completion cost and duration variations. Before running the SPECIESS program the remaining cost and duration of activities, already started but still in process, were estimated using Method 2[Eqs. (5) and(6)] which assumes that their future performance is correlated with their past performance.
Table 6 shows SPECIESS output for at-completion project performance with respect to the forecasted project duration and final cost. From this table can be observed that at-completion time variation and total cost variation were found with negative values(ATV-3 days and ACV S-2,270, respectively). If such negative values were considered as unacceptable, corrective ac- tions should be proposed and evaluated
Once the project data had been updated with the corrective action, a new project performance forecast could be run in order to evaluate effects in the probabilistic schedule and the revised at-completion performance forecast. If revised at-completion cost and duration variances were improved with respect to the originally forecasted values, the proposed corrective action could be considered as acceptable.
Summary and Discussion
A probabilistic forecasting method has been proposed in this paper for evaluation of at-completion project performance. This method presents the management advantages of: consideration of an integrated performance analysis: (2) consideration of performance variability of future activities; and(3) consideration of correlation between past performance and future performance.
The probabilistic forecasting method, proposed herein, uses the progress-based representation of elapsed time and cumulative cost(PB-S curves). Using a simulation approach, a sample of stochastic PB-S curves(SS curves) can be obtained which provide cost and time distributions at any percent of work performed(project progress). Comparing planned SS curves and forecasted SS curves(including actual performance), estimations of at-