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Retail & E-commerce · Fortune 100 Retailer

SKU-level demand forecasting that cut error by 60% across 2M items

12%
MAPE, down from 31%
22%
fewer stockouts
18%
lower inventory holding cost

The Challenge

Legacy statistical forecasts ran at 20–35% MAPE, producing simultaneous overstock and stockouts across a two-million-SKU catalog.

Planners had no way to incorporate weather, promotions, and local events into a signal that updated fast enough to act on.

Our Approach

Stood up a distributed training fabric with a shared feature store, blending gradient-boosted and deep sequence models in a per-category ensemble.

Fused external demand signals (weather, events, promotions) and automated retraining, delivering forecasts into the merchandising and replenishment systems.

We stopped arguing about the number in the room and started trusting the model. The working-capital impact paid for the program in a single season.

SVP, Supply Chain Planning

Capabilities Deployed

Deep Learning FabricFeature StoreDistributed TrainingMLOps

Illustrative engagement. The client is an anonymized industry archetype and figures reflect typical outcomes and published industry benchmarks.

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