IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-00966573.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://hal.science/hal-00966573/document
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    2. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
    3. Johannes Hönekopp & Audrey Helen Linden, 2022. "Heterogeneity estimates in a biased world," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-21, February.
    4. Sun-Joo Cho & Paul Boeck & Susan Embretson & Sophia Rabe-Hesketh, 2014. "Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 84-104, January.
    5. David A. Wagstaff & Ofer Harel, 2011. "A closer examination of three small-sample approximations to the multiple-imputation degrees of freedom," Stata Journal, StataCorp LP, vol. 11(3), pages 403-419, September.
    6. Warrington Nicole M. & Tilling Kate & Howe Laura D. & Paternoster Lavinia & Pennell Craig E. & Wu Yan Yan & Briollais Laurent, 2014. "Robustness of the linear mixed effects model to error distribution assumptions and the consequences for genome-wide association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 567-587, October.
    7. repec:jss:jstsof:43:i11 is not listed on IDEAS
    8. Ng'ombe, John, 2019. "Economics of the Greenseeder Hand Planter, Discrete Choice Modeling, and On-Farm Field Experimentation," Thesis Commons jckt7, Center for Open Science.
    9. Soekhai, V. & Donkers, B. & Levitan, B. & de Bekker-Grob, E.W., 2021. "Case 2 best-worst scaling: For good or for bad but not for both," Journal of choice modelling, Elsevier, vol. 41(C).
    10. R. N. Rattihalli, 2023. "A Class of Multivariate Power Skew Symmetric Distributions: Properties and Inference for the Power-Parameter," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1356-1393, August.
    11. Tübbicke Stefan, 2022. "Entropy Balancing for Continuous Treatments," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 71-89, January.
    12. Dorinth van Dijk & David Geltner & Alex van de Minne, 2018. "Revisiting supply and demand indexes in real estate," DNB Working Papers 583, Netherlands Central Bank, Research Department.
    13. Alex Minne & Marc Francke & David Geltner & Robert White, 2020. "Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models," The Journal of Real Estate Finance and Economics, Springer, vol. 60(4), pages 514-553, May.
    14. Dubey, Subodh & Bansal, Prateek & Daziano, Ricardo A. & Guerra, Erick, 2020. "A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 114-141.
    15. Dorinth W. Dijk & David M. Geltner & Alex M. Minne, 2022. "The Dynamics of Liquidity in Commercial Property Markets: Revisiting Supply and Demand Indexes in Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 64(3), pages 327-360, April.
    16. Gurprit Grover & Vinay K. Gupta, 2015. "Multiple imputation of censored survival data in the presence of missing covariates using restricted mean survival time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 817-827, April.
    17. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Adrian Quintero, 2022. "Bayesian Model Selection for Longitudinal Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 516-547, November.
    18. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2017. "A Variational Maximization–Maximization Algorithm for Generalized Linear Mixed Models with Crossed Random Effects," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 693-716, September.
    19. Richard R Snell, 2015. "Menage a Quoi? Optimal Number of Peer Reviewers," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.
    20. Sarah C. Lotspeich & Bryan E. Shepherd & Gustavo G. C. Amorim & Pamela A. Shaw & Ran Tao, 2022. "Efficient odds ratio estimation under two‐phase sampling using error‐prone data from a multi‐national HIV research cohort," Biometrics, The International Biometric Society, vol. 78(4), pages 1674-1685, December.
    21. Chaitra H. Nagaraja & Haikady N. Nagaraja, 2020. "Correction to ‘Distribution‐free Approximate Methods for Constructing Confidence Intervals for Quantiles’," International Statistical Review, International Statistical Institute, vol. 88(2), pages 519-519, August.

    More about this item

    Keywords

    Monte-Carlo Method; Cost Estimation; IT Project; PERT;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-00966573. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.