Project managers may use risk models to explore the possible outcomes using risk simulations. For a model in Excel, software such as Frontline's Risk Solver, may be used to perform a Monte Carlo simulation on a model. Project managers can run a simulation that performs many (thousands of) experiments or trials -- each one samples possible values for the uncertain inputs, and calculates the corresponding output values for that trial.
The first run of a simulation model can often yield results that are surprising to the modelers or to management -- especially when there are several different sources of uncertainty that interact to produce an outcome. Even before an in-depth analysis of the results, simply seeing the range of outcomes -- for example, how low and how high Net Profit can be, given our model and sources of uncertainty -- can encourage a re-thinking of the risks we face, and the actions we can take.
Because a simulation yields many possible values for the outcomes we care about -- from Net Profit to environmental impact -- some work is needed to analyze the results. It is very useful to create charts to help us visualize the results -- such as charts and cumulative frequency charts. We can summarize the range of outcomes using various kinds of statistics, such as the mean or median, the standard deviation and variance, or the 5th and 95th percentile or Value at Risk.
Another tool for assessing model results is sensitivity analysis, which can help us identify the uncertain inputs with the biggest impact on our key outcomes. For example, a tornado chart can give us a visual summary of uncertainties with the greatest positive and negative impact on net profit.