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, constructing the resilient control system of variable structure, researching the impact of supply chain resilient interaction mechanism undersupply disruption risk. On this basis, a kind of deep learning mechanism is put forward 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 disruption 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.
[1] Moses M, Sephardic S. Policy mechanisms for supply chain coordination. ⅡE Transactions, 2000, 32(3):245-262.
[2] Sheffi Y. Supply chain management under the threat of international terrorism. The International Journal of Logistics Management, 2001, 12(2):1-11.
[3] Kleindorfer P R,Saad G H. Managing disruption risks in supply chains. Production and Operations Management, 2005, 14(1):53-68.
[4] Bakal I S, Katakana S. Quantity flexibility for multiple products in a decentralized supply chain. Computers & Industrial Engineering, 2013, 64(2):696-707.
[5] Li Xin. A quantity-flexibility contract in two-stage decision with supply chain coordination//International Conference on Computational Intelligence and Security. IEEE, 2015:109-112.
[6] Awoken S R, Singh G. Using process capability analysis to evaluate supply chain flexibility based on order lead time and order processing cost deviations. European Journal of Management, 2013, 13(1):95-106.
[7] 李琳,范体军.零售商主导下生鲜农产品供应链的定价策略对比研究.中国管理科学,2015,23(12):113-123.
[8] Lummis R R, Delos R J V L K. Delphi study on supply chain flexibility. International Journal of Production Research, 2005, 43(13):2687-2708.
[9] Seedbacher G, Winkler H. A citation analysis of the research on manufacturing and supply chain flexibility. International Journal of Production Research, 2013, 51(11):3415-3427.
[10] Dietrich D M, Coney E A. Methods and considerations for the development of emerging manufacturing technologies into a global aerospace supply chain. International Journal of Production Research, 2011,49(10):2819-2831.
[11] Pereira J, Hamada L, Parades F. Flexibility and amplification measures in a supply chain model. IEEE, 2008:5510-5514.
[12] Stevenson M. Supply chain resilience:Definition, review and theoretical foundations for further study. International Journal of Production Research, 2015, 53(18):1-32.
[13] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7):1527-1554.
[14] Chen I J, Paulraj A. Understanding supply chain management:Critical research and a theoretical framework. International Journal of Production Research, 2004, 42(1):131-163.
[15] Ho D C K, Au K F, Newton E. Empirical research on supply chain management:a critical review and recommendations. International Journal of Production Research, 2002, 40(17):4415-4430.
[16] 张雨濛,王震.多级双渠道供应链的联合契约研究.中国管理科学,2015,23(S1):537-542.
[17] Simchi-Levi D. Review of:Competing through supply chain management:Creating market-winning strategies through supply chain partnerships david frederick ross Chapman & Hall. ⅡE Transactions, 2008, 30(8):762-763.
[18] Mistral D, Gunasegaram A, Papadopoulos T, et al. Big data and supply chain management:A review and biometric analysis. Annals of Operations Research, 2016:1-24.
