Abstract: Systems and methods to optimize a user plan while predicting and selecting units for a user. A user plan is received for a user to be provided a plurality of units at a given frequency. A type of each unit of the plurality of units is determined based on the user plan. The plurality of units are selected for the user in accordance with the determined type of each unit. User input that changes the type of at least one unit of the selected plurality of units is received. The user plan is then optimized based on the user input.

Abstract: Systems and methods to predict and select groups of units for users. An artificial intelligence mechanism is used to generate a model to predict a next group of units for a target user based on a plurality of sequence of tags for a plurality of sequences of groups of units. A corresponding sequence of tags is generated for each corresponding sequence of groups of units based on start and end tags for each group in each corresponding sequence of groups of units, start and end tags of each unit in each corresponding sequence of groups of units, and tags for each unit in each corresponding sequence of groups of units. The generated model is employed to predict multiple groups of units for a target user based on a sequence of tags for a sequence of groups of units previously provided to the target user.

Abstract: Generation of an available to promise (ATP) value. A method identifies business constraints and goals for an omnichannel retailer based on input quantitative and qualitative data. The method determines numerical parameters for use in an executable optimization model and using a machine learning model. Additionally, the method builds the executable optimization model using at least one of the goals as a respective at least one objective in the optimization model, at least one of the constraints as a respective at least one constraint in the optimization model, and at least one of the numerical parameters as at least one additional parameter in the executable optimization model. In addition, the method executes the executable optimization model and generates an ATP value, and outputs the ATP value to an e-commerce system.

Abstract: A method, system and computer program product for fulfilling an online order. An online order to purchase an item(s) is received. The “candidate locations” that stock the item(s) of the online order and that can be used to fulfill at least a portion of the online order are determined. A stockout cost for each of these candidate locations for fulfilling an item of the online order may be calculated, where the stockout cost is a cost of a potential lost sale of the item of the online order by the candidate location if the candidate location fulfills the item of the online order. A shipping location among the candidate locations to fulfill the item is then determined based at least in part on the stockout cost for each of the candidate locations for fulfilling the item. The item is then shipped to the customer from the determined shipping location.

Abstract: A method, system and computer program product for fulfilling an online order. An online order to purchase an item(s) is received. The “candidate locations” that stock the item(s) of the online order and that can be used to fulfill at least a portion of the online order are determined. A stockout cost for each of these candidate locations for fulfilling an item of the online order may be calculated, where the stockout cost is a cost of a potential lost sale of the item of the online order by the candidate location if the candidate location fulfills the item of the online order. A shipping location among the candidate locations to fulfill the item is then determined based at least in part on the stockout cost for each of the candidate locations for fulfilling the item. The item is then shipped to the customer from the determined shipping location.