Level: Advanced
Author(s): Feng Jin; Wenwu Xiang; Bochen Zhang
Venue: 2017 AACE International Annual Meeting, Orlando, FL
Abstract: Many megaprojects experience major cost overruns and schedule delays; this phenomenon often reflects the characteristics of chaos. The failure mode of these projects appears to be one of crossing a tipping point from an orderly into a disorderly state. Further, some projects fail in a particularly spectacular manner indicating a second tipping point. This paper proposes a two-tiered project cost risk analysis model that can be used to quantify these two levels of disorder due to chaos. The models are based on quantifying or rating the probability of occurrence and impact of three categories of chaos risk drivers; complexity and stressors on the first level and detractor risks on the next. These tipping point risks are modeled using Monte Carlo Simulation in a way that reflects chaotic failure modes; i.e., blowouts. The risk quantification outcomes of these models can then be used to guide effective risk control by management to increase the likelihood that the project will not cross the tipping points. Finally, a case study is provided to illustrate the application of the proposed models.
Author(s): Feng Jin; Wenwu Xiang; Bochen Zhang
Venue: 2017 AACE International Annual Meeting, Orlando, FL
Abstract: Many megaprojects experience major cost overruns and schedule delays; this phenomenon often reflects the characteristics of chaos. The failure mode of these projects appears to be one of crossing a tipping point from an orderly into a disorderly state. Further, some projects fail in a particularly spectacular manner indicating a second tipping point. This paper proposes a two-tiered project cost risk analysis model that can be used to quantify these two levels of disorder due to chaos. The models are based on quantifying or rating the probability of occurrence and impact of three categories of chaos risk drivers; complexity and stressors on the first level and detractor risks on the next. These tipping point risks are modeled using Monte Carlo Simulation in a way that reflects chaotic failure modes; i.e., blowouts. The risk quantification outcomes of these models can then be used to guide effective risk control by management to increase the likelihood that the project will not cross the tipping points. Finally, a case study is provided to illustrate the application of the proposed models.