AI/ML models or site for FMCG goods performance

Where a manufacturer can point at equipment it owns, the numbers are specific. Unilever's ice-cream supply chain runs 35 factories and roughly three million freezers across 60 countries, and the company credits AI weather analysis with cutting waste through adjusted sales forecasts and data from 100,000 connected freezers with lifting retail orders and sales by up to 30%. Kellanova put $4–5m into sensors, machine learning and a digital twin of the dough on the Pringles line at Kutno, and reports 10% more capacity from existing equipment and a return above 40% after four years of development with Siemens. A smaller pilot through Unilever's accelerator cut machine cleaning time 20% and utility use 10% at one foods factory, worth €100,000 a year.

SoftBank Group's 2026 applications, most of them titled simply "System", arrive as a single bloc with a common construction: retail data goes in, a generative model sits in the middle, and a recommendation comes out. Demand forecasting for restaurants and retailers blends sales history with weather, footfall and store-camera demographics. A companion filing proposes menus and issues order instructions against live inventory; another wraps the same loop in supply-chain management with an emotion engine reading customer facial expressions. An LLM sets and then dynamically adjusts inventory levels.

The harder recognition step is deciding which of two near-identical packs a box contains, and the industry filings answer it by reading the label rather than the packaging. Walmart extracts text from shelf tags and attributes from the items and maps the two together, catching a mismatched price tag as a distinct anomaly class. Urstore matches against GTINs using image embeddings from a MobileNet-based similarity class, with the whole model configured to run offline on a handheld. Trax disambiguates lookalikes by their price ranges, and Salesforce skips annotated training data entirely by segmenting shelves and reading product names off the image.

Turkcell's filings treat the store as a sensing problem rather than a retail one. One uses millimetre-wave fixed wireless units as radar, reading channel state to track customer movement and derive placement and marketing metrics. Another puts microphones and cameras at the counter to read speech and facial expression and prompt staff with a suggested response. A third builds campaign targeting from online, offline, telecom and sensor data combined, and the same filer has a supply-chain recommendation system classifying supplier and sentiment risk.

A cluster of filings puts the electronic shelf label at the centre of the store rather than at its edge. One matches products to labels from shelf images and runs a reinforcement-learning price on inventory, sales, competitor prices and shelf-space share, then flags the gap between the recommended price and the displayed one as a "price lag" and updates the tag. Another positions the monitoring cameras themselves with a learned model, and detects missing products and mismatched labels from the same image. Toppan generates display content from past promotion performance, Trax drives transparent displays from detected availability, and a Korean filing puts the camera and facial analysis inside the tag itself.

Several documents are less about accuracy than about how many alerts a system is allowed to emit. A shelf-void detector uses an asymmetric cost function that penalises a missed gap more heavily than a false alert, reaching [email protected] of 0.875 at about 41 ms per frame. An end-to-end empty-shelf pipeline reports a mean average F1 of 68.5% and argues the binding constraint is annotation quality rather than architecture. Trax patents the gate directly: a notice-override condition suppresses an alert when earlier frames show the same state, and product models are updated automatically when candidate confidence falls below a threshold. Zebra fires the camera only when a shelf sensor crosses a threshold; Fujitsu fuses image and depth through four separate determination indices before declaring a stockout.

Blue Yonder's allocation work computes a constrained quantity from a reinforcement-learning reward over margin, carrying cost and opportunity cost, then transmits instructions to automated warehousing equipment at a distribution centre to retrieve the product; a later filing models allocation as a state-action process and again hands the retrieval instruction to DC automation. Its planogram line ends the same way, with simulated annealing over layout options passed to robotic shelving that executes the arrangement.