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152 Decision trees and influence diagrams
Do not launch product
First-year sales
0.185
15000 units sold
0.63
25000 units sold
Launch product 10000 units sold
0.185
Probability/Probability density
0.7
0.6
0.5
0.4
0.3
0.2
0.1
(a) (b)
0
Discrete distribution
Continuous distribution
10000 15000 20000 25000
Number of units sold in first year
Figure 6.6 – The extended Pearson-Tukey (EP-T) approximation method
As Keefer and Bodily point out, the EP-T approximation does have
its limitations. It would be inappropriate to use it where the continuous
probability distribution has more than one peak (or mode) and the
approximation would probably not be a good one if the shape of
the continuous distribution was very asymmetric. Moreover, in some
decision problems a subsequent decision depends upon the achievement
of a particular level of a variable. For example, in our product launch
problem the manager may decide to discontinue the product after
the first year if sales do not reach 12 000 units. In this case, clearly
attention should be focused on the probability of this critical sales
level being reached, rather than on the three points used in the EP-T
approximation. Nevertheless, in general, there are clear advantages in
using this approximation. Above all, it is simple and each distribution
requires only three estimates to be made which has the obvious effect of
reducing the decision maker’s judgmental task.
Practical applications of decision trees
A large number of applications of decision trees have been published
over the years, and we give below a summary of a few of these
applications to show the variety of contexts where the method has been
successfully used.
Ulvila 6 used the technique to help the US postal service to decide on
whether to continue with the nine-digit zip code for business users. The