The Collaboration Paradox: Why Generative AI Requires Both Strategic Intelligence and Operational Stability in Supply Chain Management

2025-08-19

AI Summary

This study identifies a “collaboration paradox” in multi-tier supply chain coordination involving generative AI agents: LLM-based autonomous agents implementing vendor-managed inventory (VMI) policies trigger systemic collapse through uncoordinated inventory hoarding, underperforming non-AI baselines. To address this, we propose a dual-layer framework integrating strategic intelligence and operational stability—the first to systematically identify, attribute, and rectify AI agent collaboration failures. The framework autonomously generates, evaluates, and quantifies diverse coordination strategies. Using LLM-driven controllable simulation experiments, we demonstrate that our approach effectively mitigates inventory overaccumulation, restores system stability, and significantly enhances supply chain resilience and operational efficiency. Empirical results show consistent, substantial outperformance against conventional benchmarks across key metrics including inventory turnover, fill rate, and demand distortion.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The rise of autonomous, AI-driven agents in economic settings raises critical questions about their emergent strategic behavior. This paper investigates these dynamics in the cooperative context of a multi-echelon supply chain, a system famously prone to instabilities like the bullwhip effect. We conduct computational experiments with generative AI agents, powered by Large Language Models (LLMs), within a controlled supply chain simulation designed to isolate their behavioral tendencies. Our central finding is the "collaboration paradox": a novel, catastrophic failure mode where theoretically superior collaborative AI agents, designed with Vendor-Managed Inventory (VMI) principles, perform even worse than non-AI baselines. We demonstrate that this paradox arises from an operational flaw where agents hoard inventory, starving the system. We then show that resilience is only achieved through a synthesis of two distinct layers: high-level, AI-driven proactive policy-setting to establish robust operational targets, and a low-level, collaborative execution protocol with proactive downstream replenishment to maintain stability. Our final framework, which implements this synthesis, can autonomously generate, evaluate, and quantify a portfolio of viable strategic choices. The work provides a crucial insight into the emergent behaviors of collaborative AI agents and offers a blueprint for designing stable, effective AI-driven systems for business analytics.

Problem

• Investigating AI agents' emergent strategic behavior in supply chains
• Addressing catastrophic collaboration paradox in AI-managed inventory systems
• Resolving operational instability from AI hoarding in supply chains

Innovation

• AI-driven proactive policy-setting for targets
• Collaborative execution with proactive replenishment
• Autonomously generate and evaluate strategic choices