Skip to content
DEMAND.AI FORECASTING FOR RETAIL & CPG

Predict What Will Sell — Not Just What Sold

Traditional forecasting uses corrupted signals. Demand.AI predicts true demand — correcting for shelf failures, stockouts, and execution gaps that suppress the observed sales record — by combining consumer behaviour from Demand.AI with real shelf availability from Execution.AI.

retail-asset-monitoring-hero

Trusted by the largest CPGs and retailers

340+

Clients

75+

Countries

11+

Years of retail data

2M+

Assets Monitored

When Cooler Problems Show Up Early

bullet-4

Teams get notified the moment temperatures drift or performance drops.

bullet-4

Stock levels stay visible between visits—not just when someone checks in person.

bullet-4

Stores avoid spoilage, warm product, and last-minute replacements.

bullet-4

Field teams know which locations to prioritize before shoppers feel the impact.

WHY TEAMS RELY ON DEMAND.AI 

Separates true demand from lost sales.

→ Corrects for shelf failures — when a shelf is empty, Demand.AI identifies this as suppressed demand rather than low consumer interest

→ Combines transaction behaviour signals with Execution.AI shelf availability data — a combination no standalone forecasting vendor can replicate

→ Produces more accurate demand predictions than historical POS alone — because it corrects the signal before forecasting

 

→ Feeds supply chain planning systems with a better signal — SAP, Kinaxis, RELEX, o9, Blue Yonder all benefit from a corrected input

→ Store-level demand prediction at the granularity needed for Assortment.AI assortment decisions — not just banner averages

Product.AI
Demand.AI
 Assortment.AI Space.AI Execution.AI

Signal flow

Receives: Transaction data capability behaviour + Execution.AI shelf availability + Product.AI product attributes.
Produces: Demand Prediction — true demand corrected for execution failures, feeding Assortment.AI and supply chain systems.

Why teams rely on Demand.AI

Separates true demand from lost sales.

Corrects for shelf failures — when a shelf is empty, Demand.AI identifies this as suppressed demand rather than low consumer interest Combines transaction behaviour signals with Execution.AI shelf availability data — a combination no standalone forecasting vendor can replicate Produces more accurate demand predictions than historical POS alone — because it corrects the signal before forecasting Feeds supply chain planning systems with a better signal — SAP, Kinaxis, RELEX, o9, Blue Yonder all benefit from a corrected input Store-level demand prediction at the granularity needed for Assortment.AI assortment decisions — not just banner averages

Signal flow

Receives: transaction data capability behaviour + Execution.AI shelf availability + Product.AI product attributes
Produces: Demand Prediction — true demand corrected for execution failures, feeding Assortment.AI and supply chain systems

How Demand.AI produces true demand.

01

Execution data ingested

Execution.AI shelf availability data identifies where and when shelves were empty, misplaced, or incorrectly priced.

02

Signal corrected

Demand.AI removes execution failures from the historical record separating true demand from observed sales.

03

Consumer behaviour applied

Demand.AI demand transfer models and decision trees are applied to model true substitution patterns.

04

Prediction produced

Demand.AI outputs true demand by store cluster — feeding Assortment.AI for assortment decisions and supply chain for replenishment.

Continuous Learning Loop of

How Demand.AI works

What Demand.AI delivers

Bullet Blue
True demand prediction
What consumers actually wanted to buy — corrected for execution failures that suppressed the observed signal.
Bullet Blue

Demand transfer modelling
Where demand actually goes when a product is unavailable — in-brand, cross-brand, or category exit..

Bullet Blue

Store-level granularity
Demand predictions at the store cluster level, not just banner averages — the resolution Assortment.AI needs.

Bullet Blue

Supply chain integration
Corrected demand signals feed directly to SAP, Kinaxis, RELEX, o9, and Blue Yonder — better inputs, better outputs.

Bullet Blue

Execution-adjusted
Continuously updated as Execution.AI data identifies new execution failures and corrects the historical record..

Near-term opportunity

Demand.AI is built and ready for commercialisation as a standalone product — the highest near-term revenue opportunity in the platform.

Platform context

Demand.AI is Layer 03 — Demand Intelligence. It is built and operational. It is not yet commercialised as a standalone product — it currently powers Assortment.AI assortment intelligence and feeds supply chain integrations. Commercialising Demand.AI as a standalone demand intelligence product is the highest near-term revenue opportunity in the Vision Retail Intelligence Stack.

See Demand.AI in action.Talk to a Vision specialist about your retail setup.

Book a Demo
Retail inventory control with built-in AI

Turn every cooler into a live performance tracker

Replace vending headaches with autonomous coolers that use AI to manage stock, usage, and performance on their own. Computer vision and AI give your equipment a live view of what's happening inside your cooler.

How Vision Group's Retail Asset Monitoring Solutions Works

 

→ Predict stockouts and restock at the right time

→ Monitor cooler conditions to avoid sales loss

→ Track what gets purchased or ignored

→ Reduce maintenance visits

→ See performance by product, machine, or outlet

Watch
Coolers and sensors capture product interactions in real time. Read
AI scans each SKU, flags low stock or tech issues, and tracks performance. Report
Dashboards summarise sales, usage, and alerts by machine or product. Act
Clear next steps to restock, rotate, or repair.

Why retailers are replacing traditional vending machines

Mechanical vending machines break or stop working, missing sales without anyone noticing. Smart coolers skip the moving parts and track performance directly — using AI to show what's stocked, what's selling, and what needs attention. Our software is compatible with most commercial coolers, freezers, and vending shells.

AUTONOMOUS RETAIL

iVending

Upgrades any cooler with AI and smart tracking. Turns existing coolers into self-monitoring, self-reporting units.

autonomous-retail-feature
iot-feature
IoT RETAIL SENSORS

iVentory

Adds deeper analytics, AI forecasting, and product-level visibility. Helps spot and solve inventory issues faster.

What Demand.AI captures

Real behaviour. Not panel estimates.

Cashier-free autonomous checkout. Smart coolers that track their own inventory, sales, and performance. Sensor-equipped retail units that monitor inventory and uptime — a smarter take on vending that reduces maintenance overhead. POS and loyalty data ingestion. Multi-year transaction history from enterprise customers ingested and modelled — not just the last quarter, but the full behavioural pattern over time. AI consumer decision tree modelling. Maps the hierarchy of choices shoppers make at the fixture. Built from real observed purchase sequences, not stated preference surveys. Brand first? Price tier? Occasion first? Powers Demand.AI, Assortment.AI, and every upstream intelligence layer. Demand.AI-generated signals feed Demand.AI demand predictions and Assortment.AI assortment simulations — every product in the stack gets smarter from transaction data capability data. Execution-adjusted forecasting. Execution.AI shelf availability data continuously corrects the Demand.AI demand signal — distinguishing true low-demand stores from stores with execution failures suppressing sales.
How autonomous commerce works

The frictionless shopping experience.
Computer vision inside the cooler.

The autonomous commerce capability uses computer vision mounted inside coolers and cabinets to track exactly what is taken — with 97% item recognition accuracy. No cashier. No scanner. No friction.

VS Group Assets Frame 8

Consumer approaches

Finds an Execution.AI-enabled cooler or cabinet near them. Scans the QR code on the door.

VS Group Assets Frame 7

Payment validated

Payment information stored securely in the app requests bank authorisation — before the door opens.

VS Group Assets Frame 7 (1)

Door opens

Consumers pick their desired items. Computer vision inside the cooler sees and identifies products in real time.

VS Group Assets Frame 7 (2)

Door closes

Transaction completes automatically. Bank debits the chosen payment method.

VS Group Assets Frame 7 (3)

Data captured

Every transaction recorded — what was taken, what was left, replenishment signal triggered.

97%

Item recognition accuracy inside coolers

Retrofit

Works with existing coolers, freezers, and cabinets — no new hardware required

No friction

QR code → pick → walk out. No cashier. No scanner. No queue.

What Demand.AI predicts

True demand — not observed sales corrupted by execution failures.

Corrected demand signal. Shelf failures — OOS events, misplacements, pricing errors — removed from the historical record before it enters any forecasting model. Demand.AI predicts what consumers wanted to buy, not just what they managed to buy. Demand transfer modelling. When a product is removed or goes OOS, Demand.AI shows exactly where demand went: how much transferred within your brand, how much transferred to a competitor, and how much exited the category entirely. Store-level demand prediction. Demand predictions at store-cluster granularity — not banner-wide averages. Different stores get different predictions because different shoppers behave differently. Supply chain integration signals. Demand.AI demand predictions feed directly into SAP, Kinaxis, RELEX, o9, and Blue Yonder — providing a corrected demand input that makes existing supply chain models more accurate. Execution-adjusted forecasting. Execution.AI shelf availability data continuously corrects the Demand.AI demand signal — distinguishing true low-demand stores from stores with execution failures suppressing sales.
Common questions

Frequently asked about Demand.AI

Stay Ahead of Outages, Stock Dips, and
Equipment Failures

Book a Demo