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RICHARD DE NEUFVILLE:
So I recommended

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a process of how to approach.

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The five steps, they're
one way to think about it.

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You can have six or seven.

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In the text we
wrote some time ago,

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we had seven put out because
we went to more complexity.

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But I'm now going to talk
about these central five

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but it's mostly to
cover the basics.

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Five is not the magic number.

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So the first one we
need a valuation model.

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And for those of you
who are filling out

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your initial project
form, a number of you

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weren't quite
understood that what

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was involved when I asked about
having a model of your system.

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But you need to have
some way of saying, OK,

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if I design the model or I
set up my model in my system

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in a particular place,
which is not only

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the machinery and the
production of a process

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but also my employees,
my marketing, my sales,

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my productivity,
and so forth, you

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need to have a way
of transforming

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those assets and [? ERUs ?]
into some kind of valuation.

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It can be an economic valuation
as in this particular case

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or it can be a
performance valuation.

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I'm running an emergency medical
effort to, say, combat Ebola.

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How quickly and how well
do I contain the disease?

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So it may be performance
based rather than money based.

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And in this case
as from the garage,

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I have things about the demand,
the capacity, the revenues,

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cost, and so forth.

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And I have a present
value and so forth.

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So I need a model which says,
if the following happened

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and you've made these
design decisions,

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here's what the
performance is going to be.

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Secondly, you need to think
about which are the most

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important uncertainties.

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As you can imagine,
and as those who

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filled out the initial
project report were doing,

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you can list them.

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And there are lots and
lots of uncertainties.

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All kinds of things can happen.

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The question is what should
you really pay attention to.

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So you need to think
about which are

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the issue, the uncertainties,
that you want to focus on,

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not that all of them
exist, but some of them

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are second order effect.

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So which are the
first order effects?

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One of the ways to do that is
to use a tornado diagram, which

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I will illustrate here but
I'll describe in more detail

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later on in the course.

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But you need to have
some way of filtering out

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the important
issues to focus on.

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In the garage case, it
was the level of demand.

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I didn't talk about the strength
of the concrete, or parking

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registrations, or
things like that

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but focusing on the right issue.

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And I say that in the context
that here from the garage case,

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you can have all
kinds of uncertainties

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that are playing out.

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And each of these will give you
different results at the end.

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So which ones am I
really going to focus on?

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I don't want to be swamped
by everything else.

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So one way to do it
is a tornado diagram,

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which the end result is that you
have various uncertainties that

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is taken from somebody's
project some years back.

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The demand, the revenues,
the operating costs,

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et cetera down here.

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And from the base case, that is
the median case that you want,

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the forecast case, you
have a performance.

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And if the demand went
up in one way or down,

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it'd have this range of effects.

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And it's called
a tornado diagram

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because, when you look
at the sensitivity

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to each of these uncertainties,
you stack the results

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so the ones that they're least
sensitive are at the bottom.

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The ones that are most
sensitive are at the top.

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And therefore it has
this funnel shape.

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And that's why it's
called a tornado diagram

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because it is the way a tornado
looks like prototypically.

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So the idea is
basically you think

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about how much your
uncertainties might vary

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within a standard
deviation and if it's

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symmetric or not symmetric.

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And you run the analysis
with those extremes.

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And you see which are the ones
that have the most effect.

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And in this case, for
example, the demand

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is the most important one
in this particular case.

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And so this if you focus
on, the revenue may be next.

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But the other ones don't
make that much difference

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so that, at least
for the beginning,

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we focus on the top
ones and proceed ahead.

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So at some level,
once you consider

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that things can
be uncertain, you

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may be swamped by all
the possibilities,

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all the things that can happen.

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And you don't want to do that.

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You want to have some way
of focusing our attention

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on the things that matter.

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And a tornado diagram
is one way to do it.

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You may also want
to think about how

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things may change over time.

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This was the basic forecast
that we use for the garage case.

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I'm not pretending
it's the right one,

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but I'm simply saying it's
an illustration of the kind

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of issues that you have.

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So you start at a
particular level.

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And you think it might
grow particularly quickly

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at that time.

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And it might level off.

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And then one of the
forecasts that we

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were looking at today, which
was the renewable energy--

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the energy production
by renewables,

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you saw that many
forecasts had a growth

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and then it leveled off.

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So thinking of this
may be appropriate

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for your problem is
something you will do.

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That is your forecast may have
a certain uncertainty all along,

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but it may be around a
particular pattern of growth,

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which might be an
S-shaped pattern.

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It might be this shape.

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It might be a straight shape.

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It might be exponential.

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But you want to think about a
distribution aspect over time,

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not just at any one time.

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