Model Based Systems Engineering

Systems Engineering is, very broadly, the work of researching, designing and managing complex physical or electronic systems over their lifecycles. It focuses on the whole system and typically involves a number of sub-disciplines such as requirements, reliability, logistics, design, testing and maintenance; it considers not only the system itself but also processes, optimization and risk management, and requires sophisticated project management techniques.

In earlier decades a large but localized team might consider a very specific set of objects within a very specific and controlled environment, to be delivered to a small user base and maintained by perhaps an, again, localized team of experts who each might have responsibility for only a part of the system. Even for such a controlled and structured scenario, a huge volume of documentation was required to define the system requirements, the components, the engineering process, the standards applied and complied with, and the tests to be run on the system. Keeping this documentation cross-referenced, up to date and integrated was a major task.

Advancing into computing and basing Systems Engineering work on graphical models (Model Based Systems Engineering) provided huge benefits, allowing engineers to store and retrieve data from repositories, associate data with documentation also held in the repositories, and develop both master structures and variants from templates, all of which reduced the need to recreate and repeat work. The model initially represented the organization of the developing system, but grew to reflect the development process and the factors that supported and directed that process. As computing capabilities grew, and more specialized and sophisticated applications were made available, it became possible to represent the components of a system with increasingly varied and detailed model elements, and with increasingly varied and detailed relationships between them. Engineers could 'load' the model components and relationships with an array of properties, characteristics and parameters, which could be varied to reflect different scenarios. The standards that the system must apply or meet could be automatically enforced on the components as constraints, conditions and rules.  More and more of the development process - such as testing - could be represented by element or model features, and more and more aspects of the process could be performed on the model by the application - such as automatic generation of code to make the system operational, and simulation of the system in action under various conditions.

These days, the Systems Engineer is likely to be a member of an interdisciplinary team that has to consider a wide range of factors in designing and modeling a system - a much broader, diverse and inexpert user base, a much broader maintenance base, how the system interacts with many other systems, how the system operates in many different and sometimes extreme environments, the impact the system has on the global environment - both within its operating framework and within its pre-use production and final disposal - the socio-economic environment controlling its acceptability and popularity, and how the system compares with its increasing range of competitors. To see how the work of the Systems Engineer has become vastly more complex one has only to think of a single development, such as the quantum leap from the relatively recent fixed-site landline telephone handset for making voice calls, to the modern mobile smartphone used as a camera, computer, cinema, music center, navigator, and audio, visual and text communicator.

Projects and, indeed, industries are being developed around systems and products for use cases that are further and further beyond the control of the engineer, increasing the risk to the product, user and manufacturer - one might think of passenger air bags being fitted to many different makes of car globally, or the development of space probes intended to travel to the planets of the solar system and beyond. The parameters of risk have, in such cases, increased dramatically.

It is the advances in Systems Engineering tools and methodologies that have enabled and supported this complexity and greater risk-taking, but at the same time minimizing the difficulty and effort involved in reflecting the complexity in the model and managing the risk.

For additional information, see the Representing Systems with Models section of the 'SEBoK - Guide to the Systems Engineering Body of Knowledge' website.

Model-Based Systems Engineering in Enterprise Architect

Enterprise Architect provides a Model Based Systems Engineering platform that integrates many high-end features for Systems Engineers and model-based development, with these built-in features.

Learn more

• Representing Systems with Models (SEBoK - Guide to the Systems Engineering Body of Knowledge, online resource)