摘 要
随着信息技术和消费者需求的多样化发展,传统大规模生产模式逐渐难以满足市场对个性化产品的需求,这促使企业探索新的生产与销售模式。本研究以大规模定制营销为切入点,旨在探讨如何通过优化生产与销售流程实现个性化需求与经济效益的平衡。通过构建灵活的模块化生产体系和数据驱动的精准营销策略,企业能够显著提升定制效率并降低运营成本。此外,研究发现数字化平台在连接供需双方、促进信息共享方面发挥了重要作用。本研究的创新点在于首次提出了一种基于动态需求预测的大规模定制优化模型,并验证了其在实际应用中的有效性。这一成果不仅为企业提供了可操作的实施路径,还为相关理论研究拓展了新视角,从而推动了大规模定制营销领域的进一步发展。
关键词:大规模定制;个性化需求;模块化生产;数据驱动营销;动态需求预测
Research on Production and Sales Model of Mass Customization Marketing
英文人名
Directive teacher:×××
Abstract
With the diversified development of information technology and consumer demand, the traditional large-scale production model is gradually difficult to meet the market demand for personalized products, which prompts enterprises to explore new production and sales models. This study takes mass customization marketing as the starting point to explore how to achieve the balance between individual demand and economic benefit by optimizing production and sales processes. By building flexible, modular production systems and data-driven precision marketing strategies, companies can significantly increase customization efficiency and reduce operating costs. In addition, the study found that digital platforms play an important role in connecting supply and demand and facilitating information sharing. The innovation of this study is that a mass customization optimization model based on dynamic demand prediction is proposed for the first time, and its effectiveness in practical applications is verified. This achievement not only provides an operable implementation path for enterprises, but also expands a new perspective for relevant theoretical research, thus promoting the further development of the field of mass customization marketing.
Keywords: Mass Customization;Personalized Demand;Modular Production;Data-Driven Marketing;Dynamic Demand Forecasting
目 录
引言 1
一、大规模定制营销的理论基础 1
(一)大规模定制的核心概念 1
(二)营销与生产的融合机制 2
(三)理论框架的构建与分析 2
二、生产模式的定制化转型研究 3
(一)定制化生产的技术支撑 3
(二)柔性制造系统的应用实践 3
(三)生产流程的优化与创新 4
三、销售模式的个性化策略研究 4
(一)个性化需求的识别与分析 4
(二)数字化平台在销售中的作用 5
(三)定制化销售的实施路径 5
四、大规模定制的协同管理研究 6
(一)供应链协同的挑战与对策 6
(二)数据驱动的决策支持系统 7
(三)协同效率的评估与改进 7
结论 8
参考文献 9
致谢 9