Frequently Asked Questions
Model Based Systems Engineering Services
What is Model Based Systems Engineering?
The domain of Systems Engineering (SE) is practised in the industry to deal with an interdisciplinary process for supporting the system lifecycle. The SE process lifecycle activities performed by systems engineers can be clearly distinguished into two approaches.
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- Document-Based Systems Engineering (DBSE) is well known as a classic case where life cycle activities generate documents as artifacts.
- Model-Based Systems Engineering (MBSE) generates, instead, a set of model elements with relationships forming a system model.
Model-Based Systems Engineering (MBSE) as defined by the International Council on Systems Engineering (INCOSE) is “the formalized application of modelling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases.”
The term MBSE comprises multiple modeling concepts: modeling language, modelling method, and modeling tools in order to produce one system model or more. A system model contains model elements (e.g., requirements, use cases, functions, logical components…) and relationships in between (e.g., re‐ fine, allocate, derive…).
Indeed, the MBSE approach does not necessarily change the “what to do” by systems engineers, instead changes the “how to do it”. Particularly, MBSE goes beyond the DBSE approach by considering the use of system models instead of documents as the primary artifacts produced during the life cycle activities. Moreover, such models are specified, reviewed, and released using a systems modelling tool (following a modelling language such as SysML) and not just a drawing or documentation tool as Visio, PowerPoint or Excel.
Our SysDICE team is specialized in the adoption of MBSE across all its phases including competency development of the involved team members, method development for the intended MBSE applications, and MBSE tools customization to achieve a valuable MBSE adoption.
What projects should MBSE be used for?
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What are the common issues with implementing MBSE?
Model-Based Systems Engineering (MBSE) has been challenged concerning its successful adoption in real-world applications. Although MBSE remains to be the focal point of any systems engineering activities, its adoption still faces significant hurdles to demonstrate its return on investment.
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Based on our MBSE research and industrial work, we explored the topic of MBSE adoption challenges and found out the importance of alignment among MBSE community members for effective understanding of the actual status of MBSE adoption challenges. Therefore, in early 2017, we decided to identify a set of MBSE adoption challenges and then ask, through an online survey, participants to collect the opinion concerning these challenges, what phase of MBSE adoption they occur in, and the dependencies between them. We presented the publication described in this post at the EMEA Sector Systems Engineering Conference 2018.
The results of this survey work demonstrate how the survey objectives have been accomplished, they open the opportunity to overcome MBSE adoption challenges and thus to achieve effective MBSE implementation. We believe that the challenges faced are not only due to MBSE but the way it is adopted. A long-term vision should not only consider the tools aspect but also target and solve MBSE methodology, technological, educational, MBSE training and human factor perspectives. This is part of our SysDICE consultancy services where we help our customers to optimize their MBSE journey.
How will SysDICE help me adopt MBSE successfully?
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Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.
Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur?
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Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.
Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur?
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Why is MBSE Important?
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Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.
Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur?
Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?
What are the benefits of MBSE for my organisation?
While the benefits of MBSE are well described in the literature, each organization needs to identify the specific benefits that are driving them to move toward deploying MBSE techniques.
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The general expected benefits include the following:
- Increasing the quality of the system design specification (e.g., requirements, use cases, functions, architecture…) during the early phases of the system life cycle.
- Managing the high level of complexity of the information and data ex‐ changed during the system design and development.
- Achieving a centralized knowledge management database, i.e., the MBSE models, for sharing it as the single source of truth among the involved team members.
- Providing the traceability between key model elements, e.g., requirements, operational, and architecture which shall reduce the effort for future change impact analysis of these key model elements.
- Enhancing the communication among the involved team members and optimizing the knowledge exchange and knowledge management.
- Ensuring a common way of working based on the common agreed MBSE methods and thus achieving a common understanding of the deliverables across the whole system life cycle.
- Enabling a managed reusability of the system models data for the long term towards a model-based product line engineering approach.
Artificial Intelligence for Systems Engineering
What is Artificial Intelligence?
The aim of the Artificial Intelligence (AI) field is to build intelligent entities and machines that can perceive the environment and compute how to act effectively and safely in a wide variety of novel situations.
Is Artificial Intelligence important in engineering?
Artificial Intelligence (AI) is playing a major role in the fourth industrial revolution. The main contribution of AI is to automate different engineering tasks and to solve various engineering problems. In a nutshell, AI will fundamentally change the entire engineering profession.
What is Natural Language Processing?
Natural Language Processing (NLP) is a subset of Artificial Intelligence (AI) that enables machines to understand, interpret, and manipulate human language. NLP has its roots in linguistics, where it makes it possible for computers to extract entities and sentences, understand the meaning of words, translate that to another languages, or generate new answers.
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In addition to rule-based techniques derived from the field of computational linguistics, NLP can use both Machine Learning (ML) and Deep Learning (DL) methodologies in order to effectively take and process unstructured text and speech datasets, thus overlapping with AI.
How is Artificial Intelligence used in engineering?
In the engineering industry, Artificial Intelligence (AI) enables different technological innovation capabilities, such as robot guidance, intelligent materials handling, computer-aided inspection, human-computer interaction, and process control, that reflect a positive impact on economic.
Are AI Engineering, Machine Learning and Deep Learning the same?
Artificial intelligence (AI) is the discipline that covers anything related to making machines intelligent. However, Machine Learning (ML) is a subset of AI. ML refers to systems that can learn by themselves. Systems that get intelligent over time without human intervention. Otherwise, the term Deep Learning (DL) refers to Machine Learning (ML) using multiple layers of simple, adjustable computing elements, and applied to large data sets.