一种用于智能供应链分析的混合智能体AI框架

中文摘要 AI 辅助

高效利用供应链分析进行决策对规划者而言仍是一项重大挑战，因为数据库查询、关键绩效指标（KPI）分析、需求预测和性能诊断等关键任务需要涵盖数据工程、运筹学和领域知识的异构专业知识。在本工作中，我们提出了一种用于供应链分析的智能体系统，弥合了业务决策与技术专长之间的差距，其中协调智能体解释用户意图并将子任务委派给专门智能体。该系统支持探索性分析和确定性工作流，使规划者能够在临时问题与结构化流程之间切换。领域逻辑被封装在专家智能体和提示中，产生了可扩展、模块化且可审计的设计，并通过以提示为中心的开发降低了功能扩展的成本。我们在一个模拟多级库存管理操作的测试环境中评估了所提出的架构。结果表明，我们的多智能体设计达到了90%的准确率，与单智能体基线相当，同时将输入令牌使用量减少了约四倍，显著提高了可扩展性和成本效益。此外，我们提供了案例研究，展示了可解释的次优性检测和自动化预测优化，说明了智能体架构如何有效结合开放式探索性分析与确定性供应链分析工作流，并为更易访问和可扩展的决策支持系统提供了实用途径。

英文摘要

Efficient utilization of supply chain analytics for decision making remains a significant challenge for planners, as critical tasks such as database querying, key performance indicator (KPI) analysis, demand forecasting, and performance diagnosis require heterogeneous expertise spanning data engineering, operations research, and domain knowledge. In this work, we propose an agentic system for supply chain analytics that bridges the gap between business decision-making and technical expertise, where a coordinator agent interprets user intent and delegates sub-tasks to specialized agents. The system supports both exploratory analysis and deterministic workflows, enabling planners to transition between ad hoc questions and structured processes. Domain logic is encapsulated within specialist agents and prompts, yielding a scalable, modular, and auditable design and lowering the cost of functional extension through prompt-centric development. We evaluate the proposed architecture on a test environment that replicates multi-echelon inventory management operations. Results show that our multi-agent design achieves a 90\% accuracy, which is competitive with a single agent baseline while reducing input token usage by roughly fourfold, substantially improving scalability and cost-efficiency. Furthermore, we provide case studies to demonstrate interpretable suboptimality detection and automated forecast optimization, illustrating how agentic architectures can effectively combine open-ended exploratory analysis and deterministic supply chain analytics workflows, and provide a practical pathway toward more accessible and extensible decision-support systems.

发表机构

• Industrial AI Lab（工业人工智能实验室）
• Hitachi America（日立美国）

机构由 AI 辅助整理，请以论文原文为准。