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Empirical Validation of Structural Complexity Metric and Complexity Management for Engineering Systems
Author(s) -
Sinha Kaushik,
Weck Olivier L.
Publication year - 2016
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.21356
Subject(s) - structural complexity , complexity management , computational complexity theory , metric (unit) , computer science , observer (physics) , worst case complexity , mathematical optimization , algorithm , theoretical computer science , mathematics , artificial intelligence , engineering , operations management , physics , marketing , quantum mechanics , business
Quantitative assessment of structural complexity is essential for characterization of engineered complex systems. In this paper, we describe a quantitative measure for structural complexity, conduct an empirical validation study of the structural complexity metric, and introduce a complexity management framework for engineering system development. We perform empirical validation of the proposed complexity metric using simple experiments using ball and stick models and show that the development effort increases superlinearly with increasing structural complexity. The standard deviation of the build time for ball and stick models is observed to vary superlinearly with structural complexity. We also describe a generic statistical procedure for building such cost estimation relationships with structural complexity as the independent variable. We distinguish the notion of perception of complexity as an observer‐dependent property and contrast that with complexity, which is a property of the system architecture. Finally, we introduce the notion of system value based on performance‐complexity trade space and introduce a complexity management framework for system development.