A digital twin is a digital replica of a physical asset, device, system, place, or person.

In manufacturing, digital twins are used to represent “real-world” equipment, machines, factories, and plants.

Sensors placed upon machinery across an entire production line, for example, feed data through an IIoT network to be accessed via a dashboard. The data is displayed as a visual model – the twin – and acts as a live representation of that line.

This offers the ability to drill down into specific assets for improved root cause analysis and a host of new use cases for improved manufacturing operations.

Digital twins are helping companies surpass previous performance levels in various aspects of manufacturing, from the product design phase to supply chain management.

Here’s how:

1. Monitoring

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

Asset and production line performance data is presented in the context of the physical device, usually with a 3D visualization of the part/machine/line.

This enhanced visual interaction improves the ability to formulate actionable insights based upon the presented data.

By combining next-level monitoring capabilities offered by digital twin with machine learning algorithms, manufacturers are able to perform automated root cause analysis to prevent recurring asset failures and quality deviations.

The Seebo Predictive Maintenance Software Platform
The Seebo digital twin dashboard. Predictive maintenance and quality alerts are provided in the context of the production process.

2. Maintenance

The improved monitoring capabilities mentioned above feed directly into maintenance efficiency.

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

A digital twin isn’t only a graphical model. Predictive analytics powered by machine learning and other AI algorithms dissect the data, search for correlations, and formulate predictions about remaining useful life (RUL).

Digital twin solutions take maintenance from a reactive to a predictive approach. Repairs are only performed when needed and components aren’t switched out unnecessarily.

Since maintenance is planned and only done when necessary, technical teams need not be so large and arrive on-site with the right tools and parts, as well as precise instructions for the repair procedure.

3. Training

Digital twin is an excellent tool for professional training due to its visual interface and the fact that it mirrors real-life scenarios from the production floor.

Digital twin can be used for broad-topic training such as site orientation and safety protocols or specific technical training such as repair and installation procedures.

4. Communication

A digital twin can play a vital part in helping employees share knowledge about production issues.

Automatic alerts about predicted failures or quality deviations can be viewed by all the relevant personnel. Tips and advice can be shared, including highly specific technical details thanks to the visual nature of the twin.

This information is archived and accessible to personnel in other facilities, streamlining root cause analysis and preventing the unnecessary repetition of mistakes.

5. Strategy

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

Major changes can be made to the operation without going into downtime.

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

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


How to find the right digital twin solution for your operation

Manufacturers looking to deploy a digital twin should evaluate candidate solutions against the following key capabilities:

Simple visual interface – The digital twin platform should be clear and intuitive, easy to navigate, and simple to interact with for personnel of all backgrounds: process engineers, maintenance and quality teams, plant managers etc.

Detailed and process-based – The twin should be capable of displaying a wide range of data from individual components to production lines consisting of multiple machines as well as data on HVAC, inventory, and supply chain systems.

Big Data Processing – The twin, along with the industrial IoT network set up in the facility, must be able to handle large volumes of data simultaneously for real-time processing.

Analytical – The digital twin solution should offer a variety of AI algorithms that are easy to apply and that output actionable insights that are relevant to the needs of your operation.

Customizable – Every manufacturing facility is different. Dashboards, alerts, and other system settings should be configurable to suit the nature of the work at your plant or factory.

Download our free digital twin whitepaper, or get in touch for a 1-on-1 demo to see how a digital twin can significantly improve your operation’s uptime and output quality.