Method
How the simulation works
The Journey produces governance-ready outputs by combining a deterministic baseline plan with probabilistic risk sampling and dependency-aware schedule propagation.
Baseline first
We start with a deterministic plan: task dates, dependencies, and budgets define the baseline cost curve and finish date. This baseline is the reference for all governance decisions.
Monte Carlo sampling
Each iteration samples risk uncertainty: trigger probability is sampled within its range, trigger is a Bernoulli event, then cost and time impacts are sampled from the configured impact ranges.
Schedule propagation
Time impacts apply to the linked tasks and propagate through finish-to-start dependencies. That means downstream activities move, and the project finish date moves with them.
Cost timing shifts
When the schedule shifts, spending shifts in time as well. Total cost still reflects sampled risk cost, but the monthly placement changes—creating a cumulative fan chart over time.
Governance outputs (and why they matter)
P-levels (e.g. P50, P85) summarize the simulated distribution for total cost and finish date. They let you state requirements as confidence: “fund to P85” or “commit to P50”.
Governance targets (budget and date) are evaluated directly against the per-iteration arrays to compute chance-within-target and late & over exposure—without re-running the simulation when only the thresholds change.
The Scale step extends the same logic to a program: multiple projects are simulated and aggregated so portfolio governance remains mathematically defensible (not a percentile multiplier).