The combination of IoT and machine learning growing at the same time is leading to a rise in the use of digital twins in the supply chain, as a digital replica that can be used for various purposes. The connection with the physical model and the corresponding virtual model is established by generating real time data using sensors.
The digital twin in the supply chain allows a comparison between current and historical data on performance, wherever a sensor is located. It could be a component such as a thermostat, an asset such as a truck or a machine, an employee such as a service technician, or a process, as in manufacturing.
Part of the capability of the digital twin is driven by this complexity of having models of models to describe complex assets, processes, and systems.
In the supply chain, the digital twin model can encompass items packed in containers, moving through the physical world to distributors and customers. The model could inherit data from the process that created the product at one end of the chain, and inform a customer model at the other end.
Supply chains and manufacturing assets are just the beginning. As this technology becomes better understood, and deployments become easier, use will grow into increasing complex spaces. There is already development of digital twins in life sciences in support of systems biology modeling complex organs like the human heart.
See the articles below regarding how they are being applied in healthcare and clinical trials.
See “Virtual Twins: Their Roles in Healthcare, Drug Discovery and Pandemic Response,” in BioITWorld
https://www.bio-itworld.com/news/2020/07/09/virtual-twins-their-roles-in-healthcare-drug-discovery-and-pandemic-response
And
“The Role of Virtual Twins in Clinical Trials”
https://www.clinicalresearchnewsonline.com/news/2020/07/13/the-role-of-virtual-twins-in-clinical-trials
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