7 Steps to Monte Carlo

Known globally as The Risk Doctor, David has been working in risk management for about 30 years. He has worked in 48 countries on every continent except the Antarctic (too cold!), with clients in most industries.

  Risk Management   ProjectsAtWork  

Applying Monte Carlo methods to risk analysis does not need to be complex, and it should not be feared or avoided. Following these seven simple steps can help ensure robust and realistic modelling, and allow you to gain the benefits of this powerful technique.

Monte Carlo methods are a class of computational algorithms that use repeated random sampling to model significant uncertainty in inputs. Monte Carlo is the most common way to analyze business or project risk using numbers. But many project management practitioners view this type of quantitative risk analysis as too difficult or time-consuming, perhaps because it involves mathematics, statistics and computers. As a result, they miss out on the insights available from this powerful technique.

The following seven steps can make it easier to apply Monte Carlo methods to risk analysis:

1. Define your purpose. Why do you need to do this analysis? What is the scope? You might only be interested in one type of risk exposure, such as risk to cost, schedule, resource levels, profitability or cashflow. Or maybe you need an integrated view of overall exposure to several types of risk. The questions to be answered should be clearly defined at the start. For example, are we making a “go/no-go” decision, or working out how much contingency we need, or assessing what outcomes are possible, or trying to find the …

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