WEBVTT

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[AUDIO LOGO]

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RICHARD DE NEUFVILLE:
So I'm going

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to explore this basic trade-off
between big versus small

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from an economic point of view
in this short presentation.

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And it's all based on an old
analysis from a simple case.

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And the real situation
is more complex.

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But this analysis leads you
to some simple, intuitive

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understanding of the trade-offs,
which is what I want to use.

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And by the way, this was
built upon the experience

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of building steel plants in
India back half a century ago.

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So here's the picture
when it all comes out to.

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Let me explain the
picture first, then

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let's talk about
the explanations.

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So I have on the horizontal
axis is this alpha,

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which I've been talking about.

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And he ran the
numbers because it's

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a simple analysis actually,
which I'm not going to repeat.

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You can look it up.

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But it's not a crucial
to the understanding.

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So that from our purposes,
the economy of scale factors

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is in the range
between 0.6 and 100.

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And so this is the economies of
scale on the horizontal axis.

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On the vertical
axis is how often,

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if you're going to have
modules, how often should we

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repeat them to be efficient.

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And it's based upon
the simple analysis

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that it's a linear growth.

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But again, having
made that analysis,

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it gives you the
trade-offs, which

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I'm going to be looking for.

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So here is the cycle time, which
also translates in the size

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because, if you have
10% growth a year,

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if you're supposed to
build it every eight years,

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it's talking about 80% growth.

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So the size of the plant,
of the module, basically,

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is, in this way.

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And this is the
economy of scale.

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And it shows the--

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that's the setup for it.

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And now let me explain
what's happening.

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It's the same graph as before.

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So it says if I have
higher discount rates--

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so this is from 10% to 15%--

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higher discount rates-- sorry--

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higher discount
rates mean that you

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ought to build smaller,
which says, again,

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that, in fact, if you're
looking at it from here to here,

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you go from say 70% factor
for economies of scale,

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you go from about 7
down to about 4 and bit.

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So you make it
basically about 40%

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smaller is your
size of your module.

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And on the other hand, if
you have greater economies

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of scale, you build it larger.

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If you have no
economies of scale,

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there's no benefit to building
more than the existing demand.

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So you have the two
factors that it's

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a trade-off between economies
of scale and discount rate.

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Greater discount
rate, build smaller.

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Greater economies of scale--

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sorry-- greater
economies of scale,

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which means smaller
numbers here--

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there's a little bit of
cognitive dissonance.

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Smaller numbers mean
greater economies of scale.

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You build larger.

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That's the basic
trade-off, which I

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was trying to express earlier.

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So that is a graphical
representation

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of the kind of thing
that can happen.

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Now well, how
sensitive is all this?

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So here's the
thing that happens.

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So here is now the
total cost of the plant,

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and the cycle time is how
big you make it considering

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a constant growth per year.

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If I go from 4 to 8,
I double the size.

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So the response
is, the observation

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is the optimum is flat.

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What do I mean by that?

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If we're here, since these
things can't be predicted that

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precisely, that going
from say a size of based

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on four times the growth rate
to up to about here, about 10

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or 12 times the growth
rate, it doesn't

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make that much difference.

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So it makes a great
deal of difference

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that you want to build
some time in advance.

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But the optimal is
not that significant.

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But you don't want to
be building for 15 or 20

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years in advance because then
it really starts hitting you.

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Now this was calculated for
a particular set of values.

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And it changes at other times.

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But the notion is, well, if
you look at a relatively small

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unit, you can have it
better, not for 15 years,

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not for 20 years.

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So the implications
here are, which

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I wanted to bring
out through this,

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is that, for industries
with economies of scale

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such as electric power,
chemical processes,

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and so forth, and assuming
that you have a steady growth

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indefinitely, small plants,
those very small ones

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are uneconomical.

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The optimal size is
associated with growth

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from between about 5
and 10 years of growth.

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And they're not especially
sensitive to the higher end,

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and that's good because the
forecasts are not accurate, so

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modest size.

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But now, this analysis
assumes that growth is going

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to continue on indefinitely.

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But what happens if the
growth stops, decreases?

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What's the risk?

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Well, you risk building
capacity that's not needed.

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What can you do about it?

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Well, you build
smaller, so there's

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less reliance on the future.

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This is similar but not
the same as Eli's comments

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that the oil company, I think
it was that he was working with,

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built two plants so as not to
be vulnerable in case something

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happened, in this case, not
that the capacity would stop

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but because they wanted to be
able to be assured that they

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could meet their client's
needs or their market share

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and, therefore, wanted
to have them smaller.

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So the trade-offs that
we're talking about here

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is between the higher cost
per units for small plants

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when operating capacity
versus saving by not

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paying for extra capacity.

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So there's a trade-off
here that you

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need to think about very
particular to the specifics

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of your situation between
taking advantage of economies

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of scale, which is a typical
driving force behind planning

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for a lot of industry,
versus building it

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incrementally and smaller, which
gives you advantages of it.

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And I will be talking
later on about doing

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this in South Australia where
they are building not only--

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they had build a
water supply plant,

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which was built at a terrific
size for economies of scale.

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And they were thinking about
doing the same thing for an LNG

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plant.

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And it illustrates the
advantages of that trade-off

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and how you want to modulate
between the capacity

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and when you implement it.

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So the takeaways
from the discussion

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so far is, if there's
no economy of scale,

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don't build in
anticipation of demand.

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Expand as needed.

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If there are, then
you might want

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to build somewhat in the
future but not aggressively

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in the future.

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The more uncertainty there
are, the small increments,

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when big capacity is not
fully used or even needed,

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that is the risk of not
needing it for whatever reason

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is a reason not to build as
big as the engineering might

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like to have.

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So I want to now add in
the effect of learning,

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which I mentioned emphasizes
the effect of the discount rate.

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So here is the tail end of the
Manne's analysis basically.

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So I've taken it
from 0.6 to 0.95.

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There's basically a
discrete representation.

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That's why they're bumps in
these curves with no learning

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and at different interest rates.

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And I'm comparing this to
another case with learning.

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And you have no learning,
which is up here.

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And if you have higher
amounts of learning,

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it really decreases it.

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So I can attend by this as
an illustration of the idea

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that learning adds on to
the effect of discount rates

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in terms of arguing against
larger plants, which

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is all in the context that
it's nice to have modules

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as a way of dealing
with uncertainty.

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So the summary here is
uncertainty is a main driver.

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Against are the pervasive
economies of scale

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that impel designs
for large capacity.

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And for are the discount
rate and the learning,

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in addition to the uncertainty.

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So the big picture I want
you to leave you with

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is that economies of
scale are widespread.

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And in that way, they have been
a mantra for engineering design

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to build big plans.

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And you see this very
easily in the electric power

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industry where this has
been the norm for a bigger

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plant as opposed to smaller
plants, which you can then

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distribute around the
landscape and don't have

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the same distribution costs.

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And it's also been the pattern
of oil platforms of being

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large, as being more
efficient, and so on as

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opposed to smaller
ones, which reduce

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the risks or the uncertainties
associated with the performance

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of the field.

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And in that context,
smaller units may be better.

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So there's an argument for the
investigation of smaller units.

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And our work with BP and
subsequently with the one

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we did with Eli
Dweck, in particular,

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was really interesting
with BP because it

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was so against the idea of
looking at smaller units

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but is proven actually
quite productive for them.

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