Often seen as a little too sophisticated for many project planners new to Monte Carlo simulation, a Probabilistic Calendar is an elegant way of modelling the uncertainty in resource availability and resource allocation in a project plan. Any project plan—whether in construction, oil & gas exploration and production, manufacturing, environmental remediation, operations management, etc.—is subject to uncertainty. This uncertainty may be due to, for example:
- Weather conditions or other uncontrollable but seasonal/calendar related events (e.g. rain or annual leave),
- Possible bottlenecks caused by sickness or the availability of key resources due to the demands of other projects,
- Initial learning curves of new resources recruited to the project.
Using Probabilistic Calendars, @RISK for Project (Pro version only, a Monte Carlo software add-in to Microsoft Project) will ‘take-out’ working days from either the standard working week or from an individual resource’s working calendar, according to the probabilities specified by the user, and therefore lengthen the tasks affected.
For example, let’s assume that the finish date distribution for a Construction project looks like this, excluding any lost time for Probabilistic Calendars:
Under this decision evaluation, you might decide to tell the client (or your bosses) that you are 95% sure you will finish by 25th October.
Now let’s model the same Construction schedule but with a 20% chance of non-working on each day between 17 Oct and 31st Dec (i.e. ‘Autumn’). The Probabilistic Calendar dialogue would look like this with a (binomial) sample being taken each day in the date range:
(click to view larger image)
The resulting Finish date distribution will then look like this:
The P95, mean and range of uncertainty have all increased due to the possibility of lost working days in the Probabilistic Calendar. In fact, the probability of meeting the 25th October is now lower than 5% – so beware!
This new risk assessment allows your client the most informed decision making under uncertainty.
Ian Wallace, ACMA
Palisade Training Team