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Prediction Model of Final Project Cost using Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem

Yoo, Wi Sung   (Civil & Environmental Engineering & Geodetic Science, The Ohio State UniversityUU0014688  ); Hadipriono, FAbian C.   (Civil & Environmental Engineering & Geodetic Science, The Ohio State UniversityUU0014688  );
  • 초록

    This paper introduces a tool for predicting potential cost overrun during project execution and for quantifying the uncertainty on the expected project cost, which is occasionally changed by the unknown effects resulted from project's complications and unforeseen environments. The model proposed in this stuff is useful in diagnosing cost performance as a project progresses and in monitoring the changes of the uncertainty as indicators for a warning signal. This model is intended for the use by project managers who forecast the change of the uncertainty and its magnitude. The paper presents a mathematical approach for modifying the costs of incomplete work packages and project cost, and quantifying reduced uncertainties at a consistent confidence level as actual cost information of an ongoing project is obtained. Furthermore, this approach addresses the effects of actual informed data of completed work packages on the re-estimates of incomplete work packages and describes the impacts on the variation of the uncertainty for the expected project cost incorporating Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem. For the illustration purpose, the Introduced model has employed an example construction project. The results are analyzed to demonstrate the use of the model and illustrate its capabilities.


  • 주제어

    Bayes' Theorem .   change of uncertainty .   cost overrun .   multivariate probabilistic analysis (MPA).  

  • 참고문헌 (9)

    1. CII (1994) 'Pre-Project Planning: Beginning a project the right way' Published by Construction Industry Institute, Texas 
    2. Pugh, LA and Soden, R. G. (1986) 'Use of risk analysis techniques in assessing the confidence of project cost estimates and schedules' Project Management, Vol. 4, No. 3, August pp. 158-162 
    3. Clark, D.E. (2001) 'Monte Carlo Analysis: Ten Year of Experience' Cost Engineering, Vol. 43, No.6, pp. 40-45 
    4. Zwikael, O., Globerson, S., and Raz, T. (2000) 'Evaluation of models for forecasting the final cost of a project' Project Management Journal, March, pp. 53-57 
    5. Johnson, R.A. and Wichern, D.W. (2002) 'Applied Multivariate Statistical Analysis' Published by Prentice Hall, New Jersey 
    6. Ford, D.N. (2002) 'Achieving multiple project objectives through contingency management' Journal of Construction Engineering and Management, Vol. 128, No.1, pp. 30-39 
    7. Nassar, K. (2005) 'Cost contingency analysis for construction project using spreadsheets' Cost Engineering, Vol. 47, No. 9, pp. 24-29 
    8. Karlsen, J. T. and Lereim, J. (2005) 'Management of project contingency and allowance' Cost Engineering, Vol. 47, No.9, pp. 24-29 
    9. Touran, A. (2003) 'Probabilistic model for cost contingency' Journal of Construction Engineering and Management, Vol. 129, No. 3, pp. 280-284 

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