Case Study: Steinemann achieves more than 90% forecasting accuracy and significantly reduces food waste with Qlik AutoML

Significant improvements in production planning with AI-supported forecasting

Steinemann is a mid-sized German meat producer (five plants, ~850 employees) that struggled to plan raw material purchases for perishable products. Production decisions relied on manual historical research and gut instinct, causing frequent shortages or excess stock and inefficient use of packaging, warehousing and procurement resources.

Working with SLA and Qlik, Steinemann implemented Qlik AutoML trained on several years of sales and operational data and integrated forecasts into its ERP. Forecasts are now more than 90% accurate, enabling real-time production management, lower error rates, optimized purchasing and packaging, reduced waste (thousands of tonnes annually) and improved efficiency and sustainability.

Steinemann

Ralf Lenger

Head of IT