With the size of the connected products market estimated to be $519 billion to $685 billion by 2020, manufacturers are expected to add non-physical skills, such as digital engineering, to their traditional physical skills base. Through increased computing speed, storage capacity, and processing capabilities, digital engineering has empowered a paradigm shift from the traditional design to build methodology to a model-analyze-build methodology.
Product-based business models are being disrupted by service-based business models, new skills are needed in a world of smart products, and innovation success depends on the effectiveness of a company’s open ecosystem.
From drawings to simulations, engineers are increasingly using advanced technologies to capture data and craft design in a digitized environment. From connected coffee makers, vibration monitoring of industrial pumps to remote patient monitoring devices, the network of physical objects embedded with software, sensors and network connectivity is growing by the day. Through progressive applications, the art of digital engineering enables designers to explore possibilities and develop innovative solutions.
So what makes digital engineering innovative?
Well, in short, the flow of data and the ability to act on data, fast or in an automated fashion.
Manufacturers often find themselves dealing with a legacy system that isn’t pro digital continuity and as a result, many of the functions operate in silos. There is an inherent need to streamline data flow and reduce discontinuity throughout the entire product lifecycle, starting from engineering to manufacturing, to services.
What makes this transformation so complex is not just the availability of the right toolchain needed to make the data consistent across different silos, but also the scarcity of the expertise needed to engineer the entire connectivity of the data flow?
With productivity, speed of collaboration and faster time to market as the major goals of most manufacturers, enabling a system that supports an effective flow of data is increasingly becoming challenging. As a result, manufacturers are tapping into an ecosystem of service providers that have the expertise and skillsets needed to digitally engineer a system that can streamline data flow and increase connectivity.
To oversimplify, the transformation needed to build a manufacturing environment that enables digital engineering can be broken down into three steps:
- Assess the minimum amount of data that is needed by each domain and subdomain of the process
- Centralize the data into one coherent system
- Build an execution model that enables the handshake of data across systems