In an effort to bring hands-on Practical Project Management to Nigerians, PMtutor integrates a computerized mathematical technique into its PMtutor Enhanced curriculum – this allows delegates to account for risk during project analysis and decision-making. Despite we have unprecedented access to information, we can’t accurately predict the future. Monte Carlo simulation lets you see all the possible outcomes of your decisions and assess the impact of risk, allowing for better decision making under uncertainty.
The simulation is named after the casino resort city in Monaco, France during the World War 2. It performs risk analysis by building models of possible results by substituting a range of values (probability distribution) for any factor that has inherent uncertainty. With probability distribution, variables can have different type of outcomes occurring. It can be applied to any project cost estimate and it is one of the best way to analyze uncertain decision.
The features of Monte Carlo Simulation include:
1. Probabilistic Results. Results show not only what could happen, but also how likely each outcome is.
2. Graphical Results. Because of the data a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders.
3. Sensitivity Analysis. With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results.
4. Scenario Analysis: In deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis.
5. Correlation of Inputs. In Monte Carlo simulation, it’s possible to model interdependent relationships between input variables. It’s important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly.
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