Using the Monte Carlo Simulation in Risk Management

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  • 0:04 The Monte Carlo Simulation
  • 1:05 Mitigating Project Risk
  • 3:02 How It Works
  • 3:55 Value of Monte Carlo…
  • 5:06 Lesson Summary
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Lesson Transcript
Instructor: Michael Fanning

Mike has been a Project Management Professional (PMP) for 12 years and has a master's degree in environmental, health and safety management.

In this lesson, students will learn how the Monte Carlo simulation enables testing of preliminary task estimates to help reduce project uncertainty and risk.

The Monte Carlo Simulation

There is an interesting story to how the Monte Carlo simulation got is name. In World War II, the United States was attempting to develop an atomic bomb that could end the war. Everything related to this effort was top secret and its code name was the Manhattan Project. The science behind this new weapon was based on theoretical physics, and one of the most important parts was its forecasting model for how much energy the bomb would produce.

Stanislaw Ulam was one of the scientists who developed a method for probability simulation to test the concepts before actually exploding an atomic bomb. His work was code named Monte Carlo since he had an uncle who borrowed money from family members to gamble in games of chance at Monte Carlo's casinos.

Calculating risk and forecasting future events may be important to gamblers, but it is even more important for project managers who use the Monte Carlo simulation to better understand and manage uncertainties and risk related to cost estimates and to contingencies related to these uncertainties and risks.

Mitigating Project Risk

One of the important elements in planning a project is to identify, assess, and mitigate risks. In doing so, project teams must make assumptions about frequency and severity of incidence, cost impacts, and time estimates. Since the future is unknown, estimating an expected value is part of the best management practice.

Estimating, however, is not optimal. Even when these estimates are based on past experience or historical data, forecasts based on estimates carry risk since they are based on estimates of unknown values.

This makes sense, but what should you do when faced with a real-world problem? Basketball players are sometimes faced with a split-second decision when their team trails by three points in the last moments of the game. Should the player attempt a three-point shot, or should he try a higher percentage two-point shot and then foul an opponent to get the ball back? There are many potential permutations of outcomes, which can affect the result of a basketball game or the schedule, budget, resource allocation and other elements of your project. This is where a Monte Carlo simulation provides tremendous value for risk management by providing sensitivity analysis.

Sensitivity analysis assesses the impacts of various computations and assumptions on the final outcome of a project. This also attempts to predict alternative outcomes based on the likelihood of different assumptions happening. Taken as whole, this provides an opportunity to reduce uncertainty by identifying those project elements that contribute the greatest risk. Conversely, this analysis shows the range for minimal and maximum outcomes.

Monte Carlo simulation is the most widely used form of sensitivity analysis and provides a road map to an optimal outcome of a project for the project team. Monte Carlo simulations are popular with project teams since these simulations can tell you how likely potential outcomes are to happen. These likelihoods are based on the ranges of estimates created by the project team.

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