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Green Physics AI metadata uses an asset's metadata and the mapped time period to provide an in-depth view of the asset's history, condition and usage history within production. It sits within the Lean. Green. Digital. strategy of the BMW iFACTORY project and aims to help monitor, analyse and optimise various industrial assets for maximum performance and efficiency.

SORDI.ai considers factors such as potential deformations, wear and tear and contamination that may have occurred during the specified time period and calculates them automatically. This results in an accurate simulation of an asset's condition over time, even providing predictions for its status in the coming years, and where optimisation and reduction of elements such as CO2 emissions exist. This technology also allows for parametric ageing on industrial assets, which also means that the assets are more realistic digital versions of their real life items, resulting in better accuracy for AI training and simulations.

Two animations and a series of rendered images were delivered by the Academy to TechOffice, to communicate the Green Physics AI and metadata functionality. Common BMW iFACTORY and logistics assets were used to show ageing, wear and defects, plus the metadata used.