The combination of forecasting, demand planning, and automated forecasting models creates a new level of quality in supply chain management practice. Companies benefit from greater transparency, faster decision-making processes, and better adaptability to volatile markets. The white paper "AI-supported demand forecasts for more efficient supply chain management" classifies key developments in data-based planning and shows how supply chain optimization can be further developed in a traceable manner with SAP Integrated Business Planning (IBP).
Improve sales forecasts – with data instead of gut feelings
Many companies still plan their sales volumes using simple methods or manually. These approaches are often prone to errors and hardly take external influences into account. This leads to inaccurate forecasts – with consequences for inventory levels, production, and delivery capabilities.
Modern solutions such as SAP Integrated Business Planning (IBP) rely on AI-supported planning and digital transformation. They analyze historical data, recognize patterns, and take external factors such as market trends or seasonal fluctuations into account. The result: objective, traceable forecasts with significantly higher accuracy. This gives companies a reliable basis for their decisions – and enables them to manage their supply chains in a more targeted manner. This topic is becoming increasingly important, as data-based planning is now a key success factor. In dynamic market environments, this approach also creates a more stable basis for decisions and supports traceable planning.
The three pillars of a forecasting model – algorithm, data, indicators (Source: KPMG 2024)
This figure shows how modern forecasting models are structured and which components are crucial for high prediction quality.
SAP IBP as a platform for intelligent planning
The forecasting models can be integrated directly into SAP Integrated Business Planning (IBP). The data flows seamlessly into the planning processes – for example, into demand, production, or financial planning. No additional tools are required.
The forecast becomes particularly effective when external indicators are integrated. These so-called signal-based forecasts take into account factors such as price developments, consumer behavior, and weather data. This increases the forecast accuracy from an average of 65 percent to up to 99 percent. At the same time, the effort required to create forecasts is significantly reduced. The processes run automatically and deliver results in minutes instead of days. This close integration creates a planning framework that allows you to respond flexibly to changes after early detection. Planning software such as SAP IBP thus enables end-to-end supply chain optimization.
End-to-end workflow from data provision to forecasting in SAP IBP (Source: KPMG 2025)
This illustration shows how AI-supported forecasting is technically integrated into SAP IBP – from data import to the finished plan.
Greater efficiency, lower costs – and a clear competitive advantage
The benefits are immediately apparent: companies can reduce storage and transport costs because they know more precisely when and how much needs to be produced or delivered. The supply chain becomes more robust because risks can be identified early on and managed in a targeted manner using real-time scenario analyses.
The solution is scalable and can be applied to various business areas. It offers transparency, can be manually overridden, and integrates seamlessly into the existing IT landscape. Companies that rely on AI forecasting actively shape their planning and secure a real competitive advantage – today and in the future. The clear structuring of data and processes strengthens the basis for sound decisions along the entire value chain in the long term.
Advantages at a glance:
- Forecast accuracy of up to 99% thanks to AI and external indicators such as price developments, inflation rates, or market trends
- Seamless integration into SAP IBP for automated planning processes
- Reduced manual effort in creating sales forecasts
- Cost savings through optimized inventory and transportation planning
- Greater supply chain resilience through data-based scenario analyses
