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Title
OTTO improves demand forecasting accuracy up to 30% with Google Cloud's TiDE model
…demand behavior, such as seasonal items or new products with uncertain demand. The Time-series Dense Encoder (TiDE), trained on Vertex AI and deployed on GKE, has enabled OTTO to improve the accuracy of demand forecasts by up to 30%. TiDE is particularly useful for handling complex multivariate time series, such…
Description
OTTO, a leading German ecommerce retailer, used Google Cloud's Time-series Dense Encoder (TiDE) model trained on Vertex AI and deployed on GKE to analyze multivariate time-series data such as seasonal demand patterns, pricing changes, and promotions. TiDE improved demand forecasting accuracy by up to 30%, helping OTTO reduce inventory costs, minimize stock-outs and overstocks, and support dynamic pricing, with particularly strong responsiveness for volatile products like iPhones versus more stable items like men's jeans.
…(TiDE) model revolutionized their demand prediction capabilities. In practice, OTTO’s employees leverage TiDE to analyze vast datasets containing multivariate time-series data, such as seasonal demand patterns, pricing changes, and external factors like promotions. For example, when planning seasonal inventory for gaming consoles, TiDE’s advan…
…and ensures accurate forecasts, even for highly dynamic products like iPhones." The adoption of Google’s solutions resulted in an up to 30% improvement in forecasting accuracy, reduced inventory costs, and increased customer satisfaction through timely product availability. One of the key improvements is TiDE’s ability to adapt rapidly to short-term fl…
…et fluctuations, manage inventory more efficiently and reduce associated costs. For highly stable products, such as men's jeans, TiDE converged to results comparable to our baseline. However, for more dynamic items like iPhones, the model demonstrated superior responsiveness to shifts in demand levels and provided better adaptability to changes. Dr. Christian Wiel Senior Data Scientist and Developer In practice, OTTO employ…
Company
OTTO
…ducing waste. OTTO elevates demand forecasting with Google Cloud’s AI solutions OTTO , a leading German ecommerce retailer, embarked on a journey to enhance the accuracy of its demand forecasting processes to improve inventory management and customer satisfaction. OTTO’s forecasting team, Team Lumen, implemented Google Cloud’s advanced AI too…
Country
Germany
…mize supply chain efficiency, and maintain its position as an industry pioneer. Industry: Retail Location: Germany Products: BigQuery , Vertex AI , Vertex AI Workbench , Vertex AI Pipeline , Goo…
Industry
Retail
…mize supply chain efficiency, and maintain its position as an industry pioneer. Industry: Retail Location: Germany Products: BigQuery , Vertex AI , Vertex AI Workbench , Vertex AI Pipeline , Goo…
Problem
OTTO needed to improve the accuracy of its demand forecasting to improve inventory management and customer satisfaction, and found working with complex multivariate time-series data challenging using its baseline approach.
…ducing waste. OTTO elevates demand forecasting with Google Cloud’s AI solutions OTTO , a leading German ecommerce retailer, embarked on a journey to enhance the accuracy of its demand forecasting processes to improve inventory management and customer satisfaction. OTTO’s forecasting team, Team Lumen, implemented Google Cloud’s advanced AI too…
Solution
OTTO's forecasting team, Team Lumen, implemented Google Cloud's Vertex AI, BigQuery, and Google Kubernetes Engine (GKE), integrating the Time-series Dense Encoder (TiDE) model trained on Vertex AI and deployed on GKE to analyze multivariate time-series data such as seasonal demand patterns, pricing changes and promotions.
…orecasting processes to improve inventory management and customer satisfaction. OTTO’s forecasting team, Team Lumen, implemented Google Cloud’s advanced AI tools, including Vertex AI , BigQuery , and Google Kubernetes Engine (GKE) , to achieve this. The integration of the Time-series Dense Encoder (TiDE) model revolutionized th…
…demand behavior, such as seasonal items or new products with uncertain demand. The Time-series Dense Encoder (TiDE), trained on Vertex AI and deployed on GKE, has enabled OTTO to improve the accuracy of demand forecasts by up to 30%. TiDE is particularly useful for handling complex multivariate time series, such…
Business value
The adoption of Google's solutions resulted in an up to 30% improvement in forecasting accuracy, reduced inventory costs, and increased customer satisfaction through timely product availability.
…and ensures accurate forecasts, even for highly dynamic products like iPhones." The adoption of Google’s solutions resulted in an up to 30% improvement in forecasting accuracy, reduced inventory costs, and increased customer satisfaction through timely product availability. One of the key improvements is TiDE’s ability to adapt rapidly to short-term fl…
AI capabilities
Predictive Analytics
…demand behavior, such as seasonal items or new products with uncertain demand. The Time-series Dense Encoder (TiDE), trained on Vertex AI and deployed on GKE, has enabled OTTO to improve the accuracy of demand forecasts by up to 30%. TiDE is particularly useful for handling complex multivariate time series, such…
Technology
Vertex AI, BigQuery, Google Kubernetes Engine (GKE)
…aintain its position as an industry pioneer. Industry: Retail Location: Germany Products: BigQuery , Vertex AI , Vertex AI Workbench , Vertex AI Pipeline , Google Kubernetes Engine (GKE) menu Overview Solutions Products Pricing Resources Docs Support Contact us …
Deployment model
cloud
…demand behavior, such as seasonal items or new products with uncertain demand. The Time-series Dense Encoder (TiDE), trained on Vertex AI and deployed on GKE, has enabled OTTO to improve the accuracy of demand forecasts by up to 30%. TiD…
Deployment options
cloud
…demand behavior, such as seasonal items or new products with uncertain demand. The Time-series Dense Encoder (TiDE), trained on Vertex AI and deployed on GKE, has enabled OTTO to improve the accuracy of demand forecasts by up to 30%. TiD…
Headline outcome
…demand behavior, such as seasonal items or new products with uncertain demand. The Time-series Dense Encoder (TiDE), trained on Vertex AI and deployed on GKE, has enabled OTTO to improve the accuracy of demand forecasts by up to 30%. TiDE is particularly useful for handling complex multivariate time series, such…
Use case type
Predictive operations
…nclude financial advantages. For example, thanks to improved sales forecasting, OTTO can adjust its stock levels more accurately, reducing the costs associated with excess inventory while increasing product availability. In addition, improved accuracy enables dynamic pricing strategies, allowing OT…
