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Standardized design approaches such as those embodied by concurrent design facilities have many benefits, such as increased efficiency of the design process, but may also have hidden costs. Specifically, when their standardized organizational decomposition is a poor fit for the particular design problem, important design trades might be missed or poor decisions made. Before we can understand how this lack of fit impacts the design process, we must be able to empirically observe and measure it. To that end, this paper identifies measures of "fit" from the literature along with attributes likely to impact design process performance, then evaluates the measures to determine how well the measures can detect and diagnose potential issues. The results provide comparative insights into the capabilities of existing fit measures, and also build guidance for how the systems engineering and design community can use insights from the "fit" literature to inform process improvement. [ABSTRACT FROM AUTHOR]

Engineering systems design is a dynamic socio-technical process where the social factors, such as interdisciplinary interactions, and technical factors, such as design interdependence and the design state, co-evolve. Understanding this co-evolution can lead to behavioral insights, resulting in efficient communication pathways and better designs. In that context, we investigate how to generate behavioral insights to inform effective structuring of interdisciplinary interactions in engineering systems design teams. We present an approach that combines the predictive capabilities of computational modeling with contextual information from empirical data. A stochastic network-behavior dynamics model quantifies the co-evolution of design interdependence, discipline-specific interaction decisions, and the changes in system performance. We employ two datasets, one of the student subjects designing an automotive engine and NASA engineers designing a spacecraft. Then, we apply Bayesian statistical inference to estimate model parameters and compare insights across the two datasets. The results indicate that design interdependence and social network factors such as reciprocity and popularity have strong positive effects on interdisciplinary interactions for the expert and student subjects alike. An additional modulating impact of system performance on the number of interactions is observed for the student subjects. Inversely, the total number of interactions, irrespective of their discipline-wise distribution, has a weak but statistically significant positive effect on system performance in both cases. However, we observe that excessive interactions mirrored with design interdependence and inflexibility in design exploration reduced the system performance. These insights support the case for open boundaries in systems design teams to improve system performance. [ABSTRACT FROM AUTHOR]

The engineering of complex systems, such as aircraft and spacecraft, involves large number of individuals within multiple organizations spanning multiple years. Since it is challenging to perform empirical studies directly on real organizations at scale, some researchers in systems engineering and design have begun relying on abstracted model worlds that aim to be representative of the reference socio-technical system, but only preserve some aspects of it. However, there is a lack of corresponding knowledge on how to design representative model worlds for socio-technical research. Our objective is to create such knowledge through a reflective case study of the development of a model world. This "inner" study examines how two factors influence interdisciplinary communication during a concurrent design process. The reference real world system is a mission design laboratory (MDL) at NASA, and the model world is a simplified engine design problem in an undergraduate classroom environment. Our analysis focuses on the thought process followed, the key model world design decisions made, and a critical assessment of the extent to which communication phenomena in the model world (engine experiment) are representative of the real world (NASA's MDL). We find that the engine experiment preserves some but not all of the communication patterns of interest, and we present case-specific lessons learned for achieving and increasing representativeness in this type of study. More generally, we find that representativeness depends not on matching subjects, tasks, and context separately, but rather on the behavior that emerges from the interplay of these three dimensions. [ABSTRACT FROM AUTHOR]