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Programmatic risk analysis
Dillon, Robin L. Pamplin College of Business, Polytechnic Institute and State University, Falls Church, Virginia.
- APRAM framework
- Independent-project programs
- Dependent-projects programs
- Progress and prospects
- Related Primary Literature
- Additional Reading
Traditional risk modeling tools focus on the quantification of either the technical risk or the management (cost or schedule) risk of a project. Yet, a comprehensive programmatic risk analysis needs to consider both because they may be linked. Technical failures generally occur during operations when a project does not perform its functions. The causes of these failures are often system design flaws that were not detected before operations. The failure may have been caused by human and organizational factors. Probabilistic risk analysis (PRA) methods are useful tools for analyzing a system, measuring quantitatively its technical failure risk, and supporting design decisions to minimize this risk. Probabilistic risk analysis was originally developed for the commercial nuclear power industry in the 1970s, and has been applied successfully to many other sectors, including aerospace, marine oil platforms, and chemical processing. The purpose of a technical probabilistic risk analysis is to examine the system for all potential damage states and the probability of each state. This process, however, does not include management failures such as project cancellations due to budget overruns. Management risks focus on factors that affect both the schedule and the budget of a project. Many projects have failed (that is, were canceled) because of large cost or schedule overruns. Monte Carlo simulation in conjunction with budgets and work-breakdown structures is often used to examine the risks of cost escalation or schedule slippage for a project. While beneficial, these tools (probabilistic risk analysis and simulation of cost and schedule risks) are too often used separately. Because it is difficult to balance simultaneously project cost, schedule, and performance given the dependencies between the types of risks, an advanced programmatic risk analysis framework known as APRAM has been developed to support decisions involving both management and technical risk factors.
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