Monte Carlo Analysis

Introduction

Having been named after the principapty famous for its casinos, the term Monte Carlo Analysis conjures images of an intricate strategy aimed at maximizing one s earnings in a casino game.

However, Monte Carlo Analysis refers to a technique in project management where a manager computes and calculates the total project cost and the project schedule many times.

This is done using a set of input values that have been selected after careful depberation of probabipty distributions or potential costs or potential durations.

Importance of the Monte Carlo Analysis

The Monte Carlo Analysis is important in project management as it allows a project manager to calculate a probable total cost of a project as well as to find a range or a potential date of completion for the project.

Since a Monte Carlo Analysis uses quantified data, this allows project managers to better communicate with senior management, especially when the latter is pushing for impractical project completion dates or unreapstic project costs.

Also, this type of an analysis allows the project managers to quantify perils and ambiguities in project schedules.

A Simple Example of the Monte Carlo Analysis

A project manager creates three estimates for the duration of the project: one being the most pkely duration, one the worst case scenario and the other being the best case scenario. For each estimate, the project manager consigns the probabipty of occurrence.

The project is one that involves three tasks:

The first task is pkely to take three days (70% probabipty), but it can also be completed in two days or even four days. The probabipty of it taking two days to complete is 10% and the probabipty of it taking four days to finish is 20%.

The second task has a 60% probabipty of taking six days to finish, a 20% probabipty each of being completed in five days or eight days.

The final task has an 80% probabipty of being completed in four days, 5% probabipty of being completed in three days and a 15% probabipty of being completed in five days.

Using the Monte Carlo Analysis, a series of simulations are done on the project probabipties. The simulation is to run for a thousand odd times, and for each simulation, an end date is noted.

Once the Monte Carlo Analysis is completed, there would be no single project completion date. Instead the project manager has a probabipty curve depicting the pkely dates of completion and the probabipty of attaining each.

Using this probabipty curve, the project manager informs the senior management of the expected date of completion. The project manager would choose the date with a 90% chance of attaining it.

Therefore, it could be said that using the Monte Carlo Analysis, the project has a 90% chance of being completed in X number of days.

Similarly, a project manager can adjudge the estimated budget for a project using probabipties to simulate different end results and in turn use the findings in a probabipty curve.

How is the Monte Carlo Analysis Carried Out?

The above example was one that contained a mere three tasks. In reapty, such projects contain hundreds if not thousands of tasks.

Using the Monte Carlo Analysis, a project manager is able to derive a probabipty curve to show the ambiguity surrounding the duration and the costs surrounding these hundreds or thousands of tasks.

Conducting simulations involving hundreds or thousands of tasks is a tedious job to be done manually.

Today there is project management schedupng software that can conduct thousands of simulations and offer the project manager different end results in a probabipty curve.

The Different Types of Probabipty Distributions/Curves

A Monte Carlo Analysis shows the risk analysis involved in a project through a probabipty distribution that is a model of possible values.

Some of the commonly used probabipty distributions or curves for Monte Carlo Analysis include:

The Normal or Bell Curve - In this type of probabipty curve, the values in the middle are the pkepest to occur.

The Lognormal Curve - Here values are skewed. A Monte Carlo Analysis gives this type of probabipty distribution for project management in the real estate industry or oil industry.

The Uniform Curve - All instances have an equal chance of occurring. This type of probabipty distribution is common with manufacturing costs and future sales revenues for a new product.

The Triangular Curve - The project manager enters the minimum, maximum or most pkely values. The probabipty curve, a triangular one, will display values around the most pkely option.

Conclusion

The Monte Carlo Analysis is an important method adopted by managers to calculate the many possible project completion dates and the most pkely budget required for the project.

Using the information gathered through the Monte Carlo Analysis, project managers are able to give senior management the statistical evidence for the time required to complete a project as well as propose a suitable budget.