Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System

In this study, we investigate inventory allocation and pricing strategies for retailers by incorporating demand information into the issue of inventory allocation during the presale period. In a presale system, retailers offer presale goods at a price lower than the retail price. By offering products at a discount, retailers may attract additional demand. In addition, this system enables retailers to reduce the uncertainty of market demand and establish a strategy for inventory allocation based on the results of presales. A Bayesian approach was employed to analyze and update demand information, and inventory allocation was formulated as a newsvendor problem to determine the optimal policy that maximizes retailer profit . A numerical analysis was conducted to validate the effectiveness of the proposed strategy. Results suggest that the proposed strategies can support retailers by more accurately predicting demand and achieving higher profits with less inventory. Furthermore, retailers can experience greater benefits from risk-averse customers than from risk-neutral customers.

Dynamic Inventory Allocation for Seasonal Merchandise at Dillard’s

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.

Online Demand Fulfillment Under Limited Flexibility

We study online demand fulfillment in a class of networks with limited flexibility and arbitrary numbers of resources and request types. We show analytically that such a network is both necessary and sufficient to guarantee a performance gap independent of the market size compared with networks with full flexibility, extending the previous literature from the long chains to more general sparse networks. Inspired by the performance bound, we develop simple inventory allocation rules and guidelines for designing such network structures. Numerical experiments including one using some real data from Amazon China are conducted to confirm our findings as well as some of the flexibility principles conjectured in the literature. This paper was accepted by Chung Piaw Teo, optimization.