We consider how to allocate inventory of seasonal goods in a two-echelon distribution network for Dillard’s Inc., a large department store chain in the United States. Our objective is to allocate products with limited inventory from a distribution center to multiple retail stores over the selling season to maximize total sales revenue. Under the assumption that the true demand distributions are available to the retailer, we develop an effective dynamic inventory allocation heuristic. We further consider a more realistic and challenging setting for seasonal goods, where demand distributions are unknown to the retailer, and propose two “learning-while-doing” extensions of our inventory allocation heuristic; these policies update demand distribution estimates in a rolling horizon using censored point-of-sales data. We evaluate the performance of the policies using simulation on Dillard’s historical sales data. Dillard’s Inc. has incorporated the proposed policy into their current replenishment methodology and has been using the policy to set order levels for its seasonal merchandise.
Dynamic Inventory Allocation with Demand Learning for Seasonal Goods
