Abstract: A method and system optimizing source selection of an online order with the lowest fulfillment cost by considering multiple types of parameters, including shipping costs, backlog costs and markdown savings of the order. The method includes obtaining an order from the order retrieval subsystem of the OMS, selecting the candidate sources, and retrieving data from retailers or shipping companies of each selected candidate sources. The system then calculates the costs and savings parameters of the candidate sources from the retrieved data. The system identifies all possible candidate sourcing selections of the order and calculates the total fulfillment cost of each sourcing selection of the order by adding the shipping costs with the backlog costs, and subtracting the markdown savings of all candidate sources in each sourcing selection. The system identifies the optimized sourcing selection of the order with the lowest fulfillment cost and renders the selection to the OMS.

Abstract: In one embodiment, a computer-implemented method includes receiving historical transaction data related to a product. A demand model is calibrated to forecast demand for each of one or more zones and each of one or more channels over which the product is sold. A time-and-virtual-space (TVS) network is constructed, by a computer processor, to include one or more supply nodes and one or more sink nodes. Each of the supply nodes represents inventory of the product at a corresponding physical location, and each of the sink nodes represents a calibrated demand for the product. Based on the TVS network, a low-cost plan is determined for an omni-channel retail environment. The low-cost plan specifies at least one of allocation of the product across physical stores, partitioning of the inventory of the product for virtual sales, and pricing of the product.

Abstract: In one embodiment, a computer-implemented method includes receiving historical transaction data related to a product. A demand model is calibrated to forecast demand for each of one or more zones and each of one or more channels over which the product is sold. A time-and-virtual-space (TVS) network is constructed, by a computer processor, to include one or more supply nodes and one or more sink nodes. Each of the supply nodes represents inventory of the product at a corresponding physical location, and each of the sink nodes represents a calibrated demand for the product. Based on the TVS network, a low-cost plan is determined for an omni-channel retail environment. The low-cost plan specifies at least one of allocation of the product across physical stores, partitioning of the inventory of the product for virtual sales, and pricing of the product.