WHITE PAPER Breaking Down the Silos How Systems Architecture Enables Interdisciplinary Collaboration
Michael Pfenning, Senior Product Manager, Aras 2 Breaking Down the Silos: How Systems Architecture Enables Interdisciplinary Collaboration
With the ever-increasing complexities of today’s systems, organizations struggle to address the interdisciplinary collaboration across a product’s entire lifecycle. This is because the collaboration must extend beyond the traditional BOM-driven and mechanically- oriented physical structures. It must embrace functional aspects of the system that are allocated and cross-optimized across multiple disciplines whose data models today reside in individual silos.
Systems Engineering has a central role in enabling such interdisciplinary collaboration because it is the only authoritative source of the system’s design intent. The other disciplines rely on that source to implement or to verify that design intent. Systems Engineering creates a formal system definition during the design phase to formalize early decisions. It does that by documenting the system’s requirements, behaviors, and structures. Unfortunately, Systems Engineering today, in most cases, also exists in its own data silo and therefore design-intent driven interdisciplinary collaboration remains unresolved.
Figure 1: MBSE systems model sections in SysML
Source: http://www.omgsysml.org/what-is-sysml.htm Breaking Down the Silos: How Systems Architecture Enables Interdisciplinary Collaboration 3
From Documents to Models and Data What systems engineers did over decades in the form of documents now needs to be done in a more formal way. Systems engineers have been accustomed to working with Systems Engineering Management Plans, Design Specifications, Concepts of Operation, Functional Flow Block Diagrams (FFBD), and of course spreadsheets, a lot of them—documents a human being may understand but a computer does not.
This is where formal models are stepping in. Models that can be interpreted by an algorithm and also easily understood by users via their graphical notation. Model-Based Systems Engineering (MBSE) aims to do exactly that. Allowing systems engineers to capture information efficiently in a graphical manner and formalizing that information in a way a computer understands it—to automate, to reuse, and to trace.
There is No Green Field Anymore The tools and methods of today’s Systems Engineering practices are not completely new. Because of that, a lot of Systems Engineering data already lives somewhere—in some organizational unit, in some tool, in some database such as requirements documents, behavioral simulation models, parameters in CAD designs. In other words, the information is scattered among many legacy documents and tool specific data silos. Recreating all that data in yet another “MBSE silo” is inefficient and doomed to failure. Migrating existing tools and databases to one big monolithic data management solution is expensive and fails eight out of ten times due to the complex nature of those migration projects1.
The only way to achieve the MBSE goal of becoming a common reference point over the whole of a system’s lifecycle is to connect structures in the MBSE authored system model to information management solutions that already contain some of the related information. Unfortunately, most legacy PLM software in use today are architected around mechanical BOM-driven data structures and are therefore not extendable to representing MBSE data. Therefore, modern PLM architectures like the Aras Innovator Platform are required. The Aras Platform is architected to model new data structures (like MBSE) along the existing data structures (like mechanical) and the relationships between them. The Platform allows you to include these newly added MBSE data structures to the product’s Digital Thread. And Digital Thread is key to traceability between the elements of various design representations, even if the underlying data is spread among different data containers. 4 Breaking Down the Silos: How Systems Architecture Enables Interdisciplinary Collaboration