As a project progresses throughout its lifecycle, it is important for the project team to learn from prior completed activities in the system. This can be used to adjust the remaining contingency for the project. In this paper, this situation is modelled using Erlang Distribution. Using Bayes’ Law, the associated cost for the remaining work packages is adjusted and fit to the required confidence based on the updated arrival rates of the bottleneck resource.