Case Study: NEW YORKER reduces stockouts and boosts in-store product availability with Cloudera Data Platform

Harnessing Data Insights to Identify Fashion Trends and Reduce Stockouts

NEW YORKER is a German fashion retailer with over 1,100 stores in 47 countries and 21,000 employees, focused on affordable trends for young customers. Facing costly stockouts and the need to reliably anticipate fast-changing fashion demand, the retailer’s 27‑person data team sought to better use ERP data (sales, pricing, supplier orders, product and logistics) to improve pricing, demand forecasting, order optimization and distribution.

By adopting the Cloudera Data Platform and a data lakehouse architecture, NEW YORKER eliminated data silos, enabled large-scale analytics and improved outlier detection and data quality. The result: fewer out-of-stock situations and better in-store availability, optimized supplier orders and allocation of stock to where demand exists, higher profitability by avoiding markdowns, and a more reliable, scalable platform ready for further ML-driven optimizations.

New Yorker

Steffen Minz

Head of Data Science