Abstract: A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.

Abstract: A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.

Abstract: A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.

Abstract: A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.

Abstract: A method, system, and computer program product for asset service and maintenance lifecycle management and supply chain planning. Some embodiments commence upon receiving a database record corresponding to an individually identified asset to be individually tracked through a corresponding asset lifecycle. Each individually identified asset has an asset-specific scheduled maintenance plan. During the performance of activities pertaining to the asset-specific scheduled maintenance plan, observations are made and events are recorded to generate a series of observations that are in turn collected into a learning model. The learning model and a predictor based on the learning model is used to predict a future demand or a forecast for items in quantities that are not given in the asset-specific scheduled maintenance plan. In exemplary cases, the forecast comprises items and/or quantities that are not given in the scheduled maintenance plan.

Abstract: Disclosed is an improved approach to implement and handle promotion optimization by an enterprise software application. Promotion optimization functionality is provided to permit customer controlled rules to be processed for the promotions. The promotion optimization functionality in some embodiments include adding exclusions sets, improving the buy-down calculation, increased flexibility when entering event costs, better support for different causal types, and/or new parameters that provide greater flexibility.