The Power of 3D Twins in Retail

A 3D Model of a Doritos Packet

In today’s competitive retail environment, staying ahead means embracing new technologies that streamline operations and improve efficiency. 

Neurolabs is leading the charge with our innovative approach to Digital Twins and Synthetic Image Recognition. These technologies optimise how CPGs manage their products, offering unprecedented insights and automation in tasks like shelf auditing and planogram compliance.

But why are they so crucial for modern consumer goods? Let’s dive in.

What Are Digital Twins and Synthetic IR?

A. Synthetic IR

Traditional IR relies heavily on real-world images to train algorithms for tasks like object detection. However, this process is costly, time-consuming, and challenging to scale, especially for retail applications. Enter Synthetic IR, which uses Synthetic Data—virtual recreations of the real world—to train algorithms.

With synthetic data, retailers don’t need thousands of real images. Instead, algorithms are trained using rendered images of virtual products and environments, making the process more efficient and scalable. 

This method is particularly beneficial for improving object detection in challenging conditions, such as when products are deformed or displayed in varied retail environments.

B. Digital Twins

A Digital Twin is a virtual replica of a real-world object. These digital replicas are used to create virtual retail environments where synthetic data can be generated for training purposes.

Digital Twins play a crucial role in retail by enabling simulations of how products appear in different scenarios. This virtual representation can be used to train algorithms for tasks like product recognition, shelf monitoring, and auditing. 

By replicating the real world, retailers can run more efficient, scalable operations without needing constant manual data collection.

Why Use Digital Twins?

Overcoming Traditional Challenges

Traditional Image Recognition (IR) systems often struggle with recognising products in retail environments due to challenges like inconsistent lighting, product deformations, or partial obstructions. 

These systems require large amounts of real-world data, which can be expensive and time-consuming to gather. Moreover, traditional IR often fails when faced with new products or product packaging variations.

Using Digital Twins mitigates these issues by creating a virtual dataset of multiple diverse scenarios—ensuring that models can learn to recognise products even under challenging conditions, like distorted packaging or complex environments. This eliminates the need for extensive manual data collection and makes Synthetic Computer Vision a powerful solution for solving traditional IR challenges.

Enhancing Retail Operations

Using Digital Twins also empowers CPGs to automate essential tasks such as shelf auditing and shelf monitoring. These processes are critical to ensuring products are always in stock, correctly displayed, and easy for customers to find. 

By automating these tasks through Synthetic Computer Vision, retailers can significantly reduce costs, improve operational efficiency, and provide a seamless customer experience.

In addition, this technology reduces human error in auditing processes. It enables faster, more accurate insights into product availability and presentation—allowing retailers to act swiftly to correct inventory and display issues.

How to Create Digital Twins

Creating Digital Twins is simple and easy with Neurolabs. With Neurolabs’ enterprise platform ZIA, and the ZIA Capture App, retailers can generate high-quality digital twins of products by using images or artwork files. The process involves:

1. Artwork Approach: Existing product artwork (such as PDF or PNG files) can be used to create 3D models of products.

2. ZIA Capture: Neurolabs’ ZIA Capture app allows users to capture images and quickly create digital twins of new products. These digital twins are then integrated into a retailer’s system for use in Synthetic Computer Vision.

This seamless approach ensures that products are always accurately represented, even when new items are added to the catalogue.

Conclusion,

The combination of Digital Twins and Synthetic Computer Vision offers an optimal solution for retail. By addressing the limitations of traditional image recognition and enabling the automation of key processes like shelf auditing, these technologies are paving the way for more efficient and scalable retail operations.

Looking to the future, Digital Twins and Synthetic Data will continue to play a pivotal role in optimising in-store execution. As more retailers adopt these technologies, we can expect further innovations that will redefine how products are managed, tracked, and displayed in retail environments.

Neurolabs is at the forefront of this transformation—helping retailers worldwide enhance their operations, reduce costs, and deliver an outstanding customer experience.