What challenges did OTTO face before AI implementation?

OTTO faced challenges with demand forecasting accuracy to optimize inventory levels and reduce costs in ecommerce retail. The need to handle complex multivariate time-series data containing trends influenced by seasonal and external factors. Supporting rapid model experimentation and deployment at scale to maintain forecasting responsiveness.

What was the impact of the AI solution?

Achieved up to 30% improvement in demand forecasting accuracy. Reduced inventory waste and costs while boosting product availability and customer satisfaction. Enhanced financial outcomes through dynamic pricing strategies optimized by accurate forecasts. Strengthened operational efficiency and responsiveness to market fluctuations. Supported sustainability goals by minimizing waste.

Source

GCPScaled productionEvidence: Medium65/100

OTTO: Improving Demand Forecasting with Vertex AI in Ecommerce

Use case typeDemand forecastingUpdated Jun 13, 2026

OTTO implemented Google Cloud AI tools including Vertex AI and BigQuery to improve demand forecasting accuracy dramatically for better inventory management and customer satisfaction. Using the Time-series Dense Encoder (TiDE) model on Vertex AI, OTTO analyzes complex multivariate time-series data to capture both short- and long-term demand dependencies. Google Kubernetes Engine (GKE) is used to manage large-scale model deployment and enable rapid model experimentation and adjustments based on real-time data. OTTO benefits from up to 30% improvement in forecasting accuracy which reduces inventory costs, minimizes waste, and boosts revenues. The AI-driven forecasting capability allows OTTO to optimize stock levels and pricing strategies, resulting in better product availability and satisfaction.

Organization: OTTO
Industry: Retail
Location: Germany

Reported outcomes

+30%

accuracyQuality & accuracy

Strategic outcomes

Better decisions & insightImproved demand forecasting accuracyNew product / capabilityCaptured multivariate demand dependenciesCost efficiencyReduced inventory waste and costs

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Accuracy: 30% increase

Google Cloud Customer StoriesCustomer storyInferred claimMedium evidence strength

Achieved up to 30% improvement in demand forecasting accuracy.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: OTTO
Provider: GCP
Maturity: Scaled Production
Linked source: Google Cloud Customer Stories

Supporting rapid model experimentation and deployment at scale to maintain forecasting responsiveness

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 2 of 2

1Demand Forecasting
2Inventory Optimization

OTTO faced challenges with demand forecasting accuracy to optimize inventory levels and reduce costs in ecommerce retail.
The need to handle complex multivariate time-series data containing trends influenced by seasonal and external factors.
Supporting rapid model experimentation and deployment at scale to maintain forecasting responsiveness.

OTTO leveraged Google Cloud AI and ML services including Vertex AI, BigQuery, and GKE to develop and deploy advanced forecasting models.
The Time-series Dense Encoder (TiDE) model running on Vertex AI was used for precise demand prediction capturing multiple dependencies in the data.
GKE provided scalable infrastructure for managing large-scale model deployment and real-time experimentation.
Collaboration with Google Research and Google Professional Services supported model development and reduced time to market.

Technologies

Vertex AI BigQuery Google Kubernetes Engine TiDE model

Achieved up to 30% improvement in demand forecasting accuracy.
Reduced inventory waste and costs while boosting product availability and customer satisfaction.
Enhanced financial outcomes through dynamic pricing strategies optimized by accurate forecasts.
Strengthened operational efficiency and responsiveness to market fluctuations.
Supported sustainability goals by minimizing waste.

Architecture

Uses Google Cloud AI including Vertex AI for training TiDE forecasting model, BigQuery for data storage and analytics, and Google Kubernetes Engine for scalable deployment and experimentation.

Sources & evidence1

Evidence: Medium65/100Evidence strength