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META Metrics Toolbox for quantifying complexity and adaptability of system designs

META Metrics Toolbox for quantifying complexity and adaptability of system designs. Main goal of META is to achieve a 5x improvement in system development speed over current practice. In order to do this metrics need to be used to synthesize and select the best possible system architecture:

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META Metrics Toolbox for quantifying complexity and adaptability of system designs

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  1. META Metrics Toolbox for quantifying complexity and adaptability of system designs • Main goal of META is to achieve a 5x improvement in system development speed over current practice. In order to do this metrics need to be used to synthesize and select the best possible system architecture: • A key element is to choose system architectures and designs that achieve desired performance with minimumnecessary structural and dynamic complexity as well as maximum adaptability. • Structural Complexity is based on the type and number of system elements, their interconnections as well as the structural arrangement. • Captures both component an interface complexity • Key concept: Graph Energy • Dynamic Complexity is based on the number and correlation amongst functional performance attributes of the system. • Captures correlation and uncertainty • Key concept: Shannon Entropy • Organizational Complexity captures the number of staff required and rework • Predicts META Program Cost and Schedule • Key Concept: The Rework Cycle (System Dynamics) • Adaptability– the ease with which changes can be made to a design in the future Spectrum of System Metrics Structural Dynamical 19x19 Structural DSM B – Matrix captures correlations amongst Functional attributes Other dynamic complexity metrics Sample Results: dimensional_complexity20 computational_complexity=24.9869 nonlinear_complexity=7.7852 timescale_complexity = 185.5963 Organizational (Design Flow) META Metrics Takeaway Metrics Toolbox • It does not seem possible to compress META design evaluation into a single scalar metric. • A vector of metrics is most useful, containing: • Structural Complexity (parts, interfaces, architecture, Graph Energy) • Dynamic Complexity (functional interdependence, uncertainty, Shannon Entropy) • Organizational Complexity ( number and productivity of staff, organizational assignments, Schedule, NRE) • Main Purpose of META Metrics • Architecture Selection • Outcome prediction   • Sample Results for 3-spool Turbofan: • Dynamic Complexity: 708.2 • Functional Interdependency E(B) = 34.5 • Shannon Entropy = 20.5 • Sample Results for B777 EPS: • Schedule completion time: • Actual 61 m, META: 16 m • ECRs: 300, META: 100 • META Speedup Factor: 3.8 • Sample Results for GTF: • # of components: 73 • # of interfaces: 377 • Graph Energy: 124.8 • Structural Complexity: 717.7 Conclusion: META-speedup factor of 5 compared to current practice appears feasible, but will require multiple “mechanisms” to work together (C2M2L library coverage > 0.8, structural complexity reduction of 40% thanks to extensive architecture enumeration, 75% probability of early detection of design flaws, >2 layers of abstraction etc…). The current META metrics toolbox produced by the UTRC team contains structural, dynamic and organizational metrics of complexity. Dynamic Complexity drives the amount of change and number of iterations required to achieve satisfactory performance in all Functional requirements Structural Complexity drives the amount of Design and Integration Work required as well As the Engineering Change Generation Rate Sensitivity Analysis of META outcomes META System Dynamics Model Contact Dr. Olivier de Weck (deweck@mit.edu) or Dr. Brian Murray (MurrayBT@utrc.utc.com) for more information on this effort.

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