Case Study: Target achieves 20% shelf availability and 20% inventory reduction with Algo

How Target optimized demand planning and retail product allocation across multiple categories with Algo

Target, a major retail company, faced challenges in demand planning and product allocation across its disparate departments. Each product category, from fashion to hard goods and electronics, had different supply chain models, demand profiles, and planning cycles, making it difficult for merchandise teams to align all requirements and meet consumer needs effectively.

To solve this, Algo implemented its configurable demand planning and allocations platform, which utilizes AI and ML forecasting on the MS Azure cloud. This provided Target with a single environment to manage planning, forecasting, and replenishment across numerous departments. As a result, Target achieved a 20% increase in shelf availability, a 20% reduction in inventory, and a 10% boost in productivity.

Target

Nicole Szujda

Former General Manager of Merchandise