CASE STUDY
How did Danone, Nutricia and Żywiec Zdrój optimise their demand forecasting?
About the project
We implemented a comprehensive demand planning system for three key Danone entities in Poland: Danone Polska, Nutricia Polska, and Żywiec Zdrój. Danone is a global leader in specialized nutrition (for infants, children, and adults), dairy and plant-based yogurts, and bottled water. Among its most recognizable brands are Bobovita, Nutricia, Danone, Activia, Fantasia, Actimel, and Żywiec Zdrój.
The project covered several business segments, each with unique demand characteristics—from short shelf-life fresh products requiring daily precision, to highly seasonal and promotion-sensitive categories. Differences in sales dynamics, order cycles, and market strategies required a flexible forecasting approach and integration of data from multiple sources. Implementing a unified system enabled process automation, improved forecast accuracy, and optimized inventory levels, tailored to the needs of each product category.
Project Goals and Challenges
Improved Forecast Accuracy
Manual forecasting did not account for all relevant variables, resulting in errors and inefficient inventory management.
Time Savings for Planners
Planners spent significant time manually preparing forecasts.
Inventory Quality Optimization
Maintaining the right stock levels, reducing out-of-stocks, and improving inventory turnover.
Migration to a Common Demand Planning Tool – SAP IBP
Standardization of planning processes on a single platform.
Reliable Input Data
Ensuring consistent, high-quality data as a basis for forecasting.
Forecasting Process Automation
Implementation of statistical models and machine learning algorithms.
Integration of Financial and Promotional Data
Automatically incorporating the impact of promotions and market factors into demand forecasts.
Diverse Sales Channels
Each product category was distributed through different channels, requiring tailored forecasting models.
External Factors Affecting Demand
Forecasts needed to consider macroeconomic variables like inflation, changes in consumer purchasing power, and evolving health and environmental trends.
Seasonality and Demand Volatility
Categories such as bottled water and dairy products were strongly affected by seasonality and promotional campaigns, requiring flexible planning and detailed analysis of historical sales data.
Complex Product Portfolio Structure
A wide range of SKUs with different life cycles, including new product launches, required precise forecast management to avoid both overstocking and product shortages.
Our Approach and Solutions
Implementation of the SAP IBP (Integrated Business Planning) System
Tailored IBP configuration to match specific business requirements.
Defined aggregation and disaggregation rules to enable forecasting at multiple levels aligned with product segment needs.
Implemented automatic data cleansing and automated data uploads into the system.
Deployed machine learning algorithms and statistical forecasting models, customized for each business unit.
One integrated system supporting all processes, eliminating discrepancies in units of measure and data standards.
Statistical Models and Machine Learning
Time-series analysis to select the best-fitting model by business segment.
Use of a broad range of forecasting methods—from traditional statistical techniques to ML algorithms that consider more than just historical data.
Automatic model selection and adaptation per product type, with the option for manual overrides when needed.
Implementation Results
9%
Forecast accuracy improvement with ML models – Gratka: from 90.2% to 99.2%
10,2%
Forecast accuracy improvement with ML models – Activia: from 88.3% to 98.5%
7,8%
Forecast accuracy improvement with ML models – Danonki: from 90.2% to 98.0%
0,3%
Forecast accuracy improvement with ML models – Alpro: from 92.6% to 92.9%
Business Benefits
Automated Demand Planning
One unified tool supporting all business units.
Reduced Product Expiry Losses
Crucial for dairy, where shelf life is short.
Avoidance of Out-of-Stocks
Better availability increased customer satisfaction and prevented lost sales.
Improved Inventory Turnover
Faster product movement supported sales growth and profitability.
Migration of All Interfaces
All data available in a single system, regardless of unit of measure.
Monthly Time Savings for Planners
Less time spent on manual forecasting thanks to automation.
Lower Workload
Greater Predictability
More accurate, data-driven forecasts enabled more informed decisions.
Inventory Optimization
Lower capital tied up in stock while ensuring product availability.
Summary
The implementation of SAP IBP enabled full automation and harmonization of demand planning across Danone Polska, Nutricia Polska, and Żywiec Zdrój. By leveraging advanced statistical models and machine learning, the companies significantly improved forecast accuracy, reduced losses, and minimized manual effort—delivering tangible operational benefits.
Any questions?
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Luke Krukowski
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