Monte Carlo Simulation for Sensitivity Analysis

This article describes how Monte Carlo Simulation and PERT distribution can be used to model data for sensitivity analysis of the risk register. The PERT distribution can be used to simulate different scenarios of cost and schedule impact for an identified risk. We know risks exist in all types of projects (small, medium, large scaled), and a good risk management is critical to its success. Following the completion of a risk management plan, we now have a risk register. The advantages of a risk register are records identified risks, their severity, and the actions to be taken.

Monte Carlo 1 

With advances in computing, the process of risk management can now include - simulating the potential risk value for the identified risk. To construct a distribution of the risk for cost and schedule impact, you will need a range of estimates – minimum value, maximum value, and most likely value. As with any analytical simulation, the quality of the inputs limits its usefulness. The better the estimates, the better results you can derive from a simulation. There are however different ways you can model the distribution to generate sample values – uniform distribution, triangle distribution, and PERT distribution. 

The three point estimates are used to construct a distribution curve of all values between the minimum and maximum values and the likelihood of the estimates is calculated. Using these results, a score is calculated for the identified risk. Sensitivity analysis of the risk based on the score for cost and schedule impact – The identified risks can be sorted based on the most potential impact. Monte Carlo simulation is then used to simulate the project risk distribution based on the identified risk at project level. A respective score is calculated for its cost and schedule impact.

A Pareto analysis at the risk register allows the project manager to focus on those risks that have the most impact on the project. Taking this a step further, A Pareto analysis at programme level would allow a programme manager to focus on those projects whose risks have the most impact on the project.

Within our Programme Insight Manager (PIM) software, Monte Carlo analysis is able to produce a number of dashboards to provide Project Managers a clear visual of how different factors affect the risk, helping users prioritise risk.

See below examples of some of the charts which can be produced within PIM.

Monte Carlo 2

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