According to the PMBOK® Guide (specifically within the Perform Quantitative Risk Analysis process) and the PMI Standard for Risk Management, a Monte Carlo simulation is a technique used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables.
The results of a Monte Carlo simulation are typically presented in two main formats:
A Histogram: Showing the frequency of various outcomes.
An S-curve (Cumulative Probability Distribution): This curve is formed by plotting the cumulative frequencies of the results.
Key characteristics of the S-curve in this context:
X-Axis: Represents the project values (e.g., total cost or completion date).
Y-Axis: Represents the cumulative probability (ranging from 0% to 100%).
Interpretation: The S-curve allows project managers to determine the probability of achieving a specific target. For example, it can show that there is an 80% chance (P80) of completing the project for $1M or less. This helps in determining necessary contingency reserves.
Analysis of other options:
B. Individual project risks (Tornado Diagram): A Tornado diagram is used in quantitative risk analysis to show which risks have the most influence on the project outcome, not the S-curve.
C. Best alternative (Decision Tree Analysis): Decision trees are used to evaluate different paths or choices under uncertainty to find the best alternative based on expected monetary value (EMV).
D. Diagram for all uncertainties over time: This is a general description and does not specifically define the mathematical function of an S-curve in simulation results.
In summary, PMI documentation identifies the S-curve as the primary graphical tool for communicating the cumulative probability of meeting project objectives, providing a quantifiable level of confidence for stakeholders.
