Algorithmic pricing in supply chains: implications for product quality, pricing, and profits

The rapid adoption of algorithmic pricing by retailers, enabled by big data analytics, is reshaping decisions in supply chains and affecting consumer surplus. We develop a game-theoretic model to examine how a retailer’s operation under an algorithmic-pricing regime, compared with a uniform-pricing regime, influences the manufacturer’s product quality and wholesale pricing decisions, as well as profits and consumer surplus. We uncover three key findings. First, algorithmic pricing affects product quality through two opposing effects: the demand segmentation effect, which encourages quality improvement by better matching products to heterogeneous consumers, and the profit compression effect, which discourages quality investment when the consumer distribution is highly skewed. Second, algorithmic pricing generates asymmetric profit impacts for supply chain members. While the retailer benefits more directly from pricing precision, both firms can benefit, particularly under a balanced mix of consumer types, through increased market coverage and reduced channel conflict. Third, when algorithmic reliability is high and consumer heterogeneity is moderate, algorithmic pricing can improve consumer surplus by aligning prices with willingness-to-pay and incentivizing higher quality. As reliability improves and the consumer distribution becomes more balanced, the system can achieve a tripartite win–win that benefits the manufacturer, the retailer, and consumers. These findings highlight the dual, condition-dependent role of algorithmic pricing as both a coordination tool and a quality-enhancement mechanism in supply chains. They also offer managerial implications for the strategic deployment of algorithmic pricing tools and inform policy debates on regulating algorithm-driven markets.