In recent years, understanding the structure and function of complex networks has become the foundation for explaining many different real-world complex biological, technological and informal social phenomena. Techniques from statistical physics have been successfully applied to the analysis of these networks, and have uncovered surprising statistical structural properties that have also been shown to have a major effect on their functionality, dynamics, robustness, and fragility. This paper examines, for the first time, the statistical properties of strategically important organizational networks - networks of people engaged in distributed product development (PD) - and discusses the significance of these properties in providing insight into ways of improving the strategic and operational decision-making of the organization. We show that the structure of information flow networks that are at the heart of large-scale product development efforts have properties that are similar to those displayed by other social, biological and technological networks. In this context, we identify novel properties that may be characteristic of other information-carrying networks. We further present a detailed model and analysis of PD dynamics on complex networks, and show how the underlying network topologies provide direct information about the characteristics of this dynamics. We believe that our new analysis methodology and empirical results are also relevant to other organizational information-carrying networks.
Cambridge, MA - Scientists at The New England Complex Systems Institute (NECSI) analyzed a series of major companies' large-scale product design and development projects, defining statistical properties that impact speed and cost-effectiveness. Available in the July issue of Management Science, the study examines data from designers of vehicles, software, pharmaceuticals and hospitals.
"We're aiming to offer insight into improving networks that are at the heart of industry," said Dr. Dan Braha, NECSI researcher. "Development often involves hundreds of designers working on an intricate set of interconnected tasks. Consequently, managing the network between them is vital."
Though statistical physics has been used to characterize many networks, this study is the first to apply the technique to essential corporate networks like product development. Among its findings, the study notes several factors common to biological and social networks also impact how quickly a product develops--for example, the need to balance incoming and outgoing information flows. Typically, development is impeded by coupling parts of a system that do not need to be joined, as well as by "information bottlenecks" (i.e. parts of the network with high volumes of both incoming and outgoing information).
"These networks can work even when some parts fail, but the information bottlenecks are their Achilles heel," said Dr. Yaneer Bar Yam, NECSI President. "As with other systems we've studied there are dangers in depending too much on a single node in a network.
"Information bottlenecks along with certain types of coupling can cause development to become an endless series of revisions," said Braha. "Informed managers will boost system performance by preventing them."
The study suggests a number of solutions to these problems, including the alteration of network connections and flows, preferential resource allocation, decoupling task activities, task modularization, and reuse of prior solutions.
The Statistical Mechanics of Complex Product Development: Empirical and Analytical Results appears in the July issue of Management Science.
The New England Complex Systems Institute (NECSI) is an independent non-profit organization promoting the science of complex systems, the mathematical study of how parts of a system give rise to its collective behaviors. Based in Cambridge, MA, NECSI conducts original research, education and dissemination of the understanding of complex systems and its application to the improvement of society.