Monte Carlo Simulation

A Monte Carlo analysis is an important technique used in risk management that many PMP and PMI-RMP exam study books gloss over. 

Most of the guides say it is a complex technique requiring a computer’s assistance, implying that PMP aspirants do not need further detail. This assumption is not true, and this simulation is a straightforward technique. 

Monte Carlo Analysis

A Monte Carlo analysis is a quantitative analysis technique used to identify the risk level of achieving objectives. 

This technique was invented by a nuclear scientist named Stanislaw Ulam, in 1940; it was named Monte Carlo after the famous casino city in Monaco. 

A Monte Carlo simulation is a mathematical technique that allows you to account for risks in decision-making. It helps you determine the impact of identified risks by running multiple simulations and finding a range of outcomes. 

Every decision has a degree of uncertainty, a Monte Carlo simulation helps you get more info. It allows you to make sound choices and avoid surprises later. You can run this simulation to analyze the impact of the risks on cost, schedule estimate, and much more. 

You get a range of possible outcomes and probabilities for any course of action.

Monte Carlo Analysis Example

Let’s discuss using Monte Carlo analysis when creating the project schedule. Suppose that you have three activities with the following duration estimates (in months):


According to the PERT estimate, you can complete these activities in 17.5 months. 

In the best case, it will only take you 16 months, and in the worst, 21 months. 

Now, if we run the Monte Carlo simulation for these tasks 500 times, it will show the following:

table 3 for monte-carlo-simulation

(Please note that the above data is for illustration purpose only and it is not from an actual simulation test.) 

(Please note that the above data is for example purposes only, not from an actual simulation.) 

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, allowing you to make a better-informed decision.

Limitations of the Monte Carlo Analysis

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, you will get a false result.
  • The Monte Carlo analysis shows the probability of completing the tasks, not the actual time required.
  • This technique is not useful for a single activity; you must have several and have 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 Analysis

This 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 in schedule and cost overrun.
  • It converts risks into numbers for easy assessment.
  • It helps you build a realistic budget and schedule.
  • It helps you gain management support.
  • 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 analysis is an essential technique in risk analysis that helps you make decisions under uncertain conditions. Although it is often not used in projects, it increases the chances of achieving project success within the approved project baselines when applied. 

Have you used the Monte Carlo analysis in your projects? How was it useful? Please share your thoughts in the comment section.