Abstract: Techniques for processing relational data with a user-defined function (UDF) are provided. Relational input data being requested by the UDF, from within a relational database system, is intercepted and normalized. The UDF is called with the normalized input data and as the UDF produces output data in response to the normalized input data that output data is captured and normalized. In an embodiment, the normalized output data is used to dynamically update a data model within the relational database for the input data.

Abstract: Techniques for processing relational data with a user-defined function (UDF) are provided. Relational input data being requested by the UDF, from within a relational database system, is intercepted and normalized. The UDF is called with the normalized input data and as the UDF produces output data in response to the normalized input data that output data is captured and normalized. In an embodiment, the normalized output data is used to dynamically update a data model within the relational database for the input data.

Abstract: A method and system for distributing inventory of a replaced or discontinued product from a warehouse or distribution center to a plurality of stores. The method optimally distributes the inventory of the replaced or discontinued product from the distribution center, keeping in mind the demand for a replacing product at the stores supported by the distribution center. The described system and method calculates replaced (old) and replacing (new) store product order quantities; rounds the replaced and replacing product order quantities if necessary due to product packaging; and ensures that the aggregate of the replaced product order quantity does not exceed distribution center on-hand inventory for the replaced product.

Abstract: Data consumed by a threaded forecasting module is acquired in advance of a request for that data by the forecasting module. The data is used for servicing multiple instances of the threaded forecasting module as they concurrently process. The instances concurrently produce different portions of the forecasting results.

Abstract: A method for forecasting daily sales patterns for retail products. The method uses historical daily demand data for a product to calculate a daily weight value for the product for each day of the week, each daily weight value representing the ratio of the historical daily demand for a corresponding day of the week to a total of the historical daily demands for the entire week. A daily demand forecast for each day of a forthcoming week is determined by applying the daily weight values to a predetermined weekly demand forecast for the forthcoming week. Store closure coefficients are applied to the daily demand forecasts for days immediately preceding and following store closures or holidays to adjust the daily forecasts to accommodate changes in customer buying patterns resulting from the store closures.

Abstract: A method and system for forecasting distribution center (DC) or warehouse product suggested order quantities required to meet future product demands for a retailer. In determining DC/warehouse order quantities, a bias factor and Adaptive Forecast Error (AFE) are calculated from prior product demand and sales data and applied to DC/warehouse effective inventory calculations to account for forecast errors in DC/warehouse suggested order quantities. If the bias indicates a forecast that is too high, the method and system will attempt to compensate by increasing the suggested order quantity. If the bias indicates a forecast that is too low, the method and system will attempt to compensate by decreasing the suggested order quantity.

Abstract: A method for optimally distributing inventory in short supply from a warehouse to retail stores served by the warehouse. The method compares future store product requirements with future warehouse inventory levels to identify potential product inventory constraints. Once a constrained product is identified, the method automatically adjusts store orders based on store “must have” requirements, prioritization rules selected and ranked by the user, and historical store sales performance.

Abstract: Methods and systems for demand forecasting are provided. Historical demand data for a related product is acquired. The historical demand data corresponds to a period of time during which a promoted product was promoted. The demand effect of the related product is determined during this period of time and used to project or forecast a demand for the related product when the promoted product is subsequently promoted. The demand effect can be positive or negative. A positive demand effect identifies an affinity relationship between the promoted product and the related product. A negative demand effect identifies a cannibalization relationship between the promoted product and the related product.