智能代理模型自动补货，降库存、减缺货、提商品周转
Agentic AI Framework for Smart Inventory Replenishment
- 构建多智能体系统，自动监控库存并谈判采购
- 缺货率下降，库存持有成本降低，商品周转率提升
- 适合中型零售场景，可扩展且支持持续优化
在现代零售中，商品种类繁多（如服装、食品、化妆品、冷冻品），导致需求预测困难、缺货频发且难以识别高潜力产品。本文提出一种智能代理AI框架，用于实时监控库存、向合适供应商发起采购请求，并扫描趋势或高利润商品进行引入。系统融合需求预测、供应商选择优化、多智能体协商与持续学习机制。我们在一家中型商场的门店部署原型系统，在三组真实与人工数据集上测试性能，并与基础规则方法对比。结果表明，该系统显著降低缺货率，减少库存持有成本，并提升商品组合周转效率。研究还讨论了约束条件、可扩展性及未来改进方向。
原文摘要 · Abstract (English)
In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate. The system applies demand forecasting, supplier selection optimization, multi-agent negotiation and continuous learning. We apply a prototype to a setting in the store of a middle scale mart, test its performance on three conventional and artificial data tables, and compare the results to the base heuristics. Our findings indicate that there is a decrease in stockouts, a reduction of inventory holding costs, and an improvement in product mix turnover. We address constraints, scalability as well as improvement prospect.
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