In addition to the hard metrics, consider a scenario where a full product has to be built and tested to identify functional failures. These expensive tests should be done in a less risk-prone virtual environment, incorporating common standards and platforms while utilizing HiL (Hardware in the Loop), SiL (Software in the Loop), and MiL (Model in the Loop) practices. These identify serious gaps earlier in the development cycle.
Another scenario that is even more startling is when a product ships to the field and functional failures are realized between the mechanical, electrical, and software systems. Could this have been prevented by the clear and concise identification of stakeholder needs through a good systems engineering requirements process and/or by developing the product using a platform-based approach leveraging a common systems engineering and product data model? We must also ask: are we constantly putting out fires? Why? And if we don't have time to do it right the first time, how do we find the time to do it over?
Implementing new approaches is challenging. Developing a model-centric system engineering environment means the creation of digital threads within and across the different domains and disciplines involved for the specific design objectives. Simply creating huge amounts of digital data does not guarantee program success. A strong foundational governance around the virtual product development process, along with physical test in combination with system verification and validation, is essential. When combined with a simulation data management system inside the platform, it enables proper capturing and reuse of data, knowledge across disciplines, and makes proper data available to all stakeholders involved in the lifecycle of a product, its system, and the surrounding engineering environment.
The modern reality demands that we make use of the created and available data in real time. We cannot just use the data anymore to provide business analytics that develop new scenarios for the field upfront. We need to be able to run scenarios in real time to make decisions on the spot, based on the situation in the field and operating environment. This real-time collaboration around a visual, model-based design environment will be the catalyst to affect a cultural transformation that achieves the model-centric engineering vision. However, this cultural change cannot be successful without management support at all levels; most importantly, at the highest level.