一种减少供应链牛鞭效应的资产组合管理方法

考虑一个由单个供应商和多个零售商组成的供应链系统,零售商面临无促销活动和有促销活动两种动态需求环境,采用周期性检查库存策略,基于当前市场需求信息向供应商订货.同时,市场中的零售商由于订货决策行为的相互影响而存在一定的相关性.本文探讨零售商之间具有不同相关性订货决策时,运用资产组合管理方法调整零售商之间的供应量,减少订货的总方差,实现减少订货所产生牛鞭效应.随后,通过对比分析零售商订货量调整前后库存水平、库存成本、缺货损失和利润,验证了零售商调整订货量的动机和积极性.数值算例的结果表明,运用资产组合管理方法能够减少供应商的总方差,同时能够激励具有不同相关系数的零售商调整订货量,在一定程度上减少供应链中的牛鞭效应.零售商之间的相关系数越大,供应链中牛鞭效应减少的效果就越显著;且在同一相关系数下,零售商对市场需求预测的方差越大,运用资产组合管理方法所达到的牛鞭效应减少的效果就越大.     

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.     

在ARIMA(0,1,1)需求下的牛鞭效应与信息共享的评价

本文考虑一个包含一个供应商和一个零售商的两级供应链,研究在需求模型ARIMA(0,1,1)下牛鞭效应的量化和信息共享的价值,比较信息共享之前和之后的差异,其结果表明信息共享能给供应商带来减轻牛鞭效应、减少现有平均库存以及降低成本等好处。     

不同库存管理策略下建设供应链牛鞭效应量化对比分析

本文在分析总承包模式下建设供应链与其他行业供应链对应关系的基础上,给出了(s,S)库存管理策略和周期性检查(PeriOdic Review,PR)库存管理策略下牛鞭效应的量化分析模型,建立了两种库存管理策略下牛鞭效应的比较分析模型,分析了供货提前期、库存检查周期、自相关系数和预测样本数对两种库存管理策略下牛鞭效应差值的影响,得出PR库存管理策略比(s,S)库存管理策略下牛鞭效应的表现更强烈的结论,建议总承包商采用PR库存管理策略时,应当更加注意对牛鞭效应的控制.     

需求信息滞后下的零售商决策与牛鞭效应分析

牛鞭效应是一种在企业经营中广泛存在的普遍现象,对企业的生产经营产生了极大的负面影响。本文考虑一个供应商和一个零售商组成的简单两级供应链系统。在这个系统中,首先建立在需求信息滞后情况下的零售商库存决策和混合决策(库存决策和定价决策)模型;然后进行决策对牛鞭效应的影响分析。结果显示:在需求信息存在滞后的情况下,零售商最优决策的定价和订货点的期望值是确定的,与需求信息滞后期的长度无关;零售商的库存决策能产生牛鞭效应,且牛鞭效应会随需求滞后期增加而逐渐减少;零售商的混合决策能否产生牛鞭效应,取决于需求自相关系数的变化,而与需求滞后期无关,但随着需求滞后期会改变期变化程度。此外,库存决策还是混合决策,在需求不具有滞后时较具有滞后时牛鞭效应表现得较为为减弱;零售商的混合决策与库存决策相比,只有在需求的自相关系数取较小值时,才表现得更为强烈,否则会减弱。因而对于零售商而言,适当的需求信息滞后以及供应商对零售商的定价柔性能减少牛鞭效应。     

需求信息预测与处理中的牛鞭效应分析与控制

研究了不稳定的市场需求下一个生产商和一个零售商所组成的两层次的简单供应链系统.提出当零售商预测信息的过程中并不存在牛鞭效应现象,而在生产商和零售商利用库存策略处理需求信息的过程中却显著存在牛鞭效应现象.证明了利用信息共享的方式可以控制牛鞭效应,但不能完全消除牛鞭效应.     

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.     

基于两单链合作下的集群式供应链牛鞭效应的H∞控制研究

以集群式供应链跨链间库存合作为背景,针对两单链情形,考察零售商的紧急库存补充渠道来自于另一供应链的零售商,建立了集群式供应链跨链间库存合作下的库存模型和牛鞭效应量化方法。并用H_∞控制理论来寻求最优订货系列,以达到降低安全库存水平,提高顾客满意度和减弱牛鞭效应的目的。仿真实验表明,实施H_∞控制和跨链间库存互补策略可以有效抑制牛鞭效应。     

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。     

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.     

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.     

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.     

Price promotions,operations cost,and profit in a two-stage supply chain

The phenomenon in which demand variability increases as one moves upstream in the supply chain has been often observed in practice. This so-called “bullwhip effect” often increases upstream operations costs, including inventory holding and transportation costs. Price variations are considered to be one of the primary causes of the bullwhip effect, and thus everyday low price (EDLP) strategies are commonly recommended to counter the negative impacts of the bullwhip effect. However, trade promotions continue to play an important role in the U.S. supermarket industry as well as other industries. This paper investigates this apparent inconsistency between the literature and practice by employing a deterministic, two-stage supply chain model composed of a single supplier and a single retailer. We demonstrate that even though the use of trade promotions can indeed increase a retailer's and supplier's operations costs, these costs may be more than offset by increased revenues, even in the absence of explicit coordination. That is, if the supplier judiciously applies a trade promotion strategy and the retailer passes some of this discount to its customers, then under certain conditions, the resulting supply chain profit can exceed that under an EDLP strategy. We provide a broad set of computational results that validate this conclusion and discuss the resulting managerial insights.     

The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts

We quantify the bullwhip effect (which measures how the variance of replenishment orders is amplified as the orders move up the supply chain) when both random demands and random lead times are estimated using the industrially popular moving average forecasting method. We assume that the lead times constitute a sequence of independent identically distributed random variables and the correlated demands are described by a first-order autoregressive process. We obtain an expression that reveals the impact of demand and lead time forecasting on the bullwhip effect. We draw a number of conclusions on the bullwhip behaviour with respect to the demand auto-correlation and the number of past lead times and demands used in the forecasts. We find maxima and minima in the bullwhip measure as a function of the demand auto-correlation.     

Changing risk preferences in supply chain inventory decisions

The risk preferences of managers may cause their inventory ordering decisions to deviate from the optimal policy. Past studies in operations management have produced mixed results. This study examines this proposition using decision data collected from a supply chain experiment. This article finds that changing the risk preferences of managers with respect to demand changes and supplier failures is a significant behavioural factor in explaining deviations in ordering decisions. This result provides an additional behavioural cause in explaining the bullwhip effect in supply chains. It also provides insights on the challenges for reducing the bullwhip effect in supply chains.     

An Empirical Study of the Bullwhip Effect in China

In this study, we investigate the bullwhip effect in China using data on over 1200 companies listed on the Shanghai and Shenzhen stock exchanges from 2002 to 2009. Specifically, we estimate the ratio of the volatility of production to the volatility of demand as a proxy for the bullwhip effect. Our results show that more than two‐thirds of the companies we studied exhibit the bullwhip effect. We also find that several hypotheses proposed in the existing literature are supported by firm‐level data from China, and that the intensity of the bullwhip effect in China declined during the period from 2002 to 2009.