Project schedule risk analysis is a common application of the Monte Carlo simulation technique. A network of activities, usually derived from the project plan, is combined with risks, typically from the project risk register, to form a risk model. The Monte Carlo process then analyses the schedule, by randomly simulating the effects that a mixture of good and bad luck may have on the outcome of key milestones and the project completion date. Iterations in which there is a disproportionate amount of bad luck result in a relatively late completion and, similarly iterations in which there is a run of good luck result in much earlier completion. The result is that the simulation output should include all the possibilities that are, realistically, possible. Typically, this output is shown on an S-curve graph of the type shown below.

Monte Carlo schedule risk analysis is particularly useful at strategic points during a project when managers need a realistic understanding of the time at which key milestones can be achieved. They can also be used to distinguish between targets (by which an early finish might be realistically incentivised) and expected durations or commitments. A further advantage is that statistics from the simulation can be used to identify which activities and risks are the ones that have the most influence over the project’s schedule performance. This is an insight that is not available from simpler approaches to risk analysis that do not model risks quantitatively in the context of the overall plan.

Monte Carlo schedule risk analysis is therefore a powerful approach to project schedule performance forecasting. However, as with all modeling, it does have a potential downside; if the data input is poor the output is likely to be misleading. Preventing this problem requires careful attention to the development of the model and estimates. Usually, the key things to get right are:

• Structuring the activity network that forms the basis of the risk model
• Making realistic estimates for activity uncertainty
• Selecting and positioning risks
• Estimating risk probabilities and impacts
• Introducing appropriate correlation of risks and activities
• Identifying and reporting major assumptions

Detailed advice on each of these points can be obtained from papers that can be downloaded from this site or from the APM’s PRAM Guide. As an example, the paper that can be downloaded below includes check lists of questions that can used to verify whether or not the modeling is of good quality.

Download a paper – “Schedule Risk Analysis: critical issues for planners and managers”

HVR recommends Pertmaster as a schedule risk analysis tool. Details on Pertmaster and other products can also be found on the Risktools.com website.

Contact: martin.hopkinson@hvr-csl.co.uk