Abstract.Model-based systems engineering (MBSE) is an emerging technique widely used in current industry. It is a leading way expected to become a next generation standard practice in the systems engineering. Fundamental principles of systems engineering can be supported by a model-based approach to minimize design risks and avoid design changes in late development stages. The models can be used to formalize, analyze, design, optimize, verify and validate target products integrating engineering developments, organizations and products across domains. Though model-based development is well established in specific domains, such as software, mechanical system, electric systems, its role in integrated development from a system perspective is still a big challenge for current Chinese industry. In this paper, a survey from volunteers related with MBSE is taken by using questionnaires. The purpose of this survey is to highlight the usage and status of MBSE in current Chinese industry and address the rough understandings of MBSE concepts among system developers in China based on the answers about usages, advantages, barriers, concerns, trends of MBSE, particularly the perspective of tool-chain development.
Today's development environments in the manufacturing industry require different development tools to work together. These complex environments are highly heterogeneous and constantly changing, and the development tools are producing a huge amount of data. As a result, these development environments must overcome a significant problem related to data integration. In this paper, we examine a case study from the automotive industry using the linked enterprise data approach to integrate data from different development tools. The study explains and applies a data quality assessment methodology as a post-integration phase for linked enterprise data. In this study, important data quality dimensions from the literature are merged with empirical rules that have been defined by Scania CV AB employees. As a result, a comprehensive methodology is developed and introduced to assess these data quality dimensions. This paper presents the methodology, which aims to develop a data quality assessment tool-a dashboard-in addition to policies and protocols to manage data quality. Moreover, the proposed methodology includes systematic guidelines for planning the data quality assessment activity, extracting requirements for the data quality management, setting priorities to expedite the adaptation, identifying dimensions and metrics to ease the understanding, and visualizing these dimensions and metrics to assess the overall data quality.
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