What advanced quantitative risk analysis technique simulates thousands of project scenarios to predict the probability distribution of completion dates and costs?

The advanced quantitative risk analysis technique that simulates thousands of project scenarios to predict the probability distribution of completion dates and costs is called Monte Carlo Simulation. This technique is used to model the probability of different outcomes when multiple input variables contain inherent uncertainty, providing a probabilistic forecast instead of a single-point estimate.

The process begins by identifying key project variables that are uncertain, such as the duration of individual project activities or the cost of resources. For each of these uncertain input variables, a probability distribution is defined. A probability distribution mathematically describes the range of all possible values for that variable and the likelihood or frequency of each value occurring. Common distributions used include triangular (defined by optimistic, most likely, and pessimistic estimates), normal, or uniform distributions, depending on the nature of the uncertainty.

Once the input distributions are defined, the simulation proceeds through thousands of iterations, also known as scenarios. In each iteration, the simulation software randomly samples a value for each uncertain input variable from its assigned probability distribution. For example, for an activity duration with a triangular distribution, a duration value between the optimistic and pessimistic estimates is randomly selected based on the defined probabilities. These randomly sampled values for all input variables are then used to calculate a specific outcome for that particular scenario, such as the total project completion date or the total project cost.

This process of randomly sampling values for inputs and calculating an outcome is repeated thousands or even tens of thousands of times. Each repetition represents a unique "scenario" of how the project could unfold based on different combinations of the uncertain variables. The results from all these thousands of scenarios are then collected and analyzed. This collection of outcomes forms an output probability distribution for the project's overall completion date and cost. This output distribution illustrates the full range of possible completion dates and costs, along with the associated probability of each outcome or range of outcomes occurring. For instance, it can show that there is an 80% probability of completing the project by a certain date or within a specific cost range, providing a comprehensive and fact-based understanding of project risk.