Trucks today are computers on wheels. Thanks to modern telemetry systems and other technologies they are constantly communicating data points, such as location, motion or engine status, to various business units and systems of their organization.
The problem: Trucks often send multiple messages containing the same data over multiple channels at different times. Fleet management needs the same information on fuel consumption as the team for truck maintenance, and both have their own separate copy of the data — sometimes even separate channels to transfer the data.
The rise of digital twins coincides with the rise of the IoT
“The traditional approach wastes effort and resources because the data is overlapping and redundant,” says W. Roy Schulte, Distinguished Vice President Analyst, Gartner. “Additionally, it’s very complex and expensive to establish a new channel for every new application that needs to access the data.”
The solution: The digital twin — a design pattern that decouples each system from the physical thing. Instead of communicating with each data receiver separately, the truck just sends all the data to its digital twin. Business units that need information are connected to the twin and can access the data.
This design approach is not limited to logistics. According to Gartner research, 24% of organizations that either have Internet of Things (IoT) solutions in production or IoT projects in progress already use digital twins; another 42% plan to use twins within the next three years.
Digital twins are becoming increasingly popular because they possess capabilities that significantly decrease the complexity of IoT ecosystems while increasing efficiency.
Although digital twins vary greatly in their purposes and the amount of data they hold, all follow the same principle. There is exactly one twin per thing. The twin is continuously updated to mirror the current state of the physical thing.
When combining the twin data with business rules, optimization algorithms or other prescriptive analytics technologies, digital twins can support human decisions or even automate decision making. For example, a capability of the digital twin of a truck could be to track the state of the brakes. Current and historical data in the twin will provide the estimated time for the next needed maintenance, which the maintenance system can use to schedule at the optimal time.
The main purpose of a digital twin is to act as a proxy for its thing, so any application that needs data from the thing deals with the proxy. As the twin is a piece of software, it can be programmed to encapsulate data so that changes can be made within the twin without affecting any connected applications, and vice versa.
Encapsulation significantly reduces the work needed to maintain and enhance applications and physical things. Changes made to things — like adding sensors — often require changes to the twin but should not require changes to applications that won’t use the new sensor data. Likewise, many kinds of changes can be made to applications without changing the device or twin.
As most twins will be built by the asset manufacturer and delivered together with their physical counterparts, they should be included in every purchase decision, as their capabilities will be as vital for the success of the organization as the physical asset.
“The rise of digital twins coincides with the rise of the IoT. When buying machines and other assets, support for digital twins and continuous development of twin capabilities should be a selection factor,” says Schulte.