What Is Monte Carlo Analysis in Project Management
Monte Carlo Analysis is a risk management technique that is used for conducting a quantitative analysis of risks. This mathematical technique was developed in 1940, by an atomic nuclear scientist named Stanislaw Ulam. It’s meant to be used to analyze the impact of risks on your project. For example, if this risk occurs, how will it affect our schedule and/or the cost of the project? Monte Carlo gives you a range of possible outcomes and probabilities to allow you to consider the likelihood of different scenarios.
For example, let’s say you don’t know how long your project will take. You have a rough estimate of the duration of each project task. Using this, you also come up with a best-case scenario (optimistic) and worst case scenario (pessimistic) duration for each task.
You can then use Monte Carlo to analyze all the potential combinations and give you probabilities on when the project will complete.
The results would be something like this:
- 2% chance of completing the project in 12 months. (In other words, if every single task finished by the optimistic timeline.)
- 15% chance of completion within 13 months.
- 55% chance of completion within 14 months.
- 95% chance of completion within 15 months.
- 100% chance of completion within 16 months. (If everything took as long as the pessimistic estimates.)
Using this information, you can now better estimate your timeline and plan your project.
Benefits of Monte Carlo analysis in project management
The primary benefits of using Monte Carlo analysis on your projects are:
- Provides early identification of how likely you are to meet project milestones and deadlines.
- Can be used to create a more realistic budget and schedule.
- Predicts the likelihood of schedule and cost overruns.
- Quantifies risks to assess impacts better.
- Provides objective data for decision making.
Limitations of Monte Carlo analysis in project management
- You must provide three estimates for every activity or factor being analyzed.
- The analysis is only as good as the estimates provided.
- The Monte Carlo simulation shows you the overall probability for the entire project or a large subset of it (such as a phase). It can’t be used to analyze individual activities or risks.