A complexity-based heuristic decision analysis model to recommend systems engineering domain

All science and engineering involves abstraction of the complexity of the world into approaches and models that use simplifying assumptions, which allow generalization from one complex situation to another. The best engineering methods take advantage of the simplicity in the models without diverging so far from reality that behavior can no longer be predicted and controlled, if possible. As a system’s complexity increases, however, the risks associated with using simpler methods and simplifying assumptions also increases, and something more than traditional system engineering (TSE) or conventional system engineering (CSE) is needed to deal effectively with system-of-systems, enterprises, and complex systems. While system engineering (SE) has evolved beyond TSE with additional SE bodies of knowledge including systems-of-systems engineering, enterprise systems engineering, and CSE, there is no agreed upon best practice for SE domain selection so the SE practitioner must apply personal judgment to select an appropriate SE domain and tailor applicable SE processes. We provide a heuristic decision analysis model, based on complexity and the Cynefin framework, as a novel approach to recommend an appropriate SE domain to eliminate or reduce misclassifying systems and by extension systemfailure. We demonstrate this model using a United States National HealthCare case study.