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Model-Based Systems Engineering (MBSE) supports the development of complex systems through capturing, communicating, and managing system specifications with an emphasis on the use of modeling languages, tools, and methods. It is a well-known fact that varying levels of effort are required to implement MBSE in industries based on the complexity of the systems a given industry is associated with. This paper shares the results of a survey to industry professionals from Defense, Aerospace, Automotive, Consultancy, Software, and IT industry clusters. The research goal is to understand the current state of perception on what MBSE is and the use of MBSE among different industry clusters. The survey analysis includes a comparison of how MBSE is defined, advantages on the use of MBSE, project types, specific life cycle stage when MBSE is applied, and adoption challenges, as reported by the survey participants. The researchers also aim to trigger discussions in the MBSE community for identifying strategies to address MBSE related challenges tailored to a specific industry type. 

Academia or workforce development workshops can both increase the plausibility of a streamlined transition from a document-centric approach to MBSE frameworks, and aid the integration of Model-Based Systems Engineering (MBSE) within the current industry and the challenges faced, introducing MBSE concepts, tools, and languages. This paper reports on an online model-based system engineering Bootcamp conducted in collaboration with The University of Texas Rio Grande Valley and The University of Texas at El Paso. The importance of MBSE is emphasized throughout the online Bootcamp to a diverse group of audience i.e., students, faculty, and industry professionals unfamiliar with systems engineering. A set of predefined questions through pre and post Bootcamp surveys aided in determining the perceptions of MBSE and the effectiveness of the Bootcamp in increasing the knowledge of MBSE amongst participants. A positive knowledge gain was observed on the importance of systems modeling and MBSE across students, faculty, and industry personnel participants indicating the effectiveness of the online Bootcamp. A set of open-ended questions were targeted specifically for industry professionals from manufacturing, aerospace, healthcare, transportation, and software domains attending the Bootcamp for capturing the perceived challenges and obstacles according to them for implementing Model-Based Systems Engineering in their organizations. 

An observed increase in systems scale and complexity has led to a significant momentum in exploring, identifying, and adopting model based systems engineering (MBSE) tools and techniques amongst research communities and industry practitioners. Several attempts to transform systems design and engineering practices through the use of MBSE in academia and industry has led to a considerable increase in the number of articles published containing the keyword “MBSE.” This growth serves as the motivation in this paper to explore the MBSE landscape with the help of text mining techniques to identify the most often used key terms, tools, and languages, in the context of research in MBSE and the thematic aspects defining the use of MBSE by researchers and practitioners. The objective of this paper is to provide a structured comprehensive overview of research contributions across the MBSE landscape by employing text mining techniques for: (a) identifying the concepts and methodologies inferred upon in relation to MBSE, and (b) classifying the literature published to identify commonalities across academic researchers and practitioners using MBSE tools and methods. For this purpose, the abstracts of 2380 relevant articles published in the period of the last two decades from five different databases are mined. It is found that the terms “SysML,” “Cyber Physical Systems,” and “Production” are the most used terms among researchers across the MBSE landscape with SysML being the most widely used modeling language. Further, six major thematic topics are identified that classify articles from over the last two decades with an increasing interest observed in the use of MBSE to support manufacturing and production engineering activities, especially in the cyber physical systems domain. The contributions of this paper provide a leeway on using text mining techniques to understand the research directions that are currently of interest in the field of MBSE and thereby identify potential future research directions.