Complexity characteristics and measurement within engineering systems

Complexity is a significant factor in the development of new products and systems; generally speaking, the higher the complexity, the more difficult products and systems are going to be to design and develop. There are a number of different factors that influence complexity within systems, namely: interoperability; upgradability; adaptability; evolving requirements; system size; automation requirements; performance requirements; support requirements; sustainability; reliability; the need for increased product lifespan; and finally, the length of time systems take to develop. There is, at present, no common language to describe complexity within engineered systems; this language needs to be developed in order to help industry cope with increasing product complexity and thus meet customer demands. This thesis represents a start in the development of that language, and thus an understanding of systems complexity. The thesis offers a framework for complexity analysis within systems, one which identifies some of the key complexity characteristics that need to be taken into consideration, and which embraces complexity problems, definitions, concepts and classifications, origins and coping mechanisms. It has also has been developed in terms of a measurement approach, thereby allowing for a meaningful comparison between products, and an understanding of the complexities within them. This framework was developed using information collected from academic literature and from more specific case studies. Each complexity characteristic was investigated, and the interactions between characteristics were identified; these interactions allow us to understand complexity and help to develop a common language. The thesis develops a measurement technique that quantifies various complexity characteristics in terms of the framework laid down, thus enabling a quantified understanding of complexity within systems. This new measurement approach was tested on a set of recent case studies, and the complexity characteristics produced by the measurement technique were, in turn, tested against attributes of the system. The framework itself is always evolving - it incorporates new complexity characteristics. Nevertheless, such evolution can only further our understanding of complexity. Further work, to explore and integrate the approach demonstrated in this thesis into an automated tool, and test its robustness, along with a continual development of other elements of the framework, such as a classification of complexity, is recommended.