Search

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]

Systems engineering and design (SE&D) researchers increasingly tackle questions at the intersection of technical and social aspects of complex systems design. Practical challenges of access, limited observation scope, and long timescales limit empirical study of SE&D phenomena. As a result, studies are typically conducted in model world settings abstracted from the real world, such as behavioral experiments with student subjects. Model worlds must be representative of the phenomena being studied to ensure insights generalize to the real‐world settings. Currently, there is a lack of shared understanding and standards within the SE&D research community to evaluate representativeness of model worlds. This communication captures the results of ongoing efforts to build consensus on this topic: it defines the concept of model worlds, disambiguates representativeness from related concepts, and draws comparisons to other research domains. It outlines a potential path forward and calls for community participation in establishing shared standards for model world representativeness in SE&D research. [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses the role that qualitative methods can and should play in engineering systems research and lays out the process of doing good qualitative research. As engineering research increasingly focuses on sociotechnical systems, in which human behavior and organizational context play important roles in system behavior, there is an increasing need for the insights qualitative research can provide. This paper synthesizes the literature on qualitative methods and lessons from the authors’ experience employing qualitative methods to study a variety of engineering systems. We hope that by framing the key issues clearly, other engineers who hope to join the qualitative path will build on what we have learned so far to enable greater insight into engineering systems. [ABSTRACT FROM AUTHOR]