In our most recent publication in the Omega - The International Journal of Management Science, various multivariate regression models have been compared in a large computational experiment to validate and test the best performing methods for measuring and controlling the time of projects in progress.
Abstract: This paper explores the use of multivariate regression methods for project schedule control, within a statistical project control framework (Colin and Vanhoucke 2014). These multivariate regression methods monitor the activity level performance of an ongoing project from the earned value management/earned schedule (EVM/ES) observations that are made at a high level of the work breakdown structure (WBS). These estimates can be used to calculate the longest path in the project and to produce warning signals for project schedule control. The effort that is spent by the project manager is thereby reduced, since a drill-down of the WBS is no longer required for every review period. An extensive computational experiment was set up to test and compare four distinct multivariate regression methods on a database of project networks. The kernel principal component regression method, when used with a radial base function kernel, was found to outperform the other presented regression methods.
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Cite as: Vanhoucke, M. and Colin, J., 2016, "On the use of multivariate regression methods for longest path calculations from earned value management observations", Omega - The International Journal of Management Science, 61, 127–140 (doi:10.1016/j.omega.2015.07.013).