Monte Carlo Simulation
What is it?
Monte Carlo simulation is a technique for modeling uncertainty by running thousands of randomized experiments and aggregating the results into a probability distribution. In project planning, it answers the question:
Given the uncertainty in task durations and risk levels, what is the probability distribution of our project’s end date?
Rather than producing a single deterministic delivery date (which is almost always wrong), Monte Carlo gives you a range of outcomes with associated probabilities — which is a far more honest and useful way to reason about the future.
Why It Matters for Project Planning
All schedules are estimates. Tasks take longer than expected. External dependencies are delayed. The “optimistic” plan is almost never what ships.
Monte Carlo simulation forces you to make your uncertainty explicit — by assigning risk levels to tasks — and then tells you the statistical consequences of that uncertainty. You stop pretending you know exactly when things will finish, and start understanding how likely different outcomes are.
How Lineo Runs It
Lineo-PM’s simulation engine runs N iterations (a configurable number of passes) over your project schedule. In each iteration:
- Task durations are sampled randomly — based on the task’s baseline duration and its assigned risk level. Higher risk tasks have wider variance in their sampled duration.
- The full schedule is simulated with the sampled durations, respecting all dependency relationships.
- The resulting project end date is recorded for that iteration.
After all iterations complete, the results are aggregated into statistical outputs.
Simulation Outputs
| Output | Description |
|---|---|
| Slip Probability | The percentage of simulated runs in which the project completes after the target end date |
| Typical Delay | The median (P50) delay over the target date across all simulations |
| Percentile Delays | P75, P85, P95 — how bad the delay gets at increasingly conservative confidence levels |
| Delay Distribution | A histogram of simulated end dates, showing the shape of the uncertainty |
| Per-Task Slip Risk | The probability that each individual task finishes late in the simulation |
| Critical Index | How often each task appears on the critical path across simulations — a measure of structural importance |
| Critical Path | The sequence of tasks that most frequently determines the project end date |
Interpreting Results
A slip probability of 30% means that under the modeled uncertainty, roughly 1 in 3 simulated projects misses the deadline. Whether that is acceptable depends on the stakes.
The critical index is particularly valuable: a task with a high critical index is not just on one critical path — it is on many of the simulated critical paths, meaning it is a consistently high-leverage point for risk reduction.
Use the Risk Analysis guide for a walkthrough of running a simulation and interpreting the results dashboard.