Simulation
Simulation is a data analysis technique that uses mathematical models to evaluate the probability of various project outcomes by running multiple iterations with variable inputs, most commonly through Monte Carlo analysis.
Explanation
Simulation in project management typically refers to Monte Carlo simulation, a technique that uses probability distributions for activity durations, costs, or other variables to calculate the likelihood of different project outcomes. The simulation runs thousands of iterations, each time randomly sampling values from the defined distributions, to produce a probability distribution of possible results.
Monte Carlo simulation is most commonly applied to schedule risk analysis and cost risk analysis. For schedule analysis, it produces a probability curve showing the likelihood of completing the project by various dates. For cost analysis, it shows the probability of completing within various budget amounts. This information helps project managers set realistic targets and determine appropriate contingency reserves.
Simulation requires specialized software tools and well-defined probability distributions for input variables. While more complex than deterministic techniques, it provides significantly more insight into project risk by quantifying the range of possible outcomes rather than relying on a single-point estimate.
Key Points
- •Monte Carlo is the most common simulation technique in project management
- •Runs thousands of iterations with randomly sampled input variables
- •Produces probability distributions of possible project outcomes
- •Used primarily for schedule risk analysis and cost risk analysis
Exam Tip
Monte Carlo simulation produces a probability distribution, not a single answer. It tells you the likelihood of meeting a target date or budget. Know that it requires specialized software.
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