Teams derive a great deal of benefit simply from adopting a model-based approach. They expose their system model for greater team alignment and understanding (in document-centric approaches, the model is still there, it’s simply obscured by the communication medium). They gain insight from the thought process of developing the model. They gain richness and explicitness not generally achievable in document-centric approaches. Plus they achieve savings in countless ways – from increased productivity, early detection of errors, improved and streamlined impact analysis, and the elimination of production costs for specifications. All that said, the greatest value of the model-based approach comes not from developing the model but from exploiting the model.
To complement the embedded ability to directly simulate your model for dynamic verification, CORE includes a rich framework of embedded model diagnostics. With a library of over 70 completeness checks, countless consistency diagnostics, and a customizable framework for including your own rules, these model diagnostics go far beyond simple diagram-centric checks. Put simply, these diagnostics aid in the bookkeeping and validation of systems engineering, freeing you to focus your valuable time on the critical inspiration of systems engineering.
The first component of model diagnostics – the completeness checks – evaluates your model completeness on the fly. Much like TurboTax® dynamically informs you of missing data, CORE will tell you which key attributes and relationships have not yet been completed. Of course, not all rules are valid on day one of your project. As your design matures, your completeness checks should become more sophisticated and robust. With this in mind, for each project you specify one of four levels of completeness checks – from none to rich – tuning to your specific needs.
The second component of model diagnostics – the consistency checks – is a pre-defined (and extensible) set of script-based rules to highlight inconsistencies in your model. As you work across the systems engineering domains or across levels of detail, it’s trivial to introduce inconsistencies as your problems become more complex. With the push of a button, you can highlight these issues for a given element, a folder, a package, or the entire project. Unchecked, these inconsistencies can doom your project or the system you produce. Addressed, these inconsistencies can be easily managed.
The third component of model diagnostics – the custom checks – is a framework into which you can plug your specific rules. Whether reflecting a style guide for your project team, a corporate or customer standard, or an extended underlying schema, complying with your custom rules is critical for project success. By providing an easily extended framework alongside the existing capability, your project can quickly implement the specific diagnostics you need.
Moving beyond the results embedded in individual property sheets:
One of the rules of systems engineering is that it costs 3 to 10 times as much to correct an error for each successive stage it moves through the life cycle (assuming it can still be resolved without degraded system performance). Helping to identify these issues early on and expose them for your review is priceless.