Modern retail supply chains face rising volatility, shorter product cycles, and growing pressure to anticipate demand with greater precision. Traditional forecasting approaches often rely on limited signals and disconnected systems, which can lead to costly imbalances. MathCo’s demand forecasting and replenishment framework equips retailers with a unified intelligence layer that aligns demand drivers, improves visibility, and supports faster, more informed inventory decisions across global markets.
Problem Statement
A global fashion retailer sought to improve the accuracy and consistency of its demand planning across regions. Forecasts were generated using fragmented data sources, and replenishment decisions varied widely by market. The client needed a unified, data-driven approach that could consolidate demand signals, enhance visibility, and reduce the financial impact of stock imbalances.
Impact
- Achieved an average forecast accuracy of ~88% in the US and ~82.7% across EU markets
- Reduced more than 36% of combined lost sales and excess inventory cost through category and SKU level replenishment planning
- Improved visibility and consistency across global S&OP workflows by unifying demand signals and standardizing planning processes
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