Quality management in supply chain: Strategic implications and the paradox of AI inspection

Abstract Artificial intelligence (AI) has transformed the quality control process with AI inspection technology, which reduces the need for costly physical resources and mitigates retail returns. Despite its revolutionizing impact on supply chain quality management, there is a notable gap in research on the implications of a manufacturer's adoption of AI inspection. This article addresses this gap by presenting a two‐stage model that explores the consequences of AI inspection adoption for a downstream manufacturer and an upstream supplier. Our results show that higher AI‐based inspection accuracy may not always benefit the manufacturer. This is because when the supplier's traditional inspection accuracy falls within an immediate range, the manufacturer's incentive to improve AI inspection accuracy diminishes, and the positive effect of AI inspection on retail returns cannot fully offset the technology expense. Moreover, our study explores the dynamics of technology‐sharing strategies between the manufacturer and supplier. Despite potential revenue gains, the manufacturer may hesitate to share technology due to the risk of increased defective products with lower AI inspection accuracy, leading to a paradox where profitability coexists with losses. Surprisingly, the successful collaborative technology‐sharing strategy may paradoxically lead to reduced technology investment. This occurs because technology‐sharing enables significant marginal cost savings in retail returns, rendering the manufacturer to achieve a comparable inspection level with lower investment. Overall, this research highlights that adopting AI inspection does not guarantee benefits for the supply chain members and can sometimes be detrimental. Our study offers strategic guidance for decision‐makers in supply chain quality management.