How are Digital Twins used in retail?

Unlocking real world automation potential using virtual products

Digital twins like this enable automation of visual-based tasks like shelf auditing for the perfect store.

In retail, Digital Twins replicate real world Consumer Packaged Goods and Fast Moving Consumer Goods in a virtual environment. This provides computer software like Synthetic Computer Vision with the necessary visual data (Synthetic Data) to learn how to recognise those products in images and videos.

In traditional Computer Vision, the most widely used version of the technology, an algorithm is trained to detect real world objects using hundreds and thousands of real images of those objects such as images of a supermarket product. Sourcing and preparing this high-quality training data is extremely costly and time-consuming. Therefore, the process it is not feasible to adapt and scale traditional Computer Vision to the demands of most retailers. It is therefore unrealistic for most companies to consider its use for domain-specific applications such as retail.

Synthetic Computer Vision is not burdened by this unnecessary barrier to adoption i.e. access to the necessary training data. This is because Synthetic Computer Vision does not rely on real data to train its algorithms. Instead, Synthetic Computer Vision is powered by Synthetic Data, a virtual recreation of the real world data that is used to train Computer Vision models to detect real world objects.

For real world object detection in retail, Synthetic Data encompasses rendered images and videos of a 3D, digital twin of a real world Stock-Keeping unit including the virtual supermarket scenes that it is placed in. This data represents the attributes of the product as well as possible retail environments in which it may be found in real life. It is used to train Synthetic Computer Vision models to detect that real world product for the purposes of automating in-store processes such as Shelf Auditing and Shelf Monitoring.

A digital twin of a supermarket fridge and its products that has been used to train a Computer Vision model to detect those products in the real world for real-time Shelf Auditing.

Written by Luke Hallinan, Product Marketing Manager at Neurolabs.

Retailers worldwide lose a mind-blowing $634 Billion annually due to the cost of poor inventory management with 5% of all sales lost due to Out-Of-Stocks alone. 🤯

Neurolabs helps optimise in-store retail execution for supermarkets and CPG brands using a powerful combination of Computer Vision and Synthetic Data, called Synthetic Computer Vision, improving customer experience and increasing revenue. 🤖 🛒