Digital twins are touted as “becoming a business imperative, covering the entire lifecycle of an asset or process and forming the foundation for connected products and services. Companies that fail to respond will be left behind”. (Forbes, 2017) The global market size was valued at USD 3.1 billion in 2020 and it is projected to reach USD 48.2 billion by 2026 (M&M). The current pandemic drives an increasing demand from healthcare and pharmaceutical industries, in addition to the traditional users in the automotive and manufacturing industries.
What is a digital twin?
Digital twin is a virtual model of some real asset, system, or process. The potential for large-scale implementation has only lately been enabled by internet-of-things, increased connectivity, cloud computation, and algorithmic advances in artificial intelligence. Digital twins are used for:
- Monitoring and analysis – sensors tie the twin to the real entity. This enables detection of anomalies, reduction in variability, root cause analysis, and improvement in model accuracy.
- Prediction and simulation – prediction of future performance, what-if simulations.
- Optimization and control – prevention of hazards (predictive maintenance), developing new opportunities, and planning for future using simulations. Having access to a reliable simulator enables greater experimental throughput for optimization.
How to create them?
These models lead to new applications of data science in extracting knowledge of operations, taking into account rich domain knowledge of product experts. A constant influx of sensor data can be used to assemble and improve the digital twin. Insights and solutions found in virtual must be transferable to the real-world object, which is a delicate matter to achieve. Models must be improved so that necessary accuracy tradeoffs are done in the best way for the intended use. Optimization can be done using reinforcement learning where the model can be improved by collecting safe examples in the regions near interesting policies, and the model fit is prioritized in „interesting“ regions at the expense of model performance elsewhere. This is, in a way, similar to how cutting planes are generated within mathematical optimization procedure — on-the-fly, only in promising areas.
Applications of digital twins
Here are some illustrative examples of applications, on different scales:
- Automotive: In Tesla, every car has its own digital twin that is used in monitored for problems.
- Production industry: Schott AG, with the help of NNAISENSE, used neural digital twins of their production process in order to optimize the glass production.
- Supply chain: Working with Ireland’s An Post, Accenture created digital twins of hundreds of vehicles, delivery routes, sorting centers and processes. The created system was used to test different improvements to last-mile delivery. This was much cheaper in virtual and with much higher experimental throughput than would be possible in physical.
- City management: Virtual Singapore is a digital twin of Singapore that is used for: experimentation, test-bedding, R&D, and decision-making.
Downstream, digital twins are additionally combined with Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), as well as blockchain.
Tools and services
Key market players for digital twin tools and services are:
However, these tools are only enablers. There are no off-the-shelf solutions that automatically fit every need since every use case needs customization and deep expertise.
(Reproduced from the original post on LinkedIn)