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An empirical project forecasting accuracy framework using project regularity

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Abstract

Forecasting an ongoing project’s actual duration is an essential aspect of project management which received considerable attention in the research community. In studies using Earned Value Management forecasting, it has been argued that the network topology is a driver to indicate the accuracy of these forecasts. However, a new project indicator has been recently defined, i.e. the project regularity, which reflects the value accrue according to the plan. It has shown to outperform the serial/parallel network topology indicator in specifying the accuracy of project forecasts. This paper introduces a novel way to define the project regularity, which provides project managers with an improved indication of the expected forecasting accuracy for their projects. The study is carried out on an empirical database consisting of 100 projects from different sectors, and the results are compared to the academic literature. The experiments show that the new indicator provides a better categorisation compared to the existing approaches. Further, they have shown that the ability of project categorisers to indicate the expected forecasting accuracy is affected by industry sector and project size.

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References

  • Aliverdi, R., Moslemi Naeni, L., & Salehipour, A. (2013). Monitoring project duration and cost in a construction project by applying statistical quality control charts. International Journal of Project Management, 31(3), 411–423.

    Article  Google Scholar 

  • Anbari, F. (2003). Earned value project management method and extensions. Project Management Journal, 34(4), 12–23.

    Article  Google Scholar 

  • de Andrade, P. A., Martens, A., & Vanhoucke, M. (2019). Using real project schedule data to compare earned schedule and earned duration management project time forecasting capabilities. Automation in Construction, 99, 68–78.

    Article  Google Scholar 

  • Batselier, J., & Vanhoucke, M. (2015). Construction and evaluation framework for a real-life project database. International Journal of Project Management, 33, 697–710.

    Article  Google Scholar 

  • Batselier, J., & Vanhoucke, M. (2015). Evaluation of deterministic state-of-the-art forecasting approaches for project duration based on earned value management. International Journal of Project Management, 33(7), 1558–1596.

    Article  Google Scholar 

  • Batselier, J., & Vanhoucke, M. (2017). Improving project forecast accuracy by integrating earned value management with exponential smoothing and reference class forecasting. International Journal of Project Management, 35(1), 28–43.

    Article  Google Scholar 

  • Batselier, J., & Vanhoucke, M. (2017). Project regularity: Development and evaluation of a new project characteristic. Journal of systems science and systems engineering, 26(1), 100–120.

    Article  Google Scholar 

  • Bullen, P. S. (2003). Handbook of Means and their inequalities. Kluwer.

    Book  Google Scholar 

  • Davies, D. L., & Bouldin, D. (1979). A cluster separation measure. IEEE Transactions on pattern analysis and Machine intelligence, 2, 224–227. https://doi.org/10.1109/TPAMI.1979.4766909.

    Article  Google Scholar 

  • Fleming, Q., & Koppelman, J. (2010). Earned Value Project Management (3rd ed.). Newton Square, Pennsylvania: Project Management Institute.

    Google Scholar 

  • Goldratt, E. (1997). Critical Chain. Great Barrington, MA: North River Press.

    Google Scholar 

  • Hulett, D. (1996). Schedule risk analysis simplified. Project Management Network, 10, 23–30.

    Google Scholar 

  • Kelley, J., & Walker, M. (1959). Critical path planning and scheduling: An introduction. Ambler, PA: Mauchly Associates.

    Book  Google Scholar 

  • Khamooshi, H., & Abdi, A. (2016). Project duration forecasting using earned duration management with exponential smoothing techniques. Journal of Management in Engineering, 33(1), 04016032.

    Article  Google Scholar 

  • Khamooshi, H., & Golafshani, H. (2014). EDM: Earned duration management, a new approach to schedule performance management and measurement. International Journal of Project Management, 32(6), 1019–1041.

    Article  Google Scholar 

  • Labro, E., & Vanhoucke, M. (2008). Diversity in resource consumption patterns and robustness of costing system to errors. Management Science, 54, 1715–1730.

    Article  Google Scholar 

  • Lipke W (2003) Schedule is different. The Measurable News Summer:31–34

  • Martens, A., & Vanhoucke, M. (2018). An empirical validation of the performance of project control tolerance limits. Automation in Construction, 89, 71–85.

    Article  Google Scholar 

  • Martens, A., & Vanhoucke, M. (2020). Integrating corrective actions in project time forecasting using exponential smoothing. Journal of Management in Engineering, 36(5), 04020044.

    Article  Google Scholar 

  • Tavares, L., Ferreira, J., & Coelho, J. (1999). The risk of delay of a project in terms of the morphology of its network. European Journal of Operational Research, 119, 510–537.

    Article  Google Scholar 

  • Van Eynde, R., & Vanhoucke, M. (2021). New summary measures and datasets for the multi-project scheduling problem. European Journal of Operational Research, 299(3), 853–868.

    Article  Google Scholar 

  • Vandevoorde, S., & Vanhoucke, M. (2006). A comparison of different project duration forecasting methods using earned value metrics. International Journal of Project Management, 24, 289–302.

    Article  Google Scholar 

  • Vanhoucke, M. (2010). Measuring Time - Improving Project Performance using Earned Value Management, International Series in Operations Research and Management Science, (Vol. 136). Springer.

    Google Scholar 

  • Vanhoucke, M., Coelho, J., Debels, D., et al. (2008). An evaluation of the adequacy of project network generators with systematically sampled networks. European Journal of Operational Research, 187, 511–524.

    Article  Google Scholar 

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Correspondence to Mario Vanhoucke.

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Andrade, P.A.d., Vanhoucke, M. & Martens, A. An empirical project forecasting accuracy framework using project regularity. Ann Oper Res 337, 501–521 (2024). https://doi.org/10.1007/s10479-023-05269-7

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  • DOI: https://doi.org/10.1007/s10479-023-05269-7

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