Project monitoring and the related decision to proceed to corrective action are crucial components of an integrated project management and control decision support system (DSS). Earned value management/earned schedule (EVM/ES) is a project control methodology that is typically applied for top-down project schedule control. However, traditional models do not correctly account for the multivariate nature of the EVM/ES measurement system. We therefore propose a multivariate model for EVM/ES, which implements a principal component analysis (PCA) on a simulated schedule control reference. During project progress, the real EVM/ES observations can then be projected onto these principal components. This allows for two new multivariate schedule control metrics (T2 and SPE) to be calculated, which can be dynamically monitored on project control charts. Using a computational experiment, we show that these multivariate schedule control metrics lead to performance improvements and practical advantages in comparison with traditional univariate EVM/ES models. Two multivariate project schedule control metrics are presented using well-known EVM metrics.Principal component analysis is used to build a correlation reference and to test the multivariate metrics.A large simulation study compares the novel control approach with the current best practice.Improvements are obtained in comparison with the traditional project control approach.
['Jeroen Colin', 'Annelies Martens', 'Mario Vanhoucke', 'Mathieu Wauters']
A multivariate approach for top-down project control using earned value management