Project planning under uncertainty using Monte Carlo simulations

Projects in their early design include many uncertainties. They are not properly specified, their scope is not fully defined and they involve a lot of uncertainties in quantities, time durations, costs and activities. Rigid figures like CPM and other deterministic methods do not permit to make good uncertainty management, which at the end brings along a lot of mistakes and waste of limited resources. Conventional projects management has tried to give both a solution and an approximation to the problem with different tools like PERT, in order to avoid problems of conventional figures. However, some of these methods are insufficient to model variables and process behavior, which represents a lack of crucial information to make good forecasts. Monte Carlo simulation avoids many of these inconveniences. Using the probabilistic distributions of both internal and external variables and processes, Monte Carlo simulation can model their behavior, thus showing activities and different project scenarios. This paper describes an ongoing research effort to develop a methodology as a tool for project management and planning, in which the uncertainty is tackled in order to obtain an overview of the project and its environment, which results in saving costs, time and effort in planning and control.