Forecast quality is measured before it impacts inventory, procurement, and production decisions. GIB evaluates multiple statistical models — including seasonal methods, Croston for intermittent demand, and regression approaches — and scores them using established error metrics such as MAD, RMSE, tracking signal, and WMAPE. Smooth demand, seasonal products, and intermittent spare parts are treated differently to avoid systematic distortion and bias. Planners can objectively select the best-fit model per material or hierarchy and monitor bias and volatility over time. Instead of relying on static parameters, forecast logic is continuously validated against real performance.

Not all demand behaves the same and GIB ensures planning logic reflects that reality. High-variance items are clearly surfaced so planners can focus attention where forecast risk is greatest. Rather than reviewing everything equally, planning effort is directed toward what truly drives variability and service exposure. Materials can be segmented using ABC/XYZ or custom classifications to differentiate stable, volatile, seasonal, or intermittent patterns.

Forecasting adjustments and lifecycle events are managed directly within SAP. Structured phase-in and phase-out controls guide product transitions, while version-based planning enables scenario comparison without overwriting core data. Manual adjustments are visible and governed, and hierarchy-based coordination ensures alignment across regions, product lines, and organizational levels.

Forecasting operates across structured hierarchies — from product groups down to individual materials, plants, and regions. Multiple versions allow statistical baselines, consensus adjustments, and approved forecasts to coexist in a controlled framework. Decisions remain traceable and aligned.