The autonomous inventory reflex: A predictive framework for self-tuning supply chain efficiency through AI-mediated smart practices

Abstract

In contemporary supply chains, performance degradation arises not from insufficient predictive capability but from a structural gap between decision intelligence and operational execution, termed the “Advisory Trap.” This trap extends behavioral operations constructs, such as automation bias and algorithm aversion, by emphasizing system-level decision latency within organizational hierarchies, fragmented rights, and workflow delays that decouple algorithmic recommendations from action. To address this, the study introduces the Autonomous Inventory Reflex (AIR), a cybernetically informed framework that transforms inventory management from recommendation-based support to governed autonomous execution. It integrates artificial intelligence, reinforcement learning, and adaptive control to enable continuous sensing, policy formation, and constrained execution within the operational and regulatory boundaries. To address the accountability gap, the AIR implements a responsibility attribution model in which failures are traceable across the sensing, policy, and execution layers, supported by human-in-the-loop supervision and escalation protocols, ensuring that critical decisions remain auditable and subject to intervention. The framework was evaluated using simulation-based validation and reinforcement learning benchmarks to ensure comparability with state-of-the-art methods. This contributes to the behavioral operations extension of the Advisory Trap, a governance-aware architecture for autonomous inventory, and an accountability framework for autonomous supply chain execution.