Saturday, October 27, 2018

Risk Management - Monte Carlo simulation

If you are involved in risk management, you need to be aware of the Monte Carlo simulation. The Monte Carlo simulation is a quantitative risk analysis technique which is used to identify the risk level of completing the project.

Monte Carlo Simulation

The Monte Carlo simulation was invented by an atomic nuclear scientist named Stanislaw Ulam in 1940, and it was named Monte Carlo after the town in Monaco which is famous for its casinos.
This is a mathematical technique that allows you to account for risks in your decision-making process. With the help of this technique, you can determine the impact of the identified risks by running simulations many times, and identify a range of possible outcomes in different scenarios.
You can use the Monte Carlo simulation to analyze the impact of risks on forecasting models such as cost, schedule estimate, etc. You need this technique here because in these types of decisions, some degree of uncertainty exists. If you don’t use this technique, your outcome will not be sound and the results of your decision may surprise you at a later stage.
This technique gives you a range of possible outcomes and the probabilities that will occur for any choice of action.
For example, let’s discuss the use of the Monte Carlo simulation in determining the project schedule.
Example
To perform the Monte Carlo simulation to determine the schedule, you must have duration estimates for each activity.
Let’s say that you have three activities with the following estimates (in months):
table-1-monte-carlo-simulationFrom the above table you can deduce that according to the PERT estimate, these three activities will be finished in 17.5 months.
However, in the best case, it will be finished in 16 months, and in the worst case it will be finished in 21 months.
Now, if we run the Monte Carlo simulation for these tasks five hundred times, it will show us results such as:
table 3 for monte-carlo-simulation
From the above table 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
So, as you can see, this program provides you with a more in-depth analysis of your data which helps you make a better informed decision.

Limitations of the Monte Carlo Simulation

The Monte Carlo simulation has its own set of limitations. Some of these limitations are as follows:
  • To run the Monte Carlo simulation, you input three estimates for an activity. If you show some bias in determining the estimates, your result will not give you a correct analysis. Therefore, the results depend on the quality of your estimates.
  • The Monte Carlo simulation shows you the probabilities of completing the tasks. It is not the actual time taken to complete the task.
  • The Monte Carlo simulation technique cannot be applied to a single task or activity; you need to have all activities, and the risk assessment completed for each activity.
  • 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 chances of failure, and schedule and cost overrun.
  • It converts risks into numbers to assess the risk impact on the project objective.
  • 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 the support of objective data.
  • It helps you to find out the chances of achieving your project milestones or intermediate goals.

Summary

The Monte Carlo simulation is a very important tool and technique in the quantitative risk analysis process which helps you make decisions based on an objective data. Although this technique is not used frequently in low and low-medium sized projects, if used it increases the chances of achieving project success within approved baselines.

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