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Modeling Uncertainty when Estimating IT Projects Costs

Author

Listed:
  • Michel Winter

    (UniCA - Université Côte d'Azur)

  • Isabelle Mirbel

    (Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems - I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

  • Pierre Crescenzo

    (Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems - I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

Abstract

In the current economic context, optimizing projects' cost is an obligation for a company to remain competitive in its market. Introducing statistical uncertainty in cost estimation is a good way to tackle the risk of going too far while minimizing the project budget: it allows the company to determine the best possible trade-off between estimated cost and acceptable risk. In this paper, we present new statistical estimators derived from the way IT companies estimate the projects' costs. In the current practice, the software to develop is progressively divided into smaller pieces until it becomes easy to estimate the associated development workload and the workloads of the usual additionnal activities (documentation, test, project management,...) are deduced from the development workload by applying ratios. Finally, the total cost is derived from the resulting workload by applying a daily rate. This way, the overall workload cannot be calculated nor estimated analytically. We thus propose to use Monte-Carlo simulations on PERT and dependency graphs to obtain the cost distribution of the project.

Suggested Citation

  • Michel Winter & Isabelle Mirbel & Pierre Crescenzo, 2014. "Modeling Uncertainty when Estimating IT Projects Costs," Working Papers hal-00966573, HAL.
    • Handle: RePEc:hal:wpaper:hal-00966573
    Note: View the original document on HAL open archive server: https://hal.science/hal-00966573
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    References listed on IDEAS

    as
    1. Koehler, Elizabeth & Brown, Elizabeth & Haneuse, Sebastien J.-P. A., 2009. "On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses," The American Statistician, American Statistical Association, vol. 63(2), pages 155-162.
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    More about this item

    Keywords

    Monte-Carlo Method; Cost Estimation; IT Project; PERT;
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