Abstract: Methods and systems for forecasting demand for items across multiple channels are disclosed. In some implementations, multi-channel demand forecasting may be performed on a per-item, per-location basis, by selectively generating item-location forecasts for each item and location within a supply chain for each channel, or disaggregating a chain level forecast on a per-item basis to each location. Particular selection of an appropriate model, and selective training of models, allows for efficient computation of such forecasts across a large supply chain with thousands of locations and hundreds of thousands, or millions, of items for which forecasts are generated.

Abstract: Methods and systems are described for optimizing markdown schedules for clearance items at physical retail stores. For example, price sensitivity of a current inventory item at a physical retail store may be modeled using techniques that account for differences in operating behavior and data availability at physical stores versus online channels for selling items. When the current item is placed on clearance at the physical retail store, a request for a markdown schedule, including goals for the clearance, may be received. The forecasted price sensitivity of the current item may be adjusted based on actual price sensitivity of one or more past clearance items (e.g., based on actual clearance sales data) determined to match the current item. An optimal markdown schedule for the item may then be determined based, at least in part, on the adjusted price sensitivity of the item and clearance goals.

Abstract: Methods and systems for generating an optimal clearance schedule for inventory items in a retail enterprise. A method includes accessing historical sales data for a plurality of inventory items in a department of a retailer. A selection of an item is received and a set of possible discount schedules for the item are generated. Each schedule includes a set of prices and a price duration. A forecasting tool provides a sales performance of the item based on the historical sales data. An optimal clearance schedule is selected from the possible discount schedules.

Abstract: Methods and systems are described for optimizing markdown schedules for clearance items at physical retail stores. For example, price sensitivity of a current inventory item at a physical retail store may be modeled using techniques that account for differences in operating behavior and data availability at physical stores versus online channels for selling items. When the current item is placed on clearance at the physical retail store, a request for a markdown schedule, including goals for the clearance, may be received. The forecasted price sensitivity of the current item may be adjusted based on actual price sensitivity of one or more past clearance items (e.g., based on actual clearance sales data) determined to match the current item. An optimal markdown schedule for the item may then be determined based, at least in part, on the adjusted price sensitivity of the item and clearance goals.

Abstract: A computer-implemented method uses sales data to fit static parameters of a demand prediction model that predicts a current demand based in part on a previous demand. The static parameters and the sales data are then used to fit dynamic states of a structural time series model, wherein the dynamic states change over time and are different for different time periods. A time period for a future price is selected and the future price is applied to the structural time-series model using the dynamic states for the time period to generate an expected demand for the time period.

Abstract: Techniques are described for generating benefits reports for a markdown optimization system. As described herein, a markdown optimization system executes markdown optimization software that applies to compute markdown schedules for retailers. In addition, the markdown optimization system applies techniques to reliably approximate and quantify the benefit derived by a given retailer from using of a computed markdown schedule instead of utilizing a user-defined schedule.

Abstract: Techniques are described for generating benefits reports for a markdown optimization system. As described herein, a markdown optimization system executes markdown optimization software that applies to compute markdown schedules for retailers. In addition, the markdown optimization system applies techniques to reliably approximate and quantify the benefit derived by a given retailer from using of a computed markdown schedule instead of utilizing a user-defined schedule.

Abstract: A system and method for tuning markdown plans is provided. Such a system and method may include configuring initial rule set. Initial Demand models are generated. A first optimization for inventory pricing may be received from the price optimization system. The first optimization uses the initial demand models and cost data. A markdown plan is generated by applying the initial rule set to the first optimization. The plan is implemented. Updated data may be received which mandates a re-optimization of the plan. Demand models are refreshed using the updated data. Initial rule set is updated by cross referencing plan history with the initial rule set and subtracting rule events that have previously occurred. A second optimization is received which uses the refreshed demand models and cost data. Then, the markdown plan is re-optimized by applying the updated rule set to the second optimization. The re-optimized markdown plan is reported, approved and implemented.