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Operational Availability – otherwise known as ‘Ao’ in systems engineering is the probability a system will be ready to perform its specified function at a random point in time, with the proportion of time the system is either capable of operating or not operating, represented by the Ao metric. System downtime, as depicted below, is defined as the time during which the system is incapable of performing its primary functions, which when not occurring as a result of a reliability failure, is attributed to a variety of administrative logistics delays such as fault isolation and diagnostic time; corrective maintenance actions and the time frame to complete such actions; and supply chain and all integrated logistics support functions and processes.
Measuring Ao is a requirement for military systems, but measuring Ao effectively can be a challenge. Anecdotal and statistical evidence suggests there is a gap between predictive and reported Ao values for highly complicated systems. Such ‘system of systems’ are made up of individually functioning components that successfully meet key performance parameters as independent systems. Operational capability builds are increasingly relied upon to deliver improved functionality that current decision makers require, however the complexity of deriving Ao for systems whose complexity is a magnitude of order greater when combined, may merit another review of this particular metric. Given the level of investment the military makes in fielding and supporting systems, there is a strong desire to avoid developing erroneous Ao estimates.
Given a highly complex system of system, comprised of components purposefully strung together to achieve an operational capability build; answer the question, does a model exists that best describes and summarizes the operational availability status of the highly complex system, such that the metric communicates a higher order of confidence in its value?
To foster an atmosphere conducive to innovation and to reduce concerns for security and proprietary data; students will receive datasets that mimics the real world data normally available to approved researchers.