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Monte Carlo Simulation

A quantitative method that runs a computer model many times, randomly drawing input values from specified probability distributions and chance-based decision paths. The resulting set of simulations produces a distribution that shows the range and likelihood of possible project outcomes.

Key Points

  • Uses random sampling from defined probability distributions for uncertain inputs.
  • Models branching logic and risks to reflect different possible paths.
  • Generates probabilistic results (e.g., P50, P80) for cost, schedule, or scope outcomes.
  • Supports contingency sizing, risk-informed decisions, and communicating uncertainty.

Example

A project team assigns probability distributions to activity durations (for example, triangular estimates). They run 10,000 simulations of the schedule to estimate the chance of finishing by a contractual date. The output shows a completion date curve indicating 60% confidence by May 15 and 80% confidence by May 25, guiding buffer and contingency planning.

PMP Example Question

Which technique provides a probability distribution of project finish dates by repeatedly sampling activity duration estimates from their defined distributions?

  1. Three-point estimating
  2. Decision tree analysis
  3. Monte Carlo simulation
  4. Fast tracking

Correct Answer: C — Monte Carlo simulation

Explanation: Monte Carlo uses random sampling across many iterations to produce a probabilistic range of outcomes, such as a completion date distribution.

AI Systems with Claude, for Scrum Masters and Project Managers

Most AI pilots in project management do not fail on the model. They fail because somebody pointed the thing at a decision instead of at a task. This course is built around that distinction, and around the work that follows once you get it right.

Seven sections, taught against one running project from the first lecture to the last. You watch a system get built, then you build the same one against your own sprint. Nothing here is a demo that works only on the example.

You finish with five working systems and you keep them. The sprint report machine turns your board export and standup notes into the report you currently write by hand. The retro intelligence system tells you what the team keeps saying, not just what it said this time. The stakeholder comms engine drafts the update in the register the audience expects. The backlog health monitor flags the quiet decay nobody has time to check for. The risk and dependency tracker follows the chains that actually bite.

Seventy-six working files ship with it, across four sections, so every system is built against real sprint data rather than material invented for a slide. Four sprints of retrospectives, board exports, standup notes, backlog and risk data.

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