Simulation is an analytical technique that models the combined effect of uncertainties to evaluate their potential impact on objectives.
The effects of uncertainties are analysed and translated in to range estimates in the form of optimistic, most likely and pessimistic estimates using PERT or three point estimating techniques. From these estimates, the mean value is calculated using appropriate distribution models. The same estimates are used further in simulation techniques like Monte Carlo analysis evaluate potential impact on the overall project schedule or budget. This analysis will help to commit schedule or budget based on stakeholders risk tolerance. Also contingency reserved will be calculated based on the simulation results
Within the PMBOK® Guide, Simulation technique is used in the following processes:
Project Schedule Management
Perform Quantitative Risk Analysis
Project Risk Management
Simulation models the combined effects of individual project risks and other sources of uncertainty to evaluate their potential impact on achieving project objectives. The most common simulation technique is Monte Carlo analysis, in which risks, and other sources of uncertainty are used to calculate possible schedule outcomes for the total project.
Simulation involves calculating multiple work package duration’s with different sets of activity assumptions, constraints, risks, issues, or scenarios using probability distributions and other representations of uncertainty. Below shows a probability distribution for a project with the probability of achieving a certain target date (i.e., project finish date). In this example, there is a 10% probability that the project will finish on or before the target date of May 13, while there is a 90% probability of completing the project by May 28.
For more information on how Monte Carlo simulation is used for schedule models, see the Practice Standard for Scheduling.
PERFORM QUANTITATIVE RISK ANALYSIS
Simulation. Quantitative risk analysis uses a model that simulates the combined effects of individual project risks and other sources of uncertainty to evaluate their potential impact on achieving project objectives. Simulations are typically performed using a Monte Carlo analysis. When running a Monte Carlo analysis for cost risk, the simulation uses the project cost estimates. When running a Monte Carlo analysis for schedule risk, the schedule network diagram and duration estimates are used. An integrated quantitative cost-schedule risk analysis uses both
inputs. The output is a quantitative risk analysis model. Computer software is used to iterate the quantitative risk analysis model several thousand times. The input values (e.g., cost estimates, duration estimates, or occurrence of probabilistic branches) are chosen at random for each iteration. Outputs represent the range of possible outcomes for the project (e.g., project end date, project cost at completion). Typical outputs include a histogram presenting the number of iterations where a particular outcome resulted from the simulation, or a cumulative probability distribution (S-curve) representing the probability of achieving any particular outcome or less. An example S-curve from a Monte Carlo cost risk analysis is shown
For a quantitative schedule risk analysis, it is also possible to conduct a criticality analysis that determines which elements of the risk model have the greatest effect on the project critical path. A criticality index is calculated for each element in the risk model, which gives the frequency with which that element appears on the critical path during the simulation, usually expressed as a percentage. The output from a criticality analysis allows the project team to focus risk response planning efforts on those activities with the highest potential effect on the overall schedule performance of the project.
PMBOK® GUIDE SIXTH EDITION