供应链中长鞭效应的理论分析与研究

供应链管理是21世纪企业适应全球竞争的有效途径.要提高自身的市场章争力,企业需要从整体出发考虑供应链的协调运作情况,促使供应链绩效量大化.而长鞭效应是供应链实际运作中出现的一个主要问题,它会造成库存增加、信息延迟、顾客满意度降低等不良后果,降低供应链绩效,使供应链失调.首先分析长鞭效应带来的严重后果并全面讨论导致长鞭效应发生的各种原因,以此为基础提出弱化长鞭效应的对策,从而促进供应链协调和绩效最大化.     

供应链中库存系统的信息熵

由于市场供给和需求的不确定性、供应链结构的复杂性以及长鞭效应(Bullwhip Effect)和反长鞭效应(AntiBullwhip Effect)等因素的影响,供应链及其库存系统存在着很大的不确定性,即供应链中的库存系统存在着熵.考虑长鞭效应和反长鞭效应下供应链中库存系统的信息熵,并进行仿真计算.研究发现库存系统在简单供应链系统中充当了信息熵的"放大器",制造商的库存系统在以制造商为核心的供应链系统中充当了信息熵的"剧增器".最后给出有效降低熵值的策略,比如采用VMI模式或JMI模式,建立各种信息系统,推动式与拉动式供应链相结合,采用电子商务与传统分销相结合的供应链模式,在整个供应链系统中建立一个信息中心.     

基于联合库存的供应链系统动力学研究

采用联合库存管理为库存管理模式,运用系统动力学(Vensim5．0)软件建立了供应链动态仿真模型。通过仿真结果输出,从瓶颈、长鞭效应、成本等三方面对模型进行分析和优化;通过计算机仿真对系统未来行为进行的描述,形成了供应链管理策略的实验室。从而既保证了该系统在实际运行时的效果,又避免了系统策略实施可能带来的风险。     

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。     

仓单质押运作风险的优化

基于供应链系统的各项资金流出入量,建立仓单质押运作风险的优化模型,提出仓单质押运作风险优化的有效方法.银行、物流公司协同生产企业加强对信息流、物流、资金流的有效整合,共同构建敏捷灵活的供应链.从理论上说明了银行可以密切关注客户的经营状况,及时监控客户的生产运营和资金周转情况,降低仓单质押的运作风险.     

基于CVaR准则的供应链协同绿色创新动态优化与协调研究北大核心CSSCI

考虑消费者绿色偏好具有不确定性、产品绿色水平具有动态变化特征的情形,基于CVaR风险度量准则,构建了供应链协同绿色创新的动态优化模型。利用反馈求解法得到不同决策模式下风险规避制造商与风险规避供应商的最优均衡策略,探讨了成员风险规避水平对最优绿色创新策略及供应链绩效的影响。最后,提出双向成本分担契约对供应链进行协调。研究发现：相较于主从博弈,合作博弈下的产品绿色水平及供应链整体绩效水平均有所提高;制造商风险规避行为不利于发展绿色低碳经济,而供应商适度地规避风险可提高供应链整体运作效率;在满足一定条件时,双向成本分担契约的设计与实施能够有效提升产品绿色水平及制造商、供应商和供应链整体的绩效水平。     

基于CVaR准则的供应链协同绿色创新动态优化与协调研究

考虑消费者绿色偏好具有不确定性、产品绿色水平具有动态变化特征的情形,基于CVaR风险度量准则,构建了供应链协同绿色创新的动态优化模型。利用反馈求解法得到不同决策模式下风险规避制造商与风险规避供应商的最优均衡策略,探讨了成员风险规避水平对最优绿色创新策略及供应链绩效的影响。最后,提出双向成本分担契约对供应链进行协调。研究发现:相较于主从博弈,合作博弈下的产品绿色水平及供应链整体绩效水平均有所提高;制造商风险规避行为不利于发展绿色低碳经济,而供应商适度地规避风险可提高供应链整体运作效率;在满足一定条件时,双向成本分担契约的设计与实施能够有效提升产品绿色水平及制造商、供应商和供应链整体的绩效水平。     

基于LSSC的供应商选择与订单分配

面对物流服务供应链中不规则的客户需求,物流服务集成商如何有效分配各物流供应商的物流订单是一大难题。考虑集成商对不同分销点的多个物流供应商分配订单的情形,建立基于物流供应商现有手头订单、物流能力可获得性、配送效率、缺损率与成本的供应商选择与订单分配组合优化模型,并采用相应算法进行算例分析。结果表明:该模型及算法能够为物流服务供应链的运作确定合适数量的供应商及进行订单分配的优化;在确保满足客户期望服务水平的同时,降低供应商的运作成本,并促使其为获取更大的竞争优势而改进自身的物流服务能力。     

多级串行供应链的同步模式及库存优化研究

在Ciancimino和Cannella[1]提出的同步供应链概念模式上进行了数学建模,补充了库存更新的规则,提出同步供应链的单时段系统成本的核算方法,对同步模式与库存优化进行了结合研究。以系统成本为优化目标,获得供应链各级的最优安全库存因子,进行系统最优库存设置。通过算例模拟传统供应链和同步供应链的实际运作,利用通用的供应链评价指标对两种供应链模式进行比较分析,证实了结合库存优化的同步供应链模式的优越性。     

第三方资金流提供商的采购协同问题研究

在传统的供应链采购运作过程中,由于核心制造商一直对其供应商供货采取下线结算的方式,从而导致供应商一直承担着巨大的库存持有成本.在基于Supply Hub的供应链运作模式下,提出了一种新的资金结算方式:令传统的3PL充当第三方资金流提供商来对供应商提前支付货款.从而解决供应商库存成本压力过大的问题,并且实现对资金流、信息流、物流的整合.在新的结算模式下,通过对供应链的成本分析研究得出一种有效的供应链运作机制,从而提高供应商的响应速度及协同性,降低供应链的总成本.通过数据模拟进一步证明.在该模式下核心制造商能够获得更快的产品交货期;供应商能够有效地降低库存持有成本;第三方资金流提供商可以通过对供应商采取适当的惩罚来约束供应商,促使供应商对交货期做出最合适的选择,并且使自身获利.     

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.     

Minimizing the bullwhip effect in a supply chain using genetic algorithms

This paper presents a computational intelligence approach, which addresses the bullwhip effect in supply chains (SCs). A genetic algorithm (GA) is employed to reduce the bullwhip effect and cost in the MIT beer distribution game. The GA is used to determine the optimal ordering policy for members of the SC. The paper shows that the GA can reduce the bullwhip effect when facing deterministic and random customer demand combined with deterministic and random lead times. The paper then examines the effect of sales promotion on the ordering policies and shows that the bullwhip effect can be reduced, even when sales promotions occur in the SC.     

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.     

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

The bullwhip effect (BWE) is a phenomenon, which is caused by ineffective inventory decisions made by supply chain members. In addition to known inefficiencies caused by the bullwhip effect within a supply chain product flow, such as excessive inventory, it can also lead to inefficiencies in cash flow such as the cash flow bullwhip (CFB). The CFB reduces the efficiency of the supply chain (SC) through heterogeneous distribution of cash among supply chain members. This paper aims to decrease both the BWE and the CFB across a SC through applying a simulation-based optimisation approach, which integrates system dynamics (SD) simulation and genetic algorithms. For this purpose, cash flow modelling is incorporated into the SD structure of the beer distribution game (BG) to develop the CFB function. A multi objective optimisation model is then integrated with the SD-BG simulation model. Finally, a genetic algorithm (GA) is applied to determine the optimal values for the inventory, supply line, and financial decision parameters. Results show that the proposed integrated framework leads to efficient liquidity management in the SC in addition to cost management.     

Effective bullwhip metrics for multi-echelon distribution systems under order batching policies with cyclic demand

A large number of problems in a distribution supply chain require that decisions are made in the presence of the bullwhip effect phenomenon. The impact of the order batching policies on the bullwhip effect is analysed in this paper, when cycle demand on a multi-echelon supply chain operating is considered. While investigating which bullwhip effect metrics are more adequate to measure the bullwhip effect in these type of systems, the optimal reordering plan that minimises the operation costs of the overall system is calculated. A Mixed Integer Linear Programming (MILP) model is developed that takes into account an inventory and distribution system formed by multiple warehouses and retailers with lateral transshipments. The bullwhip effect is measured through four metrics: the echelon average inventory; the echelon inventory variance ratio; the echelon average order; and the echelon order rate variance ratio. As conclusion the inventory metrics suggest that (i) using batching policy reduces instability; (ii) batching may reduce in general order variance if using larger batches and (iii) cycle demand length has no major impact in the bullwhip effect. A motivational example and a real word case study are used and tested.     

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.     

Supply chain design and cost analysis through simulation

This paper is grounded on a discrete-event simulation model, reproducing a fast moving consumer goods (FMCG) supply chain, and aims at quantitatively assessing the effects of different supply configurations on the resulting total supply chain costs and bullwhip effect. Specifically, 30 supply chain configurations are examined, stemming from the combination of several supply chain design parameters, namely number of echelons (from 3 to 5), re-order and inventory management policies (EOQ vs. EOI), demand information sharing (absence vs. presence of information sharing mechanisms), demand value (absence vs. presence of demand ‘peak’), responsiveness of supply chain players. For each configuration, the total logistics costs and the resulting demand variance amplification are computed. A subsequent statistical analysis is performed on 20 representative supply chain configurations, with the aim to identify significant single and combined effects of the above parameters on the results observed. From effects analysis, bullwhip effect and costs outcomes, 11 key results are derived, which provide useful insights and suggestions to optimise supply chain design.     

The impact of consumer price forecasting behaviour on the bullwhip effect

As prices fluctuate over time, a strategic consumer may buy more in advance to reduce his or her future needs in anticipation of higher prices in the future, or may choose to postpone a purchase in anticipation of lower prices in the future. We investigate the bullwhip effect from a consumer price forecasting behavioural perspective in the context of a simple two-level supply chain composed of a supplier and a retailer. We consider two different forms for the demand function – linear and iso-elastic demand functions, both depending on the prices in multiple periods. Assuming that the retailer employs an order-up-to inventory policy with exponential smoothing forecasting technology, we derive analytical expressions for the bullwhip effect under the two demand functions, and extend the results to the multiple-retailer case. We find that consumer forecasting behaviour can reduce the bullwhip effect, most significantly when the consumer sensitivity to price changes is medium (approximately 0.5) for both the demand forms. In addition, for iso-elastic demand, the mitigation of the bullwhip effect induced by consumer price forecasting behaviour becomes more significant as the product price sensitivity coefficient and standard deviation of the price decrease. These findings are applicable to the development of managerial strategies by supply chain members that are conducive to bullwhip effect reduction through customer behaviour.     

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.