What is a Monte Carlo Simulation?

A Monte Carlo Simulation is a way of assessing the level of risk across a whole project. So, while you may not need to use this powerful methodology, it's vital knowledge for any project manager.

The Monte Carlo technique takes its names from the random chances at the gaming tables of Monte Carlo.

We most often use it to assess schedule risk, but we can also use it for budget risk.

How it works
For each activity on the project, estimate a range of probabilities for different possible durations. The set of durations and your estimates of their probability is the “probability distribution” for the activity durations.

The distribution we often use is called the “Beta Function”. But each task will have its own distribution of likelihoods of duration; some simple, some complex. Each will apply to different types of activity.

The Monte Carlo method calculates how long the total project will take, by adding up the durations of all of the activities each with a duration randomly assigned. The probability of each task’s duration is defined by the probability function in our curve. To simulate the random nature, the Monte Carlo simulation repeats the calculation, with a different set of random durations many, many times. This gives a distribution of all of the possible total project durations.

Monte Carlo is more than just a calculation
The calculation is the easy part. Making the estimates of the probability distribution for each task is time-consuming. The quality of the estimates you make will entirely determine the validity of the resulting distribution. If all that you do is use the same distribution for each activity, you will simply reproduce that pattern for the whole project.

There is another flaw in the Monte Carlo method, which renders its results less reliable than you might think. It contains the assumption that the probability distribution for each task is independent of all others.

Yet we know that a delay in one activity can often make others more likely to be delayed too. And, also, that one event can affect multiple tasks. As in all risk management, the strides towards rigor have only got us so far. There is no substitute for good judgment, and appropriate skepticism and caution.


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