Another problem is that the more granular the forecast – SKU at store level by week, for example – the higher the forecast error tends to be. "For sure, the greater degree of error in the store-level forecast, the greater the impact on the lost sale calculation," Fenwick said. "However, even if we hit a 70% accuracy measure, we're still capturing 70% of the potential lost demand in the store due to stock outs. Which, from a forecasting perspective, is a lot better than capturing zero lost demand. As the saying goes, 'if you only forecast to sales, you'll only ever stock to … what you sold.'"

Machine learning can be used to improve forecasts. The basic idea is that a demand forecast is made, a machine learning engine ingests data on how accurate that forecast was, and then the machine autonomously applies better math to improve the next forecast. This is explained in more depth in a previous article.