2013-R208

Use of the Risk Driver Method in Monte Carlo Simulation of a Project Schedule

Risk II Track

R2-8_Presentation_UseOfTheRiskDriverMethodInMonteCarloSimulationOfAProjectSchedule_Hulett

R2-8_Paper_UseOfTheRiskDriverMethodInMonteCarloSimulationOfAProjectSchedule_Hulett

Abstract:

Identifying the root causes of project schedule and cost risk requires that the risk to the project schedule is clearly and directly driven by identified and quantified risks. In the Risk Driver Method the risks from the Risk Register drive the simulation. (As a side note, we find that the Risk Registers are not complete—during the interviews to collect risk data the interviewees introduce important risks that are, surprisingly, missing from the Risk Register.) The Risk Driver Method differs from older, more traditional approaches in which 3-point (low, most likely and high) estimates of the activity durations are applied directly to activity durations. We notice, however, that the traditional 3-point estimate represents, often, the influence of several risks that impact the activity if they happen. Therefore the importance of each risk cannot be individually distinguished and kept track of. Also, since some risks will affect several activities, we cannot capture the entire influence of a risk using traditional 3-point estimates of impact applied to specific activities.

The Risk Driver method allows us to specify the risks by their probability of influencing the schedule as well as the uncertainty of their impact if they do occur, and to assign the risks to all detailed tasks they influence. 3-point estimates have no clear way to represent the probability of a risks’ occurring so they miss one of the two important characteristics of the risks.
Correlation between activity durations is important in determining the possible date of completion if the organization wants a fairly conservative estimate, say at the 80th percentile (P-80). With traditional 3-point estimates the correlation coefficients have to be estimated (guessed at) and applied between pairs of activities. Using the Risk Drivers method correlation between activities’ durations is created during simulation based on a common risk (or common risks) affecting the activities. We no longer need to estimate the correlation coefficients with the possibility that the coefficients determine an inconsistent correlation matrix.

The basic benefit of the Risk Driver approach comes from the ability to identify, and hence prioritize the importance of risks (as distinguished from the importance of activities or paths in the traditional 3-point estimate approach). Hence Risk Drivers facilitates risk mitigation. Plainly, we do not mitigate activities or paths, we mitigate risks. In order to determine which risks to mitigate we need to be transparent about which risks drive the results, hence the Risk Driver Method.

Author:

David T. Hulett, Ph.D.
Hulett & Associates, LLC
David T. Hulett is recognized as a leader in project cost and schedule risk management and project scheduling. Dr. Hulett has focused for the last 20 years on quantitative schedule risk analysis, integrated cost-schedule risk analysis and project scheduling best practices. His consulting and training clients represent many industries, including: aerospace and defense, oil and gas, construction, pharmaceutical development and plant construction, transportation, communications, IT, and large science. The clients are in the US, 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 and scheduling standards, including leading the risk management chapter in the Guide to the Project Management Body of Knowledge (PMBOK(R) Guide) and the Practice Standard for Project Risk Management and participating on the Core Committee for the Practice Standard for Project Scheduling. He is the primary author of the Recommended Practice 57R-09 for the Association for the Advancement of Cost Engineering International (2011, AACEI) on integrated cost and schedule risk analysis. He authored, with Mike Nosbisch, the lead article in the Cost Engineering journal of AACEI (November/December 2012). Dr. Hulett headed a group of scheduling experts from various professional associations, sponsored by the PMI College of Scheduling, to assist the US Government Accountability Office (GAO) with revising its scheduling best practices used to review government programs.
H&A has pioneered the Risk Drivers method of schedule risk and integrated cost and schedule risk analysis including specifying the requirements for Pertmaster to develop the necessary software and Beta testing that software in 2007.
H&A also provides proprietary training in project risk management and project scheduling. Some courses provide computer keystroke training within the context of project schedule and cost risk analysis.
Dr. Hulett authored 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. His second book, Integrated Cost- Schedule Risk Analysis (Gower, 2011) has been published recently. He has presented papers on quantitative risk analysis topics at conferences such as NASAs PM Challenge, PMI’s congresses, INCOSE and AACE annual conferences, user groups for Primavera, C/S Solutions, Palisade and Crystal Ball, as well as at the PMI Risk Management SIG conferences and at MarcusEvans conferences in various Asian countries.
Dr. Hulett has held strategic planning positions at TOSCO, an oil 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.