Application of Risk Methods to Assess Air Force Capabilities (PSAM-0084)


The U.S. Air Force (AF) Capabilities Review and Risk Assessment (CRRA) process is designed to implement value-based decision analysis by identifying and using specific and measurable values called Measures of Performance (MOP). These MOPs are associated with specific tasks, captured in a Master Capabilities Library (MCL), which represent all the functions the AF is required to perform as a military service. The MCL is a document in hierarchical format in which capabilities have been successively decomposed to lower-level sub-capabilities until, at the lowest level, specific and measurable MOPs are defined (e.g., with units of distance, time, velocity, location, number, etc.). ARES Corporation's efforts are to provide an improved risk assessment methodology and process that will be used to assess, manage, and mitigate risks associated with capability shortfalls identified through the CRRA process. ARES' Risk Management efforts are in accordance with contractual requirements as part of the Booz Allen Hamilton Contractor Support Team in support of the AF CRRA effort.

ARES is determining the risks (and conversely success) associated with significant operational vignettes that are based on Concepts of Operations, defined architectures, and associated capabilities. The ARES risk assessment process uses appropriate coupled Event Trees —Fault Trees to quantify the risk. The fault trees are developed to a level of depth (called a basic event) that corresponds to a CRRA specific and measurable MOP. However, most of the CRRA MOPs are not represented as failure probabilities required as input information to the fault tree basic events, but typically are values expressed in specific units. ARES has, therefore, developed an algorithm to translate the specific and measurable MOP values into a probability of success required for the fault tree basic events. The algorithm is based on four MOP Value Functions that are determined by operational experts during the CRRA process.

  • Summary/Abstract
  • Introduction
  • Algorithim for Determining the Probability of Success
  • Conclusions

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