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 also 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 these dynamics. We believe that our new analysis methodology and empirical results are also relevant to other organizational information-carrying networks.organizational studies, social networks, large-scale product development, sociotechnical systems, complex engineering systems

Concurrent engineering (CE) principles have considerably matured over the last decade. However, many companies still face enormous challenges when implementing and managing CE practices. This is due to the increased complexity of engineering products and processes, on one hand, and the lack of corresponding CE models and tools, on the other hand. This paper focuses on four critical problems that challenge management while implementing CE in complex product development (PD) projects. We refer to these problems as: iteration, overlapping, decomposition and integration, and convergence problems. We describe these problems proposing a unified modeling and solution approach based on the design structure matrix (DSM) method, which is an information exchange model that allows managers to represent complex task relationships to better plan and manage CE initiatives.

Execution of a complex product development project is facilitated through its decomposition into an interrelated set of localized development tasks. When a local task is completed, its output is integrated through an iterative c ycle of system-wide integration activities. Integration is often accompanied by inadvertent information hiding due to the asynchronous information exchanges.We show that information hiding leads to persistent recurrence of problems (termed as the design churn effect) such that progress oscillates between being on schedule and falling behind. The oscillatory nature of the PD process confounds progress measurement and makes it difficult to judge whether the project is on schedule or slipping. We develop a dynamic model of work transformation to derive conditions under which churn is observed as an unintended consequence of information hiding due to local and system task decomposition. We illustrate these conditions with a case example from an automotive development project and discuss strategies to mitigate design churn.

The last few years have led to a series of discoveries that uncovered statistical properties, which are common to a variety of diverse real-world social, information, biological and technological networks. The goal of the present paper is to investigate, for the first time, the statistical properties of networks of people engaged in distributed problem solving and discuss their significance. We show that problem-solving networks have properties (sparseness, small world, scaling regimes) that are like those displayed by information, biological and technological networks. More importantly, we demonstrate a previously unreported difference between the distribution of incoming and outgoing links of directed networks. Specifically, the incoming link distributions have sharp cutoffs that are substantially lower than those of the outgoing link distributions (sometimes the outgoing cutoffs are not even present). This asymmetry can be explained by considering the dynamical interactions that take place in distributed problem solving, and may be related to differences between the actor's capacity to process information provided by others and the actor's capacity to transmit information over the network. We conjecture that the asymmetric link distribution is likely to hold for other human or non-human directed networks as well when nodes represent information processing/using elements.