The Need for Accurate and Automated Forecasting
In the fast-changing manufacturing and retail landscape, companies face increasing pressure to automate their processes and meet customer expectations with speed and precision. This is all while managing tighter margins, resource constraints, and other external factors. As such, moving away from traditional manual methods that do not provide the high accuracy needed in today’s complex markets.
Accurate and data-driven forecasting is essential for aligning supply with demand, and for enabling proactive decision making across the value chain. For MAAG Food, Estonia’s leading meat producer, touchless forecasting has become an important part of their digital transformation. This highly automated approach leverages machine learning (ML) and exception-based logic to demand forecasting. It processes complex data, generates accurate forecasts, and adapts of changing market conditions in real time, minimizing the need for manual intervention. It allows businesses to reduce inefficiencies by enabling a scalable and collaborative approach that focuses on strategic decision-making rather than repetitive tasks.
MAAG Food, the largest manufacturing company within the Estonian-based MAAG Grupp, produces 89,000 tonnes of meat products annually across 3 manufacturing sites. With strong local brands like Rakvere, Tallegg, and Rigas Miesnieks, the company is a market leader in the Baltic region.
Implementing a Future Proof Forecasting Platform
In 2023, following a change in ownership, MAAG Food had outgrown its demand planning software. Their forecasting process which was reliant on additional spreadsheets and email exchanges between sales managers, product portfolio managers, and demand planners, so they saw there was room for improvement to gain additional visibility into future forecasts.
While they had some capability to forecast promotional and channel-specific demand, limitations in adaptability hindered their responsiveness to evolving market conditions. Driven by a desire to enhance decision-making through advanced analytics, they sought a modern, integrated solution to improve their KPIs and streamline operations.
MAAG Food selected RELEX in September 2023 due to its advanced AI-driven capabilities and strong local support in the Baltic region. The project focused on implementing the demand planning solution, including machine learning-based pooled models, promotion forecasting, and channel forecasting. The company has also leveraged RELEX for assortment planning and campaign profitability analysis, creating a unified decision-making framework.
The implementation, with a collaborative process by both RELEX and MAAG Food, focused on improving the master data quality and setting the foundation to enable cross functional teamwork, strategic decision making and scalable growth.
Transforming Operations with Touchless Forecasting
RELEX introduced capabilities that has supported MAAG Food’s growth with enhanced demand forecasting, campaign management, and data sharing across departments. The solution created a ‘single source of truth’ reducing the need to rely on emails and spreadsheets and improving planning efficiency.
Touchless forecasting has revolutionized the planning process by minimizing manual intervention and enabling a truly exception-driven approach. Up to 96% of forecasts remain untouched, as MAAG Food planners place strong trust in the system’s automated predictions. By making forecasts more precise and reliable, the planners spend less time on demand planning and their attention is focused on exceptional cases, freeing up their time and improving productivity.
MAAG Food has unlocked the full potential of accurate forecasting, improving both operational efficiency and user satisfaction. In a user survey conducted in April 2025, 100% of RELEX users reported positive satisfaction, with 43% reporting they were ‘very satisfied’. Sales, purchasing, and product portfolio teams now collaborate more effectively, benefiting from accurate 18-month forecasts and streamlined production planning. The planners have been able to focus on more complex tasks, such as production planning, where MAAG Food sees larger efficiency gains, and have such seen a 22% increase in planning efficiency.
Another notable outcome is the significant reduction in cheap sales – heavily discounted products. Over the past six months from November 2024 to April 2025, MAAG Food reduced its revenue losses from cheap sales by €98,950, reflecting the improved forecast accuracy and optimized inventory. When comparing this to February 2024, the volume and percentage of products sold at discounted prices have both declined.
“The transformation in our operations has been truly remarkable, and the exceptional support and dedication to enhancing our KPIs have made a tangible impact. This is especially the case with the reduction of cheap sales and measurable improvements in team efficiency have been a significant impact on our business,” said Martin Küüsmaa, Member of the Board and Director of Business Development, Planning, Quality, and Environment, MAAG Food. “We are fully committed to leveraging the software to its fullest potential, treating it as our single source of truth for both demand planning and assortment management.”
In a short time, MAAG Food has transformed its demand planning processes by leveraging machine learning and exception-based forecasting logic to achieve measurable value with reduced costs, improved efficiency and enhanced collaboration. By making forecasts more precise and trustworthy, touchless forecasting has not only improved demand planning outcomes but also laid the foundation for a more agile and resilient supply chain. Looking ahead at their future with RELEX, MAAG Food is exploring their expanding their use of demand planning to their dairy business units and introducing production and supply planning modules.