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。     

大规模风电并网的双供应源电力供应链牛鞭效应分析

中国当前的电力供应链除具有部分垄断特征外,还由于大规模风电并网使得电力供给也出现随机性,它与随机需求一起影响了供应链信息的准确传递,在电力供应链产生了牛鞭效应,但对这类问题的研究极少。本文在分析中国电力供应链特点的基础上,构建了由煤炭供应企业、发电厂(火力发电和风力发电)和用户组成的多级电力供应链模型,揭示了牛鞭效应在单/双供应源两种供应链类型下的变化。研究结果表明,大规模风电并网形成的双供应源电力供应链牛鞭效应较大且波动剧烈,尤其当下游用户需求较平稳时,供应链会出现牛鞭效应与反牛鞭效应共存现象,而预测技术的选择、风电场合理规划等有助于抑制牛鞭效应,保证电力安全并减小资源浪费。     

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.     

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.     

区块链技术影响下的供应链系统动态响应性研究

为解决供应链系统中信息时滞和不对称问题,加快推动区块链技术在供应链管理中的创新发展,本文运用控制理论构建由分销商和零售商组成的二级供应链系统控制模型,引入区块链技术影响下的信息校正因子,推导系统的传递函数,通过MATLAB仿真不同需求信号下的订单可变性和库存波动。仿真结果表明:(1)区块链技术的应用提高了库存系统的精准性和稳定性;(2)高库存可变性伴随着高订单可变性;(3)指数平滑系数和区块链技术影响下的信息校正因子有效抑制供应链中的牛鞭效应;(4)信息延迟时间越长,区块链技术影响下的信息校正因子对控制系统的抑制作用越显著。本研究量化了区块链技术对供应链中牛鞭效应的影响,提高了供应链系统的精准性和稳定性,丰富了区块链技术在供应链管理中的应用,为企业管理者提供新的研究思路。     

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。     

时滞供应链网络系统的切换模型预测控制方法

研究了具有时变时滞因素供应链网络系统中牛鞭效应的稳定化控制问题.由于时滞的时变特性,使得系统在不同时刻表现为不同的动态,从而将供应链库存系统建模为一类具有有限个子系统的切换系统模型.采用切换模型预测控制方法,给出了一个使得供应链切换系统指数稳定的充分条件.进而,通过在线求解一组线性矩阵不等式,给出了订单补偿控制增益的设计方法.最后,通过仿真验证了所得订单补偿控制策略能有效地抑制供应链网络系统中的牛鞭效应.     

时滞供应链网络系统的牛鞭效应切换控制方法

研究了具有时滞因素供应链网络系统中牛鞭效应的稳定化控制问题,构建了具有时变时滞的供应链库存系统模型.由于时滞的时变特性,使得系统在不同时刻表现为不同的动态,从而可将供应链库存系统建模为一类具有有限个子系统的切换系统模型.采用平均驻留时间方法,给出了一个使得供应链库存波动切换系统指数稳定的充分条件.进而,通过求解一组线性矩阵不等式,给出了订单补偿控制策略的设计方法.最后,通过仿真验证了设计的订单补偿控制策略能有效抑制供应链库存网络系统中的牛鞭效应.     

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.     

Controlling the bullwhip effect in a supply chain system with constrained information flows

The bullwhip effect problem is one of the most important issues in supply chain management. Limited information sharing increases the difficulty of reducing the bullwhip effect and leads to inefficient supply chain management. The purpose of this paper is to explore new ways to reduce the bullwhip effect in supply chain systems that face uncertainties with respect to information sharing. We first present a supply chain state transition model, based on which we explore the endogenous mechanism of bullwhip effect, especially those related to impacts from limited information sharing. Then we propose a novel inventory control method and study the corresponding control optimization problem, with the aim of reducing inventory volatility in supply chains. Both quantitative analysis and simulation study are conducted. Simulation results show the effectiveness and flexibility of our proposed method in reducing bullwhip effect and in improving supply chain performance, even under conditions of limited information sharing.     

Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains

Supply chain inventories are prone to fluctuations and instability. Known as the bullwhip effect, small variations in the end item demand create oscillations that amplify throughout the chain. By using system dynamics simulation, we investigate some of the structural sources of the bullwhip effect, and explore the effectiveness of information sharing to eliminate the undesirable fluctuations. Extensive simulation analysis is carried out on parameters of some standard ordering policies, as well as external demand and lead-time parameters. Simulation results show that (i) a major structural cause of the bullwhip effect is isolated demand forecasting performed at each echelon of the supply chain, and (ii) demand and forecast sharing strategies can significantly reduce the bullwhip effect, even though they cannot completely eliminate it. We specifically show how each policy is improved by demand and forecast sharing. Future research involves more advanced ordering and forecasting methods, modelling of other well-known sources of bullwhip, and more complex supply network structures.     

The effect of information sharing on supply chain stability and the bullwhip effect

This paper analyzes the bullwhip effect in multi-stage supply chains operated with linear and time-invariant inventory management policies and shared supply chain information. Such information includes past order sequences and inventory records at all supplier stages. The paper characterizes the stream of orders placed at any stage of the chain when the customer demand process is known and ergodic, and gives an exact formula for the variance of the orders placed. The paper also derives robust analytical conditions, based only on inventory management policies, to predict the presence of the bullwhip effect and bound its magnitude. These results hold independently of the customer demand. The general framework proposed in this paper allows for any inventory replenishment policies, any ways of sharing and utilizing information, and any customer demand processes. It is also shown as a special case that sharing customer demand information across the chain significantly reduces, but does not completely eliminate, the bullwhip effect.     

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.     

Impact of information sharing and lead time on bullwhip effect and on-hand inventory

This work analyzes a two echelon (warehouse–retailer) serial supply chain to study the impact of information sharing (IS) and lead time on bullwhip effect and on-hand inventory. The customer demand at the retailer is assumed to be an autoregressive (AR(1)) process. Both the echelons use a minimum mean squared error (MMSE) model for forecasting lead time demand (LTD), and follow an adaptive base-stock inventory policy to determine their respective order quantities. For the cases of without IS and inter as well as intra echelon IS, expressions for the bullwhip effect and on-hand inventory for the warehouse are obtained, considering deterministic lead-time. The results are compared with the previous research work and an easy analysis of the various bullwhip effect expressions under different scenarios, is done to understand the impact of IS on the bullwhip effect phenomenon. It is shown that some part of bullwhip effect will always remain even after sharing both inter as well as intra echelon information. Further, with the help of a numerical example it is shown that the lead time reduction is more beneficial in comparison to the sharing of information in terms of reduction in the bullwhip effect phenomenon.     

The bullwhip effect in supply chain networks

This paper analyzes the propagation and amplification of order fluctuations (i.e., the bullwhip effect) in supply chain networks operated with linear and time-invariant inventory management policies. The supply chain network is allowed to include multiple customers (e.g., markets), any network structure, with or without sharing information. The paper characterizes the stream of orders placed by any supplier for any stationary customer demand processes, and gives exact formulas for the variance of the orders placed and the amplification of order fluctuations. The paper also derives robust analytical conditions, based only on inventory management policies, to predict the presence of the bullwhip effect for any network structure, any inventory replenishment policies, and arbitrary customer demand processes. Numerical examples show that the analytical results accurately quantify the bullwhip effect; managerial insights are drawn from the analysis. The methodology presented in this paper generalizes those in previous studies for serial supply chains.     

Understanding supply chain dynamics: A chaos perspective

Variability in orders or inventories in supply chain systems is generally thought to be caused by exogenous random factors such as uncertainties in customer demand or lead time. Studies have shown, however, that orders or inventories may exhibit significant variability even if customer demand and lead time are deterministic. In this paper, we investigate how this class of variability, chaos, may occur in a multi-level supply chain and offer insights into how to manage relevant supply chain factors to eliminate or reduce system chaos. The supply chain is characterized by the classical beer distribution model with some modifications. We observe the supply chain dynamics under the influence of various factors: demand pattern, ordering policy, demand-information sharing, and lead time. Through proper decision-region formation, the effect of various factors on system chaos is investigated using a factorial design. The degree of system chaos is quantified using the Lyapunov exponent across all levels of the supply chain. This study shows that, to reduce the degree of chaos in the supply chain system, the adjustment parameters for both inventory and supply line discrepancies should be more comparable in magnitude. Counter-intuitively, in certain decision regions, sharing demand information can do more harm than good. Similar to the bullwhip effect observed previously in demand, we discover the phenomenon of “chaos-amplification” in inventory across supply chain levels.     

Quantifying the bullwhip effect in a supply chain with stochastic lead time

In a recent paper, Dejonckheere, Disney, Lambrecht, and Towill [European Journal of Operational Research 147 (2003) 567] used control systems engineering (transfer functions, frequency response, spectral analysis) to quantify the bullwhip effect. In the present paper, we, like Chen, Ryan, Drezner, and Simchi-Levi [Management Science 46 (2000) 436], use the statistical method. But our method extends Dejonckheere et al. and Chen et al. in that we include stochastic lead time and provide expressions for quantifying the bullwhip effect, both with information sharing and without information sharing. We use iid demands in a k-stage supply chain for both. By contrast, Chen et al. provide lower bounds using autoregressive demand for information sharing and for information not sharing (with zero safety factor for stocks). Dejonckheere et al. validate Chen et al.’s results for a 2-stage supply chain without information sharing, using both autoregressive and iid normally distributed demands. We estimate the mean and variance of lead-time demand (LTD) from historical LTD data, rather than from the component period demands and lead time. Nevertheless, we also calculate the variance amplification like Chen et al., but with gamma lead times. With constant lead times, which Chen et al. used, our method yields lower variance amplification. As for the effect of information, we find that the variance increases nearly linearly in echelon stage with information sharing but exponentially in echelon stage without information sharing.