Global Beer Leader achieves 10% forecast accuracy boost by replacing SAP APO with o9 AI-powered planning

Operating across 32+ countries with thousands of SKUs, this global brewer faced demand patterns that its legacy SAP APO system was structurally ill-equipped to handle. High seasonality and promotional spikes drove volatility that lagging indicators — the backbone of the existing forecasting process — consistently failed to anticipate. Planning ran through fragmented, siloed spreadsheets across business units, leaving no integrated view connecting market-level demand signals to upstream supply constraints. The consequences were predictable: stockouts during peak periods, excess inventory in off-seasons, elevated flex production costs, unplanned logistics expedites, and preventable food waste. At enterprise scale, even small forecast errors compound into significant lost revenue and wasted capital.

The company replaced SAP APO with o9 Solutions' AI-powered Digital Brain platform — a unified system covering all planning time horizons and all 32+ countries in a single deployment. o9's Enterprise Knowledge Graph was used to build interconnected market, demand, and supply knowledge models, enabling planners to see the full global network as one coherent system rather than isolated data silos. Machine learning forecasting algorithms, built in R and Python, replaced lagging-indicator methods by incorporating both internal historical patterns and external demand drivers such as promotional calendars and market signals. The implementation extended beyond forecasting: Multi-Echelon Inventory Optimization (MEIO), LP optimization via a Gurobi solver, a real-time Control Tower, and reverse logistics planning were all deployed on the same integrated platform — covering every category, brand, channel, and account without requiring separate tools.

The deployment delivered a 10% step-change improvement in forecast accuracy with measurably reduced bias — material for a global network where forecast errors cascade into supply chain costs at scale. Additional outcomes included:

• Lower non-standard supply chain costs: reduced flex production spend and logistics expenditure
• Significant inventory improvement across the 32-country network, freeing working capital
• Reduction in lost sales during high-demand and promotional windows
• Sustainability gains: decreased food waste and fewer emergency logistics expedites

Beyond the metrics, planners gained real-time end-to-end visibility into constraints across the global network, enabling faster and more confident responses to demand shifts.

• Replacing APO-era systems with AI-native platforms yields the greatest forecast gains when both internal history and external demand drivers are unified into a single model — lagging indicators alone cannot capture promotional or seasonal volatility.
• A knowledge graph architecture spanning all time horizons, brands, and channels is the structural prerequisite for eliminating planning silos; partial integrations preserve the root cause.
• End-to-end digital twin visibility — connecting market demand nodes to upstream supply nodes — is required for constraint-aware planning at global scale, not a post-deployment enhancement.
• Global rollouts demand open-architecture platforms capable of incorporating regional data sources, existing solvers, and diverse planning processes without forcing standardization prematurely.