Background
In an article published by The Retail Touchpoints Network, 74% of brick-and-mortar shoppers said the checkout process was the biggest pain point in their retail experience. As such, this was only further confirmation for a leading American multi-national retail corporation who was exploring, through simulation and machine learning, incorporating frictionless checkout technology across their small footprint grocery stores.
Objective & Impact
InContext was commissioned to provide high volume 3D modeling and related services for shelf stable, frozen, and select perishable inventory at one the American multi-national retail corporation’s small footprint grocery stores.
To fulfill the scope of the project, InContext Solutions performed significantly scaled product acquisition, captured required item attributes, executed studio quality photography, and created and delivered over 29,000 3D models in 9 weeks. 3D model outputs were then used as machine learning inputs to enhance object recognition in effort to train store cameras on how to validate items being sold. This would ensure shoppers were charged appropriately for the items placed in their baskets when exiting the store.
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Summary
With an ecosystem designed for high quality and scale, InContext Solutions was able to deliver on a scope of work that required flawless execution with no margin for error. 3D product models were delivered as they were completed, which averaged out to a weekly throughput of nearly 3,500 models.
