多级树形供应链牛鞭效应与综合补偿

讨论了多级树形供应链系统的牛鞭效应及其补偿问题,旨在提高供应链实际运行的效率.给出了若干牛鞭效应的量化指标,从定量的角度讨论了多级树形结构、竞争、信息传递、提前期、分配、预测等对牛鞭效应的影响作用.建立了针对信息传递、提前期,分配方式等的几种补偿方法,并结合常规补偿和模糊补偿的优点,设计了一种用模糊开关切换的综合补偿模型.对树形供应链在随机需求、竞争、促销、飞货情况下的补偿过程进行了仿真,验证了所提出的补偿方案的有效性.     

基于ExtendSim供应链牛鞭效应仿真分析

结合企业相关流程,利用ExtendSim软件对单链状4级供应链进行牛鞭效应仿真分析。通过牛鞭效应仿真图,帮助企业更直观地了解牛鞭效应曲线波动情况;改变仿真软件参数的输入值,如运输时间(备货期)、订货点库存量、每次补货量和初始库存来初步判断各因素对牛鞭效应的影响,有利于供应链上企业对各影响因素采取相应的改进措施。     

基于反馈与扰动控制的协同供应链集成模型

为了减少供应链企业间需求信息不明确而造成的牛鞭效应,采用控制系统建模思想构建企业元模型和协同供应链模型,描述供应链企业的供需状况,同时反映供应商之间的需求反馈与扰动影响.利用系统辨识方法量化供应链企业之间的供需关系,并通过共享企业间的历史数据获得协同模型参数,预测供应链系统各环节企业的需求量与生产量.仿真结果表明,该方法能够有效地减少牛鞭效应带来的供应链企业库存积累.     

基于系统动力学的闭环供应链中的牛鞭效应

为了基于闭环供应链研究逆向物流对牛鞭效应的影响,运用系统动力学原理和VENSIM软件构建四级闭环供应链模型。通过改变回收比率、回收产品再制造比例和回收延迟证的仿真,证明逆向物流的存在可以减弱正向物流中的牛鞭效应,而且逆向物流的回收比率越高,回收再制造经过的节点越少,回收延迟越小,则逆向物流对牛鞭效应的减弱效果越明显。     

基于回收比例的闭环供应链动力学行为分析

运用系统动力学构建具有普遍适用性的闭环供应链仿真模型,在不同需求条件下设置不同回收比例,研究回收对正向供应链动力学机制的影响,仿真结果显示,闭环供应链动力学行为尽管仍使正向供应链各级成员表现出牛鞭效应,但随着回收比例的增加,牛鞭效应会不断弱化。     

基于数量的供应商管理库存整合补货的牛鞭效应与成本研究

为研究在基于数量的供应商管理库存整合补货模式下，经济补货数量对两级供应链系统的牛鞭效应和系统成本的影响，以及牛鞭效应与成本的关系，利用系统动力学的方法构建了该模式的动态仿真模型。通过仿真，定量研究了基于数量的供应商管理库存整合补货模式中，供应商对随机流需求输入的订单响应率波动及系统成本的变化，并通过二次函数的回归分析探讨了牛鞭效应与系统成本之间的数学关系。结果显示，系统成本与牛鞭效应之间是一种二次凹函数的关系，可以找到一个合适的经济补货数量，实现供应链系统成本与牛鞭效应的协调优化控制。     

Internet下双渠道闭环供应链动态模型及其预测控制EI北大核心CSCD

针对Internet下既包含电子商务网络渠道和传统分销渠道的双源渠道正向物流,又包含退货、再制造的双渠道逆向物流,建立了闭环供应链网络动态模型;给出模型预测控制方法的一般步骤,以及系统牛鞭效应、正向牛鞭效应和再制造牛鞭效应的l2范数量化方法;通过仿真讨论了模型预测控制方法对系统生产、订货和库存波动的平滑作用,对于3种牛鞭效应的抑制效果,进一步讨论了渠道偏好对系统牛鞭效应的影响。所提算法的本质是通过滚动优化和滚动实施不断适时调整订货量、生产量等控制变量,使系统在稳定的前提下得到良好的动态性能,从而抑制牛鞭效应。     

基于时间的供应商管理库存整合补货模式下的牛鞭效应研究

为比较基于时间的供应商管理库存整合补货模式和传统补货模式对零售业供应链中牛鞭效应的影响,概述了供应商管理库存在零售业供应链中的应用情况,构建了两种补货模式的系统动力学模型。通过仿真,定量地比较了两种补货模式中供应商和零售商对泊松流随机需求输入的订单响应率的波动。另外,为了比较基于时间的供应商管理库存整合补货模式下补货周期对牛鞭效应的影响,通过改变参数设置模拟了不同补货周期下供应商和零售商的订单响应率波动。结果显示,与传统模式相比,基于时间的供应商管理库存整合补货对牛鞭效应有明显的改善;供应商管理库存整合补货的补货周期对牛鞭效应存在明显的影响,较长周期下的牛鞭效应相对于较短周期要严重。     

基于模块化和延迟生产的供应链建模与优化

通过改善Ricardo模型，在供应链层次上建立四种供应链结构模型，利用各节点的成本和客户服务水平指标来衡量其性能。同时基于模糊偏好关系来确定各指标的权重，由此建立多目标优化模型。模型的求解利用Matlab中的遗传优化工具箱(GAOT)，并采用单因素方差分析验证了模型的鲁棒性。最后基于优化结果，分析了四种供应链结构的牛鞭效应。     

供应链管理中的"牛鞭效应"与信息风险

分析了供应链管理中信息风险所带来的“牛鞭效应”，探讨了信息搜索在供应链管理中的作用、使用局限以及信息搜索的风险收益，提出了信息风险的防范措施。     

A measure of the bullwhip effect in supply chains with stochastic lead time

In this paper, we exactly quantify the bullwhip effect, the variance amplification in replenishment orders, for cases of stochastic demand and stochastic lead time in a simple two-stage supply chain with one supplier and one retailer. In most of the previous research, the impact of order lead time on the bullwhip effect in supply chains with pre-specified demand processes is investigated mostly for cases of deterministic lead time. In this paper, we deal with a first-order autoregressive, AR(1), demand process and investigate the behavior of a measure for the bullwhip effect with respect to autoregressive coefficient and stochastic order lead time. Extension to a mixed first-order autoregressive-moving average, ARMA(1,1), demand process is also considered.     

Impact of forecasting methods on variance ratio in order-up-to level policy

This paper considers the impact of forecasting methods on the bullwhip effect for a simple replenishment system in which a first-order autoregressive process describes the customer demand and an order-up-to inventory policy characterizes the replenishment decision. The impact of exponential smoothing and minimum mean squared error forecasting is measured for both the bullwhip effect and inventory variances. Previous similar studies have focused on investigating the impact of forecasting methods on bullwhip effect. However, little research has been carried out to explore the impact of forecasting methods for both bullwhip effect and inventory variances. Through simulation experiments, it has been found that depending on the structure of the demand process, the appropriate selection of forecasting technique can reduce, or even eliminate (i.e., “dewhip”) the bullwhip effect. However, in terms of inventory variances it has been shown that the inventory variances for the exponential smoothing are greater than the minimum mean squared error forecasting method and that gap increases as lead time increases. These findings will help companies to choose the appropriate forecasting technique depending on the nature of demand. These guidelines can help companies to reduce the bullwhip effect and inventory variances across supply chain.     

A study of lead time variation impact on supply chain performance

Lead time (LT) is a core parameter that varies and affects all supply chain (SC) partners. LT uncertainty is known as a kind of supply uncertainty in the SC literature which attracts attention of many researchers. In this paper, impact of LT variability in a serially connected supply chain with 4 levels is investigated. The study includes two main stages: development of a structural model and testing the structural relations by using simulated data. Simulation makes it possible to remove effects of intervener variables in order to measure pure impact of LT uncertainty on SC. Regarding this matter, a structural relationship model with three hypotheses about direct and indirect impact of LT uncertainty on supply chain inventories have been developed. To test the proposed structural relations, simulated data were used. The results of the study show that by increasing the lead time variance, order variances increase while no impact on the bullwhip is observed, i.e. the order variances increase uniformly throughout the entire SC. Furthermore, results show that the increase in the lead time variance will lead to inventory fluctuations. One practical recommendation of this study is to apply an investment strategy to reduce the lead time uncertainty.     

具有外包选择的闭环供应链切换模型及其鲁棒控制

建立了一类具有外包选择的闭环供应链切换系统模型,包括自行回收再制造和外包两个子系统,以及基于成本的切换信号向量。为了抑制闭环供应链系统在自行回收再制造和外包切换过程中产生的运作波动和牛鞭效应,应用鲁棒控制理论方法给出了供应链切换系统的控制律,分析了闭环供应链切换系统运作过程,并进行了控制律计算和系统仿真,验证了鲁棒控制律的有效性。     

Mitigating the bullwhip effect by reducing demand variance in the supply chain

To coordinate an agile manufacturing system with the high-level requirements of marketing services, supply chain management (SCM) plays an important role in management activities for enterprise globalization. In SCM, a phenomenon called the “bullwhip effect” has attracted considerable attention. Beyond the previous studies, we consider multiple retailers with one supplier in the supply chain and examine ways in which the supplier can reduce the total demand variance by adjusting retailers’ order sizes. The results reveal that the total demand variance can be reduced by using portfolio theory to adjust the order sizes based on the variance of order of the retailer. Therefore, the bullwhip effect can be partially reduced by our proposed method .     

An integration of assembly planning by design into supply chain planning

A supply chain needs to consider the quality of a product as well as the quality of manufacturing process to satisfy customer requirements at efficient resources planning in terms of safety stock allocation and vendor–buyer coordination. The objective of this article is to use an axiomatic approach to make assembly planning by designing and integrating assembly into supply chain planning, particularly during supply chain reconfiguration. The effect of fixture layout planning, the accuracy of demand forecast, and the supplier capability of providing the required material quality are studied. An optimum supply chain network is configured by combining the product, assembly, and supply chain planning. Heuristic-based optimization is used to validate the proposed solution. The performance of the system is measured in terms of lead time variability, the number of backorders, and the level of safety stock. The results and analysis indicate that the axiomatic approach is capable of reducing the assembly variation and employing necessary fixture layout planning to deliver product intents. In addition, the reduction of assembly variation also reduces the safety stock, lead time variability, and backorders. Finally, management decision making is discussed among other concluding remarks.     

Evaluating the value of information sharing in a supply chain using an ARIMA model

This paper considers a two-echelon supply chain, which contains one supplier and one retailer. It studies the quantification of the bullwhip effect and the value of information-sharing between the supplier and the retailer under an autoregressive integrated moving average (ARIMA) demand of (0, 1, q). The results show that with an increasing value of q, bullwhip effects will be more obvious, no matter whether there is information sharing or not. When there exists information sharing, the value of the bullwhip effect is greater than it is without information sharing. With an increasing value of q, the gap between the values of the bullwhip effect in the two cases will be larger.     

Supply chain - Insights from the aerospace sector Globalisation naturally leads to extended supply chains but customer requirements depend on a fast response. How are these two disparate trends handled in the modern supply chain?

The impact of demand change on the extended supply chain due to globalisation and the options for mitigating it are discussed. It is also shown how fast supply chain simulation can assist in comparing potential solutions. Two fundamental risks are managed: time mismatch between tc and td total, where tc is the elapsed time between the time a customer places an order to the time the finished product is received while td total is the cumulative manufacturing and physical logistics process and delivery times; and volume mismatch between forecast demand on which a supply chain has been designed and planned and the actual demand that materialises. The risks may be mitigated by a collaborative forecasting and planning processes, thereby distributing the risk as well as achieving greater consensus to the schedule