Problem Statement

The client leveraged SAP IBP Demand Planning for product demand forecasting. However, major market disruptions—including natural gas shortages, price volatility, surging demand for heat pumps, and dynamic regulatory changes in Germany—introduced substantial instability and severely impacted forecast accuracy. Most product time series showed clear signs of disruption, while the existing forecast setup lacked the robustness to respond effectively. With little preprocessing to smooth out anomalies, the models reacted sharply to erratic short-term spikes resulting in dramatic forecast shifts.

In addition, accessory products—typically sold alongside core products—were forecasted independently, ignoring joint sales dynamics.

Solution Provided

• Established essential data preprocessing routines—including outlier detection and correction—to reduce noise and stabilize input signals prior to modeling.
• Replaced the existing Best Fit approach with an ensemble forecasting strategy in SAP IBP, combining multiple models to deliver more stable, accurate predictions across all product groups.
• Developed a custom Python-based software solution to leverage correlations between core and accessory products, integrating product lifecycle data for more intelligent accessory forecasting.

Implementation & Execution

At project start, the client relied on SAP IBP’s Best Fit algorithm, which selected a single top-performing model (e.g., ARIMA, Exponential Smoothing, Gradient Boosting) based on historical data. We replaced this with a forward-looking ensemble forecasting strategy, combining multiple models into a weighted aggregate to produce more resilient and accurate forecasts. Optimal model weights were derived through time series cross-validation, simulating real-world forecast performance and enabling robust selection across a variety of demand patterns. These weights were periodically recalibrated for core products, accessories, and product segments with pronounced seasonality.

One key improvement involved implementing a robust preprocessing layer. Before feeding time series into forecasting models, we applied targeted outlier correction and sales data smoothing techniques. This reduced the influence of extreme, short-term events on the forecast, leading to more consistent performance and mitigating overreactions to volatile months.

To further enhance forecasting capabilities, we developed a custom Python-based software solution: Core Product Governed Accessory Forecasting (CPGAF). This tool capitalized on the strong correlations between core and accessory products, integrating historical relationship patterns and PLM information to deliver accessory forecasts that are both intelligent and synchronized with the broader product ecosystem.

Beyond model improvements, we provided the client with deep-dive data analytics to uncover key performance drivers—spanning product groups, countries, and forecast granularities—by examining different product hierarchy levels and time intervals.

Results & Impact

• Achieved a significant improvement in forecast stability and accuracy, boosting key performance indicators by 15% to 30% across target markets.
• Successfully implemented the Python-based CPGAF package, enabling more accurate accessory forecasting driven by core product predictions.
• Strengthened forecasting resilience in a volatile environment, positioning demand planning as a strategic, data-informed driver of business value.