Internet, smartphone, and tablet are among the three fastest-adopted technologies since technology adoption is measured.
Today’s pace of technology adoption shapes businesses across all industries.
While this disruption might get perceived as a threat by many, it is primarily a significant opportunity for those capable of harnessing it.
Physical and digital worlds are blending more than ever before.
The mass adoption of digital twin tech enabled by industrial IoT may result in one of the most significant productivity boosts for manufacturing and transportation since the invention of the assembly line.
History remembers 2002 as the year when digital twins got attention in the commercial sphere.
A consultancy firm Challenge Advisory was invited to host a lecture at the University of Michigan, where it proposed using computer modeling and simulation to create a virtual representation of a product lifecycle management center.
Over the following decades, digital twin adoption have expanded to various industries and applications. As a result, technology has become essential for optimizing operations, improving quality, and driving innovation.
However, widespread digital twin adoption accelerated only in recent years with the advances in IoT, AI, and cloud computing, enabling organizations of any size across industries to create and manage digital twins at scale.
What is a Digital Twin? Digital Twins Explained
The digital twin is a virtual representation of a physical item or process.
Digital twin is visualized as a 3D model with a comprehensive set of attributes updated in real-time with data from sensors located within the object and its environment.
The visualization helps to accurately represent its real-world counterpart’s structure, behavior, and performance.
Digital twins can be used for real-time monitoring and simulations, providing valuable insights, and enabling users to make informed decisions about the design and operation of complex systems. Here is a digital twin data model.
Figure SEQ Figure \* ARABIC 1: Digital Twin Model Schema
Digital Twin Uses
• Apollo 13 – First Known Use of Digital Twin
The first known use of a “digital twin” can be traced back to the early days of modern space exploration.
In 1970 a digital twin played a vital role in the successful outcome of the Apollo 13 mission.
During the mission, an oxygen tank exploded on the spacecraft, causing a loss of power and life support systems.
The digital twin of the spacecraft, created before the mission, allowed engineers on the ground to diagnose the problem, simulate different resolution scenarios, and develop a plan to bring the astronauts home safely.
Since then, digital twins have become essential to the most complex engineering endeavors.
• Digital Twins in Manufacturing
In manufacturing, digital twins can be used to simulate and analyze the characteristics of a product or system before deployment.
Businesses can now validate their ideas in advance, in a simulated environment, without physical prototyping.
This significantly increases the product iteration speed while at the same time decreasing its development costs.
After the deployment, a digital twin can keep track of the product’s performance over time which allows the identification of potential problems and inefficiencies before they occur.
For example, a machine’s digital twin could simulate its operation and predict when it will likely require maintenance based on factors such as temperature, vibration, and wear and tear.
This information can be used to schedule a repair at the most convenient time, avoiding unexpected downtime and ensuring that the machine continues to operate at peak performance. GE is one of the companies using digital twins.
An excellent example of this is GE Power which leverages digital twins to optimize the maintenance of its wind turbines. Using digital twins to predict when care is needed has improved their reliability and reduced maintenance costs.
Figure SEQ Figure \* ARABIC 2: Digital twin of a GE Power Wind Turbine
• Digital Twins in optimizing fleet performance
Digital twins are becoming increasingly common in the transportation industry.
They are used to analyze the performance of vehicles, trains, and ships and optimize the routing and scheduling.
With predictive maintenance, digital twins in logistics can predict potential maintenance issues to avoid costly downtime and enable just-in-time maintenance to reduce the number of visits to the maintenance shop.
In fleet management, digital twins provide operational and environmental context by capturing data about a vehicle’s location, speed, direction, and surrounding environment, such as traffic conditions and weather.
This allows fleet managers to gain a comprehensive view of their fleet and to identify and address potential problems and inefficiencies in real time.
Accelerating Digital Twins Implementation with Cloud
Cloud digital twin technologies, such as AWS IoT TwinMaker and Azure Digital Twins, can significantly speed up the adoption of digital twins.
They provide a powerful toolset for creating and managing digital twins, including visual modeling and simulation capabilities, real-time data integration and analytics, and seamless integration with other systems or services.
By using cloud digital twin technologies, organizations can significantly reduce the time required to create digital twins and begin leveraging the benefits of the technology without the need for extensive in-house expertise or infrastructure.
Harnessing the full potential of these tools can help organizations speed up their digital twin adoption and gain a competitive advantage.
Figure SEQ Figure *\ ARABIC 3: Windfarm representation in Azure Digital Twins
Standardizing Digital Twins
Standardizing digital twins defines a common set of rules and approaches enabling different digital twins to be integrated and shared, allowing for better collaboration and interoperability among other systems and organizations.
The Digital Twin Consortium is a group of organizations to develop and promote standards and best implementation practices for digital twins.
The consortium provides a forum for member organizations to share their knowledge and experience with digital twins and to collaborate on developing standards and guidelines for their use.
By promoting the standardization of digital twins, the Digital Twin Consortium helps to accelerate the adoption and integration of this technology in a wide range of industries and applications.
Figure SEQ Figure \* ARABIC 4: “Digital Twin Capabilities Periodic Table” as defined by Digital Twin Consortium, provides
a foundation of requirements to be assessed before designing Digital Twin implementations
Maximize the potential of digital twins with Ness Manufacturing & Transportation Center of Excellence
As an AWS Premier Partner and Microsoft Azure Gold Partner, Ness is one of the few digital twin companies having the expertise and experience to help your business understand the digital twin benefits and identify the areas where they can provide the most value.
Our proven track record of success makes us the perfect partner to develop proof-of-concept projects that demonstrate these benefits in a real-world setting.
Don’t miss the opportunity to revolutionize your business with digital twins – partner with us today!
What are the types of digital twins in manufacturing?
Digital twins are extensively used in manufacturing, some of them include Product Digital Twin, Process Digital Twin, Factory Digital Twin, Asset Digital Twin, and Performance Digital Twin.
What are the benefits of digital twins for manufacturing?
Benefits include better product design, improved efficiency in production, reduced downtime, optimum resource utilization and superior production quality.
What is digital twin model?
A digital twin model is virtual replica of a physical machine, equipment, or a process. it is developed using real time data, mathematical models, and simulation. The model can replicate the same behavior, characteristics, and attributes of the real-world physical object.
What are the 4 pillars of digital twin?
The first pillar on the physical machine, equipment or process which is represented by the digital twin. The second pillar is the digital twin itself which is a digital replica of the physical entity. The third pillar is data integration, here real time data form the physical entity is collected, processed, and integrated into the digital twin model. The fourth pillar is AI and Analytics, which is used to derive insights, predict behaviors, and optimize performance.
Which industry uses digital twins?
Digital twins are most commonly used in manufacturing, energy and utilities, aerospace, defense, healthcare, automotive industries.