Decision trees

A decision tree is a planning and decision making tool. Decision trees are probability and outcome maps of a scenario. Decision trees can be used by managers to determine which of a range of options should be chosen.

Decision trees

Using decision trees A figure is placed on a proposal which is calculated as follows: (likely financial outcome 1 x probability of outcome 1) + (likely financial outcome 2 x probability of outcome 2) – cost of investment This figure is then be compared with the numerical value placed on other proposals (which may in fact include doing nothing)

Example A retail organisation owns an undeveloped shop unit which could either sell now for £0.9m or refurbish and open up as one of its own stores. The cost to refurbish would be £800,000. If successful it would generate profits of £2m. The probability of this happening is 0.6. If unsuccessful, it would only generate profits of £500,000.

Example continued Success 0.6 Failure 0.4 Refurbish at a cost of £0.8m Sell undeveloped £0.9m £2m £0.5m (0.6 x £2m) + (0.4 x £0.5m) - £0.8m = £0.6m On this basis, the firm is more likely to sell the unit undeveloped for £0.9m

Benefits of using decision trees Decision trees force managers to consider a range of options rather than relying on hunch Decision trees allow comparisons to be made between different options based on numerical data Decision trees help to assess the risk involved in making decisions

Limitations of using decision trees Probabilities and outcomes are usually just estimated figures and may not be reliable Subsequent external factors may affect the likely outcomes of a scenario Decision trees, on their own, do not take into account qualitative factors such as business objectives