AI-supported warehouse optimization with Databricks at TQG
Automated picking & real-time data in the fulfillment center
The Quality Group (TQG), a leading provider of sports and nutrition products, was faced with the challenge of efficiently managing highly fluctuating order volumes in E-Commerce, with over 100,000 orders per day at peak times. Manual warehouse logistics reached its limits. The aim was an automated, scalable solution to increase efficiency in the warehouse. Together with ruhrdot, an AI-based multi-order picking solution based on Databricks was developed to reduce picking times, make real-time data usable and align logistics with scalability and flexibility.

Managing order volumes with predictive picking
The fast pace of E-Commerce and seasonal peaks such as Black Week called for intelligent process automation.
- Strongly varying order quantities with over 100,000 orders per day
- Inefficient, manually controlled warehouse processes in critical periods
- No dynamic peak load management to control fulfillment capacities
- Lack of real-time data for data-driven control of picking
- No scalable system architecture for future locations or growth

Logistics optimization with machine learning & databricks
Warehouse logistics during Black Week
The introduction of the AI solution enabled future-oriented, scalable warehouse management: with measurable impact.
Higher efficiency
Walking distances reduced by 10% and stops by 27%.
Saving time
Picking time fell by 22%, overall output increased by 20%.
Flexibility
Adaptive response to changing order patterns and storage conditions.
Scalability
Architecture designed for future warehouse locations and data volumes.
Customer satisfaction
Faster delivery times through automated fulfillment.
Data-driven decisions
Real-time analyses promote continuous process improvement.
