Engineering

Availability Impact Assessments

Availability Predictions

The combination of Reliability and Maintainability statistics are used to calculate the initial availability predictions. Other factors are often added to the equation as the design matures. Availability predictions are useful in conducting trade-off analyses of different system design and maintenance concepts.

• Inherent Availability Predictions
  
  Inherent availability is a theoretical prediction of the percentage of time that a system will be available for its intended use. These predictions factor in the mean time between failure (MTBF) and mean time to repair (MTTR) and assume that failures will occur at relatively consistent intervals and that the necessary support will be available to return the system to an operationally ready state.
  
  Inherent availability predictions are early design stage calculations that provide a starting point for determining the adequacy of new system maintenance concept and reliability with existing systems and system design goals.



• Achieved Availability Predictions
  
  Achieved availability considers mean time between maintenance (MTBM), mean preventive maintenance time (Mpmt), and mean corrective maintenance time (Mcmt) providing for the periods of time when a system will undergo preventative maintenance or when failures have occurred.
  
  Achieved availability predictions are used early in the design stage to identify areas where availability may be degraded as a result of design issues and decisions. Often, achieved availability predictions can serve as a deciding factor as to which is the more cost effective decision: a design change or preventative maintenance over the life of the system.



• Other Availability Predictions
  
  The availability of spares, support equipment, and personnel at the location where a repair is planned to be performed is critical to promptly return a failed system to service. Each of these predictions is relationally linked to operational availability of the system. In adequate spares, support equipment, or personnel can drive operational availability below required targets; the cost of these factors needed to achieve operational availability targets can drive total cost of ownership beyond feasible limits.
  
  The accurate determination of the proper range, depth, quantity, and location of spares, support equipment, and personnel is a major factor in achieving availability targets. Spares provisioning, support equipment distributions, and personnel staging activities and availability predictions are directly linked during the design and fielding stages of any system.

Availability Statistics

Availability can be statistically measured in terms of the percentage of time, when under operating conditions, an item is actually available to perform its mission. Normally calculated of year long periods, these statistics can be used as indicator of how well an item is actually performing and how support organizations are sustaining the system.

• Operational Availability Statistics
  
  Operational availability is the statistical measure of the percentage of time a system is in an operable state when called into use. Unlike availability predictions used during early design stages, operational availability is an actual measurement of the actual time, on average, that a system can be depended upon to perform its function.
  
  Operational availability is normally calculated annually. These resulting statistics can provide metrics as to how well a system is available to perform its intended mission and how well support personnel are maintaining it.