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Digital Twins: Why They Live Up to the Hype

POSTED ON: July 3, 2020 TAG: Big Data, Data Analytics

Today, forward-thinking companies across industries are implementing digital twins in increasingly fascinating and ground-breaking ways. In healthcare, researchers are creating highly accurate digital twins of the human body for diagnoses, education and training. In the energy sector, oil field operators are capturing and analyzing in-hole data to build models that guide drilling efforts, and wind farm operators are utilizing predictive maintenance to keep their windmills in peak condition. In the aircraft and automotive industries, digital twins are helping to perfect products and optimize entire manufacturing value chains.

With Internet of Things (IoT) technology improving every day and more and more compute power readily available to organizations of all sizes, the possibilities of what you can do with digital twin technology are only as limited as your imagination.

According to, 89% of IoT platforms will contain some type of digital twin technology by 2025, and digital twin capabilities will be standard in all IoT offerings by 2027. So, what is this technology and why is it commanding so much attention from today’s tech leaders?

What is a Digital Twin?

A digital twin is a virtual representation of a physical asset that is practically indistinguishable from its physical counterpart. It is made possible thanks to IoT sensors that gather data from the physical world and send it to be virtually reconstructed. This data includes design and engineering details that describe the asset’s geometry, materials, components, and behavior or performance.

When combined with analytics, digital twin data can unlock hidden value for an organization and provide insights about how to improve operations, increase efficiency or discover and resolve problems before the real-world asset is affected. Imagine testing a product before launching, simulating your disaster response, or uncovering micro adjustments to improve efficiency and performance, all before investing real-world resources or altering your physical assets. The concept of a digital twin can be applied to almost any asset across your organization. It can be built for a single machine component or deployed across a complex, interconnected network of products.

Although digital twin technology has existed in theory since the early 2000’s, it has only recently been widely adopted, thanks to technological innovations that have brought down the cost of large-scale computing, data storage, and bandwidth. As a result, digital twins technology is more accessible to organizations of all sizes, across industries. IDC projects that by 2022, 40 percent of IoT platform vendors will integrate simulation platforms, systems, and capabilities to create digital twins, and 70 percent of manufacturers will use the technology to conduct process simulations and scenario evaluations.

four key technologies

Benefits & Examples of Digital Twin Technology

Painless Prototyping: using 3D simulations that are enhanced by augmented and virtual reality, engineers can streamline the design process and eliminate many of the tedious steps typically involved in prototyping a new product. Product specifications, materials to be used, and how the design measures against relevant policies, standards, and regulations can all be determined virtually before ever investing in physical materials. This helps engineers identify any potential issues with quality and viability before designs are finalized, drastically reducing production costs.

Real-life example: Bridgestone Tires uses digital twin simulations to improve tire life and performance. According to Bridgestone director of digital engineering Hans Dorfi, “Some people ask, ‘Why do you need a digital twin if you have big data—why not just run analytics?’ I explain that while analytics plays a major role, it only augments the digital twin. The digital twin is able to capture the multidimensional performance envelope of tires and can also be applied to product in development, where no data is yet available.”1

Predictive Maintenance: Sensors embedded in machines feed performance data into a digital twin in real time, and the data can be used to train a machine learning algorithm to detect faults before they happen. Unplanned downtime is avoided, and maintenance can be done only on an as-needed basis, rather than at fixed intervals. This technology can save organizations untold amounts of money and dramatically improve efficiency and workplace safety.

Real-life example: Chevron Corp. is anticipating huge savings from its investment in digital twins. The company uses the technology to predict maintenance problems in its oil fields and refineries and aims to have sensors connected to most of its high-value equipment by 2024. Chief Information Officer Bill Braun expects that preventing the breakdowns of its most crucial equipment could save the company millions of dollars each year.2

Refined Performance: Sensors embedded in factory machines can feed performance data into AI and ML applications for analysis, identifying opportunities for output optimization, waste reduction, and elimination of substandard products. Digital twins optimize supply chains, distribution and fulfillment operations, and even the individual performance of workers.

Real-life example: Unilever, a global consumer products manufacturer, is currently using digital twin replicas of dozens of their factories to track physical conditions and enable testing of operational changes. The technology empowers the company make real-time changes to optimize output, use materials more precisely and help limit waste from products that don’t meet quality standards.3

Enhanced Employee Training: Digital twins can recreate real-life hazardous situations to train employees and run simulations. Employees can be also trained to handle equipment that isn’t physically close or is too costly to include in hands-on training.

Real-life example: GE, Airbus and Boeing have been using digital twins for several years to simulate engine failures and enhance pilot training.

There is truly no limit to the problems that can be solved using digital twin technology. In the healthcare industry doctors can use digital twins to run treatment simulations and predict outcomes, and there is even a digital twin of the entire country of Singapore that helps urban planners make decisions about utilities, disaster preparedness, zoning, and more.

Is your organization ready to adopt digital twin technology? Download our free eBook about digital twin readiness here to find out (coming soon)

POSTED ON: July 3, 2020 TAG: Big Data, Data Analytics