  With the ever increasing prominence of data in retail operations, sales
forecasting has become an essential pillar in the efficient management of
inventories. When facing high demand, the use of backroom storage and intraday
shelf replenishment is necessary to avoid stock-out. In that context, the
mandatory input for any successful replenishment policy to be implemented is
access to reliable forecasts for the sales at an intraday granularity. To that
end, we use quantile regression to adapt different patterns from one product to
the other, and we develop a stable and efficient quantile additive model
algorithm to compute sales forecasts in an intradaily context. Our algorithm is
computationally fast and is therefore suitable for use in real-time dynamic
shelf replenishment. As an illustration, we examine the case of a highly
frequented store, where the demand for various alimentary products is
accurately estimated over the day with the help of the proposed algorithm.
