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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.

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

What this fixes.

  • Forecast accuracy stuck at 60–70%

  • Safety stock over-built to compensate

  • Stockouts on top sellers anyway

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.

From signed proposal to live

The path.

01

Connect three years of SKU × location × week sales history.

02

Layer promo calendar, weather signals, regional macro indicators.

03

Run the first quarter parallel to your current planning — measure the delta.

04

Switch the replenishment trigger to model output for the top 80% of revenue SKUs first.

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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