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A Network-Based Analysis of Team Coordination and Shared Cognition in Systems Engineering

机译:基于网络的系统工程中团队协作和共享认知分析

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摘要

This paper presents a network-based approach for analyzing team coordination and shared cognition in engineering design teams. The research setting is an Integrated Concurrent Engineering (ICE) laboratory in which teams of approximately 20 engineers are collocated to conduct rapid conceptual design of scientific spacecraft. A design structure matrix (DSM) of expected interactions is constructed from technical information flow data, and DSM representations of reported interactions are created using survey data from 10 ICE design teams. A comparative analysis of expected and reported interactions is used to calculate a metric of team coordination called socio-technical congruence (STC). To examine shared cognition, pairwise shared mental models (SMMs) are measured using pre- and post-session surveys on system design drivers. Shared knowledge networks (SMMs as edges) are constructed, and team learning is measured as the change in network structure over time. Analysis reveals statistically significant correlations between team learning and each of three technical attributes (system development time, launch mass, and mission concept maturity) and between team learning and team coordination. These results indicate that team members learn most from each other when working on difficult or unfamiliar problems and when expected and reported interactions are aligned. The paper concludes that team coordination and the design product in ICE are not necessarily directly related to each other but that both are related to shared cognition. Although this study focuses on conceptual design, it lays the foundation for future work examining the role of team coordination and shared cognition in full-scale system development programs.(C) 2016 Wiley Periodicals, Inc.
机译:本文提出了一种基于网络的方法来分析工程设计团队中的团队协作和共享认知。该研究机构是一个综合并行工程(ICE)实验室,约有20名工程师组成的团队被安排进行科学的航天器的快速概念设计。根据技术信息流数据构建预期交互的设计结构矩阵(DSM),并使用来自10个ICE设计团队的调查数据创建报告交互的DSM表示形式。预期和报告的交互作用的比较分析用于计算团队协作的度量标准,即社会技术一致性(STC)。为了检查共享认知,使用在会话前和会话后对系统设计驱动程序进行的测量来衡量成对共享心理模型(SMM)。构建共享知识网络(以SMM为边缘),并根据网络结构随时间的变化来衡量团队学习。分析揭示了团队学习与三个技术属性(系统开发时间,发射质量和任务概念成熟度)之间以及团队学习与团队协调之间的统计显着相关性。这些结果表明,团队成员在解决困难或不熟悉的问题时以及在预期和报告的互动保持一致时相互学习最多。本文的结论是,ICE中的团队协调和设计产品不一定彼此直接相关,而两者都与共享认知有关。尽管此研究专注于概念设计,但它为将来研究团队协作和共享认知在全面系统开发程序中的作用奠定了基础。(C)2016 Wiley Periodicals,Inc.

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