AI Agents for Inventory Control: Human-LLM-OR Complementarity

2026-01-31 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A study on AI agents for inventory control, titled "AI Agents for Inventory Control: Human-LLM-OR Complementarity," investigates how Operations Research (OR) algorithms, Large Language Models (LLMs), and human judgment can interact to improve inventory management. Researchers developed InventoryBench, a benchmark of over 1,000 inventory instances using both synthetic and real-world demand data, designed to test decision rules under demand shifts, seasonality, and uncertain lead times. The study found that OR-augmented LLM methods significantly outperformed either method in isolation, with the OR-to-LLM pipeline achieving the best overall performance (0.538 normalized reward), a 21% improvement over OR alone. Furthermore, a controlled classroom experiment with 69 participants demonstrated that human-AI teams achieved higher profits than humans or AI agents operating independently, with Mode B (OR-to-LLM-to-Human) showing the best performance. The research also formalized an individual-level complementarity effect, estimating that at least 20.3% of individuals benefited from AI collaboration.

Key takeaway

For AI Scientists designing inventory management systems, integrating LLMs with traditional OR algorithms and maintaining human oversight is crucial. The OR-to-LLM pipeline, where OR provides recommendations that LLMs can override, combined with human final decision-making, significantly boosts performance. You should prioritize systems that allow LLMs to handle contextual reasoning and demand shifts, while humans provide critical judgment, especially in detecting anomalies like lost orders or leveraging nuanced world knowledge.

Key insights

Combining OR algorithms, LLMs, and human judgment creates superior inventory control systems through complementary strengths.

Principles

• OR provides mathematical precision for stable conditions.
• LLMs offer contextual reasoning and detect demand shifts.
• Human judgment adds value beyond automated decisions.

Method

The study constructed InventoryBench with 1,320 instances (synthetic and real) and evaluated four OR-LLM interaction methods, then conducted a human-in-the-loop experiment with 69 participants across three collaboration modes.

In practice

• Integrate LLMs to detect demand shifts and leverage world knowledge.
• Use OR for precise base-stock calculations under stable conditions.
• Design human-AI interfaces for human oversight and final decision-making.

Topics

• Inventory Control
• Large Language Models
• Operations Research
• Human-AI Collaboration
• AI Agents

Code references

• TianyiPeng/AI-human-inventory-game

Best for: AI Scientist, AI Researcher, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.