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Barraza, G A, Back, W E and Mata, F (2004) Probabilistic Forecasting of Project Performance Using Stochastic S Curves. Journal of Construction Engineering and Management, 130(01), 25–32.

  • Type: Journal Article
  • Keywords: Project management; Scheduling; Monte Carlo method; Simulation; Cost control; Forecasting; Performance evaluation; scheduling; project management; cost optimal control; forecasting theory; performance evaluation; Monte Carlo methods; civil engineering;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(25)
  • Abstract:
    This study presents a new methodology for evaluating at-completion project performance status. This new procedure uses the concept of stochastic S curves (SS curves) to determine forecasted project estimates as an alternative to using deterministic S curves and traditional forecasting methods. A simulation approach is used for generating the stochastic S curves, and it is based on the defined variability in duration and cost of the individual activities within the process. Stochastic S curves provide probability distributions for the budget and time values required to complete the project at every selected point of intermediate completion. Final project performance is determined by comparing the planned budget and project duration, with the expected forecasted final cost and elapsed time, respectively. The SS-curve methodology permits objective evaluation of project performance without the limitations inherent in a deterministic approach. The probabilistic characteristics of this approach enable users to more accurately determine at-completion cost and duration variations and evaluate the performance improvement of proposed corrective actions.