Constructing a credible Integrated Master Schedule (IMS) requires sufficient schedule margin be placed at specific locations to protect key deliverables. One approach to determining this margin is the use of a Monte Carlo simulation tool.

This probabilistic margin analysis starts with the construction of a “best estimate” Integrated Master Schedule with the work activities arranged in a “best path” network.

While there may be “slack” in some of the activities, the Critical Path exists through this network for each Key Deliverable. This network of activities must show how each deliverable will arrive on or before the contractual need date. This “best path” network is the Deterministic Schedule – the schedule with fixed activity durations.

By assigning a duration variance for each class of work activity, the Monte Carlo model shows if the at what confidence level the probabilistic delivery date occurs on or before the deterministic date. The needed schedule margin for each deliverable can be derived by the Monte Carlo simulation. This activity network is referred to as the Probabilistic Schedule – the schedule with activity durations of random variables.

With the schedule margin inserted in front of each deliverable, the Deterministic schedule becomes the basis of the Probabilistic schedule. Next is a cycle of adjusting the Deterministic schedule to assure the needed margin produces the final baselined Deterministic schedule to be placed on baseline. As the program proceeds, this schedule margin is managed through a “margin burn down” process. Assessing the sufficiency of this margin for the remaining work is then part of the monthly program performance report.

Here's an example from an upcoming workshop on building and executing a credible Performance Measurement Baseline based on the Wright Brother's work

For this to work we need several things:

- The work to be performed. This can be a network of activities in a schedule. It can be a collection of activities in a sprint. In both cases we need some approximation of how long it will take to accomplish the work. In both cases these means making an estimate of the
*Most Likely*duration or work effort to produce the needed outcomes. - This
*Most Likely*value can come from many sources. But it does need to be the*Most Likely,*not the average, not some made up number, not some cockamamie guess

Here's how to use a Monte Carlo tool for determining the likelihood of completing on or before a given date, when there is a schedule of the work with Most Likelies for the work durations and the variances in those durations