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AMSAA Planning Model based on Projection Methodology (PM2) Reliability Growth Planning Tools

Updated 4/9/2015

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AMSAA Planning Model based on Projection Methodology (PM2) Reliability Growth Planning Tools
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The AMSAA (Army Materiel Systems Analysis Activity) developed reliability growth planning methodology allows the feasibility of achieving a requirement to be assessed, given program schedules, amount of testing, and resource constraints. The model uses historical values for parameters, such as growth rate. The analysis performs trade-offs between test time, initial reliability, confidence levels, requirements, etc. to develop an achievable solution. The planning is quantified and reflected in the construction of a reliability growth program plan curve which establishes interim reliability goals throughout the program. 

Planning Model based on Projection Methodology (PM2) Continuous - The purpose of PM2 Continuous is to develop a system-level reliability growth planning curve that incorporates the developmental test schedule and corrective action strategy. The planning curve and associated steps serve as a baseline which reliability assessments may be compared against, possibly highlighting the need for reallocation of resources. Unlike the AMSAA Crow Planning Model, the PM2 Continuous model does not have a growth rate parameter, nor is there a comparable quantity. Furthermore, PM2 Continuous utilizes planning parameters that are directly influenced by program management, which include: 

  • MI , the planned initial system MTBF;
  • MS, the Management Strategy, which is the fraction of the initial failure rate addressable via corrective action;
  • MG, the MTBF goal for the system to achieve at the conclusion of the reliability growth test;
  • μd, the planned average FEF of corrective actions;
  • T, the duration of developmental testing; and
  • the average lag time associated with corrective actions. 

Planning Model based on Projection Methodology (PM2) Discrete - PM2-Discrete is the first methodology specifically developed for discrete systems and is also the first quantitative method available for formulating detailed plans in the discrete usage domain. The model has the same conditions of use as the continuous PM2 model, except for the usage domain. PM2-Discrete utilizes planning parameters that are directly influenced by program management, which include: 

  • RI, the planned initial system reliability;
  • MS, the Management Strategy, which in the discrete case is a value between 0 and 1 that decomposes RI into the factors RA and RB (which are defined below);
  • RG, the goal reliability for the system to achieve at the conclusion of the reliability growth test;
  • μd, the planned average FEF of corrective actions;
  • T, the duration of developmental testing; and
  • average delay associated with corrective actions
Processes Supported
  • Army
  • Navy / Marine Corps
  • Air Force
  • Agencies
Other information (optional)

The benefits associated with PM2 Continuous include: 

  • can determine the impact of changes to the planned test schedule and associated CAPs;
  • measures of programmatic risk are not sensitive to the length of the initial test phase (which is a limitation of the AMSAA Crow Planning Model);
  • can use different average corrective action lag time for each test phase;
  • provides an MTBF target to track against;
  • can be applied to programs with limited opportunities for implementation of corrective actions; and
  • utilizes planning parameters that are directly influenced by program management

The benefits associated with PM2-Discrete include: 

  • PM2-Discrete can determine the impact of changes to the planned test schedule and associated CAPs.
  • PM2-Discrete can use different average corrective action delay periods for each test phase.
  • PM2-Discrete provides a reliability target to track against.
  • PM2-Discrete can be applied to programs with limited opportunities for implementation of corrective actions.
  • PM2-Discrete offers several reliability growth management metrics of basic interest including:
  • Expected number of failure modes observed through trial t;
  • Expected reliability on trial t under instantaneous failure mode mitigation;
  • Expected reliability growth potential;
  • Expected probability of failure on trial t due to a new B-mode; and
  • Expected probability of failure on trial t due to a repeat B-mode expressed as a fraction of the initial B-mode probability of failure in the absence of failure mode mitigation

Disclaimer
This product support analytical tool was identified as part of a database provided solely to assist defense acquisition workforce professionals to identify best value product support solutions which optimize system readiness and life cycle cost. Neither the Department of Defense or the Defense Acquisition University provide any warranty of these tools whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the item will be error-free. This analytical tools database should under no circumstances be considered as being all-encompassing, and is in no-wise meant to endorse the capabilities or products of any particular individual, company, or organization.

Part of the AMSAA Tool Suite

This tool is part of a suite of reliability growth tools offered by AMSAA. For more information on the other tools available, visit the DAU AMSAA Tool Suite page.