Quantitative risk analysis training with @Risk
A two-day course on modelling project risks in Excel, running Monte Carlo simulations with @Risk and interpreting the results.
PRJ-R032 — Risk modelling and simulation in Excel with @Risk. Duration: 2 days.
The course covers the overall process of modelling and simulating project risks in Excel using Monte Carlo simulation.
Who the course is for
The course is intended for bid managers working ahead of projects, cost controllers, project managers and infrastructure investment managers, including those involved in public-private partnerships.
Learning objectives
Develop the following skills:
- Adapt an existing model for risk analysis.
- Create a simple Excel model suitable for risk analysis.
- Distinguish between risks and uncertainties in projects.
- Select suitable distributions to simulate uncertainty in task durations and costs.
- Select suitable models to simulate risk events.
- Configure and run a Monte Carlo simulation.
- Interpret the results and the various indicators.
- Generate a risk analysis report.
Day one alternates theoretical presentations with practical exercises using @Risk. Day two applies the principles to cases proposed by the trainer and participants.
Course programme
Day 1 — Morning, 9:00–12:30
- Introduction to risk analysis.
- Model example: cost budgeting and contingency planning.
- Discussion of risk modelling possibilities.
- Introduction to distributions.
- Risk analysis using triangular distributions.
- Running and repeating simulations.
- Interpreting simulation results.
- Using the PERT distribution.
- Using statistical functions.
- Model evaluation tools.
Day 1 — Afternoon, 13:30–17:00
- Using the binomial distribution.
- Extending the model.
- Sampling methods and number of iterations.
- Alternative parametric methods.
- Normal and lognormal distributions.
- Distribution fitting.
- Discrete distributions.
- Using multiple simulations.
Day 2 — Morning, 9:00–12:30
Case 1: risk analysis using an existing model.
- Define random variables.
- Run a simulation.
- Interpret the results: detailed statistics and sensitivity.
Case 2: building a model for risk analysis.
Modelling the variability of an input:
- Fitting distributions to historical data: GDP, exchange rates or index values.
- Converting a daily index into a monthly and annual index.
- Modelling by defining ranges with the Uniform and RAND() functions.
Day 2 — Afternoon, 13:30–17:00
Continuation of case 2:
- Modelling a rare, high-impact event: a stock market crash, a growth shock, etc.
- Calculating the correlation coefficient between two input variables and modelling it in @Risk.
- Simulation with and without correlation.
- Interpreting sensitivity charts.
- Setting model parameters with the SimTable function.
- Overlaying multiple curves.
- Generating a report.
- Scenario analysis.
Our courses are delivered by trainers holding the PMI (Project Management Institute) PMP (Project Management Professional) certification.