Novice project managers often hear the phrase “Monte Carlo simulation,” which might suggest the kind of activity that happens at an office holiday party instead of a planning meeting. After a group of physics researchers started using the phrase to refer to simulations that rely on random behavior, the nickname stuck. So, when reading about techniques that require Monte Carlo methodology, managers can expect to encounter predictions that change each time, based on random values and assessments.
Monte Carlo simulations become important when trying to determine whether team members can actually accomplish specific tasks within their budgeted time frames. Many software developers rely on Monte Carlo simulations to project task “velocity.” According to software developer Joel Spolsky, project managers can estimate the typical speed with which an individual team member can accomplish a task by making a series of calculated guesses based on past performance.
Dividing the original estimate by the actual time to complete a past task, managers following Spolsky’s method calculate a series of historical velocity factors that indicate how close that team member often comes to meeting deadlines. When building a project schedule, managers can use the Monte Carlo method to select a random velocity factor from a team member’s past. Dividing a task’s ideal duration by that random velocity factor offers a reasonable guess at how long it will actually take. And with enough velocity figures factored into a large project, the randomization evens out inconsistent guesses into a fairly accurate project timeline.
This approach benefits project managers by accounting for unforeseen delays within the mathematical model, rather than by forcing managers to break out contingency budgets during the planning phase. Managers under pressure to shorten time frames can feel confident about their plans, since contingency resources often appear as “low hanging fruit” to project sponsors and other stakeholders.
