Simulation Basics

Business risk is generally thought of as either the chance of some unfortunate event happening or as the volatility of a key performance measure such as profits. Decision trees, which are discussed in the previous chapter, provide an excellent method of planning contingent actions to take in response to events. Monte Carlo simulations, on the other hand, are best for modeling uncertainty and volatility.

Monte Carlo simulation allows you to replace uncertain quantities in your model with fuzzy numbers and then see how that uncertainty affects your results. Like decision trees, Monte Carlo simulations result in an expected value that aids in choosing the most attractive course of action. They also provide information about the range of outcome, probability of reaching specific targets, most likely outcomes, etc.

The nice thing about Monte Carlo simulation is that it is easy to apply. When you combine several uncertain values, determining the uncertainty on the result can be very complex. For example, when you add two uncertain values, the uncertainty on the result is somewhat less that the sum of the original uncertainties. Using Monte Carlo simulation, this and similar effects are handled automatically so you don't need to know much about statistics to get accurate results.

See Also

Building a Simulation Model

Running the Simulation

Interpreting the Results