A global CPG manufacturer faced chronic out-of-stock issues across key retail channels. Demand signals were scattered across ERP, POS, syndicated data, e-commerce feeds, and distributor reports. Forecasting processes were mostly manual and inconsistent across regions, resulting in missed sales, excess safety stock, and reactive replenishment cycles.
We provided a new forecasting platform that delivered measurable improvements in service levels, accuracy, and operational efficiency, enabling more confident and proactive commercial planning:
8% reduction in out-of-stocks across top retail partners
Higher forecast accuracy for predictable and volatile SKUs
Improved service levels, reducing lost sales and retailer penalties
Faster planning cycles with less manual effort
Better alignment between demand, production, and distribution
THE SITUATION: The organization operated across many international markets with differing data maturity, disconnected commercial systems, and fragmented ownership of demand data. Conflicting forecasting methods and slow manual consolidation prevented teams from establishing a single, trusted view of demand. This limited visibility into seasonality, promotions, and channel variability, making it difficult to plan accurately or respond to market shifts.
THE PROBLEM: The company struggled with deep data fragmentation and operational inefficiencies that made accurate, scalable forecasting nearly impossible, including:
Fragmented historical and real-time demand data
Inconsistent forecasting methods across teams and regions
Limited visibility into promotions, seasonality, and channel behavior
Slow manual consolidation of POS, shipment, and syndicated datasets
High error rates for fast-moving SKUs and new product launches
Reactive replenishment processes driving out-of-stocks and lost revenue
The Frame Solution
Implemented a comprehensive Data & AI modernization program to unify demand signals, improve forecast accuracy, and automate planning workflows. Our approach combined modern data engineering, lakehouse architecture, and machine learning to transform the client’s forecasting capability.
Integrated ERP, POS, syndicated data, digital commerce signals, and distributor feeds into a consolidated Lakehouse
Developed a multivariate ML forecasting model capturing seasonality, promotions, price elasticity, and channel behaviors
Implemented real-time demand sensing for high-velocity SKUs
Built forecast-accuracy dashboards with exception alerts
Automated SKU clustering and event modeling to improve long-tail performance
