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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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    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. Georgios K. Koulinas & Olympia E. Demesouka & Konstantinos A. Sidas & Dimitrios E. Koulouriotis, 2021. "A TOPSIS—Risk Matrix and Monte Carlo Expert System for Risk Assessment in Engineering Projects," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    3. 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.
    4. 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.
    5. 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.
    6. Richard E. Wendell & Timothy J. Lowe & Mike M. Gordon, 2023. "Dangers in using earned duration and other earned value metrics to measure a project’s schedule performance," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 665-680, June.
    7. She, Bingling & Chen, Bo & Hall, Nicholas G., 2021. "Buffer sizing in critical chain project management by network decomposition," Omega, Elsevier, vol. 102(C).
    8. Umar Altahtooh & Thamir Alaskar, 2018. "Understanding Relationship between Milestone and Decision-Making in Project Management: A Qualitative Study among Project Managers in Saudi Arabia," International Journal of Business and Management, Canadian Center of Science and Education, vol. 13(8), pages 184-184, June.
    9. Xing Pan & Lunhu Hu & Ziling Xin & Shenghan Zhou & Yanmei Lin & Yong Wu, 2018. "Risk Scenario Generation Based on Importance Measure Analysis," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    10. 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.
    11. Fernando Acebes & Javier Pajares & José M. González-Varona & Adolfo López-Paredes, 2021. "Project risk management from the bottom-up: Activity Risk Index," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1375-1396, December.
    12. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2021. "Using Schedule Risk Analysis with resource constraints for project control," European Journal of Operational Research, Elsevier, vol. 288(3), pages 736-752.
    13. Georgios K. Koulinas & Alexandros S. Xanthopoulos & Konstantinos A. Sidas & Dimitrios E. Koulouriotis, 2021. "Risks Ranking in a Desalination Plant Construction Project with a Hybrid AHP, Risk Matrix, and Simulation-Based Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3221-3233, August.
    14. Colin, Jeroen & Vanhoucke, Mario, 2014. "Setting tolerance limits for statistical project control using earned value management," Omega, Elsevier, vol. 49(C), pages 107-122.
    15. 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.
    16. Narjes Sabeghi & Hamed Reza Tareghian, 2020. "Using the generalized maximum covering location model to control a project’s progress," Computational Management Science, Springer, vol. 17(1), pages 1-21, January.
    17. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2020. "The impact of a limited budget on the corrective action taking process," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1070-1086.
    18. Plaza, Malgorzata, 2016. "Balancing the costs of human resources on an ERP project," Omega, Elsevier, vol. 59(PB), pages 171-183.
    19. Ö Hazır & A Shtub, 2011. "Effects of the information presentation format on project control," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(12), pages 2157-2161, December.
    20. 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.
    21. Martens, Annelies & Vanhoucke, Mario, 2019. "The impact of applying effort to reduce activity variability on the project time and cost performance," European Journal of Operational Research, Elsevier, vol. 277(2), pages 442-453.

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