The Monte Carlo simulation is an important technique in risk management that many PMP and PMI-RMP exam study books do not describe in detail.
Most of the guides say it is a complex technique that requires a computer’s assistance, and so aspirants don’t dig further. This assumption is not true; it is a straightforward technique.
Monte Carlo Simulation
The Monte Carlo simulation is a quantitative risk analysis technique used in identifying the risk level of achieving objectives.
This technique was invented by an atomic nuclear scientist named Stanislaw Ulam in 1940, it was named Monte Carlo after the city in Monaco that is famous for casinos.
Monte Carlo Simulation is a mathematical technique that allows you to account for risks in decision-making. It helps you determine the impact of the identified risks by running multiple simulations and finding a range of outcomes.
Every decision has a degree of uncertainty, and Monte Carlo Simulation helps you in such situations. It makes your decision sound and avoids surprises later. You can run this simulation to analyze the impact of the risks on cost, schedule estimate, etc.
This technique gives you a range of possible outcomes and the probabilities that will occur for any choice of action.
Let’s discuss the Monte Carlo Simulation’s use in determining the project schedule. Suppose that you have three activities with the following estimates (in months):
From the above table you can see that, according to the PERT estimate, you can complete these activities in 17.5 months.
In the best case, you can complete them in 16 months, and in the worst case, 21 months.
Now, if we run the Monte Carlo Simulation for these tasks, five hundred times, it will show us the following results:
(Please note that the above data is for illustration purpose only and it is not from an actual simulation test.)
You can see that there is a:
- 2% chance of completing the project in 16 months
- 8% chance of completing the project in 17 months
- 55% chance of completing the project in 18 months
- 70% chance of completing the project in 19 months
- 95% chance of completing the project in 20 months
- 100% chance of completing the project in 21 months
This technique provides you with a more in-depth analysis of your data, and you can make a better-informed decision.
Limitations of the Monte Carlo Simulation
The Monte Carlo simulation has a few limitations, for example:
- The results depend on the quality of your estimates, so if the data are biased, the simulation will give a false result.
- The Monte Carlo Simulation shows the probability of completing the tasks, not the actual time to complete.
- This technique is not useful for a single activity; you need to have many activities with risk assessments completed.
- You will need to buy an add-on or a software program to run the Monte Carlo simulation.
Benefits of the Monte Carlo Simulation
The Monte Carlo simulation method has many benefits in project management, such as:
- It helps you evaluate the risk of the project.
- It helps you predict the chances of failure, and schedule and cost overrun.
- It converts risks into numbers to assess the risk impact on the project objectives.
- It helps you build a realistic budget and schedule.
- It helps you gain management support for risk management.
- It helps you in decision making with objective evidence.
- It helps you to find the chances of achieving project milestones or intermediate goals.
The Monte Carlo Simulation is an essential technique in risk analysis that helps you make decisions under uncertain conditions. Although this technique is often not used in projects, if used it increases the chances of achieving project success within the approved baselines.
Have you used the Monte Carlo Simulation in your projects? How was it useful? Please share your thoughts in the comments section.