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How a global bakery company increased sales and reduced returns by redesigning PoS demand planning using predictive demand models

Multinational company, focused on snacks and pastries.
To transition from a direct to an indirect go-to-market (GTM) model, the company outsourced last-mile logistics to retailers. This shift, combined with the challenges of managing perishable products, required a highly precise and synchronized logistics chain across all channels, from fragmented and traditional trade to modern retail.

We developed a hierarchical forecasting model that operates as a single, integrated system across all logistics levels.
By incorporating key variables—such as retailer promotions, PoS visibility, and holiday calendars—we ensured a unified, accurate demand prediction at multiple resolution levels, from individual PoS to manufacturing plants. This alignment enabled better coordination across the entire supply chain, reducing inefficiencies and optimizing stock levels.

Development of a multi-level demand forecasting system
Integration of key business drivers at each level (retailer promotions, PoS visibility, logistics constraints).
Unified forecasting approach to align manufacturing, distribution, and retail execution.
Enhanced coordination between manufacturers, distribution centers, and retailers to improve logistics efficiency.

reduction in costs relating returns

decrease in retailer out-of-stock situations

Improved synchronization across the supply chain, enhancing both efficiency and service levels

By leveraging DemanLab’s analytical capabilities, we enabled a seamless transition to a data-driven logistics model. The integration of predictive insights across all levels of the supply chain ensured a robust and scalable approach, minimizing waste and maximizing product availability.