Sensitivity charts provide these key benefits:
You can find out which assumptions are influencing forecasts the most, reducing the amount of time needed to refine estimates.
You can find out which assumptions are influencing the forecasts the least, so that they can be ignored or discarded altogether.
With sensitivity information, you can construct more realistic spreadsheet models and greatly increase the accuracy of the results.
Sensitivity charts have several limitations and might be less accurate or misleading for the following:
Correlated assumptions, which are flagged on the sensitivity chart. Turning off correlations in the Run Preferences dialog may help you to gain more accurate sensitivity information.
Assumptions that have non-monotonic relationships with the target forecast. That is, an increase or decrease in the assumption is not accompanied by a strict increase or decrease in the forecast. Logarithmic curve relationships are monotonic but sine curve relationships are not.
The Tornado Analysis tool can help you discover if any of the assumptions have non-monotonic relationships with the target forecast (Measuring Variable Effects with the Tornado Analysis Tool).
Assumptions or forecasts that have a small set of discrete values. When a large percentage of assumption or forecast values are similar or identical, this loss of information grows and can significantly distort the calculation of correlations.