How accurate are machine learning (ML) demand forecasts?

This is the first question most buyers ask, and the honest answer is that there is no single universal accuracy figure, because it depends heavily on the industry, the product, and the quality of the underlying data. A fast moving staple forecasts very differently from a long tail seasonal item. What machine learning does better than a person is weigh many demand signals at once, seasonality, promotions, lead times, price changes, and spot patterns a planner scanning a spreadsheet cannot hold in their head, then update the prediction the moment new data lands rather than once a month. That predictive analytics edge is also where the time savings come from, since planners review the exceptions the model flags instead of rebuilding forecasts by hand. At Camif, a mid-market furniture retailer, that shift freed the equivalent of a full-time role, around 1,760 hours a year. The more useful benchmark is forecast value add, which measures how much better a machine learning forecast performs against a simple statistical baseline. What matters operationally is not chasing a perfect number but shrinking the error enough to carry less safety stock at the same service level.