Technology

Digital Twin: How Intelligent Data Models Can Help Shape the Future For Enterprises

Ever since the emergence of the COVID-19 pandemic, every enterprise and industry are widely adopting digital transformation strategies. Even manufacturing sectors and their operations are becoming increasingly digital or virtual. As this new trend unfolds, several organizations frequently strive to choose what they should invest in to drive and deliver substantial ROI both operationally and strategically. Indeed, their selection of technology entirely depends on the services they provide or how they develop those services. For them, digital technology may ensure considerable advantages—advantages that would never have been accomplished earlier without the advent of virtual, smart technologies. Of particular attraction of late seems to be the concept of a Digital Twin: often referred to as a precise real-time virtual representation of a physical thing in the real world.

Until lately, the digital twin and the extensive quantities of big data it analyzes often prevailed intangible to businesses because of constraints in traditional digital technology. However, with the increasing potential capabilities of cloud storage, edge computing, 5G networks, and many other technologies, the digital twin has seen exponential adoption. Comparatively lower processing expenses and advanced power capabilities have commenced a significant change that empowers the combination of (IT) and operations technology (OT) to enhance the digital twin.

Why Is The Digital Twin Important For Enterprises?

The digital twin can enable businesses to develop a comprehensive digital footprint of their products, machinery, devices, etc- anything in the real world. They can streamline the entire product from design, development and deployment through the end of the complete product life cycle. These deep insights and analytics regarding the product will enable them to experience not only the product in a virtual simulation but can help them understand the pros and cons of the product in a real-world environment.

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With the implementation of a digital twin, organizations can significantly improve the time to market of a new product, improve business operations, reduce employee stress and defects. The digital twin is capable of preventing physical errors faster by identifying them promptly in a virtual environment. The integration of advanced technologies such as Artificial Intelligence (AI) and Natural Language Processing (NLP) with digital twins helps in predicting results to a considerably higher level of accuracy. This eventually helps the manufacturing sectors in designing and building more reliable products, and, ultimately, prevents calling back products.

With this innovation in creating smart architecture design, manufacturers will realize quality and advantages faster than with traditional product manufacturing. But before anything else, businesses must understand how the digital twin technology works in order to efficiently integrate it into the operational process.

How Does A Digital Twin Technology Work?

Digital Twin is a complicated virtual system that generates huge amounts of data. All of these insights acquired from the real world are descriptive and informative in nature. That is, the data precisely and accurately indicate what occurred with the device and when it happened. By leveraging data analytics, experts can utilize this insight to be predictive and determine whether something will happen like a failure, in the future. In general, a digital twin which is a virtual reproduction of the system, product, or process will enable developers to enhance the solution, predict real-world use cases and avoid failure. Digital Twin technology integrates AI services, software or data analytics, and machine learning (ML) algorithms to design virtual simulation models that update and adapt as their physical counterparts develop. These precise real-time insights and constant informative data from various sources in the digital twin represent the status, operating condition, or capability of the physical asset.

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Digital twin includes sensors that gather big data to represent real-time data of the physical product. The sensor data is accumulated, examined, and applied in predictive analytics by the digital twin to understand the product’s performance, hence developers can streamline maintenance along with it. Digital twin technology is not only capable of learning from the current inputs but can understand previous historic data which can help them make predictions for the future. For this, various data sources are leveraged to contribute towards the digital twins’ learning curve; it makes the system more precise and accurate as more input is provided. The historical data can be accessed from previous machine usage, processes, or system usage, human inputs, algorithm changes, or any other sources.

Enterprise Benefits of Leveraging Digital Twin

Eliminates Confusion:

Digital Twin technology totally eradicates the guesswork from ascertaining the most beneficial course of response to maintain important physical assets. Therefore, this technology is significant for product manufacturers and organizations with diverse physical assets. By focussing on connecting traditional operational resources with IT administered technologies, these organizations will have straightforward access to the unparalleled combination of deep manual knowledge and intelligence about their physical assets. This will help them not only create new products at a faster ratio but will also help them develop new services by updating existing products.

Improve Consumer Experience:

Consumers play a pivotal function in determining the future plans and resolutions in any enterprise. Improving your customer’s experience and getting their input for developing a new strategy is crucial in the long run. It helps retain and explore new customer bases which would have been quite impossible with the traditional practices. By promptly creating a digital twin of the customer-facing products, businesses can get relevant feedback from existing customers. Therefore, without having to sell the real physical object, enterprises can make relevant decisions on customer feedback and then offer an improved product to customers.

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Performance Tuning:

Unlike a real-time physical product, a virtual asset can undergo multiple digital tests and online stimulations. Test automation service providers can virtually test the digital twin under multiple circumstances by leveraging traffic generators, testing labs, and real-time simulators. A digital twin helps the developers to discover the precise set of procedures that can enhance some of the fundamental performance metrics and further present estimates for long-term preparation.

Conclusion

Combined with the innumerable benefits of AI services, digital twins can turn physical assets into virtual reproductions and predict the best outcome. Manufacturing and product engineering companies can address several relevant performance and productivity metrics to further enhance their product. Overall, a digital twin will contribute to driving value and quality in applications and that can fundamentally transform how a company does business.

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