In times of demand shocks, when quantitative forecasting based on historical time series becomes obsolete, the only information about future demand is ‘‘advance demand information’’, i.e. interpreting early customer bookings as an indicator of not yet known demand. This paper deals with a forecasting method which selects the optimal forecasting model type and the level of integration of advance demand information, depending on the patterns of the particular time series. This constitutes the applicability of the procedure within an industrial application where a large number of time series is automatically forecasted in a flexible and data-driven way. The architecture of such a planning system is explained and using real-world data from a make-to-order industry it is shown that the system is flexible enough to cover different demand patterns and is well-suited to forecast demand shocks.