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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In Monte Carlo simulations, random values are assigned to each project task and are based on the range of outcomes for each task estimate. A model is then constructed and then based on these random values. The model is run and the results are recorded. This process is repeated hundreds or even thousands of times of times depending on the complexity of the project. Each run of the model is different because it uses randomly selected variables. When all of the simulations are finished, the results are then aggregated. Often these results differ from preliminary project estimates.
In basketball, rebounds from errant three-point shots bounce further away from the basket, so the shooter's team has a better chance to retain possession after a miss and to try again to score. This previously unforeseen outcome affects the risk versus reward decision facing basketball players.
Value of Monte Carlo Simulation
Since original estimates may differ from those provided by Monte Carlo simulation, this provides a low risk, low cost opportunity to test their estimates before initiating a project and incurring potential unplanned costs and schedule delays. Monte Carlo simulations provide a comfort level that their overall estimate has a good chance to succeed or discomfort if potential results differ greatly from original estimates. Differences between original estimates and Monte Carlo simulation can be valuable since they may point to areas of greatest risk and uncertainty or to crucial variables that can be changed to increase the likelihood of project success and more optimal use of resources.
So what should a basketball player do? Monte Carlo simulations will show that attempting a three-point shot offers a greater chance to tie the game when compared to the alternative. Even though the chance of making a three-point shot is lower, the risk is secondary to the reward of scoring an additional point and tying the game. The better that project teams understand about project uncertainty and risk at the beginning of the project enables the team to improve the project plan. The same holds true in basketball.
During World War II, Monte Carlo simulation helped the Manhattan Project scientists predict results. Today, Monte Carlo simulation is the most widely used method of sensitivity analysis utilized by project managers to validate their task estimates and likelihood of outcomes in order to better manage risk and uncertainty. Project managers can use this aggregate of simulation results from hundreds or thousands of simulation runs to review and possibly change crucial variables.
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