Digital Twin: The Leading Technology of Smart Industry


Digital Twin The Leading Technology of Smart Industry

Among the technologies whose importance for enterprises is progressively rising in the era of cyber-manufacturing and smart logistics belong digital twin. Digital twin ranks among the essentials of Industry 4.0 along Internet of Things (IoT), Big Data and machine intelligence.

127 new devices are connected into IoT network every second. The threshold of 30 billion interconnected devices should be reached by the next year. This number should reach to the enormous 50 billion mark in 2023. The introduction of 5G network should only accelerate the pace of IoT swelling.

Exponential growth of new technologies disrupts significantly established forms and models across all the industries. Although digital transformation permeates all areas, manufacturing (industry) and logistics (transport) belong to the forefront of early adopters when it comes to new and emerging technology.

Among the main reasons belongs the power of new technology to maximize productivity, quality and variability of manufactured products and provided services along the added value the technology is capable of generating for enterprises.

 Internet of Things (IoT) 2015 and 2020 investments

Comparison of IoT investments 2015 and 2020 (source: Statista)


Among the technologies whose importance for enterprises is increasing in the era of cyber-manufacturing and smart logistics belong digital twin. According to the international research and advisory company Gartner, 75% of enterprises utilizing IoT solutions has managed to implement the technology of digital twin or plan to do so in the upcoming 12 months. Giving the importance digital twin is harnessing, Gartner decided to rank it among top 5 technological trends of the 2019.

Digital twin entered the essential inventory of the digital age industry and enterprises along Internet of things (IoT), big data and machine intelligence. The speed of adoption implies the breadth of utilization in terms of functionalities and added value the digital twin offers to an enterprise. Gartner points out that the increased pace of digital twin deployment stems from the ever-expanding possibilities that IoT solutions offer to enterprises.

Digital Twin as Concept

Nevertheless, the concept of (digital) twin precedes the era of IoT and Industry 4.0 as well as the digital age. Its roots can be traced back to the 70s when NASA was working on the Apollo project. An oxygen tank explosion damaged the maintenance module during the mission of Apollo 13 thus putting the lives of the crew into immediate danger. The team of engineers on the Earth had to find out a solution how to remedy the situation promptly to save the space crew and minimalize the negative impact of the accident.

Since they had exact replica of the space ship with all the technological details on the Earth, they were able to credibly simulate the situation and test possible solutions. The possibility to physically try out hypothetical proceeding saved the space ship crew. Furthermore, NASA continues to employ the concept of twin - not analogical anymore but a digital one - up to now.

The basic principle remains unchanged since the digital twin is a virtual model of a physical object that enables to monitor the state of the real object remotely as well as to model or simulate various potential scenarios with precise and real data. NASA calls the use of the digital twin a paradigmatic shift since conventional methods are no longer sufficient to meet the demanding requirements for projects of the new generation.

The same principle applies to manufacturing and logistics processes, traditional technologies and methods have become obsolete and inadequate to preserve sustainable growth as well as to comply with new challenges and requirements from optimization of operating costs to consumer´s product customization in mass production.

Digital Twin as Analytical Tool

The term digital twin entered the wider awareness in 2002 when Michael Grieves coined it in regard to product lifecycle management (PLM). He employed the concept of digital twin as a virtual representation of manufactured product and it should serve as a comparison of product to its engineering design. The definition of digital twin found life outside the framework of PLM.

Currently, the term of digital twin is mostly used to describe the virtual representation of physical and non-physical objects and entities, either manufacturing or transportation machinery, equipment and tools, but processes, systems, data, staff or the whole working environment as well.

Therefore, the digital twin does not serve solely as a virtual model of a real-life counterpart but as a dynamic agent of data and state information acquired through a large number of sensors and actuators connected through the Internet of things (IoT).

In this form, the digital twin is implemented to monitor physical objects and non-physical entities and processes in real space and real-time since the technology enables to create a highly detailed digital image equipped with real data. The deployment of digital twin in complex simulation models thus accelerates and facilitates decision-making because it renders identification of potential outcomes much easier along the identification of crucial behavioral patterns in chosen processes.

This form of implementation does not solely bring deeper knowledge about the causality of all the elements in processes and environments but also an ability to uncover weak or critical spots (such as bottlenecks) in the processes that necessitate stabilization and optimization in order to assure sustainable performance growth and fortification of environment robustness.

The concept of digital twin introduced by Michael Grieves continues to be utilized in manufacturing as a part of PLM. However, besides the product itself, a virtual copy can be made of manufacturing machinery and equipment and a production line or the whole enterprise or supply chain.

Beyond simulations of manufacturing process, the virtual copy of manufacturing machinery can be employed for predictive maintenance. Modern Smart Industry systems are already fitted with predictive maintenance modules along modules for operative, scheduled, correctional and preventive maintenance.

 Digitálne dvojča: Funkcie a pridaná hodnota

Survey: In which stages of the product life cycle do you see the digital twin offering greatest value? (source: Catapult Reboot Online/ZDNet)      


Thanks to historic data regarding the technical state of machinery and equipment and real time data such as the machinery´s performance or wear and tear that digital twins harness and possess, Smart Industry systems can adequately select optimal maintenance strategy of enterprise´s machines and equipment.

Such intelligent maintenance planning contributes to minimization of undesirable downtime in manufacturing or supply processes. Enterprises are already employing Smart Industry systems outfitted with machined learning technologies to analyze collected manufacturing data in order to detect causal patterns between the degree of wear and tear (identified for example via sound of machines) and equipment downtime rate.

Regarding logistics, the digital twin is largely employed as a virtual copy of material flows enabling enterprises to effectively manage and control the supply chain. Such model has real data available regarding not solely particular materials but also other relevant elements and factors (vehicles, weather or traffic conditions, customers´ demands etc.). Enterprises have access to constant information about location of their vehicles in cooperation with geographic information systems (GIS) which is frequently necessary for proper dispatching and transportation management (TMS systems).

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