A Unified Lakehouse for Demand Forecasting

IDS built a lakehouse on Google Cloud — BigQuery as the analytical core, Dataflow for streaming and batch ingestion pipelines from POS, e-commerce, and supply chain systems — giving every division the same underlying demand signal for the first time.

Client

A pan-European grocery retail group

Services provided

About the project

Our solution

The Customer: Who they are

A pan-European grocery retail group operating thousands of stores, with separate data stacks for merchandising, supply chain, and e-commerce.

The Challenge: Identifying the villain

Demand forecasting was done per-division, using disconnected spreadsheets and legacy SQL extracts. Stockouts and overstock were common because supply chain planning didn’t see the same demand signals as merchandising. “Every division had its own truth about what customers wanted,” said the VP of Supply Chain .

The Journey: The search for a solution

The retailer had piloted a forecasting tool on top of existing siloed data, but accuracy stayed low because the underlying data wasn’t unified — a better algorithm couldn’t fix a fragmented foundation.

The Discovery: Finding IDS

The retailer’s CTO found IDS through a case study on retail data platform work and reached out for a scoping conversation on a unified lakehouse.

The Solution: The hero arrives

IDS built a lakehouse on Google Cloud — BigQuery as the analytical core, Dataflow for streaming and batch ingestion pipelines from POS, e-commerce, and supply chain systems — giving every division the same underlying demand signal for the first time.

The Implementation: The battle

Rollout started with three pilot regions before scaling network-wide, allowing the forecasting team to validate model accuracy against the new unified data before full commitment. The obstacle: point-of-sale data formats varied by acquired subsidiary; IDS built a normalization layer so history wasn’t lost in the transition.

The Results: The happy ending

  • Forecast accuracy improvement: +18 percentage points
  • Stockout rate reduction: 22%
  • Time to refresh demand data: daily batch → near real-time
  • Single unified demand dataset serving merchandising, supply chain, and e-commerce

“For the first time, three teams are planning off the same numbers.” — VP of Supply Chain

Technologies used: Google BigQuery · Google Dataflow · Google Cloud Storage

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