Simulations are indispensable but real world simulations are expensive. Therefore companies that need to learn fast (e.g. self-driving car manufacturers) heavily rely on simulations. Digital twins enable companies to simulate their shop floor or their entire business to identify optimization opportunities.
Today, businesses use digital twins in numerous ways from product development to operational performance improvement. Though the market for digital twins is relatively small (4 billion dollars in 2019), it is expected to grow to 36 billion dollars by 2025 at a CAGR of 38%. Increased digitization and IoT adoption are making it easier for businesses to build accurate digital twins and drive adoption of the technology.
What is a digital twin?
A digital twin is a virtual/ digital replica of physical entities such as devices, people, processes, or systems that help businesses make model-driven decisions. The purpose of a digital twin is to run cost-effective simulations. These examples are costly to simulate without a digital twin, that’s why data scientists and IT professionals use data to develop models that mimics the real-world assets in digital space.
The digital twin uses IoT sensors, log files and other relevant information to collect real-time data for accurate modeling of assets. These models are then combined with AI-powered analytics tools in a virtual setting.
Why are digital twins important now?
According to the IoT implementation survey by Gartner, organizations implementing IoT already use digital twins (13%) or plan to use it within a year (62%).
Digital twins can significantly improve enterprises’ data-driven decision-making processes. They are linked to their real-world equivalents at the edge and businesses use digital twins to understand the state of the physical asset, respond to changes, improve operations, and add value to the systems.
How does a digital twin work?
These digital assets can be created even before an asset is built physically. Regardless of when it is created, the process of creating a virtual twin has basic steps:
- Research the physical object or system that will be mimicked
- Integrate sensors into physical assets or monitor log files and other sources to collect data
- All this collected information along is integrated into the virtual model with AI algorithms
- By applying analytics into these models, data scientists and engineers get relevant insights regarding the physical asset.
What are the benefits of digital twins?
Digital twins are commonly used in manufacturing and provide these benefits:
- Lower maintenance costs via predictive maintenance: Digital twins enable businesses to understand potential sources of failure so that businesses minimize non-value adding maintenance activities
- Improved productivity: Gartner predicts that industrial companies could see a 10 percent improvement in effectiveness via digital twins. This is due to reduced downtime due to predictive maintenance and improved performance via optimization.
- Faster production times: IDC claims that businesses who invest in digital twin technology will see a 30 percent improvement in cycle times of critical processes including production lines. This is due to improved optimization thanks to digital twins.
- Testing prior to manufacturing: Businesses can use digital twins to understand the feasibility of upcoming products.
- Improved customer satisfaction: All of these would lead to happier customers that receive higher quality products without delays.
An emerging area for digital twins is creating digital twins of entire businesses. Benefits in this area include:
- Improved business outcomes: Digital twins enable businesses to be more resilient to shocks thanks to simulation and this can translate into more enduring customer relationships and profitability.
- Improved customer satisfaction: Gaining a deeper understanding about their services, potential disruptions and customers’ needs, businesses can deliver better, more consistent services that eventually enhance the customer experience.
What is the relation between AI and digital twins?
Artificial intelligence and digital twins have a mutualist relation where both contribute to each other.
Digital twins can help businesses generate simulated data that can be used to train AI models. Artificial intelligence can also benefit from digital twins since digital twins can virtually create an environment for machine learning test scenarios. Depending on the utility score of virtual environment data scientists and engineers can deploy artificial intelligence solutions.
Digital twins can benefit from artificial intelligence. AI and machine learning algorithms enable businesses both to build some digital twins and also to process a large amount of data collected from digital twins. For example, by leveraging AI capabilities with digital twins, engineers can accelerate the design processes by quickly evaluating many possible design alternatives.
What are its use cases?
The capacity for aggregating data from a physical product, system or process opens the way for numerous new use cases. With the aggregation of data, digital twin enables businesses to simulate, diagnose, predict and design for different industries and applications.
Top industries with digital twin applications are manufacturing and supply chain. Feel free to read all digital twin applications in detail here.
What are the leading digital twin tools?
- Ansys Twin Builder
- Autodesk Digital Twin
- Bosch IoT Suite
- CONTACT Elements for IoT
- Flutura Decision Science
- Oracle IoT Production Monitoring Cloud
- ScaleOut Digital Twin Builder
- ThingWorx Operator Advisor
If you want to create a digital twin for predictive maintenance purposes, we recommend you to read our comprehensive article about predictive maintenance.
If you still have questions about digital twins, don’t hesitate to ask. We would like to help:
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