Monte Carlo Simulation

The Monte Carlo Simulation encompasses “any technique of statistical sampling employed to approximate solutions to quantitative problems” (Monte Carlo Method, 2005). The Monte Carlo method simulates the full system many times, each randomly choosing a value for each variable from its probability distribution. The outcome is a probability distribution of the overall value of the system calculated through the iterations of the model. A standard approach to risk management of projects is outline by Project Management Institute (2004) that includes six processes: Risk Management Planning, Risk Identification, Risk Qualification, Risk Quantification, Risk Response Planning, and Risk Monitoring and Control. Monte
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Monte Carlo, by actually running through hundreds or thousands of project cycles handles these path convergence situations. Although the limitations for Monte Carlo simulation are mainly from the past due to technology issues pertaining to the use of computers and the time and resources spent to complete the simulation activity. A lack of easy to use software tools to run complex simulation against project schedules was also a problem. But with the improvements in technology these are almost obsolete concerns. The only problem with the simulation would be it is only as good as the information being entered. Which goes to the old saying “garbage in equal’s garbage out”? Monte Carlo analysis involves determining the impact of the of the identified risk by running simulations to identify the range of possible outcomes for a number of scenarios. A random sampling is performed by using uncertain risk variable inputs to generate the range of outcomes with a confidence measure for each. This is typically done by establishing a mathematical model and then running simulations using this model to estimate the impact of project risk, this technique helps in forecasting the likely outcome of an event and thereby helps in making informed