  Traditionally, manufacturers use past orders (received from some downstream
supply chain level) to forecast future ones, before turning such forecasts into
appropriate inventory and production optimization decisions. With recent
advances in information sharing technologies, upstream supply chain (SC)
companies may have access to downstream point of sales (POS) data. Such data
can be used as an alternative source of information for forecasting. There are
a few studies that investigate the benefits of using orders versus POS data in
upstream SC forecasting; the results are mixed and empirical evidence is
lacking, particularly in the context of multi-echelon SCs and in the presence
of promotions. We investigate an actual three-echelon SC with 684 series where
the manufacturer aims to forecast orders received from distribution centers
(DCs) using either aggregated POS data at DC level or historical orders
received from the DCs. Our results show that the order-based methods outperform
the POS-based ones by 6\%-15\%. We find that low values of mean, variance,
non-linearity and entropy of POS data, and promotion presence negatively impact
the performance of the POS-based forecasts. Such findings are useful for
determining the appropriate source of data and the impact of series
characteristics for order forecasting in SCs.
