Monte-Carlo simulation

Monte-Carlo simulations can be used for various purposes to analyze the behaviour of projects in (fictitious) progress. It can be used to measure the sensitivity of project activities as described in “Schedule Risk Analysis: How to measure your baseline schedule’s sensitivity?” or to evaluate the accuracy of forecasting methods used in Earned Value Management (see “Predicting project performance: Evaluating the forecasting accuracy”). In this article, a simple yet effective Monte-Carlo simulation approach is proposed consisting of nine simulation scenarios that can be used to link critical path schedules to project control information.

Risk management requires analytical skills and basic knowledge of statistics, which is often perceived as mathematically complex and sometimes theoretical and far from practice. However, a basic understanding of probability and distribution functions allows the project manager to better estimate the effects of unexpected events on the project outcome. The use of single point estimates for the project data, such as activity durations and costs or the value of the time-lags between project activities (see “Activity links: How to add precedence relations between activities?”), often leads to unrealistic project estimates due to the inherent uncertainty that typifies these projects. Therefore, the use of statistical distributions is crucial for a thorough and realistic analysis of the project as a preparation of its future progress which will be characterized by changes compared to the original point estimates.

Monte-Carlo simulations can be used in dynamic project scheduling to measure the sensitivity of project activities as described in “Schedule Risk Analysis: How to measure your baseline schedule’s sensitivity?” or to evaluate the accuracy of forecasting methods used in Earned Value Management (see “Predicting project performance: Evaluating the forecasting accuracy”). In this article, the underlying principle used during these simulations is briefly explained and illustrated on a small example. In the example, the baseline duration of an activity must be replaced by a number generated from a predefined distribution function. For more information on the use of distribution functions, see “Project risk: Statistical distributions or single point estimates?”. A simple simulation approach consisting of 8 special simulation scenarios is proposed in “Monte-Carlo simulations: Linking critical path schedules to project control”.