Digital Twin is said to be a virtual representation that matches the physical attributes of a “real world” factory, product or component in real time, production line through the use of sensors, cameras, and other data collection techniques.

It offers the ability to drill down into specific assets for improved root cause analysis and a host of new use cases for improved manufacturing. Digital twins are helping different companies surpass previous performance levels in various aspects of manufacturing, from the design of the product phase to supply chain management.

In other words, Digital Twin is a live model that is being used to drive business outcomes, and can be implemented by manufacturing companies for multiple purposes:

  • Digital Twin of an entire facility
  • Digital Twin of a production line process within a facility
  • Digital Twin of a specific asset within a production line

Now, as the emergence of the Industrial Internet of Things (IIoT) is being seen, simulations are expanding into operations. The IIoT enables the engineers to communicate with sensors and actuators on an operating product to capture data and monitor operating parameters. It results a digital twin of the physical product or the process that is responsible to monitor real-time prescriptive analytics and test predictive maintenance to optimize asset performance. The digital twin is also responsible to provide data which can be used to improve the physical product design throughout the product lifecycle.

There are certain ways of predicting and resolving the manufacturer.


A digital twin merges live all data from its physical counterpart with an interactive visual interface. It offers an unsurpassed level of monitoring.

Digital twins offered next level monitoring with machine learning algorithms, manufacturers have the ability to perform automated root cause analysis which prevent and quality deviations and recurring asset failures.


Technicians and Operators are provided with the detailed information about every asset aspect of health. It leads to insight that can be acted upon directly to improve OEE.

A digital twin is not just a graphical model. It is a Predictive analytics powered by machine learning and other AI algorithms dissect the data which search out for correlations, and formulate predictions about remaining useful life (RUL).

Training :

Digital twin solution is an excellent tool for professional training because of its visual interface and the fact that it mirrors real-life scenarios from the production floor. It can be used for broad-topic training such as safety protocols, site orientation or such specific technical training such as repair and installation procedures.

Communication :

A digital twin plays a very vital part in helping employees to share knowledge about production issues. Automatic alerts about quality deviations and predicted failures can be viewed by all the relevant personnel..

Strategy :

A digital twin solution is useful in testing new concepts for optimization without needing to disrupt production.

Major changes are being made to the operation without going into downtime.

A digital twin can also provide insight across all stages of the product life cycle by:

  • Refining assumptions by the use of predictive analytics.
  • Establishing a digital thread which connects individual systems just to improve traceability.
  • Visualizing the usage of products in the field, in real time.

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