After the previous posts discussion on the evolution of digital twins, from -basically- an academic point of view with a separation into stages and types (DT, PDT, CDT, OPDT, Hybrid…) and before looking at the further expected evolution it makes sense to take a look at how, today in 2022, digital twins are used in various sectors, a sort of reality-check. Let’s start with Manufacturing.
Manufacturing has been the first area to put the concept of Digital Twin at work and they are now an integral part of manufacturing processes in many companies. For sure they have become one of the pillars of Industry 4.0.
Manufacturing is based on tools and processes, orchestrating the use of tools and resources. Digital Twins are derived from tools (CAD) and used in tools (CAM). They have become tools in themselves supporting simulation and monitoring.
“Orchestration” is made through processes and through tools supporting them. In the case of Digital Twins the orchestration is achieved and supported through platforms throughout the whole PLM. The major manufacturing tools providers have created their own platform, like Siemens Mindsphere.
Most DTs used in manufacturing are at stage 3, i.e. the DT interacts with its physical entity only for the sake of remaining in synch with it. It can also act as a gateway for other applications (like analytics, simulation) to interact with the physical entity. An anomaly, as detected by data analytics provided by the physical entity via its associated DT, can be processed by an external application resulting in a command that will be handed over to the physical entity through the DT.
The same applies to the DTs associated to most products. They are created during the manufacturing process and remains in the ownership of the manufacturer to connect with the physical product throughout its life time.
A few of these DTs are starting to embed “intelligence” to perform data analyses and to assist the physical entity. In a way that is a tiny steps towards becoming autonomous. Some are also connecting to the cyberspace to get -autonomously- other data that can be used internally. Self driving cars are a clear users for this kind of evolution (getting a better grasp of the context by communicating -autonomously- with other DTs).
Mevea is possibly one of the most advanced user of DTs in the industry since they are basing their business model and competitive advantage of the adoption of DTs throughout the life cycle and are using the DT of their products to deliver services. They use the shadowing to get insight on the use of the products. They compare shadowing of several DTs in a given product line to improve all of them based on experiences derived from each of them. Their DTs are in many cases approaching stage 4 since some of the product functionality is actually being delivered through the DT.
General Electric is another company (one of the first -matter of fact) that is heavily rely on Digital Twins to monitor the use of their product and to provide proactive maintenance services (placing their DT somewhere between stage 3 and 4).
The Competence Industry Manufacturing 4.0, located in Turin in the Turin Polytechnic Campus and clustering many companies in the manufacturing area, is developing a digital twin infrastructure that can be used by their associated company to create a virtual lab, consisting of both physical and virtual objects that can be inspected and assembled in a hybrid mode (virtual +physical). Here DT are present at all stages 1 to 5.