Empowering efficiency with digital twin

The digital twin is a concept of replicating a process from virtual world to the real-world objects. It is built in a two-way flow of information system, which uses multiple technologies that aids in decision making, risk mitigation, and improve system performance.

This technology can replicate small objects such as wind farms, construction building to large objects in industrial environment.

The twinning process requires several hardware and software components for its operation. These components are a combination of sensors, actuators, network devices, and data management platform. Once all hardware and software components are integrated, its iterative operations are performed in virtual world. Real time data is collected from physical environment and shared with the centralised storage system, where it is analysed and fed to the digital twin. This helps in simulation, finding any obstructions, reducing turnaround time, and preventing expensive downtimes.

There are four major types of simulation process in digital twin environment, based on its area of application, it includes:

Components twin: It replicates individual components such as sensors, which helps in simulating behaviour of components to test its impact on system.

Asset twin: These are made up of individual twin components, that works together as a machine to track location and predict maintenance.

System/Unit twin: These are combination of assets that are required for operations of a finished system. It could be a part of vehicle, traffic control system or industrial manufacturing system.

Process twin: These include simulation of business process of an entire production facility. This type of simulation helps improve overall effectiveness and take business decisions in a data driven manner.

Even though there are several benefits of digital twin, its challenges remain intact. As the function of this technology is to test and build equipment in virtual world, data becomes a substantial element that structures this function, hence most of its challenges are around data.

Data provided by private companies to make digital twin operational, is limited or at times there is no real time data. In fact, some government organizations do not share their data due to privacy concerns; this limits the capabilities of digital twin.

Even if organizations overcome the issue of data availability, they need it in structured format and integrate the same in their platform.

As most of digital twin functions operate on next gen technologies such as AI, IoT, and edge computing, the initial infrastructure investment might be hard to manage for organizations on budget. Another challenge is identifying correct technology and deploying the same into its existing operations. Executives feel, with so many technologies in frame, it will be a costly affair, and might take time for initial investment/ROI to recover.

Digital twin is widely embraced for its precise virtual representation across several industries. Below are a few use cases:

  • Digital twin has a potential to be a valued asset for manufacturing. It has wide range of application; right from product design, process optimisation, operational maintenance, to supply chain management.
  • The potential for digital twin in retail has increased at an exponential rate during pandemic. Digital twin plays a key role in enhancing customer experience, instore planning, shipment management, and logistics.
  • In healthcare, digital twin along with data analytics and IoT plays a key role from offering personalised patient care to enhancing operational capacities.
  • Automobile sector benefits the most with testing and analysing overall vehicle performance.
  • In aerospace industry, digital twin is embraced for maintenance of aircraft parts, aircraft tracking and optimizing entire value chain and innovating new parts.

The Future…

The capability of digital twin is limitless, however the essential change in the existing model with digital twin will take some time. It shall be a vital part of change that will disrupt industries and its operations. With combination of next gen technologies especially IoT, AI, AR/VR, extended reality, and data analytics, organizations can improve operational capabilities, reduce cost, and asset downtime. The test and verify feature of digital twin will aid decision makers take the most precise decision.

Natasha Bhiwgade
Natasha Bhiwgade
Natasha Bhiwgade is a technology analyst since 2016. She earned a Bachelor’s degree in Electronics Engineering and Master’s degree in Marketing from Pune University, India. She is passionate about researching on emerging technologies especially artificial intelligence (AI) and internet of things (IoT). She believes “Technology is best when it makes Life Easy.”