基于W-SVR的供应链风险评价研究
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摘要
在全球经济一体化和日益发达的信息技术的双重推动下,企业为了利用外界资源,集中精力发展自己的核心竞争力,以获取快速响应市场需求的优势,逐步采取供应链管理模式。企业通过实施供应链管理,使管理效率和竞争实力有了显著提高,取得了良好的经济效益。然而,由于企业所面临环境的不确定性及供应链自身复杂性不断增加,风险成为企业供应链管理不能回避的问题。为了保证供应链的稳健运行和供应链管理目标的顺利实现,供应链风险管理日益引起企业界和理论界的重视。本文主要对供应链风险评价问题进行了研究。
     本文首先以供应链管理理论和风险管理理论为基础,系统阐述了供应链风险管理理论体系。其次,对供应链风险的来源和形成机理进行了较为深入的探讨,在此基础上进行供应链风险识别与分析,建立了一套全面反映供应链风险水平的指标体系,并对每一个定量指标进行了量化分析,给出其量化方法。再次,重点对供应链风险的评价方法进行了研究:在构建径向基小波核函数的基础上,建立了基于小波支持向量回归机(W-SVR)的供应链风险评价模型。最后,结合实例在Matlab环境下实现了该模型的评价过程,并将测试结果与标准的支持向量回归机(ε-SVR)及BP神经网络的测试结果进行了比较分析;同时对供应链风险影响因子进行了灵敏性分析。结果表明,本文建立的基于小波支持向量回归机的供应链风险评价模型取得了良好的效果,为供应链风险管理提供了科学的决策依据。
At the double driven of the global economic integration and the advancing information technology, enterprises gradually adopt the management model of supply chain in order to access to the advantage of responding quickly to the market demand, they try to make use of the external resources and concentrate on developing its core competitiveness. Through the implementation of supply chain management, enterprises’management efficiency and competitiveness have been significantly improved and they brought good economic benefits. However, in the same time risk is becoming an unavoidable problem for the enterprises in supply chain, because of the uncertainty of the environment of the supply chain and the ever-increasing complexity of its own. In order to guarantee the stable operation of the supply chain and the realization of the goals of supply chain management, the management of supply chain risk has attracted increasing attention in theoretical circles and business circles.
     In this paper, the main work is as follows: First, I elaborate and organize the theory framework of supply chain risk management systemically based on the theory of supply chain management and risk management. Secondly, I identified the risk factors after analyzing the sources and formation mechanism of the supply chain risk in depth, and set up a index system which can reflect the level of the comprehensive supply chain risk, and conducted a quantitative analysis to each indicator and indicated the quantitative method of them in the same time; Finally, I focused on the method of supply chain risk evaluation: Set up a supply chain risk evaluation model based on wavelet support vector regression machine and realized it based on Matlab in a case study, and conducted a sensitivity analysis; and a comparative analysis was also conducted with evaluation results of the standard support vector regression model and neural network model. And the results show that the risk evaluation model of supply chain based on the wavelet support vector regression achieved better results which provided a scientific decision making for the risk management of supply chain.
引文
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