Intelligence that compares branches, not just counts them.
Forecasts for every branch and SKU, anomaly detection that measures a branch against locations like it, and ranked explanations of what is driving the difference - so the next decision is specific.
How ai network insights moves through the chain.
Every step below happens against the same live record of every location, so nothing waits for a branch to send a report.
Illustrative figures.
Learn
Models train on each branch's own sales, stock and calendar history.
Forecast
Demand is projected per SKU per branch for the weeks ahead.
Detect
Branches departing from their peer group are flagged with evidence.
Recommend
Transfers, reorders and reviews are suggested with their reasoning.
What AI Network Insights includes.
Per-branch demand forecasting
A forecast for every SKU at every location, reflecting local seasonality, festivals and footfall, rather than one blended chain number.
Peer-group comparison
Branches are grouped by format, size and catchment, so each one is judged against locations that should behave like it.
Cross-location anomaly detection
Unusual discounts, voids, shrinkage or sales drops are flagged when a branch departs from its peers, with the transactions behind it.
Rebalancing engine
Combines forecast demand, current stock and route lead times to produce the transfer suggestions used across the network.
Explainable recommendations
Every suggestion shows the inputs that produced it, so managers can see why before they approve.
Ask in plain language
Ask questions such as which branches sold below forecast last week, and get an answer backed by the underlying records.
The same module, at three altitudes.
Branch manager
- Forecast for the coming week
- Items likely to run short
- Flags raised on this branch
Regional lead
- Branches departing from their peers
- Forecast accuracy by location
- Suggested actions to review
Head office
- Network performance ranking
- Accuracy and impact of recommendations
- Where capital and stock are underused
About AI Network Insights.
Something not covered here? Talk to us about how it would work on your chain.
How much history do forecasts need?
Forecasts start from as little as a few months of sales per branch and improve as more history and seasonal cycles are captured.
What about a newly opened branch?
New branches borrow patterns from their peer group until they build enough history of their own.
Is our data used to train models for other customers?
No. Models for your network are trained on your data and are not shared with or used for any other customer.
Can we override a recommendation?
Always. Recommendations are suggestions. Overrides are recorded so the models learn from local judgement.
Tell us how many locations you run. See your network.
Enter your number of branches, stores or depots. The network rebuilds at that scale with a typical mix of branch health, and the rebalancing a chain your size would usually see.
An illustrative view based on typical patterns - not a live read of your actual locations.