Artificial Intelligence (AI) is no longer a concept of the future; it’s here, and it’s revolutionising industries across the globe. One such industry is Systems Engineering, where Model Based Systems Engineering (MBSE) is gaining traction. MBSE, a contemporary methodology for systems engineering, is increasingly being adopted by organisations striving to build a ‘digital twin’ of their systems. The convergence of AI and MBSE is poised to bring about a significant shift in the landscape of Systems Engineering.
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The Evolution of MBSE with AI
AI’s potential to transform MBSE begins with automating mundane tasks involved in building a digital model, such as linking requirements and creating sequence diagrams. This automation not only streamlines the process but also eliminates human error, enhancing the overall quality of the model. As AI evolves, it could develop into a recommender system, where a neural network model, trained on vast quantities of previous project datasets, could propose a system to fulfil specific requirements. However, this vision is not without its challenges.
The Future of MBSE and AI
Three main factors currently limit the full realisation of AI-powered MBSE: datasets, the need, and modelling capability. Firstly, we lack the extensive datasets necessary to train AI models to build entire systems. Secondly, the need for such a system is not yet urgent enough to warrant the significant investment required. Lastly, our current modelling capabilities are not advanced enough to support this level of AI integration. Despite these challenges, the future of MBSE powered by AI is promising.
The Impact of AI on Systems Engineering
Imagine a world where AI takes over the ‘boring’ parts of your job, such as reviewing technical standards, writing requirements, and formulating architectures. This is the potential impact of AI on Systems Engineering. AI could augment your work by recommending routes to make your life easier and fulfil your mission, allowing you to focus on reviewing the outputs of the model and verifying the suggested results. However, this doesn’t mean that your job is at risk. Rather, AI will enhance your role, making you more efficient and effective.
The Future of MBSE Powered by AI: A Comprehensive Look
In this section, we delve into a comprehensive exploration of the future of Model Based Systems Engineering (MBSE) powered by Artificial Intelligence (AI). We discuss the potential of AI in automating routine tasks, evolving into a recommender system, enhancing decision-making, improving quality control, and increasing efficiency in Systems Engineering. We also explore how AI could augment jobs, facilitate continuous learning and improvement, promote greater collaboration, and increase accessibility in the field.
Automated Systems Engineering Tasks
AI could automate many of the routine tasks in Systems Engineering, such as linking requirements, creating sequence diagrams, and formulating architectures. This would free up engineers to focus on more complex and creative aspects of their work.
AI Recommender Systems
With sufficient training on vast project datasets, AI could evolve into a recommender system. This system could propose the most efficient and effective system designs to fulfil specific requirements, significantly speeding up the design process.
Improved Decision Making
AI could enhance decision-making in Systems Engineering by providing data-driven insights and recommendations. This could lead to more informed decisions, reducing the risk of costly errors and rework.
Enhanced Quality Control
AI could improve quality control in Systems Engineering by automatically detecting and correcting errors in the design process. This could lead to higher quality systems and increased customer satisfaction.
Increased Efficiency
By automating routine tasks and providing data-driven insights, AI could significantly increase efficiency in Systems Engineering. This could lead to faster project completion times and reduced costs.
Job Augmentation
AI is not likely to replace Systems Engineers but rather augment their work. Engineers could transition into roles that involve reviewing the outputs of AI models and verifying the suggested results, leading to more interesting and fulfilling work.
Continuous Learning and Improvement
AI models can learn from their mistakes and continuously improve over time. This could lead to continuous improvement in Systems Engineering processes and outcomes.
Greater Collaboration
AI could facilitate greater collaboration in Systems Engineering by providing a shared, data-driven understanding of the system. This could lead to more effective teamwork and better project outcomes.
Increased Accessibility
AI could make Systems Engineering more accessible by automating complex tasks and providing intuitive, user-friendly interfaces. This could open up the field to a wider range of people, including those without a traditional engineering background.
What’s next?
The integration of AI into MBSE is set to revolutionise Systems Engineering. While there are challenges to overcome, the potential benefits are immense. From automating mundane tasks to proposing system designs, AI could significantly enhance the efficiency and effectiveness of Systems Engineering. As we continue to advance our modelling capabilities and accumulate the necessary datasets, the future of MBSE powered by AI becomes increasingly promising. The revolution in Systems Engineering is coming; it’s just a matter of time.
Read our article on Unlocking MBSE: Choosing the Best Books for Model-Based Systems Engineering.
