An Introduction to Modeling and Analyzing Complex Product Development Processes Using the Design Structure Matrix

An Introduction to Modeling and Analyzing Complex Product Development Processes Using the Design Structure Matrix (DSM) Method
Ali A. Yassine Product Development Research Laboratory University of Illinois at Urbana-Champaign Urbana, IL. 61801 [e-mail] Brief description of the author Ali Yassine is an assistant professor at the Department of General Engineering at the University of Illinois at Urbana-Champaign (UIUC). He is the director of the Product Development Research Laboratory and an affiliated professor with the Technology Entrepreneur Center (TEC). His research concentrates on analyzing a set of product development problems, including the management of iteration and overlapping decisions. Abstract The design and development of complex engineering products require the efforts and collaboration of hundreds of participants from diverse backgrounds resulting in complex relationships among both people and tasks. Many of the traditional project management tools (PERT, Gantt and CPM methods) do not address problems stemming from this complexity. While these tools allow the modeling of sequential and parallel processes, they fail to address interdependency (feedback and iteration), which is common in complex product development (PD) projects. To address this issue, a matrix-based tool called the Design Structure Matrix (DSM) has evolved. This method differs from traditional project-management tools because it focuses on representing information flows rather than work flows. The DSM method is an information exchange model that allows the representation of complex task (or team) relationships in order to determine a sensible sequence (or grouping) for the tasks (or teams) being modeled. This article will cover how the basic method works and how you can use the DSM to improve the planning, execution, and management of complex PD projects using different algorithms (i.e., partitioning, tearing, banding, clustering, simulation, and eigenvalue analysis).

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