Before we dive into the details of the difference between digital twin and digital thread, here's one way to describe the two concepts:
A digital thread unifies diverse but interrelated data sets in order to uncover insights. This data unification is a necessary pre-requisite to building a true digital twin.
As with most emerging technologies, there is often some confusion around the concepts, applications, and use cases. This is especially so when the two technologies have similar names, are leveraged in the same type of environments, and are often inextricably connected.
Digital twin and digital thread are two concepts that have been around for decades, but the technology to make them powerful, useful, and accessible is emerging. This post ventures to differentiate the two, while shedding light on how the two concepts are connected in today’s industrial applications and environments.
With that baseline, let’s go in to more specifics:
A digital thread is a way to create a universal access to data – a single source of truth. When implemented across an enterprise, it can create consistency and fosters collaboration by aligning different functions around a robust set of data. The data set is enabled with real-time data synchronization so upstream and downstream information is available to all users.
A digital twin is digital model that virtually represents its physical counterpart. Digital twins are not restricted to products or physical “things”, they can also be developed for operational processes, or even a worker’s task.
They are increasingly common in industrial applications. In many digital twin use cases, the digital twin is used to better understand the physical counterpart and offer insights, or even predict how the physical counterpart will react or behave. Twins typically leverage multiple data sets to render a full representation of the physical counterpart.
This often includes business system data and sensor data, so digital twins truly reflect the physical counterpart and its environment. With higher quality and diverse data streams, the fidelity and complexity of a digital twin increases. Furthermore, by bringing together disparate and previously siloed data sets unlocks new insights and possibilities.
One of the benefits of digital twins is the ability for a single twin to be leveraged by different roles and applications. With these different “lens”, new (and additional) value can be uncovered. For example, a digital twin delivered through augmented reality could assist a service technician in a repair and the same digital twin could also improve quality assurance procedures. Because the twin has a single source of truth – generated from the foundational digital thread – users are able to access the most accurate and up-to-date information.
With digital twin technology, industrial enterprises can improve operational effectiveness, achieve product differentiation, and increase quality and productivity.
The possibilities of these two technologies (both separately and combined) are limitless. As more industrial enterprises implement digital thread and digital twin, they’ll unlock more opportunities to drive business outcomes.
Integrating augmented reality to both these technologies is an emerging method to realize value. As this technology becomes more prevalent in factories and other industrial applications, it provides the “lens” in which workers quickly and effectively view information in context from the digital thread and/or digital twin.
Among the most important steps as leaders consider these concepts for their organization is determining an impactful starting point – a project that is manageable to implement and delivers significant value.
For more on getting started on a digital twin strategy, read our whitepaper, Digital Twin: A Primer for the Industry Enterprise.