A number of cloud service providers are resolute in their support of enterprises’ digital twin efforts. Whether it’s Amazon Web Services’ (AWS) IoT TwinMaker or Alibaba Cloud’s Venue Simulation Service (VSS), various solutions exist for cloud-enabled digital twin deployment. The trend of technology providers shifting focus to cloud-driven digital twins is driven by the higher computing needs of industrial operators, manufacturers, supply chains, etc. Introducing highly specialized workloads like Artificial Intelligence (AI)/Machine Learning (ML) and intelligent Internet of Things (IoT) devices draws significant compute needs, which makes cloud platforms ideal for modern digital twin applications.
In this blog post, I’ll provide ABI Research’s insight into the following:
- Benefits of a Cloud-Based Digital Twin
- Cloud-Based Digital Twin Use Cases
- Opportunities with Cloud-Based Digital Twins
- How We See the Digital Twin Market Unfolding
Benefits of a Cloud-Based Digital Twin
From the perspective of a heavy manufacturer, automaker, supply chain manager, urban planner, or transportation operator, the allure of a cloud-enabled digital twin is hard to ignore. There are many areas of a digital twin or simulation process where cloud computing can benefit these industries. ABI Research concludes that cloud-based digital twins benefit the following areas the most:
- Scalability: A cloud-based digital twin gives enterprises a level of flexibility impossible with traditional solutions. Users can scale the digital twin up or down in line with current business requirements. Moreover, due to the optimization of cloud resources, the performance of the digital twin will be maximized.
- Computational Power: Cloud solutions such as Virtual Machines (VMs) and containers ensure that computational resources match exactly what the digital twin platform needs.
- Storage and Data Integration: Huge sums of analytics, Three-Dimensional (3D) models, and simulation results make data storage a priority for digital twin operators. Cloud storage can thankfully mitigate these storage concerns and ensure the digital replica is updated and functions smoothly.
- AI/ML Processing: A cloud solution often comes with various data analytics tools that assist users with AI/ML processing workloads. These tools include compute power hardware such as Graphics Processing Units (GPUs)/Central Processing Units (CPUs), as well as application software designed to analyze, predict, and make recommendations for specific industries.
- Reliability and Disaster Recovery: Coud-based digital twin solutions come with backup and recovery capabilities to avoid downtime and platform crashes.
Next, we take a look at some examples of how digital twins can be used by businesses in three key industries: manufacturing, smart cities, and automotive.
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Cloud-Based Digital Twin Use Cases
While digital twin technology can benefit many different industries, ABI Research evaluated its use in manufacturing, smart cities, and automotive industries in its latest report. Here’s how enterprises in these industries are leveraging cloud-based digital twins.
Manufacturing
Digital twins are an integral aspect of a manufacturer’s path to autonomous operations. Imagine having a digital protype of a product right in front of you; you can test with different designs and make revisions as you see fit. Revising the digital twin version of the product eradicates the historically time-consuming process of testing with a physical product. Ultimately, this speeds up the design cycle and improves the Time to Market (TTM).
A digital twin also enables manufacturers to simulate proposed production changes and identify areas that will most benefit from preventative repairs/maintenance. That way, operators can fix machinery, equipment, and systems before they break down and cause downtime—something that costs manufacturers about US$50 billion every year.
Deploying a digital twin supports manufacturers’ ideal end game of automating as many production processes as possible. As self-learning technology continues to advance, digital twin solutions will better mimic human behavior and perform low-complexity tasks. This provides industrial operators with more time to focus on activities and systems that are more convoluted to manage.
Smart Cities
Using digital twins for smart cities is primarily tied to improving society as a whole. For example, city officials in Seoul, South Korea deploy a city-wide digital twin to solve urban problems (e.g., traffic congestion, etc.) and improve the quality of life for residents via technological innovations.
Digital twins allow city governments to optimize facilities and urban infrastructure. The digital twin can collect droves of data about road traffic, pedestrian traffic, air quality, water usage, and other vital information. City officials can combine these data with new technologies like Augmented Reality (AR)/Virtual Reality (VR), IoT connectivity, advanced analytics, and AI to process static and historical data. From there, these revelations about city facilities and infrastructure form the basis for smart city Key Performance Indicators (KPIs). This use case would also go hand-in-hand with a smart city’s sustainability goals, as the digital twin can help optimize water, waste, and energy resources.
City planners benefit from digital twins by simulating disaster scenarios. Cities in southeastern United States, for instance, could use digital twins to gauge the city’s readiness to respond to a hurricane. That way, they can assess if more resources must be allocated for a potential natural disaster or if a building can withstand high winds.
For these reasons, smart city digital twins should be viewed through the lens of a government-citizen relationship. From this perspective, technology providers will be able to better serve the needs of smart city officials.
Automotive
A design flaw in a vehicle can be costly for automakers and lead to a widescale recall. For example, a small design flaw with the seatbelt resulted in Ford recalling more than 175,000 Broncos in May 2023. Digital twins can help prevent these types of recalls from happening, as they allow designers and engineers to realistically simulate changes to the vehicle design before it’s ever built. This can iron out any design kinks and replace them with superior modifications.
Automakers can create a virtual model of the entire physical car or just components, mechanics, electrical systems, and behavior of the car. In this digital twin application, the designer/engineer can evaluate the car’s engine performance and use sensor information to assess the performance of internal components and parts. This collectivization of vehicle data is the bedrock of automotive digital twins, and cloud computing is a key enabler of adoption due to the facilitation of collecting and processing of real-time sensor-fed information.
As carmakers are compelled to innovate and deliver sustainable products (e.g., Electric Vehicles (EVs)) to market faster, cloud-based digital twin solutions will be a necessary technology to maximize manufacturing efficiencies.
Opportunities with Cloud-Based Digital Twins
Whether it’s a public, private, or hybrid solution, a cloud-based platform is a huge benefit for businesses aiming to introduce digital twin technologies. ABI Research recognizes the following key opportunities for companies that leverage cloud-based digital twins.
Implementation Elasticity
Opportunity: Cloud computing allows enterprises to begin piloting a digital twin solution in a straightforward manner. Enterprises can scale as far up or down as their specific business needs stipulate. This can all be done without having to adopt new hardware and applications.
Impact: Due to the flexibility and scalability afforded by cloud computing, digital twin experimentation will not carry significant costs of failure. Moreover, enterprises can innovate at a quicker rate with cloud-based solutions than traditional digital twin deployments.
Shorter Time to Market
Opportunity: Pre-built tools and off-the shelf applications typically offered by cloud service providers accelerate the design and development process of digital twins. Businesses can use these tools to map the digital model to the physical asset accurately and quickly.
Impact: Not only does a shorter TTM provide an advantage over competitors, but it allows businesses to respond to changing requirements quickly and sometimes anticipate emerging business needs.
Data Management and Interoperability
Opportunity: Cloud computing can simplify and decrease the costs associated with storing, processing, and securing large sums of data collected by digital twins. Through datasets using Application Programming Interfaces (APIs) and integration services, cloud platforms also allow for interoperability between various devices involved in digital twin applications.
Impact: Businesses that leverage digital twins built on a cloud platform have easily retrievable historical insights, which can be used to optimize products. Further, cloud-driven digital twins have access to IoT devices, public API datasets, and countless other internal and external applications that improve digital twin outcomes.
Facilitates Rapid New Technology Inclusion
Opportunity: As more novel technologies are leveraged by businesses, robust AI/ML tools offered by cloud service providers help optimize and generate predictive insights stemming from these technologies.
Impact: The support for new technologies—thanks to cloud computing platforms—will allow for brand differentiation when building a digital twin.
Edge Computing Capabilities
Opportunity: A digital twin solution can be enhanced by leveraging edge processing capabilities, which cloud platforms facilitate. As enterprises adopt more and more IoT devices, local processing will be essential to keep the digital twin running smoothly.
Impact: Digital twin application outcomes will be improved as edge computing gives developers the complete scope of the real-world environment. This unparalleled level of operational transparency makes the value and information provided by the digital twin superior to non-cloud supported counterparts.
How We See the Digital Twin Market Unfolding
Undoubtedly, digital twins are a must for any organization aiming to truly understand, analyze, and maintain their physical assets, systems, or processes. While technology vendors have traditionally focused on digital twins for Operational Technology (OT) use cases, vendors are now striking a balance between OT and Information Technology (IT) use cases. Shifting focus toward OT/IT cloud-based solutions allows virtually any organization—no matter its size or industry—to take advantage of digital twin technology.
However, challenges still remain for businesses that plan to implement digital twins into their operations. Notably, enterprises must be wary of implementation complexity, data latency and bandwidth, cost management, technology vendor lock-in, and cloud service performance.
A number of OT and IT digital twin solution providers are working to address challenges such as these, which will help them further penetrate the global market. ABI Research’s recent Cloud-Driven Digital Twins: Elevating the Future of Digital Environments technology analysis report dives into more of the market background of cloud-based digital twins, and explains how various technology providers are responding to demand for cloud-based platforms. This report is one of the latest deliverables from the company’s Distributed & Edge Computing Research Service.
