Abstract: Systems, methods, and other embodiments are disclosed that are configured to generate promotion effects for use by a demand forecast model. In one embodiment, a first regression analysis is performed on historical performance data for an item to generate first promotion effect values for a plurality of promotion components. The first promotion effect values are compared to a plurality of rules. If none of the rules are violated by the first promotion effect values, the first promotion effect values are output as final promotion effect values. If at least one of the rules is violated by the first promotion effect values, a phased operation is performed on the historical performance data. The phased operation operates on subsets of the promotion components of the historical performance data over at least two regression analysis phases to generate second promotion effect values.

Abstract: Systems, methods, and other embodiments are disclosed that are configured to generate replenishment parameters for use by an external replenishment system. In one embodiment, sales statistics are generated for an item based at least in part on historical sales data for the item. A determination is made as to if demand forecast data is available for the item. If demand forecast data is not available, an order-point value for the item is generated based at least in part on the sales statistics. If demand forecast data is available, demand forecast statistics are generated and the order-point value is generated based at least in part on the sales statistics and the demand forecast statistics. An order-up-to-level is generated based at least in part on the order-point value.

Abstract: Systems, methods, and other embodiments are disclosed that are associated with forecasting and management of an item to be promoted via tier pricing. In one embodiment, historical sales data of the item are analyzed and a numerical amount, representing a total number of the item sold, is distributed across a plurality of tier discount ranges of a tier pricing promotion plan for the item. The distribution forms a plurality of distributed values and is based at least in part on discounted sales of the item reflected in the historical sales data. A combined discount value is generated based at least in part on the plurality of tier discount ranges and the plurality of distributed values. A promotional demand value is generated based at least in part on the combined discount value.

Abstract: Systems, methods, and other embodiments that are associated with a computer application configured to execute on a computing device, for providing forecasting and management of cross-promoted retail items, are described. In one embodiment, historical demand data and elasticity values associated with retail items sold at a retail location are read from a data structure. The historical demand data represents past sales of the retail items across a plurality of past retail periods, and the elasticity values represent how a change in the demand of one retail item affects changes in the demand of other retail items. Cross-promotion values for affected retail items are generated, based at least in part on the historical demand data and the elasticity values, representing a predicted future change in a demand for the affected retail items due to the planned promotion of another retail item.

Abstract: Systems, methods, and other embodiments that are associated with a computing device configured to execute a computer application, for providing a determination of retail item demand volatility, are described. In one embodiment, a historical error value is generated, at a class level, representing an uncertainty in a demand for a class of retail items. A scaling value is generated, at a retail item level, representing a variability in a demand for at least one retail item in the class of retail items. A forecast error value is generated representing an uncertainty in the demand for the at least one retail item by combining at least the scaling value for the at least one retail item and the historical error value for the class.

Abstract: Systems, methods, and other embodiments that are associated with a computer application configured to execute on a computing device, for providing forecasting and management of returned retail items, are described. In one embodiment, historical retail data associated with a retail item sold at a retail location is read from a data structure associated with the computer application. The historical retail data includes sales data and returns data for the retail item over a plurality of retail periods. Based on the historical retail data, a probability is determined and generated for which a percentage of the retail item sold in one retail period will be returned in at least one subsequent retail period.

Abstract: Systems, methods, and other embodiments for providing management of retail forecasts associated with a computer application are described. In one embodiment, historical demand data associated with a retail item sold at a retail location is read from an input data structure associated with the computer application. A determination is made as to when and where an extra retail period occurs in a forecast time domain. Forecasted demand data is generated for retail periods of the forecast time domain, except the extra retail period, based on the historical demand data. Forecasted demand data is generated for the extra retail period based on a portion of the forecasted demand data for the retail periods of the forecast time domain. An output data structure is transformed by populating the output data structure with the forecasted demand data for the retail periods, including the extra retail period, of the forecast time domain.

Abstract: A map customization module builds a customized map for a user based on the user's interest and historical activities. A database stores processed map data including layer data, element data and/or tile data related to maps. The map customization module obtains the necessary processed map data from the database and combines the map data with the user's interests to generate a customized map. The map customization module recognizes the user's interests based on explicit user input and/or implicit user input.

Abstract: A system that determines metrics for a new item in response to receiving new attributes of the new item further receives for a plurality of previous items, one or more metric values for each previous item, and one or more active attributes for each previous item. The system determines an average metric value for each active attribute of the previous items. The system then, for each of the new attributes, determines a geometric mean of the average metric values for corresponding active attributes that match the new attributes.

Abstract: A system that modifies an input sales planning curve that is a time series expands the time series from an initial length time series to an expanded length time series that includes one or more missing values. The system then fills in the missing values using linear interpolation and samples the expanded time series by selecting a subset of the expanded length time series values. The system then drops the values not selected during the sampling to generate an output sales planning curve.

Abstract: A system that generates a sales forecast for an item receives the sales history for prior sales periods that includes at least one stand-alone time period when a single promotion event is active, and at least one overlapping time period when two or more promotion events are active and overlapping. For each stand-alone time period, the system determines a stand-alone lift for each promotion event active during the stand-alone time periods. For each overlapping time period, the system determines a combined overlap lift of promotion events that are overlapping using a p-norm.

Abstract: A system that generates a sales forecast for an item receives sales history for prior sales periods that include active promotion events that are active during each sales period. The system determines one or more types of overlapping promotions during the sales periods. For each type of overlapping promotion, the system creates an overlapping promotion event that replaces the corresponding active promotion events. The system generates a set of promotion events including the overlapping promotion events and the active promotion events that were not replaced. The system then generates a lift for each of the promotion events in the set of promotion events.