- Supply Chain Management
- Resource Center
- New Releases
- Demand Planning
Release 25.0: New Demand Planning features
Enhanced precision for forecasts and strategic decisions
With Release 25.0, companies can now perform demand planning based on financial turnover (revenue) in addition to the traditional method of planning based on product quantities. This means that businesses can choose to forecast future sales in monetary terms for better financial alignment or continue to forecast in units sold for operational planning.
Additionally, the integration of seasonality tests and white noise analysis from SAP’s Predictive Analytics Library (PAL) enhances the accuracy of consumption forecasts, ensuring better resource allocation, risk management, and operational stability.
Key features in Demand Planning for Release 25.0
Align financial goals with supply chain planning using value-based turnover forecasting
This new feature allows companies to forecast future financial turnover (the total value of sales over a period) rather than just planning based on the number of products sold. These value-based planning apps are designed to work alongside your existing operational demand planning processes, not replace them. While operational demand planning manages product quantities and supply chain logistics, the new apps provide a financial perspective, focusing on revenue forecasts and cost implications to support broader business decisions.
In the graphic, the blue bars represent historical values that include "white noise." WN indicator = 1
Enhance forecast accuracy with automated seasonality detection and data quality insights
SAP’s Predictive Analytics Library (PAL) is a collection of advanced statistical and machine learning algorithms integrated into SAP S/4HANA systems to support data-driven decision-making.
Seasonality test:
- What it does: Analyzes historical sales data to determine if there are seasonal patterns—periodic fluctuations in demand at specific times of the year (e.g., higher sales during holidays).
- Business impact: The seasonality test in SCX Demand Planning automatically calculates the optimal season length for each material based on historical data, eliminating the need for a single manual setting across all materials. The system uses the Autocorrelation Function (ACF) metric — ranging from -1 to 1 — to determine the strength of seasonal patterns. Materials with an ACF value of 0.6 or higher will automatically apply the calculated season length, while those below this threshold will continue using the manual standard value, ensuring more accurate and tailored forecasts for each product.
White noise analysis:
- What it does: Identifies random, unpredictable fluctuations in historical sales data that do not follow any trend or pattern.
- Business impact: White noise analysis identifies time series data that lack any consistent pattern, trend, or seasonality, indicating that the data points are purely random with no correlation over time. In SCX Demand Planning, the White Noise (WN) indicator serves as a tracking signal to alert planners when historical data may be too random for reliable statistical forecasting. This ensures that forecasts are based on stable data, prompting users to review or adjust predictions when white noise is detected.