Dan Braha

Complex Concurrent Engineering and the Design Structure Matrix Method

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.

The Statistical Mechanics of Complex Product Development: Empirical and Analytical Results

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.

The design process: properties, paradigms, and structure

In this paper, we examine the logic and methodology of engineering design from the perspective of the philosophy of science. The fundamental characteristics of design problems and design processes are discussed and analyzed. These characteristics establish the framework within which different design paradigms are examined. Following the discussions on descriptive properties of design, and the prescriptive role of design paradigms, we advocate the plausible hypothesis that there is a direct resemblance between the structure of design processes and the problem solving of scientific communities. The scientific community metaphor has been useful in guiding the development of general purpose highly effective design process meta-tools.

Data mining for improving a cleaning process in the semiconductor industry

As device geometry continues to shrink, micro-contaminants have an increasingly negative impact on yield. By diminishing the contamination problem, semiconductor manufacturers will significantly improve wafer yield. This paper presents a comprehensive and successful application of data mining methodologies to the refinement of a new dry cleaning technology that utilizes a laser beam for the removal of micro-contaminants. Experiments with three classification-based data mining methods (decision tree induction, neural networks, and composite classifiers) have been conducted. The composite classifier architecture has been shown to yield higher accuracy than the accuracy of each individual classifier on its own. The paper suggests that data mining methodologies may be particularly useful when data is scarce, and the various physical and chemical parameters that affect the process exhibit highly complex interactions. Another implication is that on-line monitoring of the cleaning process using data mining may be highly effective.

Information Flow Structure in Large-Scale Product Development Organizational Networks

This paper analyzes the statistical properties of real-world networks of people engaged in product development (PD) activities. We show that complex PD networks display similar statistical patterns to other real-world complex social, information, biological and technological networks. The paper lays out the foundations for understanding the properties of other intra- and inter-organizational networks that are realized by specific network architectures. The paper also provides a general framework towards characterizing the functionality, dynamics, robustness, and fragility of smart business networks.

Complex Engineered Systems

Dan Braha: Good morning. Welcome to the complex engineered systems section. You know, it is often believed that engineering is fundamentally different from the natural sciences. This belief is based on the contention that engineering is a prescriptive science as opposed to the natural sciences, which are descriptive in nature. Consequently, it’s said that engineering is placed at a lower rank as far as complexity is concerned.

Anticipating Economic Market Crises Using Measures of Collective Panic

Predicting panic is of critical importance in many areas of human and animal behavior, notably in the context of economics. The recent financial crisis is a case in point. Panic may be due to a specific external threat or self-generated nervousness. Here we show that the recent economic crisis and earlier large single-day panics were preceded by extended periods of high levels of market mimicry—direct evidence of uncertainty and nervousness, and of the comparatively weak influence of external news. High levels of mimicry can be a quite general indicator of the potential for self-organized crises.

On the complexity of the design synthesis problem

Presents and analyzes a formal model of a design process, emphasizing the synthesis part. The design artifact description is identified as an algebraic structure. The desired function and constraints are mapped to the artifact description using an evolutionary process that can be visualized as a feedback loop of analysis, synthesis and evaluation. A special case of the synthesis activity, called the basic synthesis problem (BSP), is addressed. The BSP is shown to be NP-complete. As a consequence, tractability can be obtained by enforcing constraints on the artifact structure. As such, the authors present a model having an element of descriptive design theory that is also a framework for the future development of computational support systems and automatic design tools.

Global Civil Unrest: Contagion, Self-Organization, and Prediction

Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, records of civil unrest of 170 countries during the period 1919–2008 are analyzed. It is demonstrated that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics.

Time-Dependent Complex Networks: Dynamic Centrality, Dynamic Motifs, and Cycles of Social Interactions

We develop a new approach to the study of the dynamics of link utilization in complex networks using data of empirical social networks. Counter to the perspective that nodes have particular roles, we find roles change dramatically from day to day. “Local hubs” have a power law degree distribution over time, with no characteristic degree value. We further study the dynamics of local motif structure in time-dependent networks, and find recurrent patterns that might provide empirical evidence for cycles of social interaction. Our results imply a significant reinterpretation of the concept of node centrality and network local structure in complex networks, and among other conclusions suggest that interventions targeting hubs will have significantly less effect than previously thought.