基于大数据理论的供应链需求管理研究

发布时间：2018-07-20 14:19

【摘要】：大数据时代,数据以及数据分析技术是企业在竞争中获得优势的重要资源。在企业的运营过程中每时每刻都会有海量的信息数据的产生,重视对信息数据的处理和分析可以为企业带来巨大的经济价值。然而当前供应链上需求管理模式的弊端越来越突出,已无法满足当前大数据的需要。因此本文运用大数据理论为完善供应链上需求管理理论体系提供理论依据,而且为提升供应链整体竞争力提供实际意义。本文的重点在于探讨应用大数据理论进行对需求管理的改善。其第一章介绍了国内外供应链管理中需求管理的研究现状,并第二章叙述了供应链上需求共享理论、博弈论、协同学理论、大数据等关键技术,为构建基于大数据理论的需求信息大数据平台提供理论基础和技术支撑。第三章通过对传统的需求管理中的诸多问题进行原因和危害分析,发现其共通的原因——需求信息不共享,进而在第四章提出应用大数据等技术构建以需求信息数据共享分析为核心的大数据平台。本文的核心是构建基于大数据理论的大数据平台,在结合平台构建可行性和原则的基础上,提出平台具有共享需求信息、协调链上成员企业等功能。为了实现上述功能,本文设计了平台的总体架构,并具体分为五个步骤;需求信息大数据的获取、需求信息大数据加工存储、需求信息大数据组织管理、需求信息大数据分析以及需求信息大数据的决策。在需求信息获取中引入大数据理论,旨在汇集供应链上所有成员企业经营中需求信息和链外相关需求信息以及政府政策环境信息等数据;为了保障众多的需求信息数据的完整,在大数据存储中建立需求信息数据相应的数据仓库,保障能储存越来越多的信息数据;在大数据的组织管理中,是为了实现需求信息数据高效整合和有效利用;在大数据分析中提出大数据挖掘技术中的规则性数据分析、预测性数据分析方法,并从众多的需求信息数据中预测市场需求;在最后阶段,利用数据得出的结果进行决策。在安全机制中引入了安保措施,以保障需求信息数据在供应链上保存完整和安全。最后,第五章引入传统需求管理中三个特殊现象作为分析对象,分析应用大数据技术后,这三个传统的需求管理问题与应用前进行对比,从而验证平台的优越性。本文的创新点在于:其一,将大数据理论引入供应链上需求管理中,扩大需求信息的范围,并丰富了需求信息共享化理论体系,也顺应了当前大数据时代的发展趋势。其二,从供应链的实际需要角度出发,探讨并构建了大数据平台的总体架构和应用职能,并让信息数据具有决策的功能,来帮助企业管理者进行科学决策。
[Abstract]:In the era of big data, data and data analysis technology is an important resource for enterprises to gain advantages in competition. In the process of enterprise operation, there is a huge amount of information data at every moment, so it can bring great economic value to enterprises to pay attention to the processing and analysis of information data. However, the drawback of demand management mode in supply chain is more and more prominent, which can not meet the needs of current big data. Therefore, this paper uses big data theory to provide theoretical basis for improving the theoretical system of supply chain demand management, and provides practical significance for enhancing the overall competitiveness of supply chain. This paper focuses on the application of big data theory to improve demand management. The first chapter introduces the research status of demand management in supply chain management at home and abroad, and the second chapter describes the key technologies of supply chain demand sharing theory, game theory, collaboration theory, big data and so on. It provides theoretical foundation and technical support for constructing big data platform of requirement information based on big data theory. The third chapter analyzes the causes and harm of many problems in the traditional demand management, and finds out that the common reason is that the demand information is not shared. In the fourth chapter, we put forward the application of big data and other technologies to construct a big data platform which is based on the analysis of information and data sharing. The core of this paper is to build a big data platform based on big data theory. On the basis of the feasibility and principle of constructing the platform, it is proposed that the platform has the functions of sharing demand information, coordinating the member enterprises in the chain, and so on. In order to realize the above functions, this paper designs the overall architecture of the platform, and it is divided into five steps: the acquisition of the requirement information big data, the processing and storage of the requirement information big data, the organization and management of the requirement information big data. Requirement information big data analysis and requirement information big data decision making. In order to ensure the integrity of numerous demand information, big data theory is introduced into the demand information acquisition, which aims to collect the data of all member enterprises in the supply chain, such as the demand information, the off-chain demand information and the government policy environment information, and so on, in order to ensure the integrity of the numerous demand information data. In order to realize the efficient integration and utilization of the demand information data in the organization and management of big data, the corresponding data warehouse of the requirement information data is established in the storage of big data to ensure that more and more information data can be stored. In the analysis of big data, the methods of regular data analysis and predictive data analysis in big data mining technology are put forward, and the market demand is forecasted from a lot of demand information data, and in the final stage, the results obtained from the data are used to make decision. Security measures are introduced into the security mechanism to ensure the integrity and security of demand information data in the supply chain. Finally, the fifth chapter introduces three special phenomena in traditional demand management as the analysis object. After analyzing the application of big data technology, the three traditional requirement management problems are compared with those before application, so as to verify the superiority of the platform. The innovation of this paper lies in the following: firstly, the big data theory is introduced into the demand management of supply chain, which expands the scope of demand information, enriches the theoretical system of sharing demand information, and conforms to the development trend of current big data era. Secondly, from the point of view of the actual demand of supply chain, this paper discusses and constructs the overall structure and application function of big data platform, and makes the information data have the function of decision making, to help enterprise managers to make scientific decision.
【学位授予单位】：北京建筑大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F274

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