Creating a Lean R&D System

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4

Lean Exercises for the R&D Professional

SEEING

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

researcher.

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 insignicant. It tells us that this observation

builds to a conclusion, that nothing useful can be gleaned from a dierent

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 inexibility of our mental framework. We are

in big trouble, for example, if that tiger has a full belly, but the ower is

camouaging 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 suciently 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 oen leads us to forget that

they are only useful in a narrow range and, therefore, we oen 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 dierent 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-

tied ways that a plastics parts manufacturer could insert the right raw