A knowledge-driven pricing model for supply chain coordination under correlated demand

The growing competition between national brands and store brands has intensified the need for decision-support systems to guide pricing and coordination strategies in retail supply chains. This study develops an analytical game-theoretic framework that integrates stochastic demand modeling, behavioral customer preferences, and computational optimization for dual-brand supply chains. The system models a two-echelon structure consisting of a national-brand manufacturer and a retailer who also produces a substitutable store brand, acting simultaneously as a collaborator and a competitor. Customer demand for both products is correlated and influenced by relative price and quality, introducing substantial uncertainty. A Stackelberg game framework is employed to capture the hierarchical decision-making process, in which the manufacturer sets the wholesale price, and the retailer optimizes retail prices for both products. Analytical and simulation-based evaluations reveal that conventional revenue-sharing contracts fail to coordinate the supply chain when demand correlation and substitution effects are present. To address this limitation, a two-part tariff contract is designed within the analytical framework to align incentives and achieve coordination. The results demonstrate that the proposed approach enhances profitability, system efficiency, and brand competitiveness, providing actionable insights for decision-makers in retail, apparel, and fast-food industries.