Level: Intermediate
Author(s): Luis O. Figueroa
Venue: 2017 AACE International Annual Meeting, Orlando, FL
Abstract: The reliability of quantitative schedule risk analyses (QSRA) outcomes is dependent upon using robust risk models. These models must fairly represent the project plan, be simple to follow, contain a level of detail where duration uncertainties and risks can be meaningfully assigned, and be feasible to infer from historical data. This paper expands arguments in support of fit for purpose summary schedules as an appropriate type of QSRA risk model for large EPC projects. To that end, it discusses issues associated with the stability of Critical Path Method (CPM) networks. Using examples from actual projects, it explores how those issues curtail the usability of detailed schedules CPM networks as risk models, and precludes the use of historical data as a source of risk estimates evaluation. Then it explains how lean, fit for purpose summary risk models, can be designed for improving CPM network stability and can make historical data inferences more feasible to use, thus increasing the predictability of QSRA outcomes.
Author(s): Luis O. Figueroa
Venue: 2017 AACE International Annual Meeting, Orlando, FL
Abstract: The reliability of quantitative schedule risk analyses (QSRA) outcomes is dependent upon using robust risk models. These models must fairly represent the project plan, be simple to follow, contain a level of detail where duration uncertainties and risks can be meaningfully assigned, and be feasible to infer from historical data. This paper expands arguments in support of fit for purpose summary schedules as an appropriate type of QSRA risk model for large EPC projects. To that end, it discusses issues associated with the stability of Critical Path Method (CPM) networks. Using examples from actual projects, it explores how those issues curtail the usability of detailed schedules CPM networks as risk models, and precludes the use of historical data as a source of risk estimates evaluation. Then it explains how lean, fit for purpose summary risk models, can be designed for improving CPM network stability and can make historical data inferences more feasible to use, thus increasing the predictability of QSRA outcomes.