An integrated decision making model for dynamic pricing and inventory control of substitutable products based on demand learning

Abstract

Purpose: This paper focuses on the PC industry, analyzing a PC supply chain system composed of one large retailer and two manufacturers. The retailer informs the suppliers of the total order quantity, namely Q, based on demand forecast ahead of the selling season. The suppliers manufacture products according to the predicted quantity. When the actual demand has been observed, the retailer conducts demand learning and determines the actual order quantity. Under the assumption that the products of the two suppliers are one-way substitutable, an integrated decision-making model for dynamic pricing and inventory control is established.

Design/methodology/approach: This paper proposes a mathematical model where a large domestic household appliance retailer decides the optimal original ordering quantity before the selling season and the optimal actual ordering quantity, and two manufacturers decide the optimal wholesale price.

Findings: By applying this model to a large domestic household appliance retail terminal, the authors can conclude that the model is quite feasible and effective. Meanwhile, the results of simulation analysis show that when the product prices of two manufacturers both reduce gradually, one manufacturer will often wait till the other manufacturer reduces their price to a crucial inflection point, then their profit will show a qualitative change instead of a real-time profit-price change.

Practical implications: This model can be adopted to a supply chain system composed of one large retailer and two manufacturers, helping manufacturers better make a pricing and inventory control decision.

Originality/value: Previous research focuses on the ordering quantity directly be decided. Limited work has considered the actual ordering quantity based on demand learning. However, this paper considers both the optimal original ordering quantity before the selling season and the optimal actual ordering quantity from the perspective of the retailer.