Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management

2026-05-16

AI Summary

This study addresses the “agent bullwhip effect” in multi-tier supply chains—where decision uncertainty is amplified across hierarchical levels due to multi-agent coordination challenges and information delays—and formally characterizes this phenomenon for the first time. To mitigate it, the authors propose a reasoning optimization framework integrating model selection, policy constraints, centralized data sharing, and prompt engineering. They further introduce Group Relative Policy Optimization (GRPO), a novel reinforcement learning post-training method grounded in system-level rewards. Experimental results demonstrate that the optimized LLM agents reduce supply chain costs by 67% compared to human teams, while GRPO substantially suppresses tail risk and decision volatility, effectively alleviating the agent bullwhip effect and significantly enhancing overall system reliability.

Abstract

This paper studies autonomous generative AI agents in multi-echelon supply chains using the MIT Beer Game. We identify four inference-time levers that shape performance: model selection, policies and guardrails, centralized data sharing, and prompt engineering. Model capability is the dominant factor: an out-of-the-box reasoning model exceeds human-level performance, and optimized reasoning models reduce costs by up to 67% relative to human teams. However, strong average performance masks substantial reliability risks. We introduce the agent bullwhip effect, the amplification of decision unreliability across echelons, manifesting along two dimensions: decision variance increases both across facilities at the same point in time and within the same facility across time. We develop a mathematical framework showing that this phenomenon is inherent to multi-agent systems that involve coordination and information delays, and we demonstrate that repeated sampling fails to meaningfully reduce it. To address this limitation, we propose a Group Relative Policy Optimization (GRPO)-based reinforcement-learning post-training framework that trains a shared base LLM using system-level supply-chain rewards. GRPO post-training substantially reduces tail events, curtails agent bullwhip, and improves the reliability of autonomous supply-chain agents.

Problem

• autonomous AI agents
• supply chain management
• agent bullwhip effect
• decision reliability
• multi-echelon supply chains

Innovation

• agent bullwhip effect
• Group Relative Policy Optimization
• multi-agent reinforcement learning
• supply chain reliability
• generative AI agents