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Optimizing Acquisition Results Through Simple Integration Readiness Metrics

By Zuhal Tompkins, Dr. Michael Grenn, and Dr. Blake Roberts

The U.S. Department of Defense (DoD) implemented the use of Technology Readiness Level (TRL) metrics during all new acquisitions in 2002 to evaluate the maturity of a system element, based on the technology used for critical technology element (CTE). In many system development programs, elements of a system are developed in segregation and later brought together to ensure that the integrated system functions as intended in its desired operational configuration. Integration Readiness Level (IRL) was later introduced as an integration metric to help predict the readiness of the system to achieve its desired operational functionality based on maturity and complexity of its elements. Additionally, System Readiness Level (SRL) was developed as a quantitative metric to help decision makers predict the systems readiness and maturity on the systems of systems level. DoD may use TRL metrics to assess the readiness of a specific technology or CTE’s acquisition process to evaluate an element’s maturity level, IRL metrics have not been adapted to assess the likelihood of success when the CTE and other elements are integrated into the objective system. System integration problems resulting in increased cost, schedule delays, and lower performance continue to be observed, particularly for system development programs with simultaneous design and implementation where certain system elements are being built before detailed design for other system elements is complete. A simple system integration technology readiness metric (McGee Methodology) is proposed and will be evaluated by utilizing Sauser et al., and McConkie et al. methodology. While Sauser et al uses matrix algebra with graph theory, McConkie et al. proposes utilizing tropical algebra to calculate the SRL. McGee Methodology proposes using the lowest level IRL within the overall system to forecast a more realistic system maturity assessment for complex systems. The research will focus on evaluating SRL assessment by focusing on utilizing IRL metrics and comparing these mathematical operations that have been discussed in the literature. These mathematical operations are analyzed to determine if they can be used to determine SRL assessments for future systems.