Stochastic Earned Value Analysis using Monte Carlo Simulation and Statistical Learning Techniques

TL;DR

This paper introduces an integrated project control methodology combining Earned Value Analysis, Monte Carlo simulation, and statistical learning to better predict project outcomes under uncertainty.

Contribution

It presents a novel approach that refines existing methods by integrating extensive simulations with machine learning techniques for improved project risk assessment.

Findings

Effective detection of project deviations using anomaly detection.

Accurate estimation of success probabilities in cost and time.

Enhanced project outcome predictions through combined simulation and learning.

Abstract

The aim of this paper is to describe a new an 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.