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Bias in Pert Project Completion Time Calculations for a Real Network

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  • A. R. Klingel, Jr.

    (Standard Oil Company (Ohio), Cleveland, Ohio)

Abstract

Among network techniques recently widely employed in program management, Pert is addressed to the problem of assessing the manager's chances of completing a project on time. Theory and monte carlo simulation have shown that the Pert method yields results which are biased high, and this paper discusses a real example involving conditions under which the bias is very large. The manager is thus grossly misled into thinking his chances are very good, when in reality they are very poor. If the manager's network has multiple parallel paths with relatively equal means and large variances, Pert calculations will be considerably biased. Simulation can be utilized to estimate the distribution of completion time in this case, and guide the manager in appraising and controlling his chances of completion.

Suggested Citation

  • A. R. Klingel, Jr., 1966. "Bias in Pert Project Completion Time Calculations for a Real Network," Management Science, INFORMS, vol. 13(4), pages 194-201, December.
    • Handle: RePEc:inm:ormnsc:v:13:y:1966:i:4:p:b194-b201
    DOI: 10.1287/mnsc.13.4.B194
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    Cited by:

    1. R L Bregman, 2009. "Preemptive expediting to improve project due date performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 120-129, January.
    2. 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.
    3. W Herroelen & R Leus, 2005. "Identification and illumination of popular misconceptions about project scheduling and time buffering in a resource-constrained environment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(1), pages 102-109, January.
    4. Trietsch, Dan & Mazmanyan, Lilit & Gevorgyan, Lilit & Baker, Kenneth R., 2012. "Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation," European Journal of Operational Research, Elsevier, vol. 216(2), pages 386-396.
    5. Creemers, Stefan & De Reyck, Bert & Leus, Roel, 2015. "Project planning with alternative technologies in uncertain environments," European Journal of Operational Research, Elsevier, vol. 242(2), pages 465-476.
    6. 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.
    7. Vanhoucke, Mario, 2010. "Using activity sensitivity and network topology information to monitor project time performance," Omega, Elsevier, vol. 38(5), pages 359-370, October.

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