Компьютерное зрение на полках: Coresight Research подсчитала потери ритейла от ошибок выкладки

A study by analytical company Coresight Research, conducted in partnership with Simbe and RELEX Solutions, for the first time measured the real cost of shelf stocking errors in physical retail — and showed how hardware automation of shelf monitoring using computer vision translates into concrete financial returns.

The Problem That Has Long Been Ignored

Empty shelves and incorrect product placement are not a minor annoyance for customers, but a chronic and costly failure in physical retail operations. A customer who cannot find the desired product either switches to a competitor, turns to online, or simply abandons the purchase. Multiply this across thousands of stores and millions of visits — and you get a significant hole in industry revenue.

Traditional tools are not up to the task. Manual store walks by employees with tablets are slow, expensive, and inaccurate: the average store checks the status of each SKU only a few times per shift. Between checks, problems accumulate silently — shelves empty out, price tags shift, new products end up in the wrong place. All of this remains invisible to the management system and directly erodes margins.

This is precisely this hidden leak that Coresight Research set out to measure and quantify in financial terms.

What Computer Vision Can Do on the Sales Floor

Simbe's robotic systems patrol the sales floor around the clock, scanning shelves using cameras and sensors. They never tire, never skip sections, and detect every anomaly in real time. The system tracks:

• the presence or absence of each SKU at its exact shelf location
• compliance of product placement with the approved planogram
• accuracy of price tags and promotional materials
• inventory levels and the probability of stockouts by end of day
• demand dynamics across specific floor locations

Data collected in real time flows into the RELEX Solutions inventory management platform. It automatically prioritizes tasks for staff, adjusts orders with suppliers, and generates management summaries — without having to wait for end-of-day reports.

Why the Economics Finally Make Sense

Operating margins in grocery retail are 1–3%. At these margins, any new technology must prove its financial return, not just promise to improve customer experience. This is why the study fundamentally differs from most industry reports: it builds a financial model that allows retailers to plug in their own data — traffic, average transaction value, number of SKUs, frequency of stocking errors — and get an ROI forecast before buying equipment.

The investment stops looking like a bet on the future and becomes a managed solution with a clear payback timeline.

Add to this the pressure from marketplaces: online platforms show customers real-time status for every product, while a physical store with empty shelves for hours is losing not just on convenience, but on trust.

A similar trend is observed abroad — major grocery chains in North America and Europe have moved from isolated pilots to large-scale deployment of robotic monitoring systems.

"The technology solves a concrete, measurable problem — errors in retail process execution.

This is what transforms it from an experimental tool into production infrastructure," note the study authors.

What This Means

Computer vision in retail has moved from technological hype to an operational tool with transparent economics. The Coresight Research study for the first time gives the industry a language to speak with the board: not 'we are implementing AI,' but 'we are recovering concrete money from each store.' For chains competing under shrinking margins, this shifts the conversation about automation — from 'why?' to 'when do we start?'