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(RISK-1662) Using Stochastic Optimization to Improve Risk Mitigation

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Primary Author: Eric Druker
Co-Author(s): Graham Gilmer; Dr. David T. Hulett
  
Audience Focus: Advanced
Application Type: Research
Venue: 2014 AACE International Annual Meeting, New Orleans, LA, USA

Abstract: Today’s risk analysts have several tools to help them identify and mitigate future sources of cost and schedule risk. Traditionally, the risk cube method has been used to provide probability-weighted metric for each risk’s severity by multiplying its likelihood and consequence factors together. Unfortunately, this methodology ignores secondary and tertiary impacts of risks, in particular when they could drive cost by creating a new critical path within the project plan. Integrated cost & schedule risk analysis provides greater insights by integrating the risks into the schedule. Using traditional sensitivity metrics such as Pearson’s correlation, analysts are able to identify risks contributing to cost and schedule growth. While stronger than the risk cube methodology, analysts using this method are unable to measure a risk’s contribution to a particular confidence level of either cost or schedule and cannot uncover when the impact of removing a set of risks may be greater than sum of the impacts of removing them individually. This paper will show how stochastic optimization – the optimization of simulation models – can be used to better identify risks, and combination of risks, that when mitigated will best reduce project cost and schedule risk.