AlphaInventory: Letting LLMs Evolve Inventory Policies — AI as a "Supply Chain Brain"

> Paper: AlphaInventory: Evolving White-Box Inventory Policies via Large Language Models with Deployment Guarantees > Authors: Chenyu Huang, Jianghao Lin, Zhengyang Tang, Bo Jiang, Ruoqing Jiang, Benyou Wang, Lai Wei > arXiv: 2605.00369 | 2026-04-29

1. The Problem: "Inventory Management Is Too Complex for AI"

Imagine managing inventory for a chain of supermarkets. You face:

• Demand volatility: seasonal changes, sudden disruptions, shifting trends
• Supply chain uncertainty: supplier delays, shipping issues, weather effects
• Cost trade-offs: too much stock ties up capital; too little causes stockout losses

Traditional approaches rely on human experience and simple formulas, but they cannot adapt to dynamic change, their policies lack transparency, and they come with no deployment guarantees.

2. The Proposal: LLM-Evolved Inventory Policies

AlphaInventory's core idea:

> Use LLMs to evolve inventory policies that adapt online in dynamic, non-stationary environments, with confidence-interval guarantees for safe deployment.

Technical highlights:

1. LLM-driven evolutionary search — inspired by AlphaEvolve, but optimized for the inventory setting and online, dynamic environments 2. White-box policies — interpretable and auditable by humans, not black-box neural networks 3. Confidence-interval certification — performance is validated before deployment with statistical guarantees, reducing risk 4. End-to-end framework — from policy evolution to deployment in one pipeline, minimizing manual intervention

Potential applications: retail inventory, manufacturing supply chains, logistics scheduling, and medical supply management.

An analogy: traditional inventory management is a chef following a fixed recipe; AlphaInventory is an AI head chef constantly tasting and adjusting the recipe — adapting in real time, with quality assurance.

3. Why LLM Evolution Beats Traditional Optimization

Limits of traditional optimization:

• Static assumptions: assuming a stable environment while reality keeps changing makes policies obsolete quickly
• Black-box risk: deep learning policies are uninterpretable, and their performance is unknown before deployment

Advantages of LLM evolution:

• Adaptability: online evolution keeps optimizing as the environment shifts
• Transparency: white-box policies can be understood and audited by humans
• Statistical guarantees: confidence intervals give a performance lower bound, keeping risk under control

4. Takeaway: Good Policies Evolve — They Don't Stay Fixed

Echoing Feynman's point that "knowing the name of something" differs from "understanding it":

> Freezing a policy and expecting it to work forever is like wearing unchanging glasses in a changing world. AlphaInventory's insight is to let the policy evolve itself — AI-driven continuous optimization rather than manual tuning.

Questions to ask if you work in inventory, supply chain, or operations research:

1. Does my policy adapt to dynamic environments? 2. Is it interpretable and auditable? 3. Is there a performance guarantee before deployment? 4. Could LLM-driven evolution discover strategies humans wouldn't think of?

When AI learns to *evolve* inventory policies, it moves from being a tool to being an intelligent partner. In supply chain's future, the best policy isn't the most perfect one — it's the most adaptable one. In a changing world, evolution is the only constant.

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*Source: forum post on zhichai.net discussing the AlphaInventory paper (arXiv 2605.00369).*