Abstract:
Early design of complex systems is characterised by significant uncertainty due to lack of knowledge, which can impede the design process. In order to proceed with the latter, assumptions are typically introduced to fill knowledge gaps. However, the uncertainty
inherent in the assumptions constitutes a risk to be mitigated. In fact, assumptions can negatively impact the system if they turn out to be invalid, such as causing system failure, violation of requirements, or budget and schedule overruns.
Within this context, the aim of this research was to develop a computational approach to support assumption management in model-based systems engineering, with an explicit consideration of the uncertainty in assumptions. To achieve the research aim, the objectives were to: (1) devise methods to enable assumption management in a model-based design environment; and (2) devise methods to manage risk of change due to invalid assumptions, with an explicit consideration of both assumptions and margins. The scope was limited to the early stages of aircraft design.
To evaluate this research, a demonstration was performed based on two use cases to assess whether the methods work as intended. The developed methods were demonstrated to industry experts in order to obtain feedback on expected usefulness in practice, thus assessing the impact of this research. The experts concluded that the proposed methods are innovative, useful and relevant to industry, where these methods can lead to: (i) fewer undesired iterations, due to earlier identification and management of risks associated with assumptions; and (ii) a better margin balance, due to timely and interactive margin revision. Future work includes further industrial evaluation, extending the research scope and studying the scalability and associated costs of the proposed methods.
