Digital Twin Technology: The “Looking Glass” To Project, Predict, Permutate. Possibilities In The Future.
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Power To Visualise The Future With Digital Twin Technology
There is power in being about to see and project the future so that we can perfect our decision-making. Minus all the missteps and costly mistakes.
Seeing Through “The Looking Glass”
Digital twin technology is the next top trend digital transformation archetype that can give us a glimpse into what is happening and what can happen with our physical assets now and far into the future.
It’s why top car maker, Tesla, creates a Digital Twin of every vehicle it sells. By creating a highly complex virtual model that is the exact twin of a physical thing, Tesla spans this virtual representation of the car’s lifecycle, enabling real-time data interpretation to determine optimal functioning. By merging AI and IoT, sensors in all Tesla cars continuously stream data into each car’s simulation in the factory. Tesla’s software integrations are so thorough that maintenance issues, such as a rattling door, can be fixed with software updates by simply adjusting the hydraulics.
Digital Twin - A Top Technology Archetype
Digital Twin technology is gaining momentum. Not surprisingly, Accenture calls Digital Twin technology a top trend for 2021. According to Deloitte study, the global market for digital twins is expected to grow with 38% CAGR to reach $16 billion by 2023. International Data Corporation (IDC) projects that by 2022, 40% of IoT platform vendors will integrate simulation platforms, systems, and capabilities to create Digital Twins, and two thirds of manufacturers will use the technology to conduct process simulations and scenario evaluations.
It’s why top companies such as Microsoft, Bosch, General Electric, IBM and Siemens are leading in Digital Twin technology. Is it time for business leaders to consider whether this technology is appropriate for adoption?
What is a Digital Twin?
Simply put, a digital twin is a 3D model of a physical entity. Digital Twin technology converges the virtual world with the physical world by creating virtual representations using performance data of the real-time digital equivalent of physical devices, objects or systems.
By being able to control operations at every stage, organisations can predict product performance and problems, experiment with various permutations for different outcomes, and hence avoid pitfalls. All this, even before the physical product is created.
Different Levels of Digital Twin Adoption
Depending on the business goal, there can be different levels of digital twin adoptions with different complexity levels:
At the Basic Level, companies can monitor assets to gain real-time analysis through data feed on the real-world object to create a virtual model in the cloud.
At the Middle Level, companies can augment Digital Twins on “what-if” models and run simulations to find the optimal operational configuration. This concept of being able to run simulations on devices, even before the asset is physically built, has powerful implications for various industries.
At the Advanced Level, Digital Twins can be equipped with AI-enabled systems and Machine Learning algorithms to quickly detect abnormal behaviour and initiate corrective steps in the system.
Digital Twin vs Simulations
Both simulations and digital twins utilise digital models to replicate a system’s various processes. A simulation generally focuses on one particular process and it does not benefit from having real-time data. A Digital Twin, however, is actually a virtual environment. The advantage over simulation is that a digital twin can run any number of simulations, both numerical and visual, to visualise structures, study multiple processes and predict possible outcomes. Because digital twins are designed around a two-way flow of information, it can tap on real-time data to continuously provide fresh insights that are shared back with the original source object to improve the product or processes.
Value of Digital Twin
The true value of a digital twin is its ability to provide business intelligence to help companies make better decisions about their products, enhance the management of resources, improve customer experience and create benefits (see below).
Digital Twin Applications
The highly complex virtual model of a digital twin be it a car, a jet engine or even a city, can be simulated and optimised to help us predict the future. Digital Twin Technology can be applied in various industries including healthcare, automotive, construction, architecture and more. Here are some digital twin applications in retail and supply chain, manufacturing and urban planning with case examples.
Application Of Digital Twin On Retail And Supply Chain
Retailers can apply a Digital Twin to the real physical product as it goes through different stage of its life cycle. Companies can create customer personas, in-store digital replicas, use data captured by RFID readers, motion sensors, and smart shelves to analyse customer purchase behaviour, improve customer experience and test the optimal placement of products. Beyond the store, the retailer can create supply chain simulations. The models give an overview of supply chain’s performance to effectively track inventory, manage product supplies, predict performance of packaging materials, optimise warehouse design and operational performance, avoid supply chain disruptions and optimise logistics costs.
Real Life Example:
Tapping on data from IoT-enabled shelves and sales systems, French supermarket chain Intermarché created a digital twin of a brick-and-mortar store to facilitate ease of inventory management and test the effectiveness of different store layouts.
Application Of Digital Twin On Manufacturing
Using Digital Twin technology, manufacturers can monitor equipment health, recognise anomalies, and reduce assembling and installation time while validating factory production systems. Interestingly, this technology is enabling manufacturers to move towards new profitability models where they sell services in lieu of the product itself. Customers profit from operational optimisation and usage of the equipment based on the predictive/prescriptive capabilities of the Digital Twin while the manufacturer maintains ownership and maintenance services.
Real Life Examples:
Boeing achieved a 40% quality improvement rate of parts using the digital twin concept.
Deloitte reduced 20% of time spent on changeovers to achieve higher production efficiency for their manufacturing clients.
Challenge Advisory helped their automotive client improve annual profit margins by up to 54%.
Digital Twin Application On Urban Planning
With Digital Twin technology, virtual representation of cities can now be visually re-imagined and redesigned with different permutations so that smart cities be understood as an interconnected ecosystem and be optimally operated.
Real Life Examples:
Urban planners in Berlin are using Digital Twin technology to create digitised copy of the smart city to reduce traffic through new mobility concepts, achieve decarbonisation and optimise energy generation.
Harnessing Big Data to create virtual mathematical models, specialists in Shanghai created a complete virtual clone of the city to re-imagine population growth, traffic facilitation, and integration of new facilities such as health centres, parks, schools and even bridge maintenance. Predictive data is used to simulate floods for disaster planning.
Expected Development Horizons In The Next 5 Years
As Digital Twin technology continues to advance, companies from various industries are racing to adopt, invest and apply this digital development that will have significant impact on production time, processes and cost-savings. Within a short horizon of the next five year, we will be able to see advancements such as:
1. The democratisation of simulation-based digital twins across industries with growing availability of Digital Twin as a service through big tech companies such as Microsoft Azure, Bosch and Siemens.
2. End-to-end decision-making, efficacy and optimisation by applying Digital Twin insights across application stages ranging from design to after-sales.
3. Autonomous asset optimisation and continuous manufacturing using AI- driven process optimisation and robotics.
As adoption continues to grow, we expect to see more uses of Digital Twins in many sectors from city planning to product development and factory simulation. Companies that get ahead of the game, and invest time developing Digital Twins, in line with their business goals, will take giant leaps in competitive advantage.
