Abstract:
A system that estimates elasticity and inventory effect for a product pricing or forecasting system receives a sales condition relationship for an item at a store, the relationship comprising an elasticity parameter, an inventory effect parameter and a sales constant. The system receives a demand model for sales of the item in terms of the elasticity parameter and the inventory effect parameter and a base demand for the item selling at the store. The system estimates the sales constant, the estimating comprising generating a theta parameter by taking logarithms of the sales condition relationship. The system uses linear regression to estimate a logarithm of the sales constant and a value of the theta parameter. The system determines a relationship between the elasticity parameter and the inventory effect parameter based on the value of the theta parameter.
Abstract:
A system that determines a pricing markdown schedule for a retail item at a store receives demand parameters of the retail item at the store and one or more constraints, and expresses a price curve and inventory curve as linear combinations of price and inventory coefficients for orthogonal polynomials. The system determines revenue in terms of values of the price and inventory coefficients, determines an initial guess of the price and inventory coefficients, and determines a gradient of the revenue. The system then maximizes the revenue based on the revenue, the initial guesses, the gradient, and the constraints, where the constraints are in terms of the price and inventory coefficients. Based on the maximized revenue, the system then generates the price markdown schedule.