摘 要
随着信息技术的迅猛发展，大数据已成为推动供应链管理变革的重要驱动力。本研究以大数据技术为核心，探讨其在供应链智能化升级与效率提升中的应用潜力及实现路径。研究旨在通过整合大数据分析、机器学习和人工智能等先进技术，优化供应链决策流程，降低运营成本并提高响应速度。基于对国内外相关文献的系统梳理，结合实际案例分析，本文提出了一种融合数据驱动与智能算法的供应链优化框架，并通过实证研究验证了该框架的有效性。研究结果表明，大数据技术能够显著改善信息透明度，增强预测准确性，并为供应链各环节提供实时决策支持。此外，本文创新性地引入了多源异构数据处理机制，解决了传统供应链中数据孤岛问题，同时开发了适用于复杂场景的智能优化模型，为实践提供了可操作性强的解决方案。
关键词：大数据技术 供应链智能化 多源异构数据处理
Abstract
With the rapid development of information technology, big data has become an important driving force to change supply chain management. This research takes big data technology as the core, and discusses its application potential and realization path in the intelligent upgrading and efficiency improvement of supply chain. The research aims to optimize the supply chain decision-making process, reduce operational costs and improve response speed by integrating advanced technologies such as big data analytics, machine learning and artificial intelligence. Based on the systematic combing of the relevant literature at home and abroad, combined with the actual case analysis, this paper proposes a supply chain optimization fr amework integrating data-driven and intelligent algorithm, and verifies the effectiveness of the fr amework through empirical research. The results show that big data technology can significantly improve information transparency, enhance prediction accuracy, and provide real-time decision support for all links of the supply chain. In addition, this paper innovatively introduces the multi-source heterogeneous data processing mechanism, solves the problem of data island in the traditional supply chain, and develops the intelligent optimization model suitable for complex scenarios, which provides a highly operable solution for practice.
Keyword:Big Data Technology Supply Chain Intelligence Multi-Source Heterogeneous Data Processing
目 录
1绪论 1
1.1供应链智能化升级的研究背景 1
1.2大数据驱动的效率提升意义分析 1
1.3国内外研究现状综述 1
1.4研究方法与技术路线 2
2大数据在供应链中的应用基础 2
2.1供应链数据的特征与分类 2
2.2大数据分析技术框架构建 3
2.3数据驱动的决策支持机制 3
2.4关键技术挑战与解决方案 3
2.5应用基础对智能化升级的影响 4
3智能化升级的关键环节分析 4
3.1需求预测与大数据模型优化 4
3.2供应链网络设计的智能化策略 5
3.3库存管理的智能算法应用 5
3.4物流配送的效率提升路径 6
3.5协同优化的实现方式 6
4效率提升的具体实践与评估 7
4.1实践案例的选取与分析框架 7
4.2基于大数据的效率评估指标体系 7
4.3实验结果与数据分析方法 8
4.4提升效果的验证与总结 8
4.5实践中的问题与改进建议 9
结论 9
参考文献 10
致谢 11
