摘要
供应中断是OEM供应链中企业面临的主要风险。本文基于供应链弹性分析的角度，将OEM供应链弹性运作问题描述为多变量耦合控制模型，构建了可变结构的弹性控制系统，研究了在供应中断风险冲击下OEM供应链弹性交互影响机制。在此基础上，提出了一种有针对性的提升供应链弹性的深度学习机制，此算法比传统的BP神经网络更加能够提高供应链绩效，并结合案例进行验证。研究结果表明：当供应中断发生时，深度学习算法可有效提升OEM供应链弹性，最大程度减轻企业损失。
Because the OEM supply chain may face greater disruption risk than ordinary supply chain, in this paper, the elasticity operation and promotion strategy for OEM supply chain are mainly studied. OEM supply chain resilient operation problem is described as a multivariable coupling control model, construc- ting the resilient control system of variable structure, researching the impact of supply chain resilient inter- action mechanism undersupply disruption risk. On this basis, a kind of deep learning mechanism is put for- ward to improve the flexibility of OEM supply chain. This algorithm can improve the performance of OEM supply chain more than the traditional BP neural network. The results show that; when the supply disrup- tion occurs, the deep learning algorithm can effectively enhance the OEM supply chain flexibility, and it can reduce the pecuniary loss of the enterprise to the maximum extent.
出处
《中国管理科学》
CSSCI
CSCD
北大核心
2018年第2期152-159,共8页
Chinese Journal of Management Science
基金
教育部哲学社会科学研究重大课题攻关项目(15JZD021)
天津市高等学校创新团队培养计划资助项目(TD12-5013)
关键词
OEM供应链
供应中断
多变量耦合
深度学习机制
弹性提升策略
OEM supply chain
supply disruption
multivariable coupling control
deep learning
resilientpromotion strategy
