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As a provider of engineering simulation and analysis tools to support the design and development process in many organizations, Maplesoft is in a very strong position to observe the evolution of methodologies, work-flows and best practices over the last 30 years. The customers that have adopted these practices tend to be very large organizations, working on highly complex, safety-critical products and projects such as passenger vehicles, aircraft, medical equipment and space missions. These involve multiple engineering disciplines, and thousands of stakeholders dispersed across the world, coupled with demanding customer expectations that have driven the development of highly formalized product development processes within which our products are employed.
These processes are visualized typically as some variant of a “Vee”, to reflect the fact that frequent verification (“are we building the thing right?”) and validation (“are we building the right thing?”) is an intrinsic part of the process. Tools like MapleSim play an important role in developing dynamic system-level models – or “virtual prototypes” – of the proposed design from the very early stages in the design process in order to make sure the design fulfills the goals of the product. Typically, these systems require many engineering disciplines – mechanical, electrical, chemical, and others – but, over the last decade, we have seen a new discipline emerge and play an increasingly vital role in the process: Systems Engineering.
Figure 1: The gap between the Systems Engineering process and design process introduces a major challenge. How can we keep all stakeholders in the project engaged with the systems engineering process without making it onerous?
At a very high level, system engineers (SE’s) drive the system-design workflow by creating architectural models that define what the system is expected to do, in terms of its structure, behaviors, requirements and design parameters. These models are then used to guide the design engineers who will use analytical models to determine how these expectations can be achieved. A rigorous Verification and Validation (V&V) process is then employed to establish how well the system design complies with the requirements, defined in the architectural models - the “single source of truth” - as the design evolves.
In theory, this approach should help to alleviate the many problems that arise when managing a complex project. In practice, however, there are many challenges to successful adoption, the greatest of which is a very human one: lack of engagement and buy-in with the process.
The primary challenge is that, no matter how knowledgeable systems engineers are about their products, they can only define the system architecture to a certain level of granularity. At some point, the SE needs the knowledge of the design engineer, or subject-matter expert (SME), within a specific task to decompose the higher level elements of the architectural model into smaller, actionable elements. For example, the requirement, “ the vehicle must accelerate 0-100 km/hr in less than 5 seconds”, is not an actionable requirement and needs to be decomposed to smaller requirements, such as mass and engine performance, and even smaller requirements that affect the mass and engine performance, to make it actionable.
These smaller requirements can then be used to carry out the tasks these other stakeholders need to do in order to make the necessary design or operational decisions. At this point, problems start to arise.
Figure 2: At present, engagement between the systems engineer can be very ad-hoc, leading to inconsistencies, errors and increased risk to the project.
Somehow, the results from this effort need to be fed back into the systems model in order to maintain the single source of truth. The only way for SMEs to do this directly is through the use of complicated systems engineering tools, such as Rhapsody® or MagicDraw®. Since learning to use these tools is seen as an onerous task, SMEs will resist using them, so a default approach tends to be sending this information to the systems engineer for them to interpret and update the model. This information is typically delivered ad-hoc through spreadsheets, documents, and emails. As one can imagine - or, indeed, experience - this is a very significant source of misunderstandings, miscommunications, and errors, which all require additional time and effort to correct, assuming the errors are caught at all. It is this friction between the systems engineers and the other project stakeholders that is one of the major sources of unbudgeted costs, project over-runs and, very often, project failure.When the systems engineering processes don’t work well, the result is reduced confidence in the process itself, and stakeholders see no benefit and further resist engaging with it, ultimately threatening the success of any process whatsoever.
To address this challenge, Maplesoft has developed a tool that allows this information to flow from and to the Systems model through a tool that is familiar to everyone: Microsoft® Excel®. Now, all stakeholders can access the systems models and generate a tabular view of the section of the systems model with all the data that is required for them to work on their tasks. In addition, they can add further details (design decisions and changes) directly into the systems model for all other stakeholders to access, including the systems engineers, who can now perform design reviews virtually in real-time and authorize any changes.
Figure 3: Instead of forcing all stakeholders to use complex MBSE tools to interact with the systems model, MapleMBSE allows non-systems engineers to do this through a ubiquitous user interface that everyone knows - Microsoft Excel.
Figure 4: A high-level diagram of MBSE methodologies using MapleMBSE, showing how different stakeholders can collaborate with the systems engineer and other groups on the project, all using the central systems model.
Furthermore, the information is now immediately accessible for all stakeholders to perform analyses, such as V&V tests, trade-off analyses, failure-mode studies, and optimizations - all with data directly from the systems model.
MapleMBSE provides an intuitive spreadsheet-based user interface that allows all stakeholders to add, remove, and edit requirements, in natural language and numeric constraints. That information is used to automatically populate the systems model, in terms of the system structure, behaviors, requirements, and parametric constraints. This new tool from Maplesoft currently supports SysML structures and is compliant with the major SysML-based MBSE tools, specifically IBM® Rhapsody and No Magic MagicDraw. An open API allows development of interfaces to other MBSE formats.
Not only does MapleMBSE ease the systems definition process, it allows for various analyses on the model. This includes impact analysis, Failure Mode & Effects Analysis (FMEA), and trade-off studies. It also provides optimized views on the model for various important tasks, such as a design structure matrix view for system structure analysis.
The productivity benefits of implementing MapleMBSE have already been proven in several commercial projects.
For example, Nissan® uses MapleMBSE as a key part of their “Nissan-7” Systems Engineering process. This process supports requirement analysis, trade-off analysis, system context definition, operational view analysis, FMEA, and traceability analysis. By using MapleMBSE, engineers can focus on these tasks using SysML models without requiring a deep knowledge of SysML itself.
Figure 5: When analyzing Nissan’s usage of MapleMBSE, several common MBSE tasks could be performed faster than with traditional techniques.
Figure 6: In a recent study published by research from IBM, using MapleMBSE helped greatly reduce certain kinds of errors when using MBSE processes.
The resulting productivity improvement using MapleMBSE is impressive. The process of developing the architectural model of an engine sub-system was compared between MapleMBSE and another commercial MBSE tool. It was shown that engineers could perform their tasks 4 times faster in MapleMBSE than in the other tool.
Other studies have demonstrated similar productivity benefits. For example, a study published by IBM Research showed the use of MapleMBSE resulted in faster task-completion and fewer errors, compared to performing the same tasks using a standard MBSE tool directly. Typically, a 4-fold reduction in errors, and the elimination of complex errors, with the use of MapleMBSE was observed. (Miyashita, Hisashi, Hideki Tai, and Shunichi Amano, IBM Research - Tokyo, “Controlled modeling environment using flexibly-formatted spreadsheets.” in ICSE 2014, ACM)
In fact, as we see in this analysis of the errors made, they were simple errors, such as spelling mistakes, which are much easier to identify and correct. Meanwhile, users of other commercial SysML tools are prone to make more complicated mistakes that arise from the complexity of SysML, forcing users to deal with concepts such as various dependencies and stereotypes having complicated slots.
The study concluded that this productivity improvement resulted from the use of a well-known and intuitive Excel user interface, the use of spreadsheets that are optimized to each task, and the lack of requirement for advanced SysML experience.
As part of the Europa Lander mission planning, the Systems Engineering team at NASA’s Jet Propulsion Laboratory in Pasadena developed an open CAE system design environment, called Open Model-based Engineering Environment (OpenMBEE). The environment consists of a range of cloud-based resources and repositories that can be accessed through a web-services API. MapleMBSE is one of many design tools that can access the OpenMBEE API and has played a key role in several stages in the Europa Lander project. While JPL is not at liberty to disclose the details of the project, they have provided several use-cases where MapleMBSE has been described by one of the team members as “one of the key enablers for effectively viewing and editing our systems models”.
Figure 7: Overview of JPL's OpenMBEE platform. Credit Source: JPL
One of these use-cases shows the integration of MapleMBSE with Siemens® NX® through Syndeia® from Intercax®, and MagicDraw and CAMEO® Systems modeler from No Magic. Through the Syndeia plug-in in MagicDraw, it is possible to access the parametric data in a CAD model, developed in Siemens NX, and integrate this information with the systems model by creating a SysML parameter structure. Not only is this data available to the systems engineer through MagicDraw, it is available to all users of MapleMBSE through the Excel interface. This allows a design engineer to perform some analysis based on the NX parameters, and if the results of this analysis require a change to the parameters, the design engineer can simply make the changes in Excel and submit them back to the systems model through MapleMBSE. Once those changes are approved, they are then reflected back through to the CAD model, thus keeping everyone synchronized and aware of any impact these changes might make to the rest of the system.
Figure 8: One of several use-cases involving MapleMBSE, in this case, integrating parametric data from Siemens NX with the systems model through Syndeia, thus providing access to the data for all stakeholders through MapleMBSE.
In this paper, we have shown how MapleMBSE can provide a productive, controlled, easy-to-use modeling environment for systems engineering.
A tool like MapleMBSE has the potential to democratize the systems engineering process by allowing a much broader range of stakeholders to contribute to the model without being MBSE experts.
Using various case studies as examples, MapleMBSE has been demonstrated to accelerate the system-definition process by simplifying information entry through a familiar spreadsheet UI, and reducing the risk of errors creeping into the design. By giving all stakeholders access to the SE process through a live, two-way connection to the systems model, MapleMBSE helps ensure that all stakeholders can collaborate through the systems model, allowing them to work faster, avoid errors, and reduce unbudgeted costs.
