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The economics of the Digital Transformation – IX

Cognitive Digital Twins include the capability of “making sense” of data, transforming them into “knowledge”. That is done through computation using Machine Learning/AI approaches. Some can be part of the CDT itself, some computation may occur outside of the CDT resulting in additional “meta-data”. When applied to a physical entity like a machine the CDT act as a minimal “brain”.

Cognitive Digital Twins as Intelligent Behaviour

As presented, Cognitive Digital Twins were invented for the factory floor to bring processes in the cyberspace. However, the very word chosen, “cognitive”, immediately link to the peculiar capability of humans: “cognition”. This is defined as:

the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.

As mentioned in 4.5.2, Digital Twins at stage 4 become autonomous entities able to act as their physical twin although independently of it.

The value of Cognitive Digital Twins in the context of manufacturing is that they:

  • provide an effective way of monitoring the ongoing physical processes (leveraging on data created by IoT in robots, video cameras along the manufacturing plant –warehouse, assembly line, delivery pods-, digital documents exchanged through the supply and delivery chain…);
  • enable quick analyses of divergences and can be used by applications to determine the cause;
  • enable simulation and what-if analyses to cope with new situations and assess their effectiveness or to fine tune for improved efficiency;
  • can automate parts of the processes as the digital transformation shift some physical activities to the cyberspace.

This latter aspect is significant since the adoption of Cognitive Digital Twins paves the way to harvest benefits from the digital transformation in a seamless way. Besides, it moves Cognitive Digital Twins from stage 3 (interaction with the processes) into stage 4 (taking over the execution of –part of- the process).

As one enters into stage 4 capabilities, new horizons open up.

An autonomous Cognitive Digital Twin in stage 4 is characterised by an intelligent behaviour (where intelligent might be considered a marketing word but in reality it means the acquisition of sufficient context, goal and resources awareness to operate in that environment). Notice that the point is for the Cognitive Digital Twin to become an autonomous system not a cog in a predefined process. By being autonomous new behaviours will be displayed as the landscape changes without the need to redefine its behaviour from outside.Tesla

At stage 4 new values can be leveraged. As an example, a Cognitive Digital Twin of a robot can learn, as the (smart) robot does, from its operation and it can also learn from the experience of other robots involved in similar activities. Take the Tesla cars. Each car is learning from its own experience and in addition every day each car communicates to the Tesla Operation centres (that has accumulated as of 2020 over 10 billion miles of data). This data swarm gathering is processed by machine learning algorithms and turned into new knowledge that is actualised into specific changes that are broadcasted on a daily base (if appropriate) to all cars.

More that that. A company through Cognitive Digital Twins accumulate knowledge (as it is the case of Tesla) and can re-apply this knowledge to other products, increasing their performance as well as to the design of new products decreasing the effort required.

This is an interesting twist in the application of Cognitive Digital Twins because it moves values into executable knowledge and it can be expected during this decade to see companies first adopting Cognitive Digital Twins to increase their process effectiveness and then starting to leverage their economic value to sell executable knowledge as a service. .

Notice the slight shift in application of Cognitive Digital Twins from mirroring a process to mirroring a physical entity behaviour (a robot, a car) translating that behaviour tracking into knowledge. One can also say that the knowledge of the physical entity (like the intelligence of the robot) is mirrored in the Cognitive Digital Twin that in turns can expand that knowledge through autonomous interactions.

This shift takes the Cognitive part of the Digital Twin very close to the definition of Cognition:

acquiring knowledge and understanding through thought, experience, and the senses

where senses translate into shadowing, experience into threads and knowledge into model. The “understanding” part is tricky because it goes deeper into what awareness is. In the case of –current- Cognitive Digital Twins the focus is on knowledge.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee and co-chairs the Digital Reality Initiative. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.