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Forecast accuracy is one of those areas that seems straightforward until you try to apply it consistently. Most people start with a basic metric, often MAPE or RMSE, because that’s what their software provides, or because it’s easy to explain. But over time, the limitations start to show. The same model performs well under one metric and poorly under another. Different teams prefer different measures. Some products look accurate, while others clearly aren’t, even though they

Not All Products Behave the Same, So Why Forecast Them That Way? Imagine trying to forecast daily demand for toothpaste the same way you would for holiday gift baskets. One sells steadily year-round, the other spikes unpredictably. Yet in many businesses, all products are still forecasted using the same models, with the same assumptions, and often with the same level of effort. The result? Overstocked warehouses, stockouts during peak demand, and wasted hours spent fine-tunin