Design structure matrix (DSM) is a straightforward and flexible modeling technique that can be used for designing, developing, and managing complex systems. DSM offers network modeling tools that ...represent the elements of a system and their interactions, thereby highlighting the system’s architecture (or designed structure). Its advantages include compact format, visual nature, intuitive representation, powerful analytical capacity, and flexibility. Used primarily so far in the area of engineering management, DSM is increasingly being applied to complex issues in health care management, financial systems, public policy, natural sciences, and social systems. This book offers a clear and concise explanation of DSM methods for practitioners and researchers. The book’s four sections correspond to the four primary types of DSM models, offering tools for representing product architectures, organization architectures, process architectures, and multidomain architectures (which combine different types of DSM models to represent multiple domains simultaneously). In each section, a chapter introducing the technique is followed by a chapter of examples showing a variety of applications of that DSM type. The forty-four applications represent a wide range of industries (including automotive, aerospace, electronics, building, and pharmaceutical), countries (among them Australia, Germany, Japan, Turkey, and the United State), and problems addressed (modularity, outsourcing, system integration, knowledge management, and others).
Many scheduling problems in project management, manufacturing, and elsewhere require the generation of activity networks to test proposed solution methods. Single-network generators have been used ...for the resource-constrained project scheduling problem (RCPSP). Since the first single-network generator was proposed in 1993, several advances have been reported in the literature. However, these generators create only one network or project at a time; they cannot generate multi-project problems to desired specifications. This paper presents the first
multi-network
problem generator. It is especially useful for investigating the resource-constrained multi-project scheduling problem (RCMPSP), where a controlled set of multi-project test problems is crucial for analyzing the performance of solution methods. In addition to the single-project characteristics handled by existing network generators—such as activity duration, resource types and usage, and network size, shape, and complexity—the proposed generator produces multi-project portfolios with controlled resource distributions and amounts of resource contention. To enable the generation of projects with desired levels of network complexity, we also develop several theoretical insights on the effects of network topology on the probability of successful network generation. Finally, we generate 12,320 test problems for a full-factorial experiment and use analysis of means to conclude that the generator produces “near-strongly random” problems. Fully “strongly random” problems require much greater computational expense.
Many firms expend a great amount of effort to increase the customer value of their product development (PD) processes. Yet, in PD, determining how and when value is added is problematic. The goal of ...a PD process is to produce a product "recipe" that satisfies requirements. Design work is done both to specify the recipe in increasing detail and to verify that it does in fact conform to requirements. As design work proceeds, certainty increases surrounding the ability of the evolving product design (including its production process) to be the final product recipe (i.e., technical performance risk decreases). The goal of this paper is to advance the theory and practice of evaluating progress and added customer value in PD. The paper proposes that making progress and adding customer value in PD equate with producing useful information that reduces performance risk. The paper also contributes a methodology-the risk value method-that integrates current approaches such as technical performance measure tracking charts and risk reduction profiles. The methods are demonstrated with an industrial example of an uninhabited combat aerial vehicle.
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.
PurposeSupply chains must rebuild for resilience to respond to challenges posed by systemwide disruptions. Unlike past disruptions that were narrow in impact and short-term in duration, the Covid ...pandemic presented a systemic disruption and revealed shortcomings in responses. This study outlines an approach to rebuilding supply chains for resilience, integrating innovation in areas critical to supply chain management.Design/methodology/approachThe study is based on extensive debates among the authors and their peers. The authors focus on three areas deemed fundamental to supply chain resilience: (1) forecasting, the starting point of supply chain planning, (2) the practices of supply chain risk management and (3) product design, the starting point of supply chain design. The authors’ debated and pooled their viewpoints to outline key changes to these areas in response to systemwide disruptions, supported by a narrative literature review of the evolving research, to identify research opportunities.FindingsAll three areas have evolved in response to the changed perspective on supply chain risk instigated by the pandemic and resulting in systemwide disruptions. Forecasting, or prediction generally, is evolving from statistical and time-series methods to human-augmented forecasting supplemented with visual analytics. Risk management has transitioned from enterprise to supply chain risk management to tackling systemic risk. Finally, product design principles have evolved from design-for-manufacturability to design-for-adaptability. All three approaches must work together.Originality/valueThe authors outline the evolution in research directions for forecasting, risk management and product design and present innovative research opportunities for building supply chain resilience against systemwide disruptions.
In product development (PD) organizations, coordinating technical dependencies among teams with different expertise in overlapping processes is a fundamental challenge. This article takes a more ...sophisticated approach than prior methodologies to improve coordination via organizational clustering, by accounting for both team structural and attribute similarity from the perspective of social network analysis. We built models to quantify the impact of the overlapping processes on the interaction strength among PD teams, which we then used to construct structural similarity by combining tie strength and social cohesion among teams via the design structure matrix. To evaluate the organization network, we propose social embeddedness-related centrality indices within (intracluster) and across (intercluster) team groupings. To facilitate knowledge sharing, we base team attribute similarity on product- and process-related expertise among teams. We integrate the modularity index and an improved silhouette index to find an optimal number of clusters, which we then incorporate with team similarity measures as inputs to a spectral clustering algorithm. An industrial example illustrates the proposed model. The clustering results reinforce several managerial practices but also yield new insights, such as how to measure similarity among teams based on organizational network characteristics and how structural and attribute similarities impact the optimal organizational structure.
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