Digital Twin: Smart Industry and Intelligent Enterprise


Digital Twin Smart Industry and Intelligent Enterprise

The technology of digital twin wields such importance because of its dual functionality - as an analytical and controlling tool. Digital twins in combination with the Internet of Things (IoT) and machine intelligence can transform regular objects into intelligent things.

Besides what can be regarded as a passive function of the digital twin thus as a tool that does not interfere directly into the manufacturing and transportation (supply) processes but only interprets them, a new form of digital twin gradually emerged equipped with actual managing and controlling functionality.

It is thus this form of “active” digital twin that constitutes the crucial qualification for properly and effectively operating cyber-physical production systems and therefore automatized operations management in manufacturing and logistics.

Considering operations the digital twin executes, it can be also labelled an autonomous information agent. The term agent refers to an unit capable of carrying out a given task. The attribute of autonomous defines the ability of an agent to act individually and unsupervised thus it has a full control over its behavior.

In the most basic form, the agent possesses a reactive ability meaning it is capable of triggering a reaction to impetuses from its environment. The example of a simple agent is a thermostat which reacts to temperature fluctuations autonomously according to a previously defined rule.

Active digital twin is basically a software that is carrying out a certain task on behalf of an object or under user assignment. Such form of digital twin is called intelligent information agent since it is already equipped with a certain form of artificial intelligence (AI).

In regard to changes in the environment and to defined tasks, such agent is capable of taking initiative and autonomously influence its environment and objects contained within it as well as to autonomously decide about the consecution of operations in regard to assuring the most effective result possible via the optimal form.

It is the active digital twin that constitutes one of the essential components of modern Smart Industry systems as well as the whole digital ecosystem of an enterprise. In such complex infrastructures as in factories, the technology of digital twin has to be equipped with additional abilities such as adaptability, scalability, agility and several cognitive competencies as well.

Cognitive technologies permit to digital twin to react to completely new and unfamiliar impetuses and that is largely thanks to previous experiences and results of preceding decisions and their following re-evaluation. Simultaneously, intelligent information agents (digital twins) need to have a predisposition for interaction in a collective of other agents. Digital twins have to be able to communicate and collaborate in a group order or in a certain hierarchy.

  Digital twin as a strategic technology for 2019

Digital Twin as a strategic technology for 2019 (source: Gartner)


Smart Industry and Intelligent Enterprise

Manufacturing enterprises as well as logistics flows represent dynamic and non-deterministic environment, thus such an environment where each action does not have a guaranteed definitive outcome. Taking into consideration the frequent occurrence of exceptional situations caused mostly by external factors (for example unexpected or erroneous human interference), a necessity to implement more robust and complex management systems emerges.

Given the complicated nature of such manufacturing and logistics environments with a lot of simultaneously on-going processes of which majority is interconnected with each other to a certain degree (or conditioned one by another) and the enormous volume of data that is being generated during these operations, it is impossible to service those processes with existing management and control systems based on a technology of simple evidence or transaction procession. As intelligent information agents form a network of decentralized and distributed (collective) intelligence, Smart Industry systems come into the forefront as intelligent operations management system in enterprises.

Decentralization and distribution of operations management is equally significant in terms of intralogistics of manufacturing enterprises (manufacturing logistics). The management of a fleet of vehicles/equipment servicing production lines becomes incredibly complex when the variability and product customization in mass and serial production increases. It is not any longer in human power to successfully manage the whole process achieving the highest efficiency possible.

One of the reasons is the fact that the sheer volume of input data entering the decision-making regarding proper and on-time material supply exceeds human cognitive faculties. Progressive deployment of robots and automated guided vehicles (AGVs) leads to growing demands for intelligence of Smart Industry management and control systems.

Consequently, that demands the implementation of the technology of multi-agent systems (MAS) which connect individual autonomous agents (digital twins) in order so that they can seamlessly interact (communicate with each other, coordinate each other and collaborate) and ensure to fulfill the defined mutual goals and tasks.

Decentralized multi-agent systems enable to attribute different roles to different agents in order to pursue variable tasks and thus establish complex systems with better response to outer and inner factors and conditions.

As the digital transformation will progress further in manufacturing and logistics, Smart Industry systems equipped with multi-agent systems will be more often implemented into the processes of production line supply where numerous fleets of AGVs will assure on-time and correct material feeding for robots in the manufacturing flow.

Compared to current systems, they won´t be anymore based on centralized management but will be capable of collective auto-configuration. The information ecosystem of a factory will grow to become an organic structure within which the current process automation will level up to process autonomization.

Digital Twin in the Mainstream

The importance of the technology of digital twin as his significance stems from its simultaneous double benefit as a simulation and controlling tool. Creating virtual replicas of manufacturing flows, the whole factories, enterprises or supply-chains via passive model of digital twin enables enterprises to monitor, simulate and test established as well as hypothetical processes.

Employees thus can identify bottlenecks, weak or critical spots or inefficient operations in company´s manufacturing and logistics, detect various anomalies or receive precautionary alerts to potentially critical situations and thus prevent their occurrence (also in the form of predictive maintenance for example) and thus reduce operational costs.

Simulation of manufacturing flows harnesses real historic data to model different options of for example assembling operations and based on the outcomes, managers or supervisors can choose to deploy into operation the process which produces products with the highest quality and therefore minimizes unplanned expenses for additional rectification and quality inspection which does not subsequently inflate the production lead time. Digital twin already generates added value for enterprises in this (passive) form.

The active profile of digital twin, i.e. intelligent information agents, has a crucial role in the intelligent operations management. Digital twin combined with the Internet of things (IoT) can transform regular objects, those that do not have original predisposition to be “smart”, into intelligent things (AIoT – Artificial Intelligence of Things).

Besides manufacturing and transportation machinery, equipment and tools, or materials and products, this ability applies to employees as well. The combination of digital twin and IoT extends employees´ cognitive capabilities. It is not possible for warehouse workers to know the precise succession of orders and the volume of tasks, especially if orders are being generated on on-going basis in real time. However, Smart Industry systems dynamically prioritize the workflow and allocate tasks to workers via intelligent information agents based on the relevant data and customer´s current demands.

Digital twin forms the primary prerequisite for Smart Industry systems assuring operative and autonomous manufacturing and supply process management. Factories and enterprises can upgrade their internal logistics to a more agile form, Intralogistics 4.0, implementing active form of digital twins and the Internet of things (IoT) which are usually already integrated into Smart Industry systems.

   The expansion of digital twin 2016-2025

The expansion of digital twin between 2016-2025 by region in USD billions (source: MarketsandMarkets)


Smart Industry system provides the necessary information infrastructure for correct operations management by both technologies. These technologies make automation and further autonomization of processes possible and such Smart Industry solutions also boost agility of factory and enterprises´ operations. Enterprises´ processes are thus becoming much more flexible and accommodating to outer factors (customer demands, new waves, markets etc.) with possibilities to augment the variability and customization of the final product or service.

Furthermore, the technology of digital twin teems with versatility utilizable not solely for dynamic, intelligent and autonomous operations management in factories and smart industry. Its functionality is currently permeating healthcare, engineering, retail, maintenance and last but not least, digital transformation of cities (the notion of Smart City).

The proclivity for universal deployment along scaling up the functionalities and generating added value contributed to the fact that the actual volume of digital twin implementation topped Gartner´s previous forecast. The international research and advisory company had already revised its own projections and is talking about mainstreaminization of digital twin.

The pivotal role of digital twin results from its organic (inter)connection to other new and emerging technologies such as Internet of Things (IoT) and Internet of services (IoS), artificial intelligence (AI), machine learning (ML), augmented analytics and big data processing.

The capability of seamless merging with other technologies augments the current unique position of digital twin despite the five-decade existence of the concept. The ample functionality, versatile application and final outcomes it yields to enterprises rank digital twin among the most influential disruptive technologies of present and near future.

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