Optimal Replenishment Strategy for Inventory Mechanism with a Known Price Increase and Backordering in Finite Horizon

For the finite horizon inventory mechanism with a known price increase and backordering, based on minimizing the inventory cost, we establish two mixed integer optimization models. By buyer’s cost analysis, we present the closed-form solutions to the models, and by comparing the minimum cost of the two strategies, we provide an optimal ordering policy to the buyer. Numerical examples are presented to illustrate the validity of the model, and sensitivity analysis on major parameters is also made to show some insights to the inventory model.

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.