The Pricing and Inventory Decision Model of a Single Regional Retailer for Strategic Consumers

Advances in Internet technology allow retailers to adjust prices at low costs and to adopt dynamic pricing strategies to attract consumers. Consumers can easily access to the price history through the Internet, forming expectations of future price reduction, resulting in an increase in strategic purchase behavior. This behavior increases the uncertainty of market demand and the operational risk of retailers. Therefore, it is an important topic to develop reasonable pricing and inventory strategies to deal with consumer strategic behavior and reduce the pressure on retailers. Considering that consumer strategic behavior is influenced by product discount price, the impact of product pricing on demand during different sales periods is modeled using a multinomial logit (MNL) selection model. A mixed-integer nonlinear programming (MINLP) model is constructed, with pricing and inventory as decision variables, to maximize the retailer’s total profit. To simplify the problem, the model is approximately linearized as a mixed-integer linear programming model (MILP). It is found that this model and method can effectively solve the problem of product pricing and optimal inventory decision during the discount period and can deal with large-scale problems efficiently. Retailers can use historical transaction data to understand consumers’ purchasing behavior, influence the sales cycle demand through pricing strategies, and guide consumers’ purchasing behavior.