Sensitivity analysis is a technique that indicates exactly how much a project's profitability (NPV or IRR) will change in response to a given change in a single input variable, other things held constant. Sensitivity analysis begins with a base case developed using expected values (in the statistical sense) for all uncertain variables. Then, each uncertain variable is usually changed by a fixed percentage amount above and below its expected value, holding all other variables constant at their expected values. Thus, all input variables except one are held at their base case values. The resulting NPVs (or IRRs) are recorded and plotted.
Although sensitivity analysis is widely used in project risk analysis, it does have severe limitations. If an input variable is not expected to vary much (is relatively certain), a project would not be very risky even if a sensitivity analysis showed NPV to be highly sensitive to changes in that variable. In general, a project's stand-alone risk, which is what is being measured by sensitivity analysis, depends on both the sensitivity of its profitability to changes in key input variables as well as the ranges of likely values of these variables. Because sensitivity analysis considers only the first factor, it can give misleading results. Furthermore, sensitivity analysis does not consider any interactions among the uncertain input variables; it considers each variable independently of the others. In spite of the shortcomings, sensitivity analysis does provide managers with valuable information. First, it provides profitability breakeven information for the project’s uncertain variables. Second, sensitivity analysis tells managers which input variables are most critical to the project's profitability, and hence to the project’s financial success. With such variables identified, managers can spend the most time forecasting the variables that “count,” so the resources expended in the analysis can be as productive as possible....
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