What are the benefits & uses of Sensitivity Analysis ?

Measurement of sensitivity analysis Below are mentioned the steps used to conduct sensitivity analysis: Firstly the base case output is defined; say the NPV at a particular base case input value (V1) for which the sensitivity is to be measured. All the other inputs of the model are kept constant. Then the value of the output at a new value of the input (V2) while keeping other inputs constant is calculated. Find the percentage change in the output and the percentage change in the input. The sensitivity is calculated by dividing the percentage change in output by the percentage change in input. This process of testing sensitivity for another input (say cash flows growth rate) while keeping the rest of inputs constant is repeated till the sensitivity figure for each of the inputs is obtained. The conclusion would be that the higher the sensitivity figure, the more sensitive the output is to any change in that input and vice versa. Using Sensitivity Analysis for decision making One of the key applications of Sensitivity analysis is in the utilization of models by managers and decision makers. All the content needed for the decision model can be fully utilized only through the repeated application of sensitivity analysis. It helps decision analysts to understand the uncertainties, pros and cons with the limitations and scope of a decision model. Most if not all decisions are made under uncertainty. It is the optimal solution in decision making for various parameters that are approximations. One approach to come to conclusion is by replacing all the uncertain parameters with expected values and then carry out sensitivity analysis. It would be a breather for a decision maker if he / she has some indication as to how sensitive will the choices be with changes in one or more inputs. Uses of Sensitivity Analysis The key application of sensitivity analysis is to indicate the sensitivity of simulation to uncertainties in the input values of the model. They help in decision making. Sensitivity analysis is a method for predicting the outcome of a decision if a situation turns out to be different compared to the key predictions. It helps in assessing the riskiness of a strategy. Helps in identifying how dependent the output is on a particular input value. Analyses if the dependency in turn helps in assessing the risk associated. Helps in taking informed and appropriate decisions Aids searching for errors in the model.