Stop Using Segmented Forecasting

With advancements in technology and data analytics, demand forecasting is experiencing a significant transformation. The traditional, widely used approach of first segmenting products and then applying distinct forecasting techniques to each segment may no longer be the most effective or accurate method. This article first discusses the drawbacks of employing segmentation, then navigates towards alternatives (optimization engines and machine learning) that promise substantial enhancements to your forecasting strategy.

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Demand forecasting has always been crucial for supply chain management. It helps businesses to plan and manage their resources effectively and avoid unnecessary costs (we recently demonstrated that there is a direct correlation between forecasting accuracy and business value). However, as market dynamics are complex, we need forecasting methods that can capture this complexity and deliver accurate predictions. In this context, we delve into two alternatives to segmented forecasting: SKU-by-SKU optimization and global machine…