Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM) Open Access
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Different classes of problems warrant different classes of solutions. There is no agreed set of unified principles and models to support systems engineering use over a wide range of domains. Nor is there a set of consistent terminology and definitions. These two deficiencies impede the adoption of systems engineering and create problems. On schedule delivery of a system meeting stakeholder needs at an acceptable cost is dependent upon selection and application of a system engineering method (SEM) appropriate for the class of system problem (COSP). Real world problems possess a degree of complexity that requires a commensurately complex approach as stakeholders are demanding increasingly capable systems that are growing in complexity, yet complexity-related system misunderstanding is at the root of significant cost overruns and system failures. INCOSE and IEEE recommend system complexity as a basis for selection and tailoring of SE processes; however, neither society provides a definition of complexity nor a methodology for SEM selection. Selection of a complexity appropriate SEM is dependent on understanding COSP which is currently difficult to define, observe, or measure. This research develops a diagnostic assessment model (DAM), based on the Cynefin framework, that infers COSP and then recommends a complexity appropriate SEM to reduce system miscategorization and therefore reduce the risk of system failure. An empirical healthcare case study is used to demonstrate SEMDAM’s application and efficacy.