Building Trust in Innovation Practices
In quick summary, an innovation framework must address the need for each type of resource, as well as the difficulties that come with each. Some of these difficulties, and possible solution approaches are discussed in the next several paragraphs.
Few companies experiment on their primary products and services. When soft drink companies release new flavors, they do not change the formula of their best-selling drinks and expect the public to welcome the disruption without protest. Sometimes new flavors perform well and become mainstays, but more often new recipes vanish from the shelves to become lessons learned.
To businesses like soft drink companies, providing a safe environment for the development and evaluation of new ideas and technologies is business critical. To the A & D industry it is mission critical. While an unfortunate decision in the former can mean the loss of income and jobs, such a failure in the latter can mean the loss of homes, freedoms, and lives.
Experimentation and innovation can be difficult in mission critical A & D. Many constraints come into play. These range from rigorously enforced security classifications, human and system safety, strict delivery schedules, to simple unavailability of spares. There may not be access to real or even representative systems and data outside of program development. While significant strides are being made in software simulations, including the use of artificial intelligence( AI) and machine learning( ML), there remain instances when there is no substitute for real hardware.
A successful innovation framework must address such constraints and concerns. An oftenencountered issue is the lack of available physical hardware or space. This leads to conversations around digital twins. The oft-cited text by Will Roper on digital acquisition [ 6 ] expounds on the growing need for high-fidelity digital doubles of components, systems, and systems of systems, to enable faster design, testing, iteration, and deployment of physical products. Such an advanced modeling and simulation capability would have significant implications on not only product lifecycles but training, maintenance, and innovation as well.
Concurrent to such digital twin ideal states, there are testing and simulation tools useful to innovation and exploration. These include items such as MATLAB, LabVIEW, other third-party software, or in-house software or hardware. Many of these tools cover one or more domains and can export data to be consumed by other tools to integrate simulations and tests. A successful innovation framework should provide options and support for such tools along with computers, networks, and data usable for development and experimentation.
In a comprehensive environment, a wide range of system configurations would be available. These might span systems with IT controls that operate both on the company networks and offline, perhaps air-gapped, or on specialized isolated networks, and virtual systems running in secure containers. Networks might be logically separated to segment company and non-company networks on the same cable, provided through entirely distinct equipment, or made available
110 May 2025