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On the use of multivariate regression methods for longest path calculations from earned value management observations

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  • Vanhoucke, Mario
  • Colin, Jeroen

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.

Suggested Citation

  • Vanhoucke, Mario & Colin, Jeroen, 2016. "On the use of multivariate regression methods for longest path calculations from earned value management observations," Omega, Elsevier, vol. 61(C), pages 127-140.
    • Handle: RePEc:eee:jomega:v:61:y:2016:i:c:p:127-140
    DOI: 10.1016/j.omega.2015.07.013
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    References listed on IDEAS

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    1. Mario Vanhoucke, 2014. "Earned Value Management," Management for Professionals, in: Integrated Project Management and Control, edition 127, chapter 0, pages 17-31, Springer.
    2. Trietsch, Dan & Mazmanyan, Lilit & Gevorgyan, Lilit & Baker, Kenneth R., 2012. "Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation," European Journal of Operational Research, Elsevier, vol. 216(2), pages 386-396.
    3. Colin, Jeroen & Vanhoucke, Mario, 2014. "Setting tolerance limits for statistical project control using earned value management," Omega, Elsevier, vol. 49(C), pages 107-122.
    4. Kettaneh, Nouna & Berglund, Anders & Wold, Svante, 2005. "PCA and PLS with very large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 69-85, January.
    5. Jörg Henseler, 2010. "On the convergence of the partial least squares path modeling algorithm," Computational Statistics, Springer, vol. 25(1), pages 107-120, March.
    6. Vanhoucke, Mario, 2011. "On the dynamic use of project performance and schedule risk information during projecttracking," Omega, Elsevier, vol. 39(4), pages 416-426, August.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Martens, Annelies & Vanhoucke, Mario, 2017. "A buffer control method for top-down project control," European Journal of Operational Research, Elsevier, vol. 262(1), pages 274-286.
    2. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2022. "Using Earned Value Management and Schedule Risk Analysis with resource constraints for project control," European Journal of Operational Research, Elsevier, vol. 297(2), pages 451-466.
    3. Mohammadreza Sharifi Ghazvini & Vahidreza Ghezavati & Sadigh Raissi & Ahmad Makui, 2017. "An Integrated Efficiency–Risk Approach in Sustainable Project Control," Sustainability, MDPI, vol. 9(9), pages 1-20, September.

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