Abstract: System and method for omni-channel retailer operations that integrate a network of brick-and-mortar stores with their online channel. The system and method includes calibrating a demand model for both brick-and-mortar sales and on-line channels over which a product is sold, the calibrating based upon a cross-channel fulfillment-aware inventory effect. An omni-channel sales prediction and fulfillment model is then constructed based on the calibrated demand model. Using constructed linear demand and revenue models, a plan is generated to optimize one or more: allocating of the product across physical stores, partitioning of the product for sales, and pricing of the product. Customer choices are jointly modeled across channels to allow switching, and a ship-from-store (SFS) inventory effect feature only for brick choice is applied to capture asymmetry.

Abstract: System and method for omni-channel retailer operations that integrate a network of brick-and-mortar stores with their online channel. The system and method includes calibrating a demand model for both brick-and-mortar sales and on-line channels over which a product is sold, the calibrating based upon a cross-channel fulfillment-aware inventory effect. An omni-channel sales prediction and fulfillment model is then constructed based on the calibrated demand model. Using constructed linear demand and revenue models, a plan is generated to optimize one or more: allocating of the product across physical stores, partitioning of the product for sales, and pricing of the product. Customer choices are jointly modeled across channels to allow switching, and a ship-from-store (SFS) inventory effect feature only for brick choice is applied to capture asymmetry.

Abstract: A computer implemented method and system of allocating a target commodity product onto an omnichannel distribution system is provided. Historical data related to the target commodity product is retrieved. Data mining is performed on the retrieved historical data to identify patterns therefrom. An omnichannel nominal demand prediction model is developed based on the identified patterns of the data mining for the omnichannel distribution system. A life-cycle demand is forecast. An allocation for the target commodity product based on the omnichannel nominal demand prediction model is created. A worst case scenario of allocation of the target commodity product for the omnichannel distribution system is identified. The allocation is adjusted to prevent the worst case scenario.

Abstract: A system that compresses data during neural network training. A memory stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An anticipatory value of inventory (VOI) optimization component calculates optimal VOI and prices for immediate-future inventory levels in parallel and writes latest price updates for respective states to a price stack; and a recommendation component provides customized pricing recommendation for a product relative to a unique customer as a function of the latest price updates for respective states to the price stack.

Abstract: A streams platform is used. Multiple streams of electricity usage data are received, each from an electrical meter providing periodic updates to electrical usage for devices connected to the electrical meter. Weather information is received corresponding to locations where the electrical meters are. Real-time predictive modeling of electricity demand is performed based on the received multiple streams of electricity usage data and the received weather information, at least by performing: updating a state space model for electrical load curves using the usage data from the streams and the weather, wherein the updating uses current load observations for the multiple streams for a current time period; and creating forecast(s) for the electricity demand. The forecast(s) of the electricity demand are output. Appliance-level predictions may be made and used, and substitution effects and load management functions may be performed.

Abstract: A computer implemented method and system of allocating a target commodity product onto an omnichannel distribution system is provided. Historical data related to the target commodity product is retrieved. Data mining is performed on the retrieved historical data to identify patterns therefrom. An omnichannel nominal demand prediction model is developed based on the identified patterns of the data mining for the omnichannel distribution system. A life-cycle demand is forecast. An allocation for the target commodity product based on the omnichannel nominal demand prediction model is created. A worst case scenario of allocation of the target commodity product for the omnichannel distribution system is identified. The allocation is adjusted to prevent the worst case scenario.

Abstract: Systems, methods, and computer-readable media are disclosed for jointly optimizing one or more parameters associated with multiple different price mechanisms. The price mechanisms may include spot market pricing, fixed-contract pricing, formula-based pricing, or the like, and may vary in duration. The optimized parameters may include an optimized price for each price mechanism that maximizes expected seller profit across all price mechanisms and all buyers.

Abstract: System and method for omni-channel retailer operations that integrate a network of brick-and-mortar stores with their online channel. The system and method includes calibrating a demand model for both brick-and-mortar sales and on-line channels over which a product is sold, the calibrating based upon a cross-channel fulfillment-aware inventory effect. An omni-channel sales prediction and fulfillment model is then constructed based on the calibrated demand model. Using constructed linear demand and revenue models, a plan is generated to optimize one or more: allocating of the product across physical stores, partitioning of the product for sales, and pricing of the product. Customer choices are jointly modeled across channels to allow switching, and a ship-from-store (SFS) inventory effect feature only for brick choice is applied to capture asymmetry.

Abstract: Systems, methods, and computer-readable media are disclosed for optimizing terms of a long-term contract between a buyer and a seller for a product. Various types of forecasting models may be generated and used to determine the optimized terms such as an optimized price for the long-term contract. A risk model may be generated and evaluated to identify market disruptions that indicate that the optimized price should be re-negotiated to distribute the associated risk between the buyer and seller. An example of such a market disruption may be a market price fluctuation that causes a difference between a spot market price for the product and a contract price to meet or exceed a threshold value.

Abstract: A streams platform is used. Multiple streams of electricity usage data are received, each from an electrical meter providing periodic updates to electrical usage for devices connected to the electrical meter. Weather information is received corresponding to locations where the electrical meters are. Real-time predictive modeling of electricity demand is performed based on the received multiple streams of electricity usage data and the received weather information, at least by performing: updating a state space model for electrical load curves using the usage data from the streams and the weather, wherein the updating uses current load observations for the multiple streams for a current time period; and creating forecast(s) for the electricity demand. The forecast(s) of the electricity demand are output. Appliance-level predictions may be made and used, and substitution effects and load management functions may be performed.

Abstract: Embodiments are directed to a computer implemented method of generating inventory valuation data for an omnichannel (OC) retail operation. The method starts with an unsolvable OC nonlinear nonconvex problem, applies transformations to generate a mixed-integer program (MIP) that is a tractable linear nonconvex form, solves the MIP, fixes prices at optimal values to achieve dimensionality reduction and eliminate all non-convexity by eliminating the pricing dimension. The method further obtains inventory flow linear programming (LP) that is linear convex, and solves the LP to recover a dual solution as initial inventory valuations.

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.

Abstract: A demand forecasting system includes a market information processing module that processes a historical sales dataset to provide a lost market rate probability dataset, a lost-sales forecasting module that processes the lost market rate probability dataset to provide a lost-sales dataset, a market size forecasting module that processes the lost market rate probability dataset to provide a market size dataset as a function of the lost market rate probability, a demand forecasting module that processes the lost-sales dataset and the historical sales dataset to provide a demand dataset and a market share dataset as functions of the lost-sales dataset and the historical sales dataset and a best fit optimization module that processes the market size dataset and the market share dataset to provide a set of best fit parameters for the market size and the market share or the demand. A corresponding method is also described.

Abstract: Price matching strategies for a seller selling products using one or more sales channels and facing competition from omni-channel competitors in marketplace may be provided. For a product and channel, a subset of candidate competitors by product and channel may be identified. For a product, channel, candidate competitor, a value-at-risk metric is computed that represents the seller's value in the sales channel that is at risk to a competitor's price change. Based on the value-at-risk metric, one or more products for price matching against the candidate competitors may be identified. A price for the identified product may be computed that is within a competitive range.

Abstract: Predicting demand of a product offered in multiple channels for a seller that has a plurality of physical store channels and one or more virtual channels, may comprise obtaining transaction log data having records of sales transactions with location identifiers in the one or more virtual channels. The one or more virtual channels may be segmented by locations based on locations of the physical stores and the location identifiers in the transaction log data. A demand model may be estimated by location that incorporates demand for the multiple channels in that location and captures cross-effect of said multiple channels in the same location based on historic sales and transaction data. Integrated price optimization may be performed across all channels and locations that compute one or more prices for each virtual channel and one price for each location in the other channels while also satisfying a plurality of inter-channel and inter-locations constraints.

Abstract: Predicting demand of a newly launched product may comprise obtaining customer sentiment data associated with the newly launched product, the customer sentiment data obtained at least from social media. A mean sentiment lag associated with the customer sentiment data may be determined. A weight given to a predicted PLC effect of the newly launched product relative to customer sentiment identified in the customer sentiment data may be determined. Numerical prediction parameters from parameter values associated with a like-item that is determined to be similar to the newly launched product may be obtained. A product utility valuation may be computed as a weighted combination of the predicted PLC effect and a lagged social media sentiment determined from the customer sentiment data accounted by the mean sentiment lag. The product utility valuation provides an indication of the future demand of the newly launched product.

Abstract: Computing a personalized deal menu for a seller may be provided. The seller may operate one or more sales channels. A customer's willingness to wait in purchasing based on purchasing history of a customer may be determined. A purchase probability model that predicts a likelihood of the customer performing a purchase now compared to waiting to make the purchase may be formulated. A personalized price menu optimization model with one or more rules as constraints that jointly determines multiple prices, a price corresponding to a different purchase option with different lead time, may be solved. The personalized deal menu may be generated based on the solving.

Abstract: Systems and methods for modeling and forecasting cyclical demand systems in the presence of dynamic control or dynamic incentives. A method for modeling a cyclical demand system comprises obtaining historical data on one or more demand measurements over a plurality of demand cycles, obtaining historical data on incentive signals over the plurality of demand cycles, constructing a model using the obtained historical data on the one or more demand measurements and the incentive signals, wherein constructing the model comprises specifying a state-space model, specifying variance parameters in the model, and estimating unknown variance parameters.

Abstract: A top-down and bottom-up approach that decomposes product bundles to components, classifies them into different groups corresponding to a component similarity measure, and detects their inherent values. The bundles are reassembled and characterized by several key attributes according to their component inherent values, and classified into segments. A normalized utility model is constructed for each product bundle segment, taking into account the additive effect among different commodity types and product families. The goodness of fit of the top-down and the bottom-up model may be validated. The model may be applied in an RFQ pricing environment.