From Implicit to Quantified: Understanding and Assessing Knowledge in System Generation Engineering

The development of complex systems, such as cyber-physical systems, heavily relies on an efficient management of available knowledge. Within the model of SGE - System Generation Engineering, which conceptualizes product development as a cross-generational process based on reference systems and their reference system elements (RSEs), knowledge provides the foundation for well-informed development decisions. However, the lack of a clear, shared understanding of “knowledge” within the model of SGE limits systematic planning and consistent application of its mechanisms, hindering the operationalization of model-based development across system generations. This contribution addresses this gap by developing a unified, model-aligned understanding of knowledge within the model of SGE. Building on a systematic analysis of key SGE literature complemented by expert workshops, recurring assumptions about knowledge were identified and structured. The findings reveal four central dimensions of knowledge: knowledge depth, knowledge context, knowledge origin, and knowledge management. These dimensions capture the multifaceted, contextualized, evolutionary, and dynamic character of knowledge in SGE and provide a consistent framework for its integration into model-based development. Based on this conceptual foundation, ongoing work will focus on operationalizing the framework through a catalog of guiding questions that enables the systematic assessment of knowledge related to reference system elements. This will facilitate informed planning, support the consistent application of variation operators within the model of SGE, and advance the operationalization of knowledge in cross-generational product development. By enabling traceable, knowledge-based development decisions, this contribution supports systematic, model-based design of the next generation of complex systems and positions knowledge as one of three pillars – alongside amount of change and complexity – for assessing consistency risks across system generations.