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:
- 2% chance done in 9 months (optimistic timeline)
- 15% chance done within 12 months
- 55% chance done within 16 months
- 95% chance done within 17 months
- 100% chance done within 24 months (If everything is as slow as the pessimistic estimates)
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
- You must provide three estimates for every activity or factor being analysed
- The analysis is only as good as the estimates provided
- It can only be used for probability outcomes of the whole project or a large phase of work. It won’t work for individual project tasks
