Digital Twins

Digital twins as a strategic enabler for sustainability 

In the late 1980s, the concept of a Digital Twin was created as a tool to aid design in the aerospace and automotive industries. With examples such as smart cities, it was later applied in various industrial areas and is today a key facilitator for driving innovation and services in the public sector. In the coming years, digital twin use is likely to skyrocket. Markets and Markets estimates that the market will increase from 3.1 billion dollars in 2020 to 48.2 billion dollars in 2026, according to their research.

Digital Twins are gaining recognition and popularity as a solution for digitally representing a product, a building or even a human being. This is enabling new opportunities to improve performance, operations, productivity, or quality of life. 

A Digital Twin strategy to drive sustainability programs can be profoundly effective. Digital Twins offer significant benefits when planning, implementing, and realizing: 

  • Reduced energy consumption 
  • Reduced material consumption and the switch to more sustainable materials 
  • Workforce activity optimization – travel, server usage, etc. 
  • Automation and robotization of hazardous and/ or repetitive tasks to improve working conditions and health.

In 2021 seven Addnode Group companies have worked together to develop the group’s first Digital Twin report, showcasing some of the group’s competence in the area. For each case described in the report, a film has also been created. 

Addnode Group Digital Twins report 2022

The Addnode Group companies’ that have created the report


Addnode Group’s definition of a Digital Twin
A digital representation of an asset, system, product or creature realized through a system with an objective to simplify management of its lifecycle and operation. Digital twins can be connected and added to each other to manage highly complex scenarios. The digital twin representation can be augmented with additional technologies such as simulation, optimization, and machine learning to realize additional benefits.