Interactive Project Risk Management Demonstration

Monte Carlo Concept Simulator

See how repeated random sampling converts uncertain cost estimates and a discrete risk event into a distribution of possible project outcomes, then interpret mean cost, P50, P80, P90, contingency and sensitivity.

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Demonstration at a glance
Level Intermediate
Typical duration 10–15 minutes
Learning format Guided probabilistic simulation
Best for Project managers, risk professionals and controls practitioners
Demonstration overview

Replace a Single-Point Estimate with a Distribution of Possible Outcomes.

Monte Carlo simulation repeatedly samples uncertain inputs and recalculates the project outcome. This concept simulator uses four triangular project-cost estimates and one discrete cost-risk event. Thousands of trials create a distribution that can be interpreted through percentiles and confidence levels.

  • Review Minimum / Most Likely / Maximum cost ranges.
  • Add a discrete risk event with probability and cost consequence.
  • Run one trial and see each sampled value.
  • Repeat thousands of times and build a histogram.
  • Read P50, P80 and P90 confidence levels.
  • Translate P80 into illustrative contingency above the deterministic baseline.
  • Review which inputs drive the most variation.
Guided Demonstration

Monte Carlo Concept Simulator — Step by Step

Complete each locked stage before the next unlocks. Every guided action changes the simulation pane.

Learning stages
Foundation Stage 1 of 8

Review the Uncertain Cost Inputs

Current instruction
Interactive Simulation Pane

Inputs, Trials, Distribution and Confidence

Uncertain cost ranges are introduced one input at a time.

100%
Input range Current sample Outcome distribution Percentile marker

Focus Mode keeps the guided-learning pane and dashboard together while hiding surrounding page content. Dashboard Full Screen shows only the simulation dashboard. Dashboard Full Screen is also available while Focus Mode is active; exiting it returns to Focus Mode. Zoom affects only the simulation pane.

Simulation status: Review the uncertain inputs before sampling.
Supporting Input Table

Simulation Inputs

The table mirrors the guided sequence.

Input Distribution Minimum Most Likely Maximum Discrete Risk
Experiment Mode

Change Uncertainty and Re-run the Simulation

Experiment Mode is isolated from the guided example. Adjust construction uncertainty, discrete-risk probability, confidence target and number of trials.

Mean simulated cost —
Selected confidence cost —
Contingency above baseline —
Probability baseline is sufficient —
Check Your Understanding

Quick Knowledge Check

Choose an answer, then check it.
Monte Carlo Simulation Quick Reference

Core Concepts and Interpretation

Concept Meaning Interpretation
Input Distribution Represents uncertainty rather than one fixed value This example uses triangular Minimum / Most Likely / Maximum cost distributions.
Iteration / Trial One random sample from every uncertain input One trial is one possible future—not the forecast.
Monte Carlo Simulation Repeat the trial many times The outcomes approximate the distribution implied by the input assumptions.
P50 / P80 / P90 50th / 80th / 90th percentiles Approximately that percentage of simulated outcomes are at or below the percentile value.
Contingency Selected percentile cost − deterministic baseline The confidence target should reflect governance and risk appetite.
Sensitivity Relative association between uncertain inputs and output Highlights which assumptions drive the most simulated variation.
Important limitation: Monte Carlo simulation does not create better information than the assumptions supplied to it. Poor ranges, unrealistic distributions, ignored correlations or missing risks can produce precise-looking but misleading output. This is a concept simulator, not a production quantitative-risk model.
Define Uncertainty
Sample → Recalculate → Repeat
Distribution → Confidence → Decision