用智能代理AI评估供应链文档处理的可持续性，降耗超90%。
Agentic AI Sustainability Assessment for Supply Chain Document Insights
- 构建多智能体代理系统，自动解析与验证供应链文档。
- 相比人工流程，能耗降低70%-90%，碳排放减少90%-97%。
- 适合关注绿色AI与ESG合规的企业和研究者。
本文提出一种面向供应链文档智能的可持续性评估框架，核心为智能代理人工智能（Agentic AI）。研究旨在提升自动化效率的同时量化环境表现。对比三种场景：纯人工、人机协同（HITL）及高级多智能体代理工作流。实证结果显示，人机协同与代理AI方案相较人工流程，能源消耗降低70%-90%，二氧化碳排放减少90%-97%，用水量下降89%-98%。尤其在结合高级推理与多智能体验证的完整代理配置下，虽资源使用略高于简化方案，但相比人工仍实现显著可持续性提升。框架整合性能、能耗与排放指标，形成面向ESG的统一评估方法。论文还提供了可复现的案例，展示该方法在真实文档提取任务中的应用。
原文摘要 · Abstract (English)
This paper presents a comprehensive sustainability assessment framework for document intelligence within supply chain operations, centered on agentic artificial intelligence (AI). We address the dual objective of improving automation efficiency while providing measurable environmental performance in document-intensive workflows. The research compares three scenarios: fully manual (human-only), AI-assisted (human-in-the-loop, HITL), and an advanced multi-agent agentic AI workflow leveraging parsers and verifiers. Empirical results show that AI-assisted HITL and agentic AI scenarios achieve reductions of up to 70-90% in energy consumption, 90-97% in carbon dioxide emissions, and 89-98% in water usage compared to manual processes. Notably, full agentic configurations, combining advanced reasoning (thinking mode) and multi-agent validation, achieve substantial sustainability gains over human-only approaches, even when resource usage increases slightly versus simpler AI-assisted solutions. The framework integrates performance, energy, and emission indicators into a unified ESG-oriented methodology for assessing and governing AI-enabled supply chain solutions. The paper includes a complete replicability use case demonstrating the methodology's application to real-world document extraction tasks.
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