Normalized claim
Quantified impact: 84 days
Tchibo can estimate online demand up to 84 days in advance.
GCPEvidence: Medium50/100
Tchibo: Optimizing demand forecasts with AI to match customer needs
Use case typeDemand forecastingUpdated Jun 13, 2026
Leading German retailer Tchibo built an automated forecasting service on Google Cloud to predict customer demand for its online sales channel and support warehouse replenishment. The solution helps Tchibo reduce overstock and handling effort while improving product availability and reducing stock-outs.
Reported outcomes
84 days
quantified impactTime & speed
Strategic outcomes
New product / capabilityBuilt automated demand forecasting serviceSpeed & agilityAutomated replenishment and forecasting workflowCost efficiencyReduced overstock and handling effortCustomer experience & trustImproved product availability and reduced stock-outs
Why do we believe this?Outcome claims, sources, and evidence checks
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Tchibo
- Provider
- GCP
- Maturity
- Unknown
- Linked source
- Google Cloud Customer Story
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1Demand forecasting
- 2Supply chain optimization
- 3Retail analytics
- Tchibo’s previous analytics solution was manually maintained and lacked the forecast quality needed for fast-changing, multi-channel retail demand.
- The company needed better demand visibility to manage warehouse supplies, lower logistics costs, and avoid stock-outs that could cause lost online sales.
- Tchibo built DEMON, an online demand forecasting service on Google Cloud.
- The service uses Vertex AI with a temporal fusion transformer model for time-series forecasting and BigQuery for data gathering and feature-store creation.
- Google Workflows orchestrates an event-driven microservice architecture covering data gathering, feature-store creation, model training, prediction delivery, reporting, and technical monitoring.
- Forecast results are stored and passed to downstream ERP and allocation systems so logistical operations can automatically replenish the warehouse.
Technologies
- The forecast service generates over six million predictions per day.
- Tchibo can estimate online demand up to 84 days in advance.
- The company reports significant time and cost savings from reduced overstock and handling efforts.
- Tchibo expects significant sales increase from fewer stock-outs and improved availability.
Architecture
DEMON is an event-driven microservice architecture. Incoming data triggers workflows that gather data, create feature stores for training and inference, run model training and production separately, deliver predictions and reports, and monitor technical operations. Google Workflows orchestrates the pipeline, BigQuery supports data gathering and feature-store creation, and Vertex AI runs the temporal fusion transformer forecast model. Forecast outputs are handed to ERP and allocation systems for automatic warehouse replenishment.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jan 1, 2024Publisher: Google Cloud Customer StoryEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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