Demand Forecasting

Predict what each SKU will sell, by location, in time to act on it, instead of reacting after the fact.

The problem it solves

Most purchasing teams still plan reorders from a rolling average and instinct, because tracking seasonality, promotions, and contract cycles across thousands of SKUs by hand isn't realistic. The result shows up on both ends at once: cash tied up in stock nobody needed yet, and empty shelves right when a customer needed to buy.

What it does for your business

Predicts at SKU and location level

Demand estimates for every product at every warehouse or store, refreshed on a schedule that matches how your ERP posts new order data.

Drives actual purchasing

Converts directly into reorder quantity, timing, and safety stock recommendations, rather than sitting in a report nobody opens.

Learns your own calendar

Trained on your order history, so it picks up your specific seasonal swings and renewal cycles instead of assuming a generic retail calendar.

Shows its reasoning

Each forecast carries the signals behind it, so a buyer can see why a number moved before deciding to act on it.

Where this moves the needle

Lower stockout rate Lower inventory carrying cost Higher accuracy than manual planning Fewer hours spent on manual replenishment
How it works
Time-series and gradient-boosted models are trained separately for each SKU-and-location combination, using features built from seasonality, promotions, and contract timing. Every model is checked against a holdout period before it is trusted with live purchasing decisions.
What we need from you
Roughly the last two years of order history (dates, SKUs, quantities, locations), your item master, and a promotion calendar if you keep one. Most of this comes from a single export out of your ERP.

See Demand Forecasting on data like yours

A short walkthrough on sample data, including how the model is trained and validated before it touches live purchasing decisions.

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