Quantitative Risk Analysis

Why bother?


Risk Doctor Briefing


Dr David Hulett

California, USA



Many projects overrun their budget and schedule targets, often due to the following causes:

  • Project plans are biased, usually towards being over-optimistic.
  • Project plans do not fully reflect the impact of uncertainty and risks (including both project-specific risks and systemic risks).

Fortunately, quantitative risk analysis can help to address both of these, through a two-stage analysis. The first stage addresses the two main causes of unrealistic plans:

  • Optimistic or biased plans. All project plans include estimates of cost and duration which are based on assumptions, and these are often optimistic. For instance, we might assume that problems that affected previous similar projects will not happen on this project. Or we might produce unrealistic estimates because of pressure from the customer, management, the competition and the economics of the project, which usually results in optimistic plans that may be unachievable. Ideally, if we could challenge assumptions and remove the effect of optimism or bias, we could ensure that the project starts with a realistic baseline plan. However, it may not be possible to counter estimating bias fully, so the uncertainty component of the risk analysis will usually include a correction for optimistic estimates of cost or duration.
  • Uncertainty and risks. Project managers must recognize that estimates of cost or duration are uncertain due to inherent variability, estimating error and estimating bias (if it exists). In addition, there are both project-specific and systemic risks that may affect achievement of schedule and cost targets. These risks must be identified and quantified, including their probability, impact and which activities they will affect. When both uncertainty and risks are incorporated in the risk analysis model, results obtained using Monte Carlo simulation will indicate a range of possible project outcomes, including the result that can be expected in the absence of actively managing the risks. These results are more realistic (and usually more pessimistic) for both finish date and total cost, but they are not the end of the story.

In the second stage, quantitative risk analysis results can be used to guide proactive risk management actions. Risks can be prioritized using the outputs of a risk analysis model, which indicate where risk management action would lead to the greatest improvement in project outcome. The prioritized risk list forms the input to a workshop or a set of interviews, where effective risk responses can be developed. Implementing these responses will result in improved project outcomes, although there will probably still be residual risks that need further action, since relatively few risks can be managed completely.

Overall, quantitative risk analysis helps the project manager in at least two ways:


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How to cite this article: Hulett, D. (2018). Quantitative Risk Analysis: Why bother? Risk Doctor Briefing; PM World Journal, Vol. VII, Issue XI – November.  Available online at https://pmworldjournal.net/wp-content/uploads/2018/11/pmwj76-Nov2018-Hulett-quantitative-risk-analysis-series-article.pdf


About the Author

David T. Hulett, Ph.D., FAACE

Hulett & Associates, LLC
California, USA



David Hulett is recognized as a leader in project cost and schedule risk analysis and project scheduling.  He has conducted many risk analyses focusing on quantifying the risks and their implications for project cost and schedule estimating and mitigation, and many schedule assessments.  He is a Fellow of AACE International.

Clients of Hulett & Associates, LLC represent many industries and are in the US, Canada, Europe, South America, South-East Asia and the Middle East.

Dr. Hulett is well-known as a leader in the Project Management Institute (PMI) for project risk standards, including leading the risk management chapter in the Guide to the Project Management Body of Knowledge (PMBOK® Guide) and the Practice Standard for Project Risk Management. He is the author of Recommended Practice 57R-09 published in 2011 by the Association for the Advancement of Cost Engineering (AACE) International on Integrated Cost and Schedule Risk Analysis and 85R-14 Use of Decision Trees in Decision Making.

Dr. Hulett has published Practical Schedule Risk Analysis (Gower, 2009) for which he was recognized by the PMI College of Scheduling for “contributions to the scheduling profession” in 2010 and Integrated Cost- Schedule Risk Analysis (Gower, 2011).

Dr. Hulett has held strategic planning positions at TOSCO, an oil shale company, and at TRW in aerospace and defense.  In the Federal government, Dr. Hulett managed offices in the Federal Energy Agency (FEA), the Department of Energy (DOE) and the Office of Management and Budget (OMB).  He was also an economist with the Federal Reserve Board of Governors.  Dr. Hulett was an Instructor in the Economics Department at Harvard University.  His Ph.D. in Economics is from Stanford University and his B.A. is from the Special Program for Public and International Affairs (Woodrow Wilson School) at Princeton University.

David Hulett can be contacted at [email protected]