering was first described by Allen Ward in his studies of the Toyota Product Development System. It has a lot of potential, yet it's been one of the most difficult and infrequently-used aspects of Lean Product Development.
That's because the traditional product development process doesn't have a place for SBCE. Most traditional programs focus on producing deliverables that are naturally point-based, and force teams to make decisions too early. . .
Teams in early product development generate a lot of ideas about how to meet customers' needs with unique products, and then they need to narrow those ideas down to a final product concept. This rhythm of divergence / convergence repeats many times as a product moves through early product development until the group has a final Proof of Concept that they can take into detailed design. This is one of the reasons why the Fuzzy Front End is so . . . fuzzy.
Rapid Learning Cycles provide structures that fit naturally with this cadence of broadening to develop alternatives and then narrowing down to the solution. Knowledge Gaps provide a natural home for the work of building knowledge, exploring alternatives and generating new ideas. Key Decisions trigger narrowing decisions that filter out the weak ideas into an initial set. Later Knowledge Gaps probe this set...
If you've attended one of our workshops on LAMDA or read about problem solving at the LPDRC, you may have heard about our problem with invasive blackberries. It's a great example of problem solving because it took us awhile to recognize that this was a problem that some LAMDA could solve.
When we did, we immediately began to understand the problem much better. This is reflected in the fact that our Problem Statement underwent at least five changes from our first pass to the final problem statement. The problem statement helped us...
Stuart Pugh is best known for the Pugh Matrix - a quantitative decision table that scores options based upon a weighted score of their ratings against a set of criteria. At Whittier Consulting, we recommend that people keep the decision table - but throw away the score.
Too much focus on the numbers leads to team arguments about the wrong things: weighting factors, numerical ratings and calculation methods. The quantification process obscures the subjectivity and imprecision that...