Lean Exercises for the R&D Professional
If you have ever seen an exceptional scientist or engineer in action, you
probably noted that the person could just “see” things that you could not.
Such people see problems that others miss, causes that seem to be removed
from the immediate problem, implications of observations that others just
cannot comprehend, and so on. is ability to see need not be unique
to them; it can be learned by anyone and is crucial to becoming a Lean
Seeing in this sense requires the ability to observe without assumptions.
As we saw in Chapter 1, our mental framework and assumptions put our
raw observations in context. It tells us that one observation is impor-
tant, but that another one is insignicant. It tells us that this observation
builds to a conclusion, that nothing useful can be gleaned from a dierent
observation. Our mental models create the context in which we winnow
apparently useful from apparently useless observations. e observable
universe is so large and complex that our mental models serve mostly to
help us focus on a narrow set of observations that allow us to take action.
It enables our ability, more than anything else, to tune out the din of the
observable so that we can focus our limited observational capability. If that
narrow capacity observes the right things (tigers about to pounce), we can
take meaningful action. If it observes the wrong things (owers near the
tiger, but not the tiger), then our actions will be meaningful, but ultimately
disastrous. In essence, our mental models are there to focus observation
on information that leads to problem solving. Using the important infor-
mation, we can quickly identify and pursue the best possible option(s).
44 • Creating a Lean R&D System
For obvious reasons, this winnowing is incredibly valuable. It is also
exceptionally limiting, because it means our problem solving is limited
entirely by the scope and inexibility of our mental framework. We are
in big trouble, for example, if that tiger has a full belly, but the ower is
camouaging an enemy with a blow gun.
e problem is that our mental models, like Newtonian physics, are
exceptionally condensed visions of reality that we carry around in our
heads. Useful as they may be, their simplicity, which is the result of this
condensation of reality, means they are always massively incomplete,
hence subtly or substantially wrong, depending on context. Newton’s
mechanics, for example, are suciently precise for most walking-around
work, but they break down to pure uselessness outside of their limited
range of time, speed, and scale.
Importantly, the usefulness of our models oen leads us to forget that
they are only useful in a narrow range and, therefore, we oen use their
winnowing implications to eliminate observations that would otherwise
lead us to greater insight. Einstein was not swayed by the assumptions
of Newtonian physics ingrained in other physicists’ minds and was able,
therefore, to absorb rather than cast aside as unimportant observations
that had been building among other scientists for decades. By accepting the
importance of these previously discarded observations, Einstein was able
to synthesize the science of physics in an entirely new and valuable way.
A few examples from polymer science might be useful. More than
a billion kilograms of polycarbonate plastic are synthesized every year.
Because of its scale, it is manufactured in large plants that cost hundreds
of millions of dollars to build. In these plants, powders and (usually) pel-
lets of the plastic are formed, which are then shipped to plastics customers
who melt, cast, or extrude these pellets into shapes and forms for nal use.
If we lock onto these original assumptions—big plants, big chemistry,
pellet forms, etc.—we limit ourselves to improving the existing process.
We could, for example, go from “solution” phase synthesis to “interfa-
cial” synthesis of the polymer. ese two approaches use the same basic
infrastructure. We might look at dierent chemistries but, again, assume
they need to be scaled up to megaton production rates. is assumption
might result in the choice of melt processing as an answer to the problem.
However, this approach fails to consider the opportunity of utilizing the
end user as a manufacturer. In this case, truly ingenious chemists iden-
tied ways that a plastics parts manufacturer could insert the right raw