Risk Management

It is moronic to predict without first establishing an error rate for the prediction and keeping track of one’s past record of accuracy
— Nassim Nicholas Taleb, Fooled By Randomness

The naturally occurring uncertainties (Aleatory) in cost and schedule create risk. These uncertainties can be modeled in a Monte Carlo Simulation tool. The result is a measure of the needed schedule and cost margin to protect the deliverable date and cost target.

The Event-Based uncertainties (Epistemic) and their resulting risk, require elicitation, impact modeling, handling strategies, modeling the effectiveness of these handling strategies, the residual risks after handling, and their impacts from both the original risk and the residual risk on the project. The result is a model (using Monte Carlo Simulation) on the cost, schedule and technical performance of the projects outcomes.

The management of uncertainties in cost, schedule, and technical performance; and Event Based uncertainty and resulting risk are both critical success factors for all programs.

Risk Management starts with capturing Event Based Risks and their impacts, then with the modeling of the statistical uncertainty of the normal work. 

This work is done through a step-by-step process, guided by Risk Management processes found in a variety of sources. Here are some trusted sources for managing risk on your project

• “Special Workshop on Risk Acceptance and Risk Communication, Aleatory or epistemic? Does it matter?” March 26-27, 2007, Stanford University
• “Representation of Analysis Results Involving Aleatory and Epistemic Uncertainty,” J. C. Helton, J. D. Johnson, W. L. Oberkampf, C.J. Sallaberry, SANDIA REPORT, SAND2008-4379, August 2008.
• “A Methodology For Project Risk Analysis Using Bayesian Belief Networks Within A Monte Carlo Simulation Environment,” Javier F. Ordóñez Arízaga, Graduate School of the University of Maryland, College Park, Maryland.
• “Quantitative Risk Assessment For Projects Schedules,” Sherif S. Hassanien and Jason B. Skow, Proceedings of the 2012 9th International Pipeline Conference, IPC2012, September 24-28, 2012, Calgary, Alberta, Canada.
• “Understanding Risk Attitude,” David Hillson and Ruth Murray-Webster, Defence Management Network, 14 September 2006.
• “The Risk Driver Method of Monte Carlo Schedule Risk Analysis,” David T. Hulett, Ph.D., Hulett & Associates, LLC
• “Assessing Risk Probability: Alternative Approaches,” Dr. David A. Hillson and Dr David T. Hulett, 2004 PMI Global Congress Proceedings – Prague, Czech Republic
• “Integrated Cost / Schedule Risk Analysis,” David Hulett, Ph.D. and Bill Campbell, III “Integrated Cost – Schedule Risk Analysis Using the Risk Driver Approach,” David T. Hulett, Ph.D., 24th Annual International IPM Conference, Bethesda, Maryland, 29-31 October 2012.
• “Decision Tree Analysis for the Risk Averse Organization,” David T. Hulett, Ph.D., PMI EMEA Congress in Madrid, Spain, May 9, 2006.
• “Summary of the GAO Best Scheduling Practices,” David T. Hulett, Ph.D., February 2012 “Integrated Cost-Schedule Risk Analysis,” David T. Hulett, Ph.D., Gower Press, 2011
• Practical Schedule Risk Analysis, David T. Hulett, Ph.D., Gower Press, 2009
• “Using Quantitative Risk Analysis To Support Strategic Decisions,” David T. Hulett, Ph.D., Consult GEE Executive Briefings in Business Risk Management, Thomson GEE, London, UK, December 2004.
• “Integrated Cost-Schedule Risk Analysis using Risk Drivers and Prioritizing Risks,” David T. Hulett, Ph.D., 8th Annual PMICOS Scheduling Conference, San Francisco, May 1-4, 2011
• “Project Cost Risk Analysis: The Risk Driver Approach Prioritizing Project Risks and Evaluating Risk Responses,” David T. Hulett, Ph.D., Keith Hornbacher, and Waylon T. Whitehead, 2008.
• “How the Risk Register Drives the Schedule Risk Analysis,” David T. Hulett and Waylon T. Whitehead, NASA PM Challenge, Daytona Beach, FL., February 26-27, 2008.
• “Integrated Cost / Schedule Risk Analysis,” David T. Hulett, Ph.D., NASA PM Challenge February 6-7, 2007, Moody Gardens, Galveston, TX.
• “Using the Risk Register in Integrated Cost/Schedule Risk Analysis with Monte Carlo Simulation,” David T. Hulett, Ph.D. and Dr. Dan Patterson, PMP, NASA PM Challenge, Daytona Beach, FL., February 24-25, 2009.
• “Eliminating Bias through Reference Class Forecasting and Good Governance,” Bent Flyvbjerg, Concept Report No 17 Chapter 6, NTNU, Norges teknisk-naturvitenskapelige universitet Høgskoleringen 7A 7491 NTNU – Trondheim.
• “Curbing Optimism Bias and Strategic Misrepresentation in Planning: Reference Class Forecasting in Practice,” Bent Flyvbjerg, Aalborg University, Denmark and Delft University of Technology, The Netherlands, European Planning Studies Vol. 16, No. 1, January 2008.
• “From Nobel Prize To Project Management: Getting Risks Right,” Bent Flyvbjerg, Aalborg University, Denmark, August 2006 Project Management Journal.
• “Over Budget, Over Time, Over And Over Again Managing Major Projects,” Bent Flyvbjerg, From Peter W. G. Morris, Jeffrey K. Pinto, and Jonas Söderlund, 2011, eds., The Oxford Handbook of Project Management (Oxford University Press), pp. 321-344.
• “Complexity in Defence Projects How Did We Get Here?,” Concept Symposium 2010, Oscarsborg Norway, Mary McKinlay NASA Systems Engineering Handbook, NASA/SP-2007-6105 Rev 1. Risk management in small construction projects, Kajsa Simu, Luleå University of Technology Department of Civil and Environmental Engineering Division of Architecture and Infrastructure.
• “Fat-Tailed Distributions For Cost And Schedule Risks,” John Neatrour, SCEA: January 19, 2011 “Evaluation of the Risk Analysis and Cost Management (RACM) Model,” Matthew S. Goldberg, IDA Paper P-3388, August 1998
• “Integrating Cost & Schedule Risk Analyses: Creating Improved Resource Allocations,” Jason A. Dechoretz, 26 October 2004
• “Monte-Carlo-Type Techniques for Processing Interval Uncertainty, and Their Potential Engineering Applications,” V. Kreinovich, J. Beck, C. Ferregut, A. Sanchez, G. R. Keller, M. Averill and S. A. Starks, College of Engineering and NASA Pan-American Center for Earth and Environmental Studies (PACES), University of Texas, El Paso, TX 79968, USA
• “Predictability of Costs, Time and Success of Development,” RAND, 1959. Risk Management Policies from DoD 5000.4-M Cost Analysis Guidance and Procedures. Risk-informed Decision-making In The Presence Of Epistemic Uncertainty, Didier Dubois, Dominique Guyonnet, "International Journal of General Systems 40, 2 (2011) 145-167
• “On different types of uncertainties in the context of the precautionary principle,” T. Aven, University of Stavanger, Norway. Risk Analysis 2011 Oct;31(10):1515-25. Reflections on Decision Making under Uncertainty, Paul R. Kleindorfer 2008/73/TOM/ISIC, INSEAD Working Paper.
• “A Hybrid Method to Deal with Aleatory and Epistemic Uncertainty in Risk Assessment,” Palash Dutta and Tazid Ali, International Journal of Computer Applications, Volume 42– No.11, March 2012
• “On the quantitative definition of risk,” Kaplan, S. and Garrick, B.J., Risk Analysis, 1, 11–27, 1981
• GAO Cost Estimating and Assessment Guide, GAO-09-3SP
• “On The Quantitative Definition of Risk,” Stanley Kaplan’ and B. John Garrick, Risk Analysis, Vol. 1, No. 1, 1981.
• “Using the Rayleigh Model to Assess Future Acquisition Contract Performance and Overall Risk,” Dan Davis, Gary Christle, and Wayne Abba, Center for Naval Analysis (CNA), January 2009.
• Distinguishing Two Dimensions of Uncertainty, Craig Fix and Gülden Ülkumen, in Perspectives on Thinking, Judging, and Decision Making, Universitetsforlaget, 21 Dec 2011. How to Lie With Statistics, Darrell Huff, Norton, 1954.