Sensitivity analysis is the use of multiple what-if scenarios to model a range of possible outcomes. The technique is used to evaluate alternative business decisions, employing different assumptions about variables. For example, a financial analyst could examine the potential profit levels that may be achieved as a result of an investment in machinery by altering the expected demand level, material costs, equipment downtime percentage, crewing costs, and residual value of the equipment.
For example, an analyst is modeling the range of profit outcomes for a prospective equipment purchase. A potential issue is that the equipment may be superseded by a new equipment model, which may reduce its resale value. Accordingly, the analyst conducts a sensitivity analysis that models the lifetime profitability of the investment, assuming a range of possible resale values at the end of the projected usage period for the equipment.
A particularly useful aspect of sensitivity analysis is to locate those variables that can have an unusually large impact on the outcome of the analysis. The decision maker can then evaluate the probability of the variables experiencing significant changes. The outcome is a better understanding of the risks associated with an investment.
One way to create a sensitivity analysis is to aggregate variables into three scenarios, which are the worst case, most likely case, and best case. The probability of occurrence for the variables used in these three cases clusters the highest probability variables in the most likely case.