供应链管理

文章聚焦于DeepSeek(深度求索)与AI(人工智能)技术在智慧供应链领域的深度融合与创新应用。通过分析传统供应链管理的固有痛点(如单模态数据局限性、复杂场景适应性差及决策滞后性),系统阐述了多模态AI与生成式AI如何重构智能管理范式，实现供应链全链路的价值跃升。文章研究对比了DeepSeek-V3与其他主流模型(如Claude-3.5、GPT-4o等)的性能，指出其在高性价比、中文处理及开源灵活性等方面的核心优势。同时，提出了“多模态AI四维重构模型”(感知—决策—协同—体验)与“生成式AI三阶跃迁框架”(战略生成—战术优化—执行纠偏),并通过采购、计划、交付等环节的案例展示了AI技术的实际应用价值。文章还展望了AI驱动的供应链优化未来趋势，包括全链路智能化、与物联网及区块链的融合、可持续性发展等，并针对技术、人才与数据挑战提出了应对策略，呼吁构建多方协同的AI供应链生态系统。文章的研究为供应链智能化转型提供了理论与实践参考。

This paper focuses on the deep integration and innovative application of DeepSeek and artificial intelligence(AI) technologies in intelligent supply chain management.By analyzing the inherent pain points of traditional supply chain management(e.g., limitations of single-modal data, poor adaptability to complex scenarios, and decision-making lag), it systematically elaborates how multimodal AI and generative AI reconstruct intelligent management paradigms to achieve value enhancement across the entire supply chain.The study compares DeepSeek-V3 with other mainstream models(e.g., Claude-3.5, GPT-4o), highlighting its core advantages in cost-effectiveness, Chinese language processing, and open-source flexibility.Furthermore, the “Four-dimensional Multimodal AI Reconstruction Model”(perception-decision-collaboration-experience) and the “Three-phase Generative AI Transition Framework”(strategic generation-tactical optimization-execution correction) are proposed, with practical case studies in procurement, planning, and delivery demonstrating the real-world value of AI technologies.The paper also explores future trends in AI-driven supply chain optimization, including end-to-end intelligence, integration with IoT and blockchain, and sustainable development, while addressing challenges in technology, talent, and data through actionable strategies.It advocates for building a collaborative AI-powered supply chain ecosystem.This research provides theoretical and practical insights for the intelligent transformation of supply chains.