Retail

Inventory management

Replenishment is an area where retailers can find an edge to beat the competition and exceed customer expectations. This case resulted in increased in sales and less waste for a large regional retailer.

Can we improve the replenishment policy?

Towards the end of a season stock in the central distribution center runs out for some items, while low performing shops sometimes still have stock left. This leads to lost sales in the high performing shops because they cannot be replenished.

Building a classification model.

We built a model that classified high and low performing shops. These rankings suggested which shops should return their stock and when.

Increasing overall sales.

Due to redistribution of stock there was 20% less unsold items at the end of the season and an increased overall sales of 8%.

Results:

0
% increased sales
0
% decrease in unsold items

This could be your case!

Our BrainMatter platform is the basis for all our applied AI cases. Share your challenges with us and we’ll quickly turn them into power apps.
Tommaso Gritti, Chief Solutions Officer
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