The Monte Carlo Simulation is an advanced risk analysis technique that uses computational models to predict probable project outcomes through a series of randomized scenario simulations. It empowers project managers and owners by evaluating various scenarios, and assessing the likelihood of reaching specific goals.
In other words, it analyzes the probabilities of risks on your project outcomes — if a risk occurs, how will it affect the schedule or the cost of the project?
The range of possible outcomes and probabilities allow PMs to consider the likelihood of project outcomes under different scenarios. This differs significantly from the Critical Path Method, which uses single-sequence project estimates, which gives a false notion that future outcomes can be predicted precisely.
Real world example of Monte Carlo Simulation
For example, if you have only a rough estimate of the duration of each project task—you can develop best-case (optimistic) and worst-case (pessimistic) scenarios for how long those tasks might take.
You can use Monte Carlo analysis to calculate the most likely completion date for a project based on numerous combinations of possible outcomes. Below is the kind of results you might obtain.
Example of expected likelihood of project completion:
Using this information, you can now more realistically estimate your budget and timeline to plan your project. It also allows for early evaluation of whether you are likely to meet project milestones and deadlines.
Downsides of using Monte Carlo simulations on capital projects
