Convert a deterministic schedule or cost estimate into a model suitable for Monte Carlo simulation.
Project Management
Quantitative Schedule and Cost Risk Analysis Using Monte Carlo Simulation
A technical course on quantitative schedule and cost risk analysis, building and interpreting Monte Carlo simulations to produce confidence-based project forecasts.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
A single-point schedule or budget hides the range of outcomes a project could actually produce, and it invites false confidence in a plan that only works if everything goes as assumed. Quantitative risk analysis using Monte Carlo simulation replaces that single point with a distribution: thousands of simulated runs of the schedule and cost model, each drawing randomly from defined uncertainty ranges and risk events, producing a confidence curve that shows the likelihood of finishing by a given date or within a given budget. The course builds this capability in stages, starting from a conventional schedule and cost estimate, adding uncertainty ranges and discrete risk events, running the simulation, and correctly interpreting the resulting P50, P80 and other confidence levels. Analysis that most courses skip gets full treatment here too: correlation between activities that share a common cause of delay, sensitivity analysis to identify which risks actually drive the outcome, and how to present a probabilistic result to a sponsor who wants one number. Participants finish able to build a defensible schedule and cost risk model, and to explain what it shows without retreating into statistical jargon.
Expected Learning Outcomes
Define uncertainty ranges and discrete risk events for individual activities in a risk model.
Run a Monte Carlo simulation and read the resulting cumulative probability distribution correctly.
Apply correlation between activities that share a common driver of delay or cost overrun.
Identify the risks and activities with the greatest influence on outcome through sensitivity analysis.
Set contingency levels based on a chosen confidence level such as P50 or P80 rather than intuition.
Present a probabilistic schedule or cost forecast to a sponsor without collapsing it into false precision.
Who Should Attend
Project controls professionals and schedulers responsible for quantitative risk analysis.
Cost engineers setting contingency and reserve levels on capital or infrastructure projects.
Risk managers who need to move beyond qualitative risk registers to numerical modelling.
Programme managers reviewing confidence-based forecasts submitted by project teams.
Project management office staff introducing quantitative risk analysis as a standard practice.
Engineers and analysts operating schedule or cost risk simulation software.
Course Modules
Select any module to see its sessions and points.
01Building the Quantitative Risk Model
2 sessions · 8 points
Session 1From Deterministic Estimates to Risk Models
- Identify which activities in a deterministic schedule carry the greatest genuine uncertainty.
- Convert a single-point duration or cost estimate into a three-point uncertainty range.
- Distinguish uncertainty inherent in an estimate from discrete risk events that may or may not occur.
- Prepare a schedule and cost model structurally ready for simulation rather than static reporting.
Session 2Selecting Distributions and Inputs
- Select an appropriate probability distribution, such as triangular or PERT, for a given activity.
- Source uncertainty ranges from historical data, expert judgement or a structured elicitation session.
- Represent a discrete risk event with a probability of occurrence and a separate impact range.
- Document modelling assumptions so the risk model can be reviewed and challenged before use.
02Running and Interpreting the Simulation
2 sessions · 8 points
Session 1Running a Monte Carlo Simulation
- Configure a Monte Carlo simulation with an appropriate number of iterations for stable results.
- Link discrete risk events to the specific activities they would affect if triggered.
- Run a simulation across a full schedule or cost model and generate the output distribution.
- Check simulation output for signs of a modelling error rather than a genuine result.
Session 2Reading Confidence Levels and Distributions
- Read a cumulative probability curve to identify the P50, P80 and other confidence levels.
- Explain the difference between a P50 date and the original deterministic finish date to a sponsor.
- Interpret a cost distribution to recommend a contingency value at an agreed confidence level.
- Recognise when a distribution's shape suggests a modelling assumption needs revisiting.
03Correlation and Sensitivity Analysis
2 sessions · 8 points
Session 1Modelling Correlation Between Activities
- Identify activities likely to be delayed together by a shared cause, such as weather or a supplier.
- Apply correlation coefficients between related activities so the model does not understate joint risk.
- Test the effect of ignoring correlation on the resulting confidence levels.
- Validate correlation assumptions against how the project has actually behaved in similar past work.
Session 2Sensitivity Analysis and Risk Prioritisation
- Run a sensitivity analysis to rank risks and activities by their influence on the outcome.
- Use a tornado diagram to communicate which few risks drive most of the schedule or cost variance.
- Prioritise risk response effort toward the risks sensitivity analysis identifies as most influential.
- Distinguish a risk that is merely likely from one that is likely and highly influential.
04Applying Results to Decisions and Communication
2 sessions · 8 points
Session 1Setting Contingency and Reserve from Simulation Results
- Recommend a contingency value tied explicitly to a stated confidence level from the simulation.
- Reconcile a simulation-derived contingency with a sponsor's fixed budget expectation.
- Distinguish contingency for known risks from management reserve for genuinely unknown events.
- Update contingency recommendations as the simulation is rerun with revised project data.
Session 2Communicating Probabilistic Results
- Translate a probability distribution into a short narrative a non-technical sponsor can act on.
- Respond to a request for a single number without discarding the value of the range behind it.
- Present simulation results alongside the qualitative risk register so both tell a consistent story.
- Build sponsor confidence in a probabilistic forecast by explaining, not just presenting, the method.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
Complete your registration
We will contact you within one business day to confirm.
Ready to start?
Reserve your seat and start building the skill.