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On the use of multivariate regression methods for longest path calculations from earned value management observations
Vanhoucke, Mario Colin, Jeroen
Vanhoucke, Mario
Colin, Jeroen
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2016
Journal
Omega - International Journal of Management Science
Book
Publication Volume
61
Publication Issue
June
Publication Begin page
127
Publication End page
140
Publication Number of pages
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Abstract
This paper explores the use of multivariate regression methods for project schedule control within a statistical project control framework. 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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Keywords
Project Management, Scheduling, Risk, Simulation