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On the dynamic use of project performance and schedule risk information during projecttracking

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  • Vanhoucke, Mario

Abstract

Project scheduling, risk analysis and project tracking are key parameters to a project's success or failure. Research on the relative sensitivity of project activities during the project scheduling phase as well as research on project performance measurement during project progress have been published throughout the academic literature and the popular press. Obviously, the interest in activity sensitivity information and project performance measurement from both the academics and the practitioners lies in the need to focus a project manager's attention on those activities that influence the performance of the project. When management has knowledge about the current project performance and has a certain feeling of the relative sensitivity of the various project activities on the project objective, a better management focus and a more accurate response during project tracking should positively contribute to the overall performance of the project. In this article, two alternative project tracking methods to detect project problems are presented and their efficiency on the quality of corrective actions to bring the project back on track is measured and evaluated. More precisely, a bottom-up and a top-down project tracking approach within a corrective action framework is applied on a large and diverse set of fictitious projects that are subject to Monte-Carlo simulations to simulate fictitious project progress under uncertainty. The top-down tracking approach relies on state-of-the-art earned value management performance metrics, while the bottom-up tracking mechanism makes use of the well-known schedule risk analysis method. A computational experiment shows that a top-down project tracking approach is highly efficient for project networks with a serial activity structure while a bottom-up approach performs better in a parallel structured project network. Moreover, it will also be shown that dynamic thresholds to trigger corrective actions, which gradually increase or decrease the project manager's attention along the project progress, outperform the static thresholds for both tracking approaches.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jomega:v:39:y:2011:i:4:p:416-426
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    References listed on IDEAS

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    1. Elmaghraby, Salah E., 2000. "On criticality and sensitivity in activity networks," European Journal of Operational Research, Elsevier, vol. 127(2), pages 220-238, December.
    2. Williams, Terry, 1999. "Towards realism in network simulation," Omega, Elsevier, vol. 27(3), pages 305-314, June.
    3. Genaro J. Gutierrez & Panagiotis Kouvelis, 1991. "Parkinson's Law and Its Implications for Project Management," Management Science, INFORMS, vol. 37(8), pages 990-1001, August.
    4. Vanhoucke, Mario & Coelho, Jose & Debels, Dieter & Maenhout, Broos & Tavares, Luis V., 2008. "An evaluation of the adequacy of project network generators with systematically sampled networks," European Journal of Operational Research, Elsevier, vol. 187(2), pages 511-524, June.
    5. Elmaghraby, S. E. & Fathi, Y. & Taner, M. R., 1999. "On the sensitivity of project variability to activity mean duration," International Journal of Production Economics, Elsevier, vol. 62(3), pages 219-232, September.
    6. Vanhoucke, Mario, 2010. "Using activity sensitivity and network topology information to monitor project time performance," Omega, Elsevier, vol. 38(5), pages 359-370, October.
    7. Durbach, Ian N. & Stewart, Theodor J., 2009. "Using expected values to simplify decision making under uncertainty," Omega, Elsevier, vol. 37(2), pages 312-330, April.
    8. Valadares Tavares, L. & Antunes Ferreira, J. & Silva Coelho, J., 1999. "The risk of delay of a project in terms of the morphology of its network," European Journal of Operational Research, Elsevier, vol. 119(2), pages 510-537, December.
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    Cited by:

    1. Wauters, Mathieu & Vanhoucke, Mario, 2017. "A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1097-1111.
    2. 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.
    3. Cui, Nanfang & Demeulemeester, Erik & Bie, Li, 2016. "Incorporation of activity sensitivity measures into buffer management to manage project schedule riskAuthor-Name: Hu, Xuejun," European Journal of Operational Research, Elsevier, vol. 249(2), pages 717-727.
    4. Womer, K. & Li, H. & Camm, J. & Osterman, C. & Radhakrishnan, R., 2017. "Learning and Bayesian updating in long cycle made-to-order (MTO) production," Omega, Elsevier, vol. 69(C), pages 29-42.
    5. Colin, Jeroen & Vanhoucke, Mario, 2014. "Setting tolerance limits for statistical project control using earned value management," Omega, Elsevier, vol. 49(C), pages 107-122.
    6. repec:eee:ejores:v:262:y:2017:i:1:p:274-286 is not listed on IDEAS
    7. Plaza, Malgorzata, 2016. "Balancing the costs of human resources on an ERP project," Omega, Elsevier, vol. 59(PB), pages 171-183.
    8. Cohen, Izack & Iluz, Michal, 2015. "When cost–effective design strategies are not enough: Evidence from an experimental study on the role of redundant goals," Omega, Elsevier, vol. 56(C), pages 99-111.

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    Keywords

    Project management;

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