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.

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 opens the guided workbench as a dedicated learning overlay while keeping the current learning stage and simulation dashboard together. Dashboard Full Screen expands only the simulation dashboard. Exiting Dashboard Full Screen returns to Focus Mode when Focus Mode is active. 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.

Construction minimum Construction most likely Construction maximum Discrete-risk probability (%) Confidence target 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

Key Takeaway

Monte Carlo simulation turns uncertain inputs into a distribution of possible project outcomes rather than a single deterministic answer.

Define realistic uncertainty ranges and discrete risks, run enough trials for stable results, interpret P50/P80/P90 as percentiles rather than guarantees, and use sensitivity analysis to identify which assumptions most strongly drive the simulated outcome range.