1. Decision Tree Analysis

2. Definition A Decision Tree is a graphical presentation of a decision-making process within a business which aims to highlight the most cost-effective decision.

3. Decision Trees Clearly lay out the problem used in situations of uncertainty Can help decision-makers select the best course of action Help form an accurate, balanced picture of the risks and rewards of a path choice Provide a structured framework to quantify the values of outcomes and also the probabilities of achieving them

4. Structure of a Decision Tree Squares represent points at which management decisions have to be made. They are referred to as decision nodes Branches show the definitive alternatives Circles represent points at which one of a number of outcomes may occur. These are referred to as chance nodes The branches coming from the circle show the likelihood of the occurrence eg 0.6 The expected values of each alternative is shown at the far right

5. Drawing a Decision tree Expected Values £ Good profit0.6 Moderate profit 0.3 Low profit 0.1 Good profit 0.5 Moderate profit 0.3 Low profit 0.2 4m 2.5m 1m 3.5m 1.2m 0.2m A B C

6. Calculating a Decision Tree 1.Start at the right side of the decision tree with the value of each outcome 2.Work out the numeric values = 3.Value assigned to the outcome * by the probabilities along each segment of the path 4.Work towards the LHS complete all numeric values for each path 5.Work out the decision value - cost of following each path 6.The highest value indicates the best decision

7. Worked Example Option B 4m x 0.6 + 2.5m x 0.3 +1m x 0.1 = £3.25m Option C 3.5m x 0.5 +1.2m x 0.3 + 0.2m x 0.2 = £2.15m Decision at box A Choose Option B as it gives a higher expected value than Option C

8. Advantages of Decision Trees Provides a pictorial approach to decision making by mapping out different courses of action, possible outcomes and values Places numerical values on each option Using decision trees encourages dialogue and forces decision makers to take account of all the risks involved Very useful in situations where there is a logical sequence of events Effective in situations where there have been past similar events Simple to understand and interpret Requires little data preparation

9. Disadvantages of Decision Trees Information based on probability so may be unreliable Time lags often occur between data collection and data analysis so information can be out of date Prone to error as some probabilities are difficult to predict Ignores important qualitative data