A Multi-Domain Analysis Framework for Product Development

Abstract

The 21st century brings many new challenges to the product development (PD) community mainly due to a drastic increase in the scale and complexity of engineered systems. This requires the collaboration of various entities and resources within and outside firm boundaries. To address these new challenges, this paper proposes a novel framework for an enterprise-wide PD information management system. The proposed framework provides an integrative view of the various dependencies and information flows that co-exist in three main PD analysis domains (i.e., people, products, and processes) and analysis methods for the discovery of gaps or ‘misalignment’ between them. These gaps could help explain why some organizations are able to provide more competitive products within a given industry. Moreover, the framework suggests that the characteristics of how an organization acquire data, interpret information, and apply knowledge will impact the final architecture of the product. Finally, we demonstrate this framework by analyzing an open source software (OSS) project, which offers some insights and new directions into how the transfer of data, information, and knowledge impacts the final (source code) architecture and design.

A framework for ad hoc information management for the building design process

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Systematical hybrid state modelling of complex dynamical systems: The quad-I/HS framework

“…The notation of reading is similar to the DynS-DSM: First of all, the prefix of the interaction is written, than the proportional coefficient and the last the connection sign between this coefficient and the element of the corresponding row. The functional relationship is merged in a function f 1 1 and is represented in detail in an added sector. The non-linear connection is summarized in the self-depending cell and the corresponding proportional coefficients are included as before.…”

“…This is becoming increasingly difficult as a result of the drastic rise in engineered system's complexity. Those large, multidisciplinary and networked systems are affected by the reams of interactions within and between several domains like components, tasks and persons [1]. Hence, already small variances can lead to unexpected or even unstable behaviour, which makes it essential to get knowledge about the dynamics, stability and robustness of such complex networks [2,3].…”