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AI Solution · Logistics, Procurement, and OperationsTravel

Anticipate the peak before it breaks your stock

Peak Radar predicts demand store by store, with special attention to seasonal peaks, so procurement and logistics can react before product runs out on the shelf.

By store
granular prediction
Seasonality
forecast ahead of time
Fewer stockouts
at key moments
The Challenge · When demand catches you off guard

Stockouts hit exactly when you sell the most

Campaigns, holidays, or season changes spike demand unevenly across stores, and aggregate forecasts don't catch it in time.

📈

Peaks you don't see coming

Demand spikes on specific dates and the general forecast doesn't distinguish store by store.

🚚

Late replenishment

By the time the stockout is spotted, the order and the transport no longer arrive in time for the peak.

💸

Lost sales

Every stockout in high season is a sale you don't recover and a customer who tries a competitor.

The Solution

Demand forecasting focused on real seasonality

It analyzes sales history, the commercial calendar, and external signals to anticipate when and where demand will rise, with enough lead time to react.

📅

Seasonality detection

Identifies recurring patterns by date, campaign, or weather that affect each category's demand.

🏪

Store-level granularity

Predicts the peak at point-of-sale level, not just at chain or region level.

🔔

Early alerts

Alerts procurement and logistics early enough to adjust orders and delivery routes.

🔄

Continuous learning

Tunes the model with every new season, improving prediction accuracy over time.

How It Works · The value, step by step

From historical data to on-time replenishment

The solution watches the calendar and the sales history to anticipate every peak with room to react.

Sales historyCalendar and signalsDetect seasonal patternPredict demand by storeAlert procurementAdjust replenishment
1
Collects sales history by storeAnalyzes sales time series, including past campaigns and holidays.
2
Cross-references calendar and context signalsIncorporates holidays, weather, or planned marketing campaigns that could affect demand.
3
Detects the seasonal patternIdentifies which categories and stores tend to repeat peaks on specific dates.
4
Predicts demand by storeGenerates a granular forecast, store by store, with the volume expected for each peak.
5
Alerts procurement and logisticsSends the forecast early enough to adjust orders, safety stock, and routes.
Integration

Connects with what you already use, frictionlessly

It builds on your sales history and the commercial calendar to anticipate every peak.

Input

ERP and sales history

Consumes sales history by store, SKU, and date from your ERP or point-of-sale system.

Grovium Solution

Peak Radar

Models seasonality and predicts the expected volume by store and category.

Output

Procurement and logistics

Sends alerts and replenishment recommendations to the procurement and supply chain teams.

Human oversight available at every critical step: the solution proposes, your team decides when it's needed.

Use Cases · The Business Value

Where timing is everything

Seasonal retail (fashion, garden, toys)

Anticipates campaign peaks and avoids running out of product in the key weeks.

FMCG and food

Predicts demand upticks from holidays or local events, store by store.

Logistics and distribution

Adjusts routes and safety stock before the order peak overwhelms delivery.

Problem → Solution

A garden center chain that ran out of stock every spring

A real example of the kind of situation this solution solves.

Before · the problem

At the start of every spring, several stores ran out of the best-selling items during the first week of the campaign, while other stores in the same chain sat on excess stock.

The forecast was done at chain level, without recognizing that the peak arrived earlier in some areas than others. The replenishment order went out once the stockout was already visible on the shelf, too late to react.

After · with the solution

Peak Radar predicts, store by store, when the spring peak will arrive, weeks ahead — enough time to adjust orders and transport.

With the history of recent campaigns and the season calendar, the solution identifies which stores will pick up first and by how much, making it possible to redistribute stock between stores before the stockout happens.

Get ahead of the next demand peak

We'll show you what Peak Radar's prediction would look like with your own stores' history.