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Cognitive Digital Twins: bridging minds and machine – IV: Managing Knowledge

A Cognitive Digital Twin for a company may turn oout to be a quite complex entity in terms of self-standing entity or as a network of CDTs. In the graphic a representation showing the CDT components (each one but the company’s CDT is actually a set of instances. The ones connected by green lines can be components of the company’s CDT or -more likely- can be seen as a network interacting with the company’s CDT. Notice that some of them, like the suppliers’ CDT differ from the actual suppliers CDT in that they represent the knowledge the company has of the suppliers. The exchange of data / knowledge from the actual supplier’s CDT can be based on a smart contract. Also notice that the company’s CDT of an employee differs from that employee’s CDT. The two can exchange data using APIs.

In the last post I discussed the application of Digital Twins to people, modelling their physiological characteristics, with obvious application in Healthcare and in the previous one in this series the use of Digital Twins to model knowledge of machines, Cognitive Digital Twins -CDTs. It is time now to consider the management of the knowledge through CDTs and to apply CDTs to people, to mirror the knowledge of a person.

Knowledge management pre-dates the idea of using CDTs. Companies, notably the HR area, have records of their employees describing their knowledge/experiences as collection of information on their education, the courses they took, projects they have been involved (and roles). They also have well documented records of the processes being used in the company to execute the various activities, from procurement to sales. Most companies have records of their preferred providers (some are certifying their providers, meaning basically that they assess the skills, type of products that can be acquired, their characteristics, the capability to deliver and so on).

All of the above is about tracking knowledge. Part of this tracking is supported by tools and is formalised, other parts consist just of data that can be accessed by the company (employees) to take decision (whom to allocate to a specific project, whom to ask for procurement…). This is represented in the graphic with the claim that all this knowledge could be formalised into CDTs and these CDTs can be used to take decisions.

An important aspect of knowledge management, from a company’s standpoint, is understanding what is missing, what is becoming obsolete and what actions can be taken to fill the knowledge gap.

This is one of the motivation to adopt CDTs (more futuristics ones will be addressed in following posts). Suppose a company needs to launch a new project to deliver a new product / service on the market. Given the rapid pace of evolution it is most likely that the knowledge space required for the project cannot be filled with the knowledge owned by the company (the knowledge space of the company is represented in the graphic, so it does include, as example, the knowledge of start ups having the advanced technology that would make a difference in the production of the new product). Hence, the first step is to assess what kind of knowledge is available  (among the one useful for the project). This can be assessed using AI tools and comparing it with the knowledge formalised in the CDTs.

Once the gap(s) is identified the following question is how to fill the gap(s). From a company standpoint it is a matter of economics (cost vs effectiveness) within a range of constraints. As an example a company may

  • change a supplier,
  • hire a consultant,
  • re-train the existing workforce,
  • seek for extending its labour force bringing in the right knowledge,
  • acquire “artificial” knowledge, i.e. the one provided by machines,

Once CDTs are adopted in the context of the enterprise KM we can turn the above list into:

  • Acquire new CDTs
  • Expand existing CDTs

As it can be seen CDTs are virtualising knowledge by moving it to the cyberspace (digital transformation of knowledge) and it is irrelevant the form of the “physical” container of knowledge. This is great, but at the same time it opens up difficult issues.

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 New Initiative Committee and co-chairs the Digital Reality Initiative. He is a member of the IEEE in 2050 Ad Hoc Committee. 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.