Digital Twins and the Enterprise Edge

BrandPost By Todd M. Edmunds
Aug 04, 2020
AnalyticsBig DataHadoop

digital twin
Credit: Dell Technologies

For years, enterprises have turned to digital technologies to increase the effectiveness and efficiency of their processes. From developing and prototyping products to simulating and managing complex physical operations, organizations now do the heavy work in software.

Today, these forward-looking digital enterprises are taking their data-driven processes to a whole new level by developing “digital twins,” or virtual representations of real-life physical products and processes.


So what is a digital twin? The Digital Twin Consortium, an industry group that is leading the way in this new era, characterizes the digital twin concept as a bridge between the physical and the digital worlds. It offers this definition:

A digital twin is an abstraction of something in the real world. It may be physical (a device, product, system or other asset) or conceptual (a service, process or notion). A digital twin captures the behavior and attributes of its physical sibling with data and life cycle state changes. …


A digital twin may be used for simulation, as a kind of prototype to understand expected behavior, [and] can also capture real-world behavior so that, for example, analytics and learning can be performed. Digital twins can also be used in virtual reality (VR) and augmented reality (AR).[1]

This is a concept whose time has come. Industry analysts predict that within the next few years, digital twins will represent hundreds of millions of products and processes. In industrial sectors, digital twins will be used to optimize the operation and maintenance of physical assets, systems and manufacturing processes. Digital twins will also represent people, development processes, things and even organizations.

Viewed from the highest level, digital twins in a factory embody the combination of all Industrial Internet of Things (IIoT) use cases, from quality and compliance to predictive maintenance, intelligent logistics, smart robotics and more. It’s the ultimate IIoT use case.

Use cases

To call out a few examples cited by the Digital Twin Consortium, the use cases for digital twins span the range of digitally driven industries. Digital twins are used for jet engines, a Mars rover, a semiconductor chip, a building and more.

An automotive manufacturer might use digital twins of each one of the robots on the factory floor to track their usage, efficiency, uptime and performance; to detect anomalies in the quality of output; to predict emerging maintenance issues; and to suggest ways to optimize throughput and execution.

In another example, the military might use digital twins to test and improve the survivability of vehicles under different combat and attack situations in order to make them safer. Simulations could be run subjecting the vehicle to hundreds or thousands of “what if” scenarios without destroying a single asset. In this use case, the insights gained could save many lives, and the cost savings could be enormous in comparison to destroying physical vehicles.

Edge Computing

Edge Computing, in which data is processed and analyzed close to the point where it is captured, is an important enabler for digital twins. An enterprise-grade edge computing strategy creates the foundation for digital twins.

This is sometimes referred to as the “Enterprise Edge.” As I note in an earlier blog on Edge Computing in Smart Manufacturing, the Enterprise Edge brings enterprise-grade infrastructure and modern IT concepts on-premises, close to where data is captured, such as the manufacturing floor or the distribution center.

Edge Computing allows organizations to avoid the substantial costs, network bandwidth constraints and latencies associated with moving massive amounts of data to and from a cloud data center for processing and analysis.

For example, an automotive manufacturer might have a thousand robots on a factory floor, each of which generates a continuous stream of data from sensors that measure everything from environmental conditions to performance metrics. In most cases, it wouldn’t be practical to move all of this data to the cloud. Processing digital twin data at the Edge closes this gap and allows significantly faster reaction times.

Key takeaways

There are compelling reasons for organizations to adopt digital twin solutions and the technologies that support them. From enabling predictive maintenance and preventing operational downtime to developing innovative products and improving the customer experience, digital twins are the ultimate, all-encompassing IoT use case.

For organizations on this path, the first step is to build the foundations of a scalable, Enterprise Edge infrastructure. This infrastructure supports all of your current Industrial IoT use cases, while creating a foundation for even more advanced digital twins and other applications in the future.

To learn more

To learn more about digital twins, and how your organization can find the resources you need to capitalize on this trend, join the Digital Twin Consortium. This organization, founded by a select group of global technology leaders, including Dell Technologies, is bringing together industry, government and academia to drive consistency in vocabulary, architecture, security and interoperability of digital twin technology.

Todd M. Edmunds is Director of Industrial IoT and Edge Strategy at Dell Technologies.

[1] Digital Twin Consortium, “Frequently Asked Questions,” accessed June 29, 2020.