Monte Carlo Simulation

Last time, I described how my team applies Flow Metrics with Scrum. Although we don’t often estimate the short-term delivery, we are sometimes asked when a big feature will be completed. In the Story Points era, we used Story Points and calculated the average velocity of the previous 3 to 5 sprints for forecasting. In theory, averaging is the easiest way to forecast. However, in reality, unexpected situations may arise. For example, someone may call in sick, there may be a public holiday in the middle of the Sprint, or a PBI may be blocked by external parties. It is easy to fall into the trap of relying solely on averages, resulting in an unrealistic forecast. Moreover, communicating Story Points to stakeholders can be challenging due to their inherent ambiguity. In such situations, Monte Carlo Simulation comes into play. ...