考虑牛鞭效应与反牛鞭效应联合作用在不完美信息下供应链决策者的合作博弈与演进

理性假设下,供应链运作过程中出现牛鞭效应也会出现反牛鞭效应,在考虑牛鞭效应与反牛鞭效应联合作用下分别构建二级供应链合作博弈模型与n级供应链合作博弈模型,得到Nash均衡策略,构建合作Nash均衡下供应链上订货模型,并通过数值算例验证订货模型及牛鞭效应特性与反牛鞭效应特性交替作用的最优化局势优于传统视角的消除牛鞭效应带来的供应链优化.研究表明:供应链上下游在周期内表现出牛鞭效应特性与反牛鞭效应特性的交替作用,会使得供应链整体上市场需求偏差减少、运作成本降低.贡献在于给出消除牛鞭效应的新思路,从整体上消除牛鞭效应或反牛鞭效应,使得整条供应链不管是短期的还是长期都是最优的,在长期还会给供应链企业带来显著的战略优势.     

牛鞭效应弱化与反牛鞭效应强化在完美信息下联合作用的博弈

在供应链运作过程中,同时存在牛鞭效应与反牛鞭效,若仅考虑到供应链的成本、需求偏差等问题,这种存在会因有限理性的驱使使得牛鞭效应弱化与反牛鞭效应强化.因此,认为供应链的上下游在周期内会表现出牛鞭效应弱化与反牛鞭效应强化的联合作用,联合作用使得单个企业达到低平均库存成本,也意味着供应链的整体库存最低且整体市场需求偏差最低,间接地、自动地从整体上消除牛鞭效应或反牛鞭效应,使得整条供应链不管是短期的还是长期来看是最佳的,若是长期,还会给供应链企业带来显著的战略优势.     

供应链交互作用对牛鞭效应的影响分析

考虑两平行供应链系统,建立了需求依赖于两种产品价格的需求函数模型,分析了平行供应链交互作用对牛鞭效应的影响。研究表明:(1)供应链交互作用可能增加或减弱牛鞭效应。(2)对于具有产品可替代性的两竞争型供应链系统,若产品价格交互敏感性不强,则较大协方差的引入可以抑制牛鞭效应。(3)对于具有产品互补性的两合作型供应链系统,若产品价格交互敏感性较强,则较小协方差的引入可以抑制牛鞭效应。     

不同需求条件下供应链系统动态仿真比较研究

本文首先建立供应链节点企业的系统动力学模型,并由此推及到供应链的系统动力学模型。通过使用Vensim对模型进行仿真,引入外生摄动的需求变化,包括阶跃需求、斜坡需求、脉冲波动需求和正弦波动需求,对比分析四种不同需求对供应链系统动态性的影响。研究表明,斜坡需求和正弦波动需求具有渐变的性质,使得供应链各节点企业的牛鞭效应较小;阶跃需求和脉冲波动需求具有突变的性质,供应链各节点企业的牛鞭效应较大。通过销售策略的调整可以改变其外生摄动的需求变化,降低供应链的动态性,提高其运作绩效。     

季节性供应链零售商补货策略研究——基于牛鞭效应视角

通过建立含有季节性自回归移动平均需求过程的供应链,零售商采用最小均方差预测技术预测提前期需求,分析(R,D)、(R,S)、(R,βS)、(R,γO)和(R,γO,βS)五种补货策略下的牛鞭效应.研究结果表明:(R,γO)补货策略是弱化牛鞭效应的最优补货策略,然而(R,γO)补货策略时出现了反牛鞭效应,无法保证供应链的安全供给.实践中当库存量调节系数和订货量调节系数较大时,(R,βS)补货策略能有效弱化牛鞭效应,当库存量调节系数和订货量调节系数较小时,(R,γO,βS)补货策略能有效弱化牛鞭效应;对于(R,βS)和(R,γO,βS)补货策略,牛鞭效应随着库存平滑系数的增大而增大;对于(R,γO)和(R,γO,βS)补货策略,牛鞭效应随着订货平滑系数的增大而增大;对于(R,S)、(R,βS)和(R,γO,βS)补货策略,牛鞭效应随着订货提前期的增大而增大;对于(R,γO)和(R,γO,βS)补货策略,牛鞭效应随着时刻t的增大而增大,但时刻t增大到一定程度时,牛鞭效应值基本不变.     

基于产品需求替代的两级供应链牛鞭效应分析

考虑两种产品需求替代下的自回归移动平均时间序列,零售商采用最小均方差技术预测市场需求,以订货点法确定订货量的两级供应链牛鞭效应量化模型,并对该模型牛鞭效应的大小及其影响因素进行理论分析和算例验证,研究表明:1)需求替代情形下牛鞭效应的表达式对有无需求替代的情况都适用;2)替代系数对被替代产品的牛鞭效应无影响,对替代产品的牛鞭效应有影响,并且替代系数同替代产品的牛鞭效应变化方向一致;3)需求替代情形下缩短提前期并不一定能减少替代和被替代产品的牛鞭效应.     

供应链中"牛鞭效应"的成因及弱化

加强供应链中信息共享，弱化“牛鞭效应”是实现供应链管理目标的关键。本在介绍了“牛鞭效应”形成过程及成因的基础上，论述了信息共享是弱化“牛鞭效应”的有效途径和方法，然后架构了一个基于Internet的信息共享系统，并对其系统进行了简要的描述和分析。     

基于SARMA需求模式的牛鞭效应分析

在自回归移动平均(ARMA)模型的基础上,建立需求过程为季节性自回归移动平均(SARMA)的时间序列,零售商采用最小均方差(MMSE)预测技术预测市场需求,库存采用补充订货至目标库存(order-up-to)策略的简单两级季节性供应链牛鞭效应量化模型,并对模型牛鞭效应的大小及其影响因素进行理论分析和实例验证,不仅刻画出各种情形下牛鞭效应存在的辨别条件和属性,而且实证结果表明煤炭供应链采用SARMA模型度量牛鞭效应优于ARMA模型.     

ARMA（1,1）需求下的信息延迟对供应链最优决策的影响

在假定顾客需求满足ARMA（1,1）过程的前提下,考虑了由一个零售商和一个供应商所组成的两级供应链系统最优订购决策问题.分别建立了需求信息不延迟与延迟这两种情形下零售商和供应商的最优订购决策模型,通过比较得出：当需求呈正相关时,需求信息延迟不仅可以减小牛鞭效应,而且可以降低供应链系统的平均总成本.     

历史订单信息对牛鞭效应的影响分析

客户需求信息的失真是导致牛鞭效应存在的原因,基于零售商的历史订单数据对其需求进行预测可以部分消除牛鞭效应。论文基于零售商-分销商二级供应链视角,分析了在零售商的需求为线性自回归模式的二级供应链中,分销商利用零售商历史订单数据和现有订单数据进行需求预测时自身库存成本的变更以及整个供应链的牛鞭效应的缓解程度。结果表明:分销商利用历史订单数据进行库存的决策可以显著地降低自己的平均库存和需求的波动,这种降低程度在零售商的订货提前期较大的情况下比较明显,但是零售商的需求预测相关系数对它影响不大。     

The incremental bullwhip effect of operational deviations in an arborescent supply chain with requirements planning

Lee et al. (1997) advocated the idea of sharing demand and order information among different supply chain entities to mitigate the bullwhip effect. Even with full supply chain visibility afforded by IT systems with requirements planning and with no information distortion, we identify a “core” bullwhip effect inherent to any supply chain because of the underlying demand characteristics and replenishment lead times. In addition, we quantify an incremental bullwhip effect as various operational deviations (inaccurate order placements, batching, lag in sharing demand forecast) contribute incrementally to the variance of the order quantity not only at the node where the deviation is taking place but also at all upstream supply chain nodes. We discuss some managerial implications of our results in the context of a UK manufacturer.     

Measure of bullwhip effect in supply chains with autoregressive demand process

In supply chain management, one of the most critical problems which require a lot of effort to deal with is how to quantify and alleviate the impact of bullwhip effect – the phenomenon in which information on demand is distorted while moving upstream. Although it is well established that demand forecast, lead time, order batching, shortage gaming and price fluctuation are the main sources that lead to the bullwhip effect, the problem of quantifying bullwhip effect still remain unsolved in many situations due to the complex nature of the problem. In this research, a measure of bullwhip effect will be developed for a simple two-stage supply chain that includes only one retailer and one supplier in the environment where the retailer employs base stock policy for their inventory and demand forecast is performed through the first-order autoregressive model, AR(1). The effect of autoregressive coefficient and lead time on this measure will then be investigated.     

Modeling and analysis of the causes of bullwhip effect in centralized and decentralized supply chain using response surface method

The “bullwhip” effect is a major cause of supply chain deficiencies. This phenomenon refers to grow the amplification of demand or inventory variability as it moves up the supply chain. Supply chain managers experience this variance amplification in both inventory levels and orders. Other side, dampening variance in orders may have a negative impact on customer service due to the increase in the inventory variance. This paper with simulating a three stage supply chains consisting of a single retailer, single wholesaler and single manufacturer under both centralized and decentralized chains. In this paper, it is intended to analysis the causes of bullwhip effect from two dimensions of order and inventory variance using the response surface methodology. The results show that in both supply chains, rationing factor is considered as the least important cause of bullwhip effect. While the wholesaler’s order batching and the chain’s order batching are considered as the main causes for the bullwhip effect in the decentralized and centralized chains, respectively.     

工业化、城镇化进程中电力需求分析及预测

随着工业化、城镇化进程的不断加快,我国电力需求量将持续上升。电力的充足供应是我国经济稳步发展的重要保证,故合理准确的对电力需求进行分析及预测具有重要的现实意义。基于此,分析我国电力需求现状,利用通径分析筛选电力消费需求的核心驱动因素。在模型选择的基础上,基于单变量(ETS、ARIMA模型)和多变量(情景分析)两个维度进行电力需求量分析及预测。结果表明:GDP每提高1%使得电力需求量提高0.5249%;工业化水平每提高1%使得电力需求量提高2.2146%,城镇化水平每提高1%使电力需求量相应提高1.0076%。“十二五”末中国电力消费需求量将近61425.96KW/h,2020年中国电力消费需求将近81410.10KW/h。     

The effect of replenishment policies on the bullwhip effect: A transfer function approach

An important phenomenon in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. This paper examines the influence of different replenishment policies on the occurrence of the bullwhip effect. The paper demonstrates that certain replenishment policies can in themselves be inducers of the bullwhip effect, while others inherently lower demand variability. The main causes of increase in variability are projections of future demand expectations, which result in over-exaggerated responses to changes in demand. We suggest that through appropriate selection and use of certain replenishment rules, the bullwhip effect can be avoided, subsequently allowing supply chain management costs to be lowered.     

Bullwhip effect in a supply chain model with multiple delivery delays

We develop a model of differential equations for a supply chain with delivery time delays between every adjacent firms. Based on the supply chain model, we provide a new perspective of the bullwhip effect and show that the bullwhip effect is intrinsic in supply chains in the sense that the equilibrium state of each firm in the supply chain is a cumulative forward product of the ratios of order fulfillment and placement between adjacent firms toward the end customer demand. We also show that it is the multiple time delays instead of the constant end consumer demand that determine the stability of the equilibrium states. However, the consumer demand has impacts on the stability of the equilibrium states of the supply chain when the end retailer’s inventory decisions are linearly related to the end consumer demand.