Edge computing: digital twin catalyst
Data is digital gold dust. Businesses can benefit greatly by channelling the ever-growing wealth of data and harnessing the combined power of edge computing and digital twins. Working in concert, these technologies can be used to plan, build and run operations at scale – maximising efficiency and minimising risk and cost. In this article, we explore digital twins, the bounds of edge-digital twin symbiosis and a real-world case study.
Digital twins are bringing in a new era of data insights
In their quest to better understand business performance and define future strategy, for many years, business leaders have turned to data. Data has provided them tangible proof points that validate their intuition and perhaps reveal a few surprises. Over the last few years, as the world has become hyper-connected, data has become more available, richer and more accurate. The proliferation of IoT equipment has meant data is harvested from a dense network of sensors, creating a detailed map of any enterprise asset or series of assets. Businesses are now able to replicate physical and logical (data) environments leveraging that very same tessellation of data checkpoints. The result – digital twins. Edge compute capacity can be used to supercharge digital twins by creating real time links to live environments, reducing network latency and time to insight.
What is a digital twin?
Coined in 1991 by Dr. Michael Grieves, the term ‘digital twin’ was originally imagined as a manufacturing concept. While digital twins are most certainly used in manufacturing today, the use cases span across industry verticals. Digital twins are used to prototype and stress-test hypotheses and are great enablers of innovation. Many technology firms are releasing digital twin products, designed to support enterprises in twin construction, in order to grasp the market opportunity. Diageo, the drinks manufacturer, used a digital twin to optimise their cask filling process by creating repeatable filling to 99% volume and reducing spillage and foaming. Connected Places Catapult, the UK government’s transport innovation agency, used the technology to improve autonomous vehicle road and human driving pattern recognition – one successful test resulted in a journey across Britain driven by an autonomous vehicle. Telecom operators use digital twins to model their networks, including the customer side, and service environments to predict and refine performance.
Source: STL Partners
The cycle for use of digital twins is illustrated in the figure below. First, data about the asset, whether it is physical or logical, is collected to give digital twin architects the information they need to build the twin. Once the twin is built, engineers can tweak and test parameters in the twin environment. Next, testing can be carried out in the live environment and the results can be fed back into the model. Machine learning is used to detect inefficiencies and suggest refinements to the model. A continuous feedback loop is therefore created, as models are optimised through rigorous and extensive testing.
Digital twin build and refinement process
Source: STL Partners
There are more options than ever for enterprises looking to develop digital twins, with a range of offerings on the market. Siemens, Palantir, QiO and IBM are just some examples of players providing platforms to build, run and manage digital twins.
Edge computing will act as a digital twin catalyst
Edge computing has seen a meteoric rise over the past few years, with conservative estimates of the 2030 revenue opportunity upward of £30bn. Facilities to process data and generate actionable insight at speed are increasingly crucial, given the growing tessellation of data-generating sensors and devices. Edge builds on the principle of centralised cloud by bringing compute power closer to the data source in regionalised or local edge nodes. This results in three key benefits: 1) Speed; 2) Privacy and Regulation; 3) Local uptime
Source: STL Partners
Hyperbat, the vehicle battery manufacturer, is an early example of an enterprise leveraging digital twins and edge computing to drive improved business outcomes. They have constructed a 3D digital twin of their product to help design, engineering and manufacturing teams engineers collaborate in real time virtual reality. Ericsson, Nvidia, BT, Qualcomm, Masters of Pie and The Grid Factory are Hyperbat’s partners in bringing this exciting approach to life. Nvidia’s edge compute hardware and software allow the 3D computer-aided design (CAD) models to integrate seamlessly into the wider factory operations – pushing changes and pulling relevant data in real time. The solution, launched in April 2021, also brings 5G into play, reducing constraints on team locations.
Hyperbat engineer operating the company’s new digital twin solution
Source: Masters of Pie
There will always be a requirement to partner with players across the value chain, but some ecosystem players have begun to develop combined digital twin and edge computing propositions. For example, Swim, an STL Partners Edge Computing Companies to Watch 2021, runs their EDX platform which uses edge data to build stateful digital twins for enterprises and governments to optimise operations. Others will undoubtedly follow.
The future is bright for the digital twin-edge market
While the digital twin industry is relatively nascent, enterprises and providers are actively exploring the potential benefits. Edge computing will turbocharge the rise of digital twin. Twins will be more accurate as edge compute ensures environment visibility is updated in real time, regardless of the quantum of sensors generating data. In many cases, enterprises will be able to make updates just as quickly to their scaled live environment after digital twin iteration. The journey for digital twin-edge computing joint solutions has just begun and considerable growth opportunities await, for enterprises and providers alike.
Author: Patrick Montague-Jones is a Senior Consultant at STL Partners, specialising in a range of topics across the telecommunications value chain
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