Abstract: A method and system for predicting the impact of replenishment levers on product service level, lost sales, and on-shelf availability for a retailer. The method and system models cost and revenue elasticity curves for a product or group of products and analyzes the cost and revenue elasticity curves, measures the impact of tuning the replenishment levers on inventory cost and sales revenue, and identifies values for the product replenishment levers to optimize replenishment system policies and product profitability.

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.

Abstract: A product demand forecasting methodology is presented that applies daily weight values to a weekly forecast to determine daily forecasts for a product or service. The method determines daily weight values for use in forecasting current product sales by blending daily weight values calculated from historical demand data for both recent weeks and year-prior weeks. Recent weeks are used to account for recent correlations and alternation effects, and year-prior weeks are used to account for seasonality effects. The method automatically calculates a measure of significance for the daily weights calculated from the recent weeks and year-prior weeks. The significance of each week is applied as a weighting factor during the blending of recent weeks and year-prior daily weight values.

Abstract: An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs information about prior promotional demand forecasts, prior product sales, and the data dispersion and the number of data samples in a product class hierarchy to dynamically determine the optimal level at which to compute promotional uplift coefficients. The methodology calculates confidence values for promotional uplift coefficients for products at each level in a merchandise product hierarchy, and uses the confidence values as a filter to determine the optimal level for promotional uplift aggregation.

Abstract: Techniques for multi-variable analysis at an aggregate level are provided. Two or more datasets having different statistical data distributions and which are not capable of being aggregated are acquired. The values for variables in the two or more datasets are normalized to produce a single integrated dataset of normalized values. The normalized values are then used to produce a demand model that represents and integrates multiple disparate products or services from the two or more datasets into a single demand model.

Abstract: A forecast response factor (RF) determines how quickly product demand forecasts should react to recent changes in demand. When a product sales pattern changes (e.g., a sudden increase in product demand), RF is adjusted accordingly to adjust the forecast responsiveness. The present subject matter provides automatic calculation of the RF, based at least in part on the nature of the product sales (autocorrelation) and the status of recent forecasts (bias).

Abstract: An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. The model is utilized to calculate the promotional uplift from the coefficients of the regression equation. The methodology utilizes a mathematical formulation that transforms regression coefficients, a combination of additive and multiplicative coefficients, into a single promotional uplift coefficient that can be used directly in promotional demand forecasting calculations.

Abstract: An improved method and system for forecasting product demand using a causal methodology, based on multiple regression techniques. The causal method uses both historical and future values of causal factors for causal forecasting. Historical values are used to build a causal model, i.e., to determine the influence of the causal factors upon the demand for a product, and future values are used to generate demand uplifts which applied to an initial demand forecast based upon historical product demand. The improved causal method provides different processes for the calculation of demand uplifts associated with seasonal variables, such as temperature, than typical, non-seasonal causal variables, such as product price.

Abstract: An improved method and system for forecasting product demand using a causal methodology, based on multiple regression techniques. The improved causal method revises product group seasonal factors used by conventional forecasting applications to best fit the sales pattern of an individual product in the product group through the calculation of an exponential coefficient which measures the deviation of the historical sales pattern of an individual product from the product group seasonal factors. The value of exponential coefficient is calculated using a causal framework through multivariable regression analysis.

Abstract: A method and system for determining distribution center or warehouse product order quantities of a slow selling product. The method includes the step of determining for each one of a plurality of stores supplied by the distribution center, a store sales forecast for the slow selling product. The method converts the store sales forecast to a stochastic forecast when the average rate of sale of the product is less than a minimum average rate of sale threshold value. Store order forecasts are thereafter determined by subtracting a store inventory value from the stochastic forecast when average rate of sale is less than the average rate of sale threshold value, and subtracting the store inventory value from the sales forecast when the average rate of sale is not less than said average rate of sale threshold value.

Abstract: A method and system for determining distribution center or warehouse product order quantities of a slow selling product. The method includes the step of determining for each one of a plurality of stores supplied by the distribution center, a store order forecast for the slow selling product. The method generates a random beginning on-hand inventory value for stores with inventories below a minimum inventory threshold value. Store order forecasts are thereafter determined by subtracting the random beginning on-hand inventory value from store sales forecasts when the beginning on-hand inventory value is less than the minimum inventory threshold value, and subtracting the actual beginning on-hand inventory value from the store sales forecasts when the beginning on-hand inventory value is not less than the minimum inventory threshold value.

Abstract: A method and system for forecasting product demand using a causal methodology, based on multiple regression techniques. In order to better predict product demand changes associated with causal variables having seasonal patterns, such as temperature, the method and system include a technique for removing the seasonal variation of causal variables, i.e., to de-seasonalize the causal factors. The de-seasonalized causal variables are utilized within the causal methodology to generate product demand forecasts.

Abstract: A method system for forecasting product demand using a causal methodology, based on multiple regression techniques. The methodology utilizes weather related data as a set of causal factors for retail demand forecasting. These weather related factors may include temperature, precipitation, snow, accumulated snow, or extreme weather conditions.

Abstract: An aggregate User Defined Function (UDF) processing used for multi-regression is provided. The aggregate UDF initializes storage space for multiple nodes of a database environment. Data is then extracted from a relational database and populated according to groupings on each of the nodes. Multiple rows or records are then processed to create a merge and multi-regression processed.

Abstract: A method, based on autocorrelation techniques, for measuring the relative significance of the systematic versus random components of product sales data. The results of this determination can be used to improve product demand forecast and product seasonal profile determinations. When a product's sales variation is primarily due to systematic patterns, the accuracy of demand predictions and forecasts can be improved by understanding and modeling the underlying pattern. On the other hand, when variations in sales are merely random, these variations can be discounted when determining demand forecasts or product seasonal profiles.

Abstract: A partitioning system that provides a fast, simple and flexible method for partitioning a dataset. The process, executed within a computer system, retrieves product and sales data from a data store. Data items are selected and sorted by a data attribute of interest to a user and a distribution curve is determined for the selected data and data attribute. The total length of the distribution curve is calculated, and then the curve is divided into k equal pieces, where k is the number of the partitions. The selected data is thereafter partitioned into k groups corresponding to the curve divisions.

Abstract: An improved method for forecasting and modeling product demand for a product. The forecasting methodology employs a causal methodology, based on multiple regression techniques, to model the effects of various factors on product demand, and hence better forecast future patterns and trends, improving the efficiency and reliability of the inventory management systems. A product demand forecast is generated by blending forecast or expected values of the non-redundant causal factors together with corresponding regression coefficients determined through the analysis of historical product demand and factor information. The improved method provides for the saving and updating of previously calculated intermediate regression analysis results and regression coefficients, significantly reducing data transfer time and computational efforts required for additional regression analysis and coefficient determination.

Abstract: An improved method for forecasting and modeling product demand for a product. The forecasting methodology employs a causal methodology, based on multiple regression techniques, to model the effects of various factors on product demand, and hence better forecast future patterns and trends, improving the efficiency and reliability of the inventory management systems. The improved method identifies linear dependent causal factors and removes redundant causal factors from the regression analysis. A product demand forecast is generated by blending forecast or expected values of the non-redundant causal factors together with corresponding regression coefficients determined through the analysis of historical product demand and factor information.

Abstract: A forecast response factor (RF) determines how quickly product demand forecasts should react to recent changes in demand. When a product sales pattern changes (e.g., a sudden increase in product demand), RF is adjusted accordingly to adjust the forecast responsiveness. The present subject matter provides automatic calculation of the RF, based at least in part on the nature of the product sales (autocorrelation) and the status of recent forecasts (bias).

Abstract: A product demand forecasting technique is presented which employs multivariable regression analysis to identify demand associated with annual events and shift demand associated with those events when the events occur in different weeks of different years. Historical weekly product demand data is acquired for one or more years. An event influencing demand for products which occurs at in different weeks in a prior year than in the forecast year is identified. Mulitvariable regression techniques are used to analyze the historical weekly product demand data to determine demand components associated with the event. These demand components can then be removed from the historical weekly demand data and re-applied to weeks in the prior year corresponding to the week the event occurs in the forecast year to create a shifted historical weekly demand for said product.