Probabilistic Risk Management in Design and Construction Projects, Version 1.1

CII created Research Team (RT) 280 to answer the following essential research question:

What would be the benefits and implications of applying a probabilistic approach to analyzing cost estimating and scheduling risks for the design and construction of capital projects?

The team’s objectives included a review of the industry’s current probabilistic controls practices and their associated risk allocation techniques. To make this review, the team surveyed 104 contractor and owner organizations worldwide, from which 12 were selected for in-depth case studies. For these case studies, team members conducted hundreds of hours of face-to-face interviews and collected risk-related documentation from risk managers and project teams. After analyzing the case studies, the interviews team conducted an industry-wide validation survey of 73 organizations. The team explored when, why, and how probabilistic approaches were used, and documented the kinds of successes organizations were achieving through probabilistic risk management.

The team’s final products include the following:

• identification of the benefits and limitations of probabilistic analysis
• a process roadmap for implementing probabilistic controls at three levels of risk analysis: identification, deterministic, and probabilistic
• Implementation Resource 280-2, Applying Probabilistic Risk Management in Design and Construction Projects, a guide to implementing probabilistic controls, with risk register templates and an example that demonstrates all three levels of risk analysis.
• Implementation Resource 280-3, What’s the Risk?, a laminated guide to selecting the appropriate level of risk analysis and interpreting probabilistic results.

To further help organizations realize the benefits of probabilistic risk management, the team developed IR 280-3, What’s the Risk?, a laminated process roadmap for implementing probabilistic controls. This resource offers guidance for selecting the appropriate level of risk analysis for a project and helps users interpret probabilistic results.