Abstract: A method for cleansing product demand data to improve product demand forecasting. The improved data cleansing methodology enhances product weekly demand forecast accuracy by adjusting stock-out week demand values, and employing separate outlier logic for regular and promotional demand periods.

Abstract: A method and system for adjusting product store order quantities when a retail store is moving a replenishment source from a current distribution center to a new distribution center. The method determines a last delivery date for a last product order from the current distribution center to be placed prior to a product replenishment source change date, and a first delivery date for a first product order from the new distribution center to be placed following the product replenishment source change date; and compares the two delivery dates to determine which delivery will occur first. When the first delivery date for new distribution center predates the last delivery date for the current distribution center, the last product order from the current distribution center is suspended.

Abstract: An improved method and system for forecasting product demand using a causal methodology, based on multiple regression techniques. The improved causal method identifies year-over-year trending effects within historical product demand data, removes the trending effects from the calculation of seasonal factors used in determining product demand forecasts, calculates trend factors from the identified trending effects, and applies the trend factors and de-trended seasonal factors to initial product demand forecasts when determining final demand forecasts for the products.