New calculation feature of RiskyProject 8
Posted: Wed Mar 04, 2026 7:44 pm
New RiskyProject version 8 allows forecasting of project and task performance using Bayesian analysis. This method uses the same input data as the traditional analysis described in Calculating and Reassessing Tasks Using Tracking Data: the percentage of task completion and the associated status date. However, the calculation itself is performed differently.
With the traditional approach, RiskyProject essentially extrapolates actual task performance to estimate future task performance. In contrast, the Bayesian approach evaluates two sources of information:
1. Prior (original) task risks and uncertainties that affect project duration
2. Actual task performance, including percent task complete and associated status dates
For example, suppose a task duration is estimated to range between 3 and 6 days, with a most likely duration of 4 days, based on data from similar tasks completed previously. In the current project, 40% of the task has been completed over 3 days. Using the traditional approach, the forecasted task duration would be 8 days. However, using the Bayesian approach, the forecasted duration would be 6 days, because the calculation incorporates the prior (original) task uncertainty.
During the Bayesian analysis process, RiskyProject calculates the probability distribution of the remaining task duration and uses it to determine the total task duration. The Bayesian approach generally provides a more realistic forecast of task and project duration, and it is recommended for projects where actual performance tracking is available and prior project risks and uncertainties have been defined. Bayesian analysis applies only to uncertainties in task duration and not to fixed costs.
Bayesian analysis can significantly affect project schedules when partially completed tasks have long durations relative to the overall project duration and lie on the critical path. For example, if a task with uncertainties and tracking data has a duration of two weeks within a two-month project, and the task is on the critical path, the project finish date predicted by Bayesian analysis may differ significantly from the date calculated using the traditional approach.
With the traditional approach, RiskyProject essentially extrapolates actual task performance to estimate future task performance. In contrast, the Bayesian approach evaluates two sources of information:
1. Prior (original) task risks and uncertainties that affect project duration
2. Actual task performance, including percent task complete and associated status dates
For example, suppose a task duration is estimated to range between 3 and 6 days, with a most likely duration of 4 days, based on data from similar tasks completed previously. In the current project, 40% of the task has been completed over 3 days. Using the traditional approach, the forecasted task duration would be 8 days. However, using the Bayesian approach, the forecasted duration would be 6 days, because the calculation incorporates the prior (original) task uncertainty.
During the Bayesian analysis process, RiskyProject calculates the probability distribution of the remaining task duration and uses it to determine the total task duration. The Bayesian approach generally provides a more realistic forecast of task and project duration, and it is recommended for projects where actual performance tracking is available and prior project risks and uncertainties have been defined. Bayesian analysis applies only to uncertainties in task duration and not to fixed costs.
Bayesian analysis can significantly affect project schedules when partially completed tasks have long durations relative to the overall project duration and lie on the critical path. For example, if a task with uncertainties and tracking data has a duration of two weeks within a two-month project, and the task is on the critical path, the project finish date predicted by Bayesian analysis may differ significantly from the date calculated using the traditional approach.