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RICHARD DE NEUFVILLE:
And the graphical

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view here, it addresses
the question of what

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is the right size of
your initial project.

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And so I have four curves
here depending upon the alpha.

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The alpha equals ones
indicate that there's

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no economies of scale.

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And as we go lower,
alpha means that there's

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lower exponent on the
economies of scale,

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which means they
are greater, and you

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get to a different design.

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And as an extension
of the garage case,

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you see that the optimal
design here increases along

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with economies of scale.

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And there are
certain fixed costs

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so that the 25 tons per day
didn't seem like the best

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solution, and so 50 seemed
to be the right one given

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the fixed costs and
everything else.

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So all this is fairly
straightforward from the garage

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case.

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So there is a
general lesson here

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that is we might
digest them, is there's

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a trade-off between economies
of scale and the plant size

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where anticipating
future growth.

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This was not part of the
MITx simple-minded homework

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assignment, but it is the kind
of thing that you think about.

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And in this particular
case, the economies

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of scale and the example
problem weren't very large

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so that building over as large
as you could over that time

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was not obviously to
be a good example.

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Now, here is, I think, a really
interesting table that is we're

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now looking at the uncertainty.

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So as shown in this graph
here, there is the projection

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that Keppel Offshore
Marine was going

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for, which had been a use,
which was there was growing,

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the red line, it sort
of disappeared here,

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but it's here, and then
leveling off over time

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over this 20 years.

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And we had applied
uncertainty parameters

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to have it vary around here.

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And, of course, that we
had no special bases on it,

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but recognize those uncertainty,
and it wasn't trivial,

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so here's the ones
we are working with.

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Now, here's where we have
the parametric analysis.

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So clearly the design depends
upon the economies of scale.

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So here is the four that we
were looking at from none

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to about the most that
I've seen in practice,

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but seemed to be
appropriate as a range.

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And if we look to the
optimal capacity in terms

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of tons per day, as
I just showed you

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with the graph, the
more economies of scale,

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the larger is the better
design from the economic point

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of view.

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The moment you
impose uncertainty,

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very similar to the garage
case, the optimal design

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shifts because you find out
that, well, you build something

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too large for the
demand, and you lost,

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and on the other hand if
the demand was higher,

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you didn't particularly have
a gain because you could only

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produce so much so that what
you see here is systematically

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when we think about
the design itself,

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the optimal design from a
deterministic point of view

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and with an uncertain
perspective can be lower.

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Now, the amount it's
lower here depends

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upon the uncertainty
that we impose, granted,

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but the effect is
it drives it slower.

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That is a kind of
effect you can imagine

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happening that you
can anticipate,

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and it may not happen if
the uncertainty was less.

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Maybe the uncertain design for
the higher economies of scale

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might be the same
here, but and that's

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why it's the lower or equal,
so this is not necessarily it,

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but it is in this case.

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By the same token, we can think
about the net present value

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of the project.

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So here is the NPV of
uncertainty is ignored,

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and it goes through
these values.

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And the moment we now
recognize the uncertainty

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as such as we're given
here, we see that again it

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is they are lesser than the ones
with a deterministic analysis.

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So these are absolutely
standard effects and the results

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from the flaw of averages
as they apply here

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to capacity-constrained systems.

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Now, this being
lesser all the time

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when you recognize
uncertainty, applies

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to this kind of situation.

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It does not apply
necessarily where

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you have network effects
such as Amazon, or internet,

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or Google where the
non-linearity is playing

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the other way, but
the point being

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is that it's a clear evidence
of the flaw of averages.

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And the reason I bother spending
time on the flaw of averages

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is precisely because it drives
these kinds of differences

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in case of capacity-constrained
installations,

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whether they're warehouses,
or electric power plants,

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or whatever, this is the kind
of effect you would expect.

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So a general lesson too
I'm suggesting to you

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is that when invested in
capacity-limited facilities,

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the realistic analysis
that recognize uncertainty

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contrast with the
deterministic analysis.

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That is are if you deal with
a deterministic analysis,

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you're dealing with
basically a fairy tale.

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That's a rather rude way
of talking about it maybe

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because people are
doing this all the time,

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but the fact is you're dealing
with an unrealistic situation,

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and you are consequently
going to get the wrong answer.

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And in particular, for the
capacity constraints situation,

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the realistic answer is that
you have lower expected value

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and probably smaller modules
are economically optimal.

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And, all right, so I want
to stop here again, Indra.

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Is there any question here
that people would like to ask?

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I want to be sure that
this point is understood,

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or if you debate it, that we
can have the debate about it,

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and I can resolve any
questions that might occur.

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INDRA: If anyone
has a question--

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yeah, we have Joshua.

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RICHARD DE NEUFVILLE:
Joshua, go ahead.

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AUDIENCE: Professor, just
looking for clarification.

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When you say that for
capacity-limited facilities

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or whatnot, you're
taking away the upside

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because you can only
go up to that capacity.

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And so the uncertainty
that's presented only

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provides you the downside up
to that mean or the average.

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RICHARD DE NEUFVILLE: And
that's the major effect, yes.

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I mean, you can
think, well, maybe we

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could raise our prices
for it, so maybe we

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could compensate for if
it's this tight demand.

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That implies there's no
competition, of course.

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But so the major effect
is in that direction.

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And of course, there are
exceptions and modulate,

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but that is the main effect
that one can account for, yes.

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That's what I meant to say.

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Are you concerned about
that or just validating it,

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or how are we thinking about?

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AUDIENCE: I think that
makes a lot of sense.

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I appreciate the clarification.

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I think it's just
how do you then

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absorb the risk
associated with allowing

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yourself to get the upside?

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To get above the
mean with capacity,

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any business would have
significant risk in that case.

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RICHARD DE NEUFVILLE:
Well, that's

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where the modularity comes in
because this is recognizing

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uncertainty, but it also
means that, OK, well,

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we're going to allow for
flexibility, and we say, right.

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If demand is picking
up, we're going

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to put it ends
some more modules.

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Now, in the garage
case, the starter set

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was waited two years to see
if the growth was there.

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Well depending
upon the business,

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and depending on how long it
takes to add the capacity,

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you might decide to do it
earlier, or later, or whatever.

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But the idea is, OK, if
we've planned this correctly,

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we haven't forgone the
upside opportunity.

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We just are going to make
sure that it's around.

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So if we're starting here
in year zero, year one,

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we're not going to build
all the way up for here.

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We're going to say, well
when year 3 comes around

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and it looks like the
demand is really growing,

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we're going to add capacity.

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We haven't closed it off, but in
that particular year when there

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was demand was higher than we
were at capacity for, well,

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we didn't gain as
much, but we'll

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catch it up somewhat later on.

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So but, yeah, that's
what I'm saying.

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For those of you
who operated things,

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I mean, you have to
realize that capacity,

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there's the plate capacity
on the production facility,

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and then there is
the actual capacity

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because there's a glitch,
or you have to close it

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for maintenance, or so forth.

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And on the other
hand, you can have

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produce more than
the plate capacity

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because you decide
to work overtime

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and forgo maintenance for a
while that you had assumed,

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and so forth.

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There are a lot of ways
in which capacity is not

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like this cup of coffee,
which will only take so much

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and you can't get
any more into it.

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But it's more like a
capacity of people on a bus

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or on a subway car.

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Well, it may say capacity for
20 standing and 30 seating,

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but you all situations where
you had greater capacity,

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and others where
right now, you're

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not allowed to sit
close together,

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so the capacity is
actually smaller.

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So capacity, in
practice, is quite a bit

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of a fluid thing, yes.

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So I'm trying to point
to the general phenomenon

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here, but the ways in
which in the real world

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it gets to be complicated,
yes, absolutely.

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And right, my work now
with this Moroccan company

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is exploring exactly
those kinds of issues.

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So just to say that I'll
pass over this slide, which

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is simply to say that once you
move to thinking about not just

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the combination of
size and timing,

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but also add that other
dimension of location,

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and in this case of
five possible locations,

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that the complexity of the
analysis increases greatly,

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and so the analysis
is more complicated,

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but that's just as a footnote.

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So now let's look at
the optimal design here,

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and this is, again, very
similar to the garage case

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where the building
at large and fixed,

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has this performance
here for these numbers

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and expected value
quite reasonable,

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but it could be if you
build it and nobody comes,

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the way it was the first
years of the salting plant,

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you can have a big loss,
and you're capacity

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limited to a certain amount.

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If you had a flexible
modular solution

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where you allowed yourself to
time the increases of capacity

00:12:40.010 --> 00:12:41.960 align:middle line:84%
according to the
demand, you weren't

00:12:41.960 --> 00:12:44.820 align:middle line:84%
going to lose very much
because you started small,

00:12:44.820 --> 00:12:48.780 align:middle line:84%
and there was not
so much to lose,

00:12:48.780 --> 00:12:55.630 align:middle line:84%
and the losses were several
couple of million dollars

00:12:55.630 --> 00:12:58.170 align:middle line:90%
by this comparison.

00:12:58.170 --> 00:13:00.020 align:middle line:84%
But if the demand
really picked up,

00:13:00.020 --> 00:13:01.760 align:middle line:84%
you could expand,
expand, expand,

00:13:01.760 --> 00:13:05.330 align:middle line:84%
so that you had a much
better performance overall,

00:13:05.330 --> 00:13:08.010 align:middle line:84%
and in this case,
as often happens,

00:13:08.010 --> 00:13:10.970 align:middle line:84%
but not necessarily,
the flexible demand

00:13:10.970 --> 00:13:12.920 align:middle line:84%
compared with the
inflexible demand

00:13:12.920 --> 00:13:17.135 align:middle line:90%
perform was better overall.

00:13:17.135 --> 00:13:19.310 align:middle line:90%
So that was that.

00:13:19.310 --> 00:13:24.260 align:middle line:84%
It was those target curves
for that comparison.

00:13:24.260 --> 00:13:28.440 align:middle line:84%
And note, by the way, that
the increase in value,

00:13:28.440 --> 00:13:30.170 align:middle line:84%
so of course, this
is not guaranteed,

00:13:30.170 --> 00:13:32.570 align:middle line:84%
but in this particular
case, was from about 14

00:13:32.570 --> 00:13:38.940 align:middle line:84%
and change to about 21, which
is about 50% increase in value.

00:13:38.940 --> 00:13:46.850 align:middle line:84%
So that with no new technology,
no sort of miraculum involved,

00:13:46.850 --> 00:13:49.310 align:middle line:84%
simply by recognizing
uncertainty,

00:13:49.310 --> 00:13:54.110 align:middle line:84%
you could increase the
prospective expected value

00:13:54.110 --> 00:13:55.610 align:middle line:90%
by 50%.

00:13:55.610 --> 00:13:59.053 align:middle line:84%
It's really quite
remarkable, and, of course,

00:13:59.053 --> 00:14:01.470 align:middle line:84%
there are details about how
it actually going to work out,

00:14:01.470 --> 00:14:07.880 align:middle line:84%
and so forth, but it
emphasizes the great prospects

00:14:07.880 --> 00:14:12.010 align:middle line:84%
for flexible design
under some circumstances.

00:14:12.010 --> 00:14:23.481 align:middle line:90%