A decision tree is a schematic model of alternatives available to the decision maker along with their possible consequences. The name derives from the tree-like appearance. The Decision tree analysis is widely used in strategic decision making. Let’s consider the following example. A retailer must decide whether to build a small or a large facility at a new location. Demand at the location can be either small or large with probabilities to be .4 and .6. If a small facility is built and demand proves to be high, the manager may choose not to expand or to expand. If a small facility is built and demand is low, there is no reason to expand and the payoff is $200,000. If a large facility is built and demand proves to be low, the choice is to do nothing or to stimulate demand through local advertising. The response to advertising may be either modest or sizable, with their probabilities estimated to be .3 and .7 respectively. Below diagram shows the decision variables and its estimated payoff of each event.
Based on the estimated probabilities and expected payoff, the retailer should build the large facility with expected payoff of $544 compare to small facility with expected payoff of $242. If demand is low, the retailer should advertise rather than do nothing as the expected payoff of advertising (payoff = 160) is higher than doing nothing. This analysis is very useful when management faced with multiple options and the likely probability of occurrence and consequences ( e.g profits/costs) associate with it.