Page 295 - DCOM303_DMGT504_OPERATION_RESEARCH

Operations Research

Notes Result

By applying this technique we can see that the best option is to develop a new product. It is
worth much more to us to take our time and get the product right, than to rush the product to
market. It is better just to improve our existing products than to botch a new product, even
though it costs us less.

Key Points

Decision trees provide an effective method of Decision-making because they:

 Clearly lay out the problem so that all options can be challenged.
 Allow us to analyze fully the possible consequences of a decision.
 Provide a framework to quantify the values of outcomes and the probabilities of achieving
them.

 Help us to make the best decisions on the basis of existing information and best guesses.
As with all Decision-making methods, decision tree analysis should be used in conjunction with
common sense – Decision trees are just one important part of your Decision-making tool kit.

Example: A private investment firm has ` 10 crores available in cash. It can invest the
money in a bank at 10% yielding a return of ` 15 crore over five years (ignore compound
interest).
Alternatively it can invest in mutual funds, of which there are currently two available.

If it invests in Mutual Fund A there is a 0.5 chance of it being a success yielding `20 crore, and a
0.5 chance of it failing leading to a loss of ` 5 crore. (over the five year period)
If it invests in Mutual Fund B there is a 0.6 chance of the project being a success yielding
` 30 crore and a 0.4 chance of it failing leading to a loss of ` 2 crore. (over the five year period)
Show the most feasible solution by the help of decision tree.
Solution:

Working out the likely outcomes:
Invest in bank – return = ` 15 cr
Expected Value of investment in Mutual Fund A

= E(X)   x P(X  x )
j
j
j
= ` 7.5 cr
Expected Value of investment in Mutual Fund B

= E(X)   x P(X  x )
j
j
j
= ` 17.2 cr
You can see that Project B yields the best result. We can illustrate this information on a decision
tree as in Figure 14.2.

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