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Industry-Specific·6–16 weeks·8–12 weeks

Demand Forecasting

For retail, FMCG, manufacturing. ML model learns from sales history, promotions, weather, macro signals and external data. Outputs SKU-level forecasts that drive replenishment + safety stock. Cuts carrying cost without breaking service level.

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OUTCOMES
Forecast accuracy
+18–28 pts
Inventory carry
−22%
Stockouts
−35%
PROBLEM
The problem

What this fixes.

  • —

    Forecast accuracy stuck at 60–70%

  • —

    Safety stock over-built to compensate

  • —

    Stockouts on top sellers anyway

CAPABILITIES
How it works

Three jobs, on rails.

Forecast

SKU-level

Per-store / per-DC weekly forecast with confidence.

Adjust

Promo + weather

External signals folded into the model.

Drive

Replenishment

Output drives PO suggestions in your ERP.

EXAMPLE
A real moment

One scenario, one outcome.

The scenario

A grocery chain with 280 stores and 18,000 SKUs runs week-3 of summer.

The outcome

Model predicts a 31% lift on chilled drinks Thursday onwards (heatwave + footy final). Safety stock is shifted that morning; stockouts on top-10 SKUs fall from 4.2% to 0.6% on the weekend.

Engagement

Scoped on a call.

Delivery

8–12 weeks

Engagement model

Pilot → retainer

Scope confirmed in a 30-minute call. Fixed scope, fixed timeline before you sign. We'll send a one-page proposal within 48 hours.

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Want this in your workflow?

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MIRROR·tested on us first · 100%AUTOMATIONS·50 ready to shipCATEGORIES·10 business functionsLIVE PRODUCTS·2 of our ownSTUDIO·Vienna, AT · since 2019MIRROR·tested on us first · 100%AUTOMATIONS·50 ready to shipCATEGORIES·10 business functionsLIVE PRODUCTS·2 of our ownSTUDIO·Vienna, AT · since 2019
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