Seeing the Unseen: AI Assimilation and Supply–Demand Visibility for Effective Risk Management in Manufacturing Supply Chains

Artificial intelligence (AI) has become a strategic resource for enhancing supply chain resilience in environments characterized by growing uncertainty and complexity. Building on the resource-based view (RBV) and organizational information processing theory (OIPT), this study examines how AI assimilation as a firm-level strategic capability improves supply–demand visibility and strengthens supply chain risk management (SCRM). Using survey data collected from 129 manufacturing firms in China, the proposed research framework is tested through structural equation modeling. The results show that AI assimilation significantly enhances both supply–demand visibility and SCRM, with visibility playing a partial mediating role in translating AI-enabled capabilities into more effective risk control. These findings indicate that AI contributes to resilience not merely through technological deployment but through its integration into organizational processes that support information processing and coordination. From a managerial perspective, the study suggests that firms should approach AI as an ongoing strategic capability development process rather than a one-time technological investment. By embedding AI into core supply chain functions such as production planning, inventory management, and demand forecasting, firms can improve visibility, anticipate disruptions, and shift toward more proactive and resilient risk management practices. This study advances the literature by integrating RBV and OIPT to explain the strategic mechanisms through which AI assimilation enhances visibility in SCRM, providing empirical evidence from a manufacturing context.