基于控制工程的牛鞭效应建模与仿真研究

"牛鞭效应"对供应链性能和绩效产生了严重的影响.本文基于控制理论建立了多级线性供应链的模型,分别利用噪声带宽和Matlab/simulink对一个可扩展多主体线性供应链系统的牛鞭效应进行了建模与仿真研究,分析了实施信息共享前后供应链系统的牛鞭效应,探讨了物流延迟时间参数和指数平滑预测参数对牛鞭效应的影响,并给出了减弱牛鞭效应影响的相应策略.     

供应链网络系统的牛鞭效应时滞因素分析与库存控制策略研究

牛鞭效应的存在严重影响了供应链系统的运作效率,增加了供应链管理的复杂性。本文基于供应链网络库存状态的内部系统动力学机制,构建了供应链网络库存系统的状态转移模型,并引入时滞影响因素,通过供应链网络库存系统的波动状态描述牛鞭效应。在此模型基础上,针对供应链网络系统的牛鞭效应问题,提出了一类新的基于库存波动状态的动态供应链库存控制策略,并运用系统稳定性理论,将该策略的参数优化求解问题转化成线性矩阵不等式的求解问题。最后通过系统仿真深入分析了供应链网络库存系统对库存控制策略参数以及系统时滞因素的敏感性,并验证了该动态库存控制策略可以有效地抑制牛鞭效应。     

基于系统动力学的供应链中牛鞭效应的研究

本文运用系统动力学的原理和VENSIM软件构建了啤酒游戏的供应链模型.通过增加供应链模型节点个数并对其仿真结果进行分析,证明随着供应链长度的增加,牛鞭效应也愈加明显;对VMI库存管理模式与传统库存管理模式的系统结构及运营绩效进行了比较,说明供应链成员间的信息共享可以有效地弱化牛鞭效应.     

对供应链中牛鞭效应问题的初探

牛鞭效应是在供应链中对供应链节点需求信息的一种扭曲现象。牛鞭效应在供应链管理中不可能被根本消除,只能弱化对供应链的影响。本文通过对牛鞭效应的介绍,产生原因的分析,提出缓解牛鞭效应影响的方法。     

多级供应链中库存不准确性对牛鞭效应的影响

目前大多数关于牛鞭效应的研究聚焦于两级供应链,极少数文献研究多级供应链系统中的牛鞭效应问题,且这些文献鲜有考虑到库存不准确的问题.本文通过建立解析模型量化分析在库存信息不准确的多级、分散供应链中的牛鞭效应,揭示牛鞭效应在高阶段供应链中的放大情况,以及库存准确性对牛鞭效应的影响.本文研究表明,库存误差会进一步恶化信息的扭曲,该信息扭曲随着订单传播过程被逐级放大和累积,使得牛鞭效应在高阶段供应链中成指数增长.共享信息可以缓解信息的扭曲,将牛鞭效应减低为线性增长.本文通过将供应链建模从两级拓展到多级,同时考虑到信息准确性问题,旨在更加完整的勾勒出需求信息在供应链中的传播情况.     

基于CPFR模型供应链"牛鞭效应"分析

牛鞭效应对企业产能与库存方面的影响很大。在不完全预先需求信息条件下,从CPFR的角度,通过建立模型对供应链各节点成员间缺乏对需求预测造成的供应链中牛鞭效应进行分析,并提出减少牛鞭效应影响的相应策略。     

信息共享可减小“牛鞭效应”

供应链其固有的"牛鞭效应"问题成为学术界和实业界关注的热点,它的负面影响造成供应链效率低下。本文通过对"牛鞭效应"的影响及产生原因加以分析,提出信息共享可在一定程度上抑制"牛鞭效应",是实现高效率低成本供应链管理的基础。     

基于APIOBPCS模式VMI下牛鞭效应仿真研究

运用系统动力学构建基于APIOBPCS采购模式和供应商管理库存的七成员供应链模型,通过该模型研究8个因素对牛鞭效应的影响.研究表明,平滑指数越小牛鞭效应越弱:库存初始值影响牛鞭效应的具体数值,但不能根本改变牛鞭效应;牛鞭效应受APIOBPCS模式库存调节系数影响巨大,DE-APIOBPCS模式下牛鞭效应相对较弱;在库存调节系数设定为1、某成员的所有下游成员提前期已经确定的前提下,该成员需求的标准方差只与紧邻上游成员的提前期有关,其余成员的提前期对该成员需求标准方差没有影响;库存检查周期越短牛鞭效应越弱;在供应链信息共享以及成员合作的前提下,瓶颈取适当值情况下的牛鞭效应与没有瓶颈情况下的牛鞭效应相比,前者更小;最低采购数量影响牛鞭效应,并且牛鞭效应对此因素表现出比较强的敏感性.     

双渠道供应链中的牛鞭效应分析

研究价格参考效应对双渠道供应链中牛鞭效应的影响．考虑由一个制造商和一个双渠道零售商构成的两级供应链网络,基于价格信息对称下消费者的价格参考行为,构建了双渠道零售商的市场需求函数,并分析补充至目标库存策略和移动平均预测技术下的牛鞭效应及其减弱控制措施．研究表明：１）价格参考效应可能会抑制双渠道供应链的牛鞭效应,但却无法完 全消除牛鞭效应;２）双渠道供应链中价格自回归度高的商品具有更小的牛鞭效应;３）当线上市场价格波动相对较大时,线上（下）市场的价格参考系数越小（大）,则双渠道供应链的牛鞭效应越小．     

企业供应链中牛鞭效应的产生原因及弱化对策

牛鞭效应是供应链中需求放大的现象,它会对整个供应链的运作水平造成严重影响。本文在讨论了牛鞭效应现象的产生及危害后,从牛鞭效应存在的普遍性出发,分析了引起牛鞭效应的六个主要原因,并给出了三条弱化牛鞭效应的对策。     

On variance amplification in a three-echelon supply chain with minimum mean square error forecasting

We analyse a three echelon supply chain model. First-order autoregressive end consumer demand is assumed. We obtain exact analytical expressions for bullwhip and net inventory variance at each echelon in the supply chain. All of the three supply chain participants employ the order-up-to policy with the minimum mean square error forecasting scheme. After demonstrating that the character of the stochastic ordering process observed at each level of the supply chain is mathematically tractable, we show that the upper stream participants have complete information of the market demand process. Then we quantify the bullwhip produced by the system, together with the amplification ratios of the variance of the net inventory levels. Our analysis reveals that the level of the supply chain has no impact upon the bullwhip effect, rather bullwhip is determined by the accumulated lead-time from the customer and the local replenishment lead-time. We also find that the conditional variance of the forecast error over the lead-time is identical to the variance of the net inventory levels and that the net inventory variance is dominated by the local replenishment lead-time.     

Decision Making and Cognition in Multi‐Echelon Supply Chains: An Experimental Study

Supply chain performance often depends on the individual decisions of channel members. Even when individuals have access to relevant information, order variation tends to increase when moving up the supply chain, a phenomenon known as the bullwhip effect. While prior research has investigated several structural/environmental factors which can mitigate the bullwhip effect, the underlying behavioral factors contributing to it are an open question. Using a production and distribution decision‐making simulation representing a four‐stage serial supply chain, we find that the cognitive profile of decision makers contributes to the bullwhip effect. We found that the specific decision tendency to underweight the supply line is linked to an individual's level of cognitive reflection. Furthermore, performance differs for entire supply chains and for specific echelons, and holds under standard mitigation efforts. The findings have implications for supply chain design, education, and industry.     

Information sharing in supply chains

Supply chain is defined as a system of suppliers, manufacturers, distributors, retailers and customers where material, financial and information flows connect participants in both directions. Most supply chains are composed of independent agents with individual preferences. It is expected that no single agent has the power to optimise the supply chain. Supply chain management is now seen as a governing element in strategy and as an effective way of creating value for customers. The so-called bullwhip effect, describing growing variation upstream in a supply chain, is probably the most famous demonstration that decentralised decision making can lead to poor supply chain performance. Information asymmetry is one of the most powerful sources of the bullwhip effect. Information sharing of customer demand has an impact on the bullwhip effect. Information technology has lead to centralised information, shorter lead times and smaller batch sizes. The analysis of causes of the bullwhip effect has lead to suggestions for reducing the bullwhip effect in supply chains by strategic partnership. Supply chain partnership leads to increased information flows, reduced uncertainty, and a more profitable supply chain. The cooperation is based on contacts and formal agreements. Information exchange is very important issue for coordinating actions of units. New business practices and information technology make the coordination even closer. Information sharing and strategic partnerships of units can be modelled by different network structures.     

Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. We report on a set of laboratory experiments with a serial supply chain that tests behavioral causes of this phenomenon, in particular the possible influence of coordination risk. Coordination risk exists when individuals' decisions contribute to a collective outcome and the decision rules followed by each individual are not known with certainty, for example, where managers cannot be sure how their supply chain partners will behave. We conjecture that the existence of coordination risk may contribute to bullwhip behavior. We test this conjecture by controlling for environmental factors that lead to coordination risk and find these controls lead to a significant reduction in order oscillations and amplification. Next, we investigate a managerial intervention to reduce the bullwhip effect, inspired by our conjecture that coordination risk contributes to bullwhip behavior. Although the intervention, holding additional on‐hand inventory, does not change the existence of coordination risk, it reduces order oscillation and amplification by providing a buffer against the endogenous risk of coordination failure. We conclude that the magnitude of the bullwhip can be mitigated, but that its behavioral causes appear robust.     

供应链中断下顾客缺货反应对牛鞭效应的影响研究

传统的关于供应链牛鞭效应的研究,仅关注供应链上游成员间（供应商、制造商或零售商）的协调运作,而忽略缺货发生后顾客缺货反应对供应链动态性的影响。本文利用高层级Petri-net对包含两个制造商、两个零售商的供应链系统进行建模与仿真,定量地研究不同的缺货强度下顾客缺货反应对两条相互竞争的供应链牛鞭效应的影响。研究结果表明,顾客缺货反应对缺货品牌及其竞争品牌的牛鞭效应均存在显著影响,但各种缺货反应对牛鞭效应的影响程度有所差异,且供应链中断强度（主要体现为发生缺货的品牌市场份额以及缺货持续时间）亦对牛鞭效应产生显著影响。我们建议不同产品类型（对应不同的顾客反应组合）的管理者根据品牌的市场占有率、缺货持续时间等因素对不同类型的顾客进行购买行为的引导,以缓解牛鞭效应并保持市场份额。     

多分销中心供应链模型及其牛鞭效应的H∞控制

本文在文^[1]具有一个分销中心供应链模型的基础上,建立了具有多个分销中心的供应链结构模型,采用供应链上游库存与订货偏差波动之和与下游顾客需求偏差波动之比描述牛鞭效应。提出顾客末端需求波动最差条件下H_∞控制方法,以抑制牛鞭效应。这一方法本质就是供应链末端需求波动最差情况下,制定供应链管理的最优决策。并且以一个石油分销系统为对象进行了供应链牛鞭效应的H_∞控制仿真实验。     

信息共享下库存量牛鞭效应的影响因素研究

目前的研究多从供应链上游角度出发考虑传统牛鞭效应,而本文从供应链下游的角度研究库存量牛鞭效应得到了不一样的管理学启示.在需求函数方面,建立的需求模型包括市场规模、价格敏感性系数等更有现实意义的要素.在此,本文建立了包括一个零售商和一个制造商的简单两级供应链,得出了制造商在采用补充至订货点策略和最小均方差预测技术,在两种不同的信息共享模式下的库存量牛鞭效应表达式,并对他们的影响因素进行了分析.而且通过数值分析对模型进行了验证并得到新的结果.通过研究发现,信息共享能够显著降低制造商的库存量牛鞭效应;零售商和制造商的库存量牛鞭效应都不受市场规模的影响;零售商的库存量牛鞭效应在一定条件下不存在;相比于零售商提前期,制造商提前期对制造商的库存量牛鞭效应影响更大.同时,价格敏感性系数、价格自相关系数等因素对制造商库存量牛鞭效应也有不同程度的影响.     

基于随机Petri网的生产提前期牛鞭效应测度研究

利用随机Petri网结合三角模糊参数,对产品生产的三工艺阶段生产周期的波动进行测定,得出前工艺阶段生产周期的波动造成后工艺阶段生产周期更大的波动,且波动逐级传递。证实了生产提前期也具有类似供应链中的牛鞭效应的现象,对不确定因素导致生产周期波动的测度提供了一种新的思路和方法。     

非平稳需求状态下虚拟企业牛鞭效应的存在性分析

本文从供应链管理的角度出发,将牛鞭效应的概念和模型引入虚拟企业的运营过程中,运用可变系数的自回归移动平均(ARIMA)模型对虚拟企业上下游成员企业需求信息进行预测,研究非平稳需求状态下虚拟企业内上下游企业订货水平的差异,证明了虚拟企业运营中牛鞭效应的存在性,最后提出除虚拟企业的敏捷经营、信息共享对牛鞭效应的影响以外,需求的波动也是影响牛鞭效应的重要因素,并讨论了信息完全共享的情况下牛鞭效应存在的条件.