A global, grower-owned beverage company that sells juice drinks, sauces, and snack products in more than 100 countries faced severe demand disruption following the COVID-19 pandemic.
Changes in consumer behavior—including increased home consumption, brand switching due to income pressure, and shifting pack-size preferences—made traditional forecasting methods unreliable.
The company needed a forecasting approach that could both improve accuracy and explain the drivers behind demand changes.
Demand Chain AI partnered with the organization to develop a Driver-Based Forecasting (DBF) model capable of capturing real-world demand drivers and enabling scenario planning across hundreds of SKUs.
The pandemic fundamentally reshaped consumer demand patterns. Increased at-home consumption, shifting income levels, and evolving retail dynamics disrupted historical trends.
Traditional statistical forecasting models, built primarily on historical data, struggled to adapt to these rapid changes.
The organization relied on multiple data sources including:
Syndicated retail data
Internal order and shipment data
External market indicators
Aligning syndicated UPC-level data with internal SKU-level data created significant complexity, particularly in channels with limited coverage such as club and dollar stores.
Existing forecasting tools generated statistical outputs but lacked transparency. Business teams could not easily understand what factors were driving forecast changes, making it difficult to support strategic decisions around pricing, promotions, or distribution.
The proof-of-concept deployment delivered strong results across forecasting performance and planning visibility.
Key Results
The DBF model delivered approximately 30% improvement in forecast accuracy compared with the company’s baseline statistical forecasting model at a three-period lag.
The model also translated consumption forecasts into shipment plans, revealing trade inventory oscillations that had previously gone undetected.
These insights helped the company better understand the relationship between consumer demand and channel inventory dynamics.
By implementing driver-based forecasting, the company moved beyond traditional historical models to a more flexible and transparent demand planning approach.
Teams can now better understand the factors influencing demand, evaluate strategic decisions, and respond more effectively to market changes.
The result is a more agile, explainable forecasting process that supports better decision-making across the organization.
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