Almost all demand forecasting and planning systems use some form of statistical forecasting methods that require historical demand data. The closest data to consumer demand is POS and/or syndicated scanner data. Although, many companies collect and store POS and syndicated scanner data, less than 40% of companies use POS data for demand forecasting, and less than 10% use syndicated scanner data. Many companies continue to manually cleanse their historical demand data as a prerequisite for forecasting and planning of their products. Manually cleansing data is an intensive process that tends to add virtually no value. The primary reason for cleansing data is that traditional demand forecasting and planning systems are unable to predict sales promotions and correct for outliers. This is a result of the statistical methods being deployed in the technology—mainly exponential smoothing methods—which are not capable of measuring and predicting sales promotions or automatically correct for shortages and outliers.