The use of digital twins as a critical business management tool is becoming more common as cloud computing, 5G, artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) gain momentum.
Digital twins are virtual representations of people, real-world assets and processes that help us understand how these might behave in different situations. Digital twins can be used to replicate difficult conditions, using machine learning gathered insights, providing options that would otherwise have gone unnoticed.
Digital twins create a mirrored environment in which interactions in the real world can be analysed and improved digitally. Organisations can now connect massive networks of intelligent twins to create real-time simulations of product lifecycles, workplaces, supply chains, and even entire cities. For this, high-performance computing infrastructure and network intelligence is crucial to unlock the potential of Digital Twin implementation.
The origins and benefits of digital twins
The origins of digital twins can be traced back to the 1960s. NASA used twinning methods to construct systems on Earth to replicate those in space. Despite digital twins having existed for 60 years, they only recently gained popularity in 2017. For most applications, the digital twin concept only took off recently due to the rise of IoT and the availability of increasing computational power.
Digital twins go beyond the computer-assisted engineering (CAE) capabilities that have been prevalent in systems development over the last two decades. The advancements in AI, machine learning, and IoT have expanded CAE capabilities by building virtual models that are seamlessly and continuously updated throughout a product or process life cycle. As a result, changes to the physical object are mirrored in the digital model, and insights gathered can be applied to real-world design decisions.
When leveraged as decision-support tools, digital twins have several benefits:
- Digital twins reproduce the real world with precision and accuracy as it capitalises on real-time data.
- Digital twins function as mathematical models, allowing decision-makers to run a limitless number of “what-if” scenarios to forecast possible outcomes and effectiveness.
- Digital twins efficiently identify possible sources of conflict, root causes of failure, early warning signs of bottlenecks or poor performance, vulnerabilities, inefficiencies, and areas for improvement.
These benefits have accelerated the adoption of digital twins across a wide variety of sectors.
Digital twin use cases
Whilst digital twins is already employed across many industries, the market is forecast to grow significantly over the next several years. The global digital twin market size was approximately USD 3.1 billion in 2020. It is predicted to continue growing rapidly through to 2026, when it is expected to reach an estimated USD 48.2 billion.
Digital twins is widely used for monitoring, modelling, and remote control of physical assets used in digital factories. However, digital twin technology can be applied to industries outside of manufacturing, including government, healthcare, supply chain, and retail.
One of the most well-known uses of digital twins is the digital factory. Design engineers can alter and optimise a design based on actual environmental conditions using engineering simulations that leverage real product data throughout the product design process.
Digital twins aid troubleshooting and diagnostics through simulations based on hypothetical scenarios. These scenarios use operational data to generate solutions on the virtual asset before real-world implementation.
Predictive models can be built using historical data and then combined with simulation models based on real-time sensor data, enabling operators and technicians to diagnose problems. This method reduces reactive maintenance costs linked to unplanned downtime.
Digital twins provide visibility into the entire manufacturing process, allowing the discovery of bottlenecks and the visualisation of process changes, increasing productivity and yields. For example, Bridgestone, the world’s biggest tyre and rubber manufacturer, uses digital twin technology to supplement simulations with sensor data to extend tyre life and performance.
Governments are actively developing smart city programs to continuously provide citizens with better services. Digital twins is an essential element of smart cities as it is used to create three-dimensional virtual representations of buildings, infrastructure, and other physical assets that are linked to the data.
Digital Twin Victoria is an innovative new digital venture launched by Land Use Victoria. The project’s primary purpose is to reproduce the State of Victoria online so that government, industry, and the community may work together to enhance real-world results via shared open-data, technology, and analytics. The Digital Twin Victoria initiative integrates rich 3D and 4D geographical data, artificial intelligence, and sensor data from around the state to visualise and model areas before making investments. This provides consumers with a comprehensive view of correct, up-to-date datasets in an easy-to-use format, all in one location, simultaneously.
While Victoria is a good example, it is not the only one. There is a larger drive to incorporate digital twins into other State Government departments. Infrastructure Australia is looking to integrate digital twins in all federally financed infrastructure projects over the next 10–15 years as part of its 2021 Australian Infrastructure Plan.
The use of digital monitoring and human body modelling is improving medical care, and the usage of digital twins is transforming clinical operations and hospital management. Digital twins are digital representations of the human body that include both individual and population data in the field of human physiology.
Researchers are using digital twins to study diseases, new treatments, and medical equipment. When healthcare systems use digital twins, they can bring life-saving technologies to market faster with lower costs and improved patient safety.
To give a fully personalised treatment experience, digital twin technology can be employed to correctly depict an individual’s DNA, physiological traits, and lifestyle factors. Clinicians can use a digital twin of a human body to detect pathology before symptoms show, test medicines, and better prepare for surgery.
Digital twins can assist health care practitioners in analysing the operational efficacy of their organisation, operational strategy, capabilities, staff, and care models.
Supply chain and retail
Retailers can visualise their supply chains as networked environments using digital twin technology. Facilities, depots, stock movement and placement, personnel, and processes can all be replicated with digital twins. The models are given real-time IoT data, including that from Enterprise Resource Planning (ERP), and other business systems to build a live execution environment.
Retailers use the digital twins of their supply chain to simulate the performance of their supply network and discover areas for improvement. Because digital twins give real-time knowledge, firms can quickly deploy alternate supply chain fulfillment models, such as curbside pick-up or micro-fulfillment, to reduce costs and increase efficiency. Leveraging network intelligence, digital twins link data from various sources, communicating vital information across the supply chain. Early adopters of digital twins realised CAPEX savings of up to 10%, sustained inventory reductions of up to 5%, and EBITDA improvements of one to three percentage points, according to BCG.
HPE Solutions for digital twins
HPE offers a comprehensive range of digital twin hardware and software solutions, including computing, storage, and networking layers.
The availability of computational power in a digital twin application allows for the development of precise and high-performance virtual models. HPE HPC systems include servers that allow for a balanced, high-performance, and scalable HPC environment. These systems make use of significant technological components that enable digital twins, ranging from micro (HPE Apollo 2000) to medium (HPE Apollo 6000) to large supercomputers (HPE SGI 8600). With the computing power of these systems, complex simulations and modelling can be handled efficiently.
HPE provides a complete end-to-end solution stack that can be adapted and customised to satisfy the business requirements imposed by digital twins. For example, the most comprehensive portfolio of supported and optimised computer-aided engineering application software, as well as a varied range of software and hardware platforms, all accompanied by high-value services
Digital twins are increasing in use as their capabilities and functionalities grow. While digital twins has historically been applied to manufacturing and industrial domains, other sectors are beginning to unlock the benefits. Realising the full potential of digital twins may require integrating systems and data across entire organisational ecosystems. To take full advantage of digital twins, it’s important your business has the right underlying IT infrastructure, including computational power and network intelligence, to support it.
As your IT partner, we can help you embrace digital twin technology by establishing the necessary HPE infrastructure solutions and assist with your business transformation, from acquisition to integration and full deployment.