I’ve always been a big believer that using downstream supply chain information can improve demand forecasting and planning. I’ve written several columns that discussed various aspects of this during my 12-year tenure in writing for the Journal of Business Forecasting (JBF). These discussed the potential use of downstream data including (as I’ve defined them): customers’ orders and replenishment forecasts, retailer warehouse withdrawals, Point-of-Sale (POS) data and forecasts, Vendor Managed Inventory (VMI), and Collaborative, Planning, Forecasting and Replenishment (CPFR) information. In these articles I proposed that one should be able to improve forecasting by leveraging these types of information since they are generated closer to the consumer. As such, demand changes ought to be detected sooner and be subject to less distortion from the “Bull Whip” effect that adds more noise to consumption demand as it moves up the supply chain. Generally, I’ve been preaching that these downstream data sources ought to provide much better “demand signals” than a manufacturer’s orders and shipments.