Probabilistic Schedule Risk Analysis
Monte Carlo simulation produces project-level confidence curves, milestone probability bands, and task-level statistical outputs — saved as named SRA packages that feed the BI pipeline.
Probabilistic schedule risk analysis extends the schedule health analytics capability with a Monte Carlo simulation surface. Where schedule health analysis evaluates the deterministic plan against quality frameworks, probabilistic SRA quantifies the uncertainty in that plan and produces confidence-based answers to scheduling questions: what is the probability of meeting the key milestone? What is the P80 project finish date? Which activities carry the most schedule risk?
From Rubric Model to Monte Carlo
The rubric-based uncertainty model described in the Schedule Risk Model feature is the direct input to Monte Carlo simulation. Because uncertainty assumptions are already embedded in the plan through rubric assignments, simulation requires no additional setup — the system runs against the current risk model and produces results immediately. This tight connection between the risk model and the simulation surface is what makes probabilistic SRA practical to run frequently rather than as a one-time exercise at the beginning of the project.
Durable SRA Packages
Completed simulation runs can be saved as named SRA packages. Each package is a durable, self-contained record of the simulation results including the full task-level analytical payload — statistical distributions, sensitivity contributions, and confidence-at-date outputs for every activity. Packages are stored in the workbook alongside the snapshot archive, and the package inventory shows storage consumption per record so teams can manage their analytical history as it accumulates over the project lifecycle.