Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques

• We improve previous methodologies for integrating EVM and risk analysis.
• We apply advanced statistical learning techniques to project monitoring and control.
• We use Monte Carlo simulation to generate the “universe” of possible projects.
• We detect if deviations from planned values stay within project expected variability.
• We predict probabilities of success and expected cost and duration of the project.

The aim of this paper is to describe a new integrated methodology for project control under uncertainty. This proposal is based on Earned Value Methodology and risk analysis and presents several refinements to previous methodologies. More specifically, the approach uses extensive Monte Carlo simulation to obtain information about the expected behavior of the project. This dataset is exploited in several ways using different statistical learning methodologies in a structured fashion. Initially, simulations are used to detect if project deviations are a consequence of the expected variability using Anomaly Detection algorithms. If the project follows this expected variability, probabilities of success in cost and time and expected cost and total duration of the project can be estimated using classification and regression approaches.