Primary Author: Dr. Gui Ponce de Leon, PE
Co-Author(s): Dr. Vivek Puri
Audience Focus: Intermediate
Application Type: Research
Venue: 2016 AACE International Annual Meeting, Toronto, ON, Canada
Abstract: The Critical Path Method (CPM) schedules activities to start on early dates, which results in an unrealistic completion distribution in CPM risk analysis. CPM risk analysis tools, thus, cannot model what commonly occurs when a project unfolds and activities start on dates later than early dates due to floating or pacing decisions based on schedule progress. Graphical Path Method (GPM®) risk analysis allows activities in each realization to float as a function of random sampling and decision rules, accurately modeling the real world where activities are delayed to take advantage of total float. This paper demonstrates how the early bias in CPM risk analysis leads to optimistic completion distributions, and how GPM risk analysis corrects for the early bias by allowing floating and pacing scenarios. A novel approach is also introduced for developing a bounding completion distribution envelope for selecting realistic probabilistic completion dates and for monitoring safe-float use as the project progresses.
Co-Author(s): Dr. Vivek Puri
Audience Focus: Intermediate
Application Type: Research
Venue: 2016 AACE International Annual Meeting, Toronto, ON, Canada
Abstract: The Critical Path Method (CPM) schedules activities to start on early dates, which results in an unrealistic completion distribution in CPM risk analysis. CPM risk analysis tools, thus, cannot model what commonly occurs when a project unfolds and activities start on dates later than early dates due to floating or pacing decisions based on schedule progress. Graphical Path Method (GPM®) risk analysis allows activities in each realization to float as a function of random sampling and decision rules, accurately modeling the real world where activities are delayed to take advantage of total float. This paper demonstrates how the early bias in CPM risk analysis leads to optimistic completion distributions, and how GPM risk analysis corrects for the early bias by allowing floating and pacing scenarios. A novel approach is also introduced for developing a bounding completion distribution envelope for selecting realistic probabilistic completion dates and for monitoring safe-float use as the project progresses.