Effective communication in product development organizations is widely recognized to be a key element of product development performance. Furthermore, management of product architecture knowledge by the development organization provides important competitive advantage for established firms facing architectural innovation. This research studies how the combination of product architecture and organizational structure determines technical communication in development teams. By documenting and analyzing both the design interfaces between the components that comprise a product and the technical interactions between the teams that design each of these components, we learn how the architecture of the product and the layout of the organization drive development team interactions. Several hypotheses are formulated to explain the unexpected cases when: 1) known design interfaces are not matched by team interactions, and 2) observed team interactions are not predicted by design interfaces. We test the hypothesized effects due to organizational and system boundaries, and design interface strength. Hypotheses are tested using both categorical data analysis and log-linear network analysis. The research is conducted using data collected describing a large commercial aircraft engine development process.
Complex engineered systems tend to have architectures in which a small subset of components exhibits a disproportional number of linkages. Such components are known as hubs. This paper examines the degree distribution of systems to identify the presence of hubs and quantify the fraction of hub components. We examine how the presence and fraction of hubs relate to a system’s quality. We provide empirical evidence that the presence of hubs in a system’s architecture is associated with a low number of defects. Furthermore, we show that complex engineered systems may have an optimal fraction of hub components with respect to system quality. Our results suggest that architects and managers aiming to improve the quality of complex system designs must proactively identify and manage the use of hubs. Our paper provides a data-driven approach for identifying appropriate target levels of hub usage.