Mathematical Properties of Interfaces
Open Access DepositedCluster-Based Application of System Readiness Levels in Acquisition
Decisions in development of complex technology relies on metrics of readiness or maturity to inform the key managers of progress throughout the lifecycle. This research builds upon previous mathematical models of system readiness level to address their shortcomings and provide a simple tool for the program manager to assess risk and effort prior to committing major resources. A key factor in technological immaturity in defense weapons acquisition is lack of understanding critical integrations at the subsystem and component level. To address this shortfall, recent research in Integration Readiness Level combines with Technology Readiness Level to form a System Readiness Level. System Readiness Level can be improved with more robust quantitative methods to provide the program manager a useful tool prior to committing to major weapons acquisition programs. This research harnesses previous mathematical models based on graph theory, Petri nets, and tropical algebra, and proposes an additional desirable mathematical property that a tightly integrated subsystem should not have a higher System Readiness Level than an inherently less coupled subsystem. This research ties the separately developed Manufacturing Readiness Level into the network representation of the system and addresses shortfalls in previous frameworks, including the lack of integration weighting and the over-importance of a single extremely immature component. Tropical algebra (based on the minimum of a set of Technology Readiness Levels or Integration Readiness Levels) allows a single immature Integration Readiness Level or Technology Readiness Level value to diminish the SRL of the entire system, which may not be reflective of actuality if that component is not critical or tightly coupled. Additionally, lack of integration is not represented by zero, but by a perfect integration maturity value. To further explore the impact of grouping subsystems, a multi-objective genetic algorithm is then used to find various clusters or communities that can be optimized for the most representative subsystem System Readiness Level. This model remains a simple, accessible tool at higher fidelity for the program manager to decide on actions for major defense acquisition program milestones.
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