摘要
随着信息技术的迅猛发展，大数据与人工智能技术逐渐成为运营管理领域的重要驱动力，为提升决策效率和优化资源配置提供了全新思路。本研究旨在探讨大数据与人工智能在运营管理中的创新应用及其实际价值，通过整合多源数据资源并运用机器学习算法，实现对复杂运营场景的深度分析与精准预测。研究采用理论建模与实证分析相结合的方法，选取典型行业案例进行深入剖析，验证了基于大数据驱动的人工智能技术在需求预测、供应链优化及客户关系管理等关键环节中的显著优势。结果表明，大数据与人工智能的融合能够有效降低运营成本、提高服务质量和增强市场竞争力，同时为管理者提供更具前瞻性的决策支持。本研究的创新点在于首次提出了一种集成式智能运营框架，将动态数据处理与自适应算法相结合，突破了传统方法在时效性和精确性上的局限。此外，研究还揭示了技术应用过程中可能面临的挑战，如数据隐私保护与模型解释性问题，并提出了相应的解决策略。总体而言，本研究不仅丰富了运营管理领域的理论体系，也为实践应用提供了有价值的参考依据，推动了大数据与人工智能技术在运营管理中的深度融合与发展。
关键词：大数据驱动；人工智能技术；运营管理；需求预测；智能运营框架
Abstract
With the rapid development of information technology, big data and artificial intelligence (AI) have gradually become crucial drivers in the field of operations management, offering new approaches to enhance decision-making efficiency and optimize resource allocation. This study investigates the innovative applications and practical value of big data and AI in operations management. By integrating multi-source data resources and employing machine learning algorithms, this research conducts in-depth analyses and precise predictions of complex operational scenarios. A combination of theoretical modeling and empirical analysis is adopted, with a detailed examination of representative industry cases, to validate the significant advantages of AI technologies driven by big data in key areas such as demand forecasting, supply chain optimization, and customer relationship management. The results indicate that the integration of big data and AI can effectively reduce operational costs, improve service quality, and enhance market competitiveness while providing managers with more forward-looking decision support. An innovative contribution of this study is the proposal of an integrated intelligent operations fr amework that combines dynamic data processing with adaptive algorithms, overcoming the limitations of traditional methods in terms of timeliness and accuracy. Furthermore, the study identifies potential challenges during the application process, such as data privacy protection and model interpretability, and proposes corresponding solutions. Overall, this research not only enriches the theoretical fr amework of operations management but also provides valuable references for practical applications, promoting the deep integration and development of big data and AI technologies in the domain of operations management.
Keywords：Big Data Driven; Artificial Intelligence Technology; Operation Management; Demand Forecasting; Intelligent Operation fr amework
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法与技术路线 2
二、大数据在运营管理中的应用基础 2
(一) 大数据技术的核心特征 2
(二) 数据驱动的运营管理框架 3
(三) 大数据在运营决策中的价值体现 3
三、人工智能赋能运营管理的创新路径 4
(一) 人工智能技术在运营管理中的适用性 4
(二) 智能算法优化运营流程的研究 5
(三) 基于运营管理的创新路径 5
四、大数据与人工智能融合的运营管理实践 6
(一) 融合技术在供应链管理中的应用 6
(二) 数据智能支持的企业资源规划 6
(三) 创新案例分析与经验总结 7
结 论 8
参考文献 9
