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Model-based systems engineering (MBSE) is being rapidly adopted in U.S. industries across various sectors. While practitioners and academics recognize many benefits of adopting MBSE, industries also report challenges such as limited tool expertise and a shortage of skilled personnel. Highlighting the difficulties in industry adoption of MBSE, prior research by the authors identified challenges such as tool limitations, knowledge gaps, cultural and political barriers, costs, and the level of customer understanding and acceptance of MBSE practices. Additionally, another study by the authors points out a gap between industry demands for MBSE skills in new hires and the current academic training programs. To further assess the MBSE industry’s workforce needs, this paper introduces a two-phase method for the Structured Extraction of MBSE competencies using large language models based on current workforce demands from LinkedIn job postings. Phase 1 involved extracting 1960 job descriptions from LinkedIn using the term “model-based systems engineer.” In phase 2, large language models (LLMs) employing deep transformer architectures were used to transform unstructured text into structured data. An AI agent was used as an autonomous software layer to manage every interaction between the raw dataset from Phase 1 and the LLM. Supported by the analyzed data, a competency framework is proposed that summarizes the tools, technical skills, and soft skills expected of a model-based systems engineer by the industry. The framework is designed to include core competencies shared across all MBSE roles, with specific competencies tailored for aerospace & defense, manufacturing and automotive, and software and IT sectors. 

System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model generation framework leveraging Python and the spaCy NLP library to process text input and generate class/block definition diagrams using PlantUML for visual representation. The intent of this framework is to aid in reducing the manual effort in creating SysML v1.6 diagrams—class/block definition diagrams in this case. We evaluate the effectiveness of the framework using precision and recall measures. The contribution of this paper to the systems modeling domain is two-fold. First, a review and analysis of natural language processing techniques for the automated generation of SysML diagrams are provided. Second, a framework to automatically extract textual relationships tailored for generating a class diagram/block diagram that contains the classes/blocks, their relationships, methods, and attributes is presented. 

As the complexity of both products and systems increases across a wide range of industry sectors, there has been an influx in demand for methods of system organization and optimization. MBSE enhances the ability to obtain, analyze, communicate, and manage data on a comprehensive architecture of a system. In this study, a military combat surveillance scenario is modeled using SysML generating state machine diagrams and activity diagrams using the Magic Model Analyst execution framework plugin. This study seeks to prove the feasibility of an MBSE-enabled framework using SysML to create and simulate a surveillance system that monitors and reports on the health status and performance of an armored fighting vehicle (combat tank) through an Unmanned Ariel Vehicle (UAV). The Magic System of Systems Architect, which actively promotes system development architectural frameworks, was used to construct SysML-compliant models, allowing the creation of intricate model diagrams. The construction of the UAV surveillance scenario emphasized the capability of modifying a diagram feature and ensuring that the alteration is communicated to all linked model diagrams. This study builds on a previously published MBSE-enabled conceptual framework for creating digital twins. The purpose of this research is to test and validate the framework's procedures. Keywords—MBSE, SysML, MBSE framework, UAV, Surveillance 

Abstract Model‐based systems engineering (MBSE) is rapidly gaining popularity among U.S. industries. Though industry practitioners and academic researchers have identified several advantages in transitioning to MBSE, several adoption challenges of MBSE in industries, such as insufficient tool knowledge, lack of skilled personnel, and resistance in organizations toward a shift to MBSE, are observed. Attesting to the challenges in industry adoption of MBSE, a previous research study by the authors characterized the adoption challenges as tools‐based, knowledge‐based, cultural, political, and cost‐related, and customer understanding and acceptance of MBSE practices. This study is motivated to explore further and address the challenge of low MBSE tool knowledge and lack of skilled personnel with MBSE knowledge for industry adoption. This paper presents a two‐phased research approach framed by an overarching question of the extent to which the MBSE academic curriculum is aligned with industry workforce requirements. In Phase 1 of the study, we survey industry professionals from Defense, Aerospace, Automotive, and other industry clusters to identify MBSE tools, languages, and concepts preferred by industry professionals in a candidate for hire. This is followed by Phase 2 of the survey targeted at academic institutions with Systems and MBSE programs to analyze the extent to which MBSE curricula reflect industry workforce hiring requirements. Further, we also identify the challenges reported in academic institutions in training the Workforce on MBSE. The contributions of this paper are two‐fold: providing a pathway for academic institutions to align their curricula to MBSE industry workforce requirements and triggering discussion in the broader MBSE community to identify strategies for addressing MBSE adoption challenges and training future model‐based systems engineers. 

Recent studies in systems engineering indicate that the design, development, and management of systems will continue increasing in complexity. The foreseen growth is expected as future capabilities require understanding the system and its operating environment, adapting to rapid-changing scenarios, integrating more independent hardware and software elements, coordinating with multiple stakeholders across the system’s lifecycle, among others. To develop the next generation of systems, alignment between industry needs and curricula from higher-education institutions should exist. Therefore, this research contributes to the human capital development of systems engineering in the United States by exploring current job opportunities and their relationship to existing academic offerings in Hispanic-Serving Institutions. The study analyzes job openings from INCOSE’s CAB Members to capture current needs in terms of role description lifecycle experience, tools and methodologies needed in the job market, and it explores the relationship of systems engineering methodologies covered in Hispanic-Serving Institutions. The outcome of this research provides a direction to support the development, adoption, and update of higher-education systems engineering curriculum that aligns with current industry needs. 

A digital twin (DT) is an interactive, real-time digital representation of a system or a service utilizing onboard sensor data and Internet of Things (IoT) technology to gain a better insight into the physical world. With the increasing complexity of systems and products across many sectors, there is an increasing demand for complex systems optimization. Digital twins vary in complexity and are used for managing the performance, health, and status of a physical system by virtualizing it. The creation of digital twins enabled by Modelbased Systems Engineering (MBSE) has aided in increasing system interconnectivity and simplifying the system optimization process. More specifically, the combination of MBSE languages, tools, and methods has served as a starting point in developing digital twins. This article discusses how MBSE has previously facilitated the development of digital twins across various domains, emphasizing both the benefits and disadvantages of adopting an MBSE enabled digital twin creation. Further, the article expands on how various levels of digital twins were generated via the use of MBSE. An MBSE enabled conceptual framework for developing digital twins is identified that can be used as a research testbed for developing digital twins and optimizing systems and system of systems. Keywords—MBSE, Digital Twin, Digital Shadow, Digital Model, SysML 

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. 

Increasing complexity in today’s manufacturing and production industry due to the need for higher flexibility and competitiveness is leading to inconsistencies in the iterative exchange loops of the system design process. To address these complexities and inconsistencies, an ongoing industry trend for organizations to make a transition from document-centric principles and applications to being model-centric is observed. In this paper, a literature review is presented highlighting the current need for an industry-wide transition from document-centric systems engineering to Model-Based Systems Engineering (MBSE). Further, investigating the tools and languages used by the researchers for facilitating the transition to and the integration of MBSE approach, we identify the most commonly used tools and languages to highlight the applicability of MBSE in the manufacturing and production industry.