Tyson R. Browning

Applying the design structure matrix to system decomposition and integration problems: a review and new directions

Systems engineering of products, processes, and organizations requires tools and techniques for system decomposition and integration. A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system that supports innovative solutions to decomposition and integration problems. The advantages of DSMs vis-a-vis alternative system representation and analysis techniques have led to their increasing use in a variety of contexts, including product development; project planning, project management, systems engineering, and organization design. This paper reviews two types of DSMs, static and time-based DSMs, and four DSM applications: (1) component-based or architecture DSM, useful for modeling system component relationships and facilitating appropriate architectural decomposition strategies; (2) team-based or organization DSM, beneficial for designing integrated organization structures that account for team interactions; (3) activity-based or schedule DSM, advantageous for modeling the information flow among process activities; and (4) parameter-based (or low-level schedule) DSM, effective for integrating low-level design processes based on physical design parameter relationships. A discussion of each application is accompanied by an industrial example. The review leads to conclusions regarding the benefits of DSMs in practice and barriers to their use. The paper also discusses research directions and new DSM applications, both of which may be approached with a perspective on the four types of DSMs and their relationships.

Modeling impacts of process architecture on cost and schedule risk in product development

To gain competitive leverage, firms that design and develop complex products seek to increase the efficiency and predictability of their development processes. Process improvement is facilitated by the development and use of models that account for and illuminate important characteristics of the process. Iteration is a fundamental but often unaddressed feature of product development (PD) processes. Its impact is mediated by the architecture of a process, i.e., its constituent activities and their interactions. This paper integrates several important characteristics of PD processes into a single model, highlighting the effects of varying process architecture. The PD process is modeled as a network of activities that exchange deliverables. Each activity has an uncertain duration and cost, an improvement curve, and risks of rework based on changes in its inputs. A work policy governs the timing of activity execution and deliverable exchange (and thus the amount of activity concurrency). The model is analyzed via simulation, which outputs sample cost and schedule outcome distributions. Varying the process architecture input varies the output distributions. Each distribution is used with a target and an impact function to determine a risk factor. Alternative process architectures are compared, revealing opportunities to trade cost and schedule risk. Example results and applications are shown for an industrial process, the preliminary design of an uninhabited combat aerial vehicle. The model yields and reinforces several managerial insights, including: how rework cascades through a PD process, trading off cost and schedule risk, interface criticality, and occasions for iterative overlapping.

Design Process Sequencing With Competent Genetic Algorithms

In product design, it is critical to perform project activities in an appropriate sequence. Otherwise, essential information will not be available when it is needed, and activities that depend on it will proceed using assumptions instead. Later, when the real information is finally available, comparing it with the assumptions made often precipitates a cascade of rework, and thus cost and schedule overruns for the project. Information flow models have been used to sequence the engineering design process to minimize feedback and iteration, i.e., to maximize the availability of real information where assumptions might otherwise be made instead. In this paper, we apply Genetic Algorithms (GAs) to an information flow model to find an optimized sequence for a set of design activities. The optimality of a solution depends on the objective of rearrangement. In an activity sequencing context, objectives vary: reducing iteration/feedback, increasing concurrency, reducing development lead-time and cost, or some combination of these. We adopt a matrix-based representation scheme, the design structure matrix (DSM), for the information flow models. Our tests indicate that certain DSM characteristics (e.g., size, sparse-ness, and sequencing objective) cause serious problems for simple Genetic Algorithm (SGA) designs. To cope with the SGA deficiency, we investigate the use of a competent GA: the ordering messy GA (OmeGA). Tests confirm the superiority of the OmeGA over a SGA for hard DSM problems. Extensions enhancing the efficiency of both a SGA and the OmeGA, in particular, niching and hybridization with a local search method, are also investigated.

Planning Concurrency and Managing Iteration in Projects

Reducing cycle times, managing costs, and improving project management are key to competitive advantage. This paper introduces an approach known as the dependency structure matrix (DSM) and discusses using the DSM to design project plans that produce greater concurrency and better iteration management. The DSM focuses management attention on the essential information transfer requirements of a project: finding essential tasks vital to begin early, developing project plans having improved throughput, removing unnecessary iteration, and simplifying project reviews, while at the same time preserving or improving deliverable quality. A DSM helps us decide what to assume and how and when to plan project reviews. Assumptions are among the greatest sources of project risks. Making assumptions and their dependencies explicit is the key to controlling risk.

Sources of Schedule Risk in Complex System Development

Schedule risk is an important category of risk in complex system product development. This paper presents a framework that facilitates understanding schedule risk from a systems perspective. Research findings from literature and a Delphi-type survey of experienced product development managers and system engineers at a major aerospace company are synthesized into a framework characterizing sources of schedule uncertainty. The framework includes not only key uncertainty drivers but also the hypothesized or theorized relationships between them. Since risk is more than just uncertainty, consequences of schedule overruns and of schedule uncertainty itself are also discussed. This research contributes a more comprehensive, systems view to the studies of product development and risk management and to the practice of both in industry. The paper also examines potential paths for future research.

Value-based product development: refocusing lean

Lean is not minimizing cost, cycle time, or waste. Lean is maximizing value. In product development (PD), sometimes getting lean requires doing more, not less. Providing a preferred combination of product performance, affordability and availability requires a lean PD process. Product value is affected not only by the presence of necessary activities in the PD process but also by the way those activities work together to ensure that they use and produce right information. Lean PD requires the right information at the right place at the right time.

Linking Cyclicality and Product Quality

This paper examines the impact of architectural decisions on the level of defects in a product. We view products as collections of components linked together to work as an integrated whole. Previous work has established modularity how decoupled a component is from other product components as a critical determinant of defects, and we confirm its importance. Yet our study also provides empirical evidence for a relationship between product quality and cyclicality the extent to which a component depends on itself via other product components. We find cyclicality to be a determinant of quality that is distinct from, and no less important than, modularity. Extending this main result, we show how the cyclicality--quality relationship is affected by the centrality of a component in a cycle and the distribution of a cycle across product modules. These findings, which are based on an analysis of open source software development projects, have implications for the study and design of complex systems.

Design Structure Matrix Extensions and Innovations: A Survey and New Opportunities

The design structure matrix (DSM), also called the dependency structure matrix, has become a widely used modeling framework across many areas of research and practice. The DSM brings advantages of simplicity and conciseness in representation, and, supported by appropriate analysis, can also highlight important patterns in system architectures (design structures), such as modules and cycles. A literature review in 2001 cited about 100 DSM papers; there have been over 1000 since. Thus, it is useful to survey the latest DSM extensions and innovations to help consolidate progress and identify promising opportunities for further research. This paper surveys the DSM literature, primarily from archival journals, and organizes the developments pertaining to building, displaying, analyzing, and applying product, process, and organization DSMs. It then addresses DSM applications in other domains, as well as recent developments with domain mapping matrices (DMMs) and multidomain matrices (MDMs). Overall, DSM methods are becoming more mainstream, especially in the areas of engineering design, engineering management, management/organization science, and systems engineering. Despite significant research contributions, however, DSM awareness seems to be spreading more slowly in the realm of project management.

Integrative mechanisms for multiteam integration: Findings from five case studies

Many product development programs contain multiple integrated product teams (IPTs) and functional support groups. Interteam information dependencies greatly affect program success. Organization integration has thus become an issue of increasing interest. This paper focuses on the realm of team interdependence and categorizes and explores several integrative mechanisms (IMs) that facilitate interteam integration. IMs are strategies and tools for effectively coordinating actions across groups within a program. The IM categorization scheme should prove useful to those developing an integration “tool kit.” This paper explores the use of IMs in real programs, summarizing findings from five case studies at Chrysler, General Electric Aircraft Engines, Boeing, Sundstrand, and Raytheon Systems. As the appropriateness of a given IM varies as a function of many parameters—such as program stage, size, complexity, risk, etc.—this research does not formulate a universal template for IM application. Rather, the hope is that the lessons learned by these five programs will help others determine the suitability of particular IMs in their situations. This paper centers on studies in the defense aerospace industry (with two commercial cases and one nonaerospace case), but the implications extend to any system development program.

Degree Distribution and Quality in Complex Engineered Systems

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.