两种库存管理方法下的牛鞭效应比较分析

建立了ARIMA供应链需求模型,利用订货量方差和需求方差的比值,分别对Order-up库存管理方法和基于MRP的库存管理方法下的牛鞭效应进行了量化.比值大于1则牛鞭效应存在且与比值成正比,反之则牛鞭效应不存在.模拟试验对两种库存管理方法作了对比,结果表明基于MRP的库存管理方法在多数情况下产生了较小的牛鞭效应.     

基于数量的VMI整合补货模式下的库存控制

利用系统动力学的原理、方法,定性分析了两级供应链环境下基于数量的VMI整合补货策略,构建了该库存管理模式的系统动力学仿真模型以刻画其系统动态性,通过对随机流需求下模型仿真结果的定量分析,研究了该模式下不同经济补货阀值对牛鞭效应及库存变化的影响.结果显示,在一定范围内,较小经济补货阀值下供应链中的牛鞭效应及各节点的库存水平及波动的情况比较理想,随着经济补货阀值设定的变大,牛鞭效应、库存水平及波动逐渐加大.     

考虑牛鞭效应的智慧供应链定价与投资决策研究

智慧供应链的发展缓解了传统供应链中牛鞭效应问题。针对制造商、零售商组成的两级供应链,考虑中小企业是否投资智慧供应链数字技术,构建不同数字技术投资组合下的博弈模型,探讨牛鞭效应对产品定价与数字技术水平决策的影响,并比较不同模式下的利润水平。研究表明,投资数字技术的供应链成员会设置更高的数字技术水平来弱化牛鞭效应带来的负面影响;制造商或零售商单独投资都会对供应链产生积极影响,两者均投资却有可能会降低供应链绩效。     

VMI技术的运作协调性及在制造业的应用研究

传统供应链因结构性内生的问题--牛鞭效应往往使企业响应迟缓,库存大量积压.本文通过定量的理论分析,认为VMI模式不仅有助于解决牛鞭效应的问题,同时有助于整个供应链的获利能力的提高.并以一个家电企业与零件供应商之间的VMI系统为例介绍VMI在制造供应链上游的应用.     

多供应链间库存互补系统动力学仿真模型

以两条或多条由生产商、批发商和零售商组成的三级供应链为研究对象,首先分别建立了两条供应链环境下零售商、批发商和生产商各级节点企业库存互补系统动力学仿真模型,然后把两链间库存互补问题扩展到多链间库存互补情况.研究表明,两条供应链各级节点企业问库存互补均能减少供应链系统的库存总量和总物流成本、减弱牛鞭效应、提高顾客服务水平...     

供应链牛鞭效应建模与仿真

该书共分九章,内容包括供应链结构与牛鞭效应、基于All（1）需求的多级供应链牛鞭效应建模与仿真、基于ARMA（1,1）需求的多级供应链牛鞭效应建模与仿真、基于不同预测技术的两级供应链牛鞭效应建模与仿真、ARMA（1,1）需求下不同预测技术的多级供应链牛鞭效应建模与仿真、信息共享下多级供应链牛鞭效应建模与仿真、航运市场中牛鞭效应实证分析、总结与展望。     

集群式闭环供应链系统动态模型及牛鞭效应分析

以集群式闭环供应链为研究对象,建立了不确定需求下两个闭环供应链跨链合作的交叉库存补充动态模型,分析了集群式闭环供应链的鲁棒运作问题.针对系统运作过程中需求不确定干扰问题,给出了解决集群式闭环供应链牛鞭效应的鲁棒H∞控制策略及线性矩阵不等式(LMI)算法.最后,通过算例仿真,对比分析了有无跨链合作时两供应链库存、订货(生...     

突发事件影响的供应链系统动态仿真

为了研究突发事件对供应链绩效的长期效应,基于系统动力学构建了传统供应链模型、渠道库存控制供应链模型及供应商管理库存供应链模型.以包含一个制造商和一个供应商的二级供应链为研究对象,基于对供应链模型的仿真实验获取了供应链总库存和需求短缺量等绩效指标在无突变风险、生产突变风险和运输突变风险三种情境下的原始数据,通过对供应链绩...     

基于库存销售比的VMI补货策略

在信任与合作的前提下,销售商提供库存与销售信息,供应商通过库存销售比(存销比)确定补货时间和补货量进行补货.这种补货策略通过信息共享与无订单补货,达到消除供应链中牛鞭效应的目的.     

信息共享对瞬时补货多级供应链牛鞭效应影响的建模分析

对不稳定市场需求下的瞬时补货多级供应链系统进行建模,定量地分析了需求信息共享前后供应链各节点企业的牛鞭效应,并进行了数值实验.研究表明:无需求信息共享时,牛鞭效应始终存在;需求信息共享后,牛鞭效应明显地减小,在一定条件下会出现反牛鞭效应.     

制造企业库存长鞭效应影响因子分析及控制方法

长鞭效应是供应链管理中由于供应链合作伙伴之间缺乏合作与协调的结果。从理论上刻画了长鞭效应的内在本质,分析了产生长鞭效应的原因,以一个跨国公司的库存管理为例,详细分析了影响长鞭效应三个方面的因子：（1）集中式与分散式管理;（2）需求与供应特征;（3）产品类型与库存再订货点。还从供需合作关系的角度提出了改善长鞭效应的几个控制方法。     

Measure of the bullwhip effect considering the market competition between two retailers

In order to consider the market competition, a new supply chain with one supplier and two retailers is established in this paper. Two retailers employ different AR(1) demand processes, respectively, and an order-up-to inventory policy characterises the inventory decisions. The bullwhip effect in this supply chain is measured under the moving average forecasting technique. We investigate the effects of the lead time, the span of forecast, market competition and the consistency of demand volatility on the bullwhip effect using the algebraic analysis and numerical simulation. Conclusions indicate that different factors lead to the bullwhip effect following different patterns in the supply chain. Moreover, some suggestions are present to help managers to control parameters that yield the lower bullwhip effect.     

Stochastic characterization of upstream demand processes in a supply chain

In the supply chain management area, there has much recent attention to a phenomenon known as the bullwhip effect. The bullwhip effect represents the situation where demand variability increases as one moves up the supply chain. In this paper, we study this effect in an order-up-to supply-chain system when minimum Mean Square Error (MSE) optimal forecasting is employed as opposed to some commonly used simplistic forecasting schemes. We find that depending on the correlative structure of the demand process it is possible to reduce, or even eliminate (i.e., "de-whip"), the bullwhip effect in such a system by using an MSE-optimal forecasting scheme. Beyond the bullwhip effect, we also determine the exact time-series nature of the upstream demand processes.     

Analysis of the bullwhip effect in multi-product,multi-stage supply chain systems–a simulation approach

This research aims to develop a simulation approach based on system dynamics modelling (SDM) and adaptive network based fuzzy inference system (ANFIS) for quantifying and reducing the bullwhip effect in a multi-product, multi-stage supply chain. The proposed model is comprised of three groups of variables influencing the bullwhip effect, namely the structure of a supply chain network, supply chain contributions (ordering process in regular situation or when a supplier has a promotion or shortage gaming) and supply chain performances (the number of defects and ordering lead time). As a result, a two layer simulation model is developed with three generic models. The flexibility of this proposed approach is its ability to model various types of ordering policies which are basic inventory policies, material requirement planning (MRP) system and just in time (JIT) approach. The supply chain of a beverage company was selected to validate and demonstrate the flexibility of the proposed model. The findings of the proposed simulation model are consistent with the results obtained from the case study. The error magnitude of the bullwhip effect level varied between 0 and 9% resulting in bullwhip effect reductions of up to 92%. Accordingly, the bullwhip effect levels are significantly decreased by using the proposed simulation model.     

Bullwhip effect and inventory oscillations analysis using the beer game model

This work examines the bullwhip effect generated and suffered by each level of a four-stage beer game supply chain when different demand scenarios are considered. The paper shows that the actors who generate lower bullwhip are those who suffer more from its effects. Moreover, a new definition of an inventory oscillations measure based on bullwhip definition is introduced. Finally the paper verifies that the new measure of inventory oscillations provides more information on supply chain performance than the bullwhip measure.     

基于提前期的需求信息放大效应定量研究

在m个分销商和一个制造商的两级供应链中考虑提前期对信息放大效应的作用,推导出分销商需求信息与最初客户群的需求信息之间的需求信息放大效应的表达式.实例分析证明在即使不存在提前期(或者提前期为零)的情况下仍然存在信息放大效应,单纯地压缩提前期不能真正减少信息放大效应的影响,提出实现企业之间的合作与实时信息共享是关键.     

Dynamic analysis and design of a semiconductor supply chain: a control engineering approach

The combined make-to-stock and make-to-order (MTS-MTO) supply chain is well-recognised in the semiconductor industry in order to find a competitive balance between agility, including customer responsiveness and minimum reasonable inventory, to achieve cost efficiency while maintaining customer service levels. Such a hybrid MTS-MTO supply chain may suffer from the bullwhip effect, but few researchers have attempted to understand the dynamic properties of such a hybrid system. We utilise a model of the Intel supply chain to analytically explore the underlying mechanisms of bullwhip generation and compare its dynamic performance to the well-known Inventory and Order-Based Production Control System (IOBPCS) archetype. Adopting a control engineering approach, we find that the feedforward forecasting compensation in the MTO element plays a major role in the degree of bullwhip and the Customer Order Decoupling Point (CODP) profoundly impacts both the bullwhip effect and the inventory variance in the MTS part. Thus, managers should carefully tune the CODP inventory correction and balance the benefit between CODP inventory and bullwhip costs in hybrid MTS-MTO supply chains.     

基于遗传算法的供应链需求预测优化模型研究

整体优化是供应链管理的核心问题之一.为降低供应链的整体运作成本,减小"长鞭效应"的影响,建立了多级供应链的Multi-agent模型,并运用遗传算法(GA)对其需求预测进行优化研究.最后利用算例进行分析,表明提出的方法能有效减小"长鞭效应",有效降低供应链的整体运作成本.     

Underestimating the bullwhip effect: a simulation study of the decomposability assumption

We investigate the assumption of decomposability as it pertains to modelling the bullwhip effect in multi-stage supply chains. Decomposing a multi-stage supply chain into a set of node pairs, each of which can be efficiently represented with a two-stage model, is a common modelling technique when analysing the bullwhip effect in supply chains. This approach depends on the validity of the decomposability assumption since most supply chains are coupled systems that are a logical fit for singular, or ‘monolithic’, multi-stage models. We utilise a simulation study to compare decomposition-based supply-chain models with monolithic models and determine if decomposition modelling significantly alters the predicted severity of the bullwhip effect. We find decomposition-based models exhibit a significantly lower level of bullwhip effect than monolithic models of the same supply chain. The systematic underestimation of the bullwhip effect by decomposition-based models indicates that the assumption of decomposability is flawed. Our study also confirms previous work showing the significant benefit of using actual, instead of approximate, lead-time demand information. We discuss implications for supply-chain modelling, supply-chain design, and data collection.