Risk analysis as a process can be improved by sensitivity analysis. From a given set of output data, we can apply statistical analysis to assess variation. To understand the source of the variation, we can resort to sensitivity analysis. By applying various techniques, among them regression analysis and scenario analysis, we can determine how the variation of the inputs, as defined by probability distribution functions, affects the variation of the output of the model. Once we establish an observed relationship through the results of the model scenarios, the ranking provided by @RISK’s sensitivity analysis can help steer our attention to the factors which most contribute to the output variation. It is these critical inputs which require the greatest attention because of their impact. If they have greater impact in the model, we need to know as much about them as possible. If the inputs have little impact in terms of the relational variation, it may be safe to ignore them in favor of those that do.
Once we establish that certain input variation is critical to the output variation of the simulated Excel model, we know we need to gather as much data pertaining to the inputs as possible. Once gathered, we can apply distribution fitting techniques to establish a suitable representation of the relevant data. These fitted distributions help define the risk variation in the model. As we refine the model with additional data and information from appropriate subject matter experts our simulations become more effective in communicating risks to our decision makers. They can then apply this information to establishing objective criteria for determining which decisions to approve, allowing for the best decision making under uncertainty.
Palisade Training Team