When resources are limited and downtime isn’t an option, IT teams need smarter ways to test, scale, and improve their systems. That’s where virtual environment replicas come in—they enable experimentation, optimization, and risk-free decision-making, all without touching live infrastructure or causing downtime.
The concept — called a digital twin — uses advanced AI-driven analytics and machine learning to enable proactive maintenance, quality improvements and performance optimization. However, because AI requires substantial compute power, many organizations that could benefit from real-time simulation lack the infrastructure to do so.
To cost-effectively reap the productivity benefits of digital twins without replacing legacy systems, organizations can combine their existing infrastructure with new middleware, API-driven integration and a hybrid cloud infrastructure. Vultr for instance specializes in enabling seamless integration between Internet of Things (IoT) devices, legacy systems and multicloud environments, often at a cost that’s up to 50% lower than leading cloud providers, according to the firm.
What is a digital twin?
A digital twin is an integrated, virtual representation of a physical object, system or process that mirrors the original’s behavior and performance. It uses real-time data to continually and accurately reflect the original’s characteristics. Organizations implement digital twins in trustworthy IT systems and use them for simulation, integration, testing, monitoring and maintenance.
In these and similar scenarios, sophisticated AI models compare actual and virtual representations. Using its pattern-recognition power, the model can analyze trends to suggest operational improvements.
Possible benefits of digital twins include:
- Less equipment downtime
- Longer equipment lifespan
- Fewer manual inspections
- Faster maintenance responses
- Improved overall efficiency
- Improved overall safety
- Long-term cost savings
Digital twin use cases
In terms of how this plays out in the real world, digital twins provide value to industries ranging from automotive to telecom; and aerospace was one of the first to integrate the technology. Overall, Gartner projects that more than 40% of large companies will use digital twins for operational excellence by 2027.
Here are a few ways organizations have used digital twins to streamline operations, enhance safety and accelerate innovation.
Healthcare and life sciences
The healthcare and life sciences sector includes hospitals and healthcare systems, as well as pharmaceutical, medical device and diagnostics companies. Health systems have used digital twins to better allocate resources to minimize staff shortages and improve clinical care. These and other uses help these complex organizations reduce costs and maintain compliance while potentially improving patient outcomes.
In drug discovery, digital twins help researchers identify potentially successful drug targets faster than traditional approaches by mimicking biochemical properties in drug development, which could lead to dramatically reduced timelines. This helps accelerate the availability of necessary therapies.
Financial services
Banks use digital twins for compliance management and to continuously monitor their operations — from customer service areas to financial transactions. With real-time information, these businesses can detect suspicious activity faster than with other technology, effectively stopping fraud before it happens.
Within customer service, a digital twin can simulate different outcomes to develop strategies to improve customer satisfaction by reducing wait times. Predictive maintenance can help prevent scenarios such as software glitches and ATM malfunctions, both of which are potentially disastrous from both financial and brand reputation standpoints.
Manufacturing and energy
Manufacturers in multiple industries use digital twins to manage the performance, effectiveness and quality of equipment, machine lines and plants.
Systems twins, a specialized type of digital twin, model the interaction between physical and digital processes to spot and rectify inefficiencies. When used for troubleshooting and streamlining, digital twins can analyze data captured by sensors embedded on machines and production lines. Modeling real-time data such as temperature, vibration, pressure and wear can be used for proactive maintenance, improving asset reliability and uptime.
How Vultr enables digital twins
McKinsey research shows digital twins have cut product development times by up to 50%, with lower costs and improved product quality. It's no wonder that executives across industries have either implemented digital twins or they’re considering the technology because of its potential to improve quality while lowering costs. However, these virtual replicas require a powerful infrastructure — one that can handle large amounts of data without security or privacy risk.
Specifically, digital twins require high-quality data, robust data storage and a secure connection between the on-premises data center and the GPU cluster, where the AI model lives. The secure connection is especially important in heavily regulated industries such as aerospace, finance and healthcare.
A real-world use case shows how digital replicas can be created using 170 AI models powered by AMD Instinct™ GPUs on Vultr's high-performance infrastructure to create real-time digital replicas. This hybrid architecture can integrate data securely from IoT sensors, existing on-premises systems and Vultr cloud. Private networking prevents exposure to the public internet and mitigates data leakage risks. Continuous real-time updates ensure accurate monitoring and predictive analytics.
This approach enables real-time virtual clones of equipment and processes, enabling precise simulation of operations and monitoring of physical assets. These new capabilities thus lead to improved decision-making and predictive maintenance.
For many enterprises looking to implement digital twins, a hybrid cloud environment from Vultr that integrates with on-premises legacy systems via middleware and API-driven integration can provide the data protection they demand, along with the scalability and flexibility of the cloud.
Learn more about the impact of digital twins: https://www.vultr.com/marketing-sales-files/use-case-oil-gas.pdf