CUSTOMER STORY // STEINEMANN
Significant Improvements in Production Planning With AI-Supported Forecasting
With Qlik and SLA, Steinemann is revolutionizing data analysis and using AI to achieve a new level of corporate success.

90%
more accurate forecasting
Forecasting of planned production quantities is now more than 90% accurate.
>1,000 tons
of food waste avoided
Eliminating food waste reduces storage capacity and costs while boosting Steinemann’s sustainability incentives.
Real-time
production figures
More accurate figures can now be used for planning, reducing error rates.
CHALLENGE
Production guided by past order volumes
Accurate raw materials planning is a significant challenge in meat production as decisions often have to be based on past order volumes. It can be difficult to make predictions as orders often come in last minute and factors such as weather, promotions and storage costs all impact the planning process.
Steinemann decided to take a data-based approach by combining machine learning (ML) with historical production data. Together with Qlik OEM partner SLA, the team investigated how these technologies could make planning more efficient and accurate.

APPROACH
Data analysis meets artificial intelligence
The company used historical sales data to develop a prototype for predicting its raw materials requirements. Initial tests saw forecasts prove more than 90% accurate when compared to the actual sales data – far better than manual estimates.
This accuracy was improved further by incorporating additional factors such as days of the week, product numbers and promotions. The Qlik results are integrated into the existing ERP system via an interface.

RESULTS
An industry pioneer – thanks to AI
The accurate forecasts generated with Qlik AutoML are significantly improving raw materials planning for the company’s meat production processes. Machine learning can handle complex variables such as market size and stock inventories.
The system also optimizes logistical aspects – such as the purchase of packaging materials and seasoning – and reduces error rates. Integrating it into the existing ERP system has led to greater efficiency, cost savings and boosted Steinemann’s sustainability credentials. Unnecessary storage and waste are reduced, thereby elevating animal welfare and helping to conserve the environment.

WHAT THIS MEANS FOR YOU
Precise requirements forecasts optimize operations
With Qlik AutoML, the company can generate in-depth analysis to help it make well-founded business decisions. Precise requirement forecasts support the company in increasing its efficiency and reducing error rates and costs, which in turn has a positive impact on sustainability.



When we compared the AI-generated forecasts with our actual sales data, even I couldn’t quite believe how close the figures were.