Primary Author: Mr. John K. Hollmann PE CCP CEP DRMP
Audience Focus: Advanced
Application Type: Application
Venue: 2014 AACE International Annual Meeting, New Orleans, LA, USA
Abstract: Empirical studies show that the distribution of actual/estimate cost data has a very long, bimodal tail on the high side. Actual p90 values are often triple the values we are estimating; traditional risk analyses is failing to predict the tail. The author hypothesizes that the bimodal tail reflects the cost outcome of project chaos. Borrowing from chaos and complex systems theory, the author developed a practical method to warn management when a project’s risks threaten to push project behavior over the edge into chaos and cost disaster. Complex systems theory is a maturing project management topic (e.g., as in Lean Construction, etc.); however, it has not found much practical application in risk quantification. This paper reviews chaos and complex systems theory and how they relate to project cost uncertainty, and presents a method that brings the understanding of chaos and complexity into a practical risk quantification toolset.
Audience Focus: Advanced
Application Type: Application
Venue: 2014 AACE International Annual Meeting, New Orleans, LA, USA
Abstract: Empirical studies show that the distribution of actual/estimate cost data has a very long, bimodal tail on the high side. Actual p90 values are often triple the values we are estimating; traditional risk analyses is failing to predict the tail. The author hypothesizes that the bimodal tail reflects the cost outcome of project chaos. Borrowing from chaos and complex systems theory, the author developed a practical method to warn management when a project’s risks threaten to push project behavior over the edge into chaos and cost disaster. Complex systems theory is a maturing project management topic (e.g., as in Lean Construction, etc.); however, it has not found much practical application in risk quantification. This paper reviews chaos and complex systems theory and how they relate to project cost uncertainty, and presents a method that brings the understanding of chaos and complexity into a practical risk quantification toolset.