The role of digital twin

In 2017, digital twin made it into Gartner’s Top 10 Strategic Technology Trends. The Gartner definition has evolved over time but, at the time of publication, the Gartner Glossary defines in as “an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction.”

Digital twin technologies may be introduced for a broad range of reasons, from accelerating the start-up process to enhancing operational efficiency or improving operator training. However, all of these could be reduced to the fundamental principle of risk management. In the real world, complex manufacturing processes are subject to, and responsible for, a broad spectrum of risk factors – financial, physical, operational and more. By abstracting the physical to the virtual, much of this risk can be reduced, if not eliminated.

Once a virtual model of the object or process has been created, the ability to abstract real-time and historical operational data from the physical and into the virtual allows for almost limitless monitoring, scenario modelling and planning.

A digital twin facilitates the most essential of business activities – informed decision making. It allows businesses to answer
the questions to “what if” scenarios without having to physically
test. It also provides a single pane of glass view of end-to-end processes, enabling real-time optimization.

To realise the true potential of the technology, it is important to recognize that a digital twin is not the representation of a moment in time; it is a connected, homogenized entity that is constantly evolving and learning.

3 key components
of digital twins

Digital twin technology continues to evolve and, as industry becomes increasingly digitalized, will create new benefits. Currently, the technology is used extensively to enhance asset reliability.
Three ways it is helping today include:

New product development
Analysis of historic data aids the identification of areas for improvement, including new product innovation and customer satisfaction.

Process improvement
Analysis of real time data enables operators to optimise production line output and efficiency under a wide range of conditions.

Predictive maintenance
Comparing real time and historical data, digital twins can recognise conditions that may result in failure and make remote, preventative adjustments.