随机需求曲线下的供应链零售商决策与牛鞭效应

本文讨论由一个供应商和一个零售商组成的简单两级供应链系统中,对于不同的随机需求函数,零售商的联合决策对牛鞭效应的影响。首先建立了基于具有随机扰动项的需求函数下的零售商优化联合决策(库存决策和定价决策)的一般模型;然后分析在随机需求函数为线性和等弹性(假设随机扰动项为独立同分布的正态分布)的情形下零售商决策对牛鞭效应的影响。对于随机线性需求函数,牛鞭效应不会发生;而对于随机等弹性需求函数,仿真实验结果显示,零售商优化联合决策能使牛鞭效应度量值随着曲线等弹性系数或刻度因子的增大而减小,但牛鞭效应始终会发生。因此,零售商在制定策略以减小牛鞭效应时,还应考虑不同需求曲线的形态和精细结构。     

基于牛鞭效应的供应链最优委托权安排

本文使用一个一阶自回归模型，分析了“牛鞭效应”的成因，并阐明当供应链各成员在信息共享行动本身存在不完全信息时，通过信息共享减少“牛鞭效应”只是一种可能性，只有当供应链各成员建立了实现信息共享的合作机制，在最优委托权安排下，共同致力于信息共享工作，并分享信息共享带来的收益，这种可能性才能转化为现实，也最终才能减少“牛鞭效应”。     

供应链管理中的牛鞭效应及对策分析

供应链管理中的一个重要现象是牛鞭效应．即沿着供应链向上游移动时，需求波动幅度逐渐增大。这种现象蔓延到整个供应链，使得从零售商到分销商、制造商、供应商，订货量呈现波动幅度递增的趋势。许多公司注意到牛鞭效应，意大利的一家面食制造商布瑞勒观察到，地方分销中心的周订单一年之内波动了70倍．     

浅析供应链需求预测与牛鞭效应

总结了供应链需求预测和牛鞭效应定量研究的方法,以H.L.Lee提出的AR(1)自相关模型为基础,定量的验证了基于一次指数平滑的需求预测方法下的牛鞭效应.需求预测方法对牛鞭效应有重要影响.     

买方抗衡势力与制造商定价决策

本文研究了买方抗衡势力的存在对上游制造商定价决策的影响,讨论了制造商在不同定价形式（线性定价、两部收费制和转售价格维持（RPM））之间的选择问题,并构建了两阶段动态博弈模型,引入买方抗衡势力,比较分析了制造商在零售商具有和不具有买方抗衡势力两种情况下,制造商最优定价形式的选择。研究发现当零售商不具有买方抗衡势力时,制造商选择两部收费制和RPM是无差异的,都能使上游制造商获得相等的最优利润;当零售商具有买方抗衡势力时,对于上游制造商来说两部收费制优干RPM,RPM又优于线性定价。此外,本文还发现存在一个由抗衡势力和零售商替代程度决定的临界条件,当满足这一条件时,两部收费制是上游的最优选择;一旦这一条件不满足时,RPM就成了上游的最优选择。     

竞争环境下绿色供应链的融资策略研究

在一个含有两个竞争型供应商和单一零售商的供应链中，生产绿色产品的供应商缺乏资金时，可通过零售商提前支付和反向保理融资两种 渠道获得融资。当使用反向保理时，银行提高支付比例可以促进供应商的绿色生产、零售商的订购量，同时也能促进绿色供应商、零售商以及整个供 应链的利润增长。提前支付则是根据市场环境的不同，会对供应链上企业决策产生不同的影响。资金约束的绿色供应链可以根据自身以及市场环境的 不同，选择不同的融资方式，制定运营策略。     

下游竞争、买方谈判能力与上游产品差异化

基于零售商谈判能力的差异,构建完全信息动态博弈模型,考察下游竞争程度、买方谈判能力对上游产品差异化策略激励的影响.结果表明,下游市场竞争与买方谈判能力对产品差异化的激励不存在替代效应;当零售商不具有买方谈判能力时,市场竞争程度通过影响上游利润激励供应商的产品差异化策略,竞争程度不影响批发价格;当主导零售商具有买方谈判能力时,谈判能力通过影响批发价格激励供应商的产品差异化策略;市场竞争削弱了消费者效用水平,而买方谈判能力改善了消费者效用水平.     

决策机制对供应链牛鞭效应的影响分析

本文论证了信息结构与供应链牛鞭效应的关系,从决策机制角度以及供应链牛鞭效应的影响,对供应链牛鞭效应的形成进行了分析。     

企业再制造条件下逆向物流的“逆牛鞭效应”分析

文章提出了企业再制造条件下逆向物流中存在"逆牛鞭效应",分析了该效应产生的原因及影响。指出该"逆牛鞭效应"的存在是内在的,是由回收产品中可再制造部分数量和质量的不确定性决定的。通过对信息方法的优化设计、逆向物流渠道的完善和有效的决策控制设计可以弱化该效应的影响。进一步说明"逆牛鞭效应"不是牛鞭效应的对偶现象,对正向供应链中的牛鞭效应有放大作用。     

基于可控提前期的供应链库存优化模型研究

本文基于“提前期决策权由下游控制,下游完全承担缩短提前期的费用”的假设研究由一个供应商和一个零售商组成的供应链决策问题,建立该供应链系统的Stackelberg模型,并给出相应的算法供参考,最后结合算例分析,证实了该模型的有效性和实用性,即合理地缩短提前期可以有效降低供应链库存成本.     

The bullwhip effect on inventory: a perspective on information quality

In recent years, there has been a vast increase in the quantity of information shared across supply chain. We investigate how the timeliness and accuracy of information quality affect the value of information sharing. We use the inventory bullwhip effect (BWE) to measure the value of information sharing in a two-level supply chain consisting of one retailer and one manufacturer. The retailer faces a price-sensitive demand and the price is an AR (1) process. Our study shows if customer demand and retailer’s immediate order are delayed, using retailer’s historical order quantity to forecast can decrease manufacturer’s BWE. If information errors happen, during delivery and utilization, information sharing is not always valuable for the manufacturer. Sometimes, no information sharing can decrease much more of BWE. If information errors occur when the retailer collects demand information, value of information sharing is more significant than when there are no information errors.     

预期作用下人民币汇率对出口价格的非线性传递效应研究

对外贸易中贸易款项的支付大多数都在远期,因此对未来汇率变化幅度的预期,会影响汇率对出口价格的传递效应,从而导致汇率传递出现非线性特点。本文对此进行了理论分析,结果显示,预期未来汇率变化幅度较大时,汇率对出口价格的传递程度会下降甚至可能出现负向传递。实证部分本文使用STR模型分析了1999年1月—2016年7月在汇率预期的作用下人民币汇率对出口价格的传递效应。研究发现当人民币汇率预期变动幅度较小时,汇率传递系数为正,人民币汇率升值会导致出口价格上涨,贬值导致出口价格的下降。但是当人民币预期汇率的变化幅度达到一定的阈值后,人民币汇率升值会导致出口价格下降,而贬值会导致出口价格上涨。因此货币当局有必要关注汇率预期尤其是大幅度汇率变化预期的存在对进出口价格和需求的影响。     

均值-标准差控制下的供应链渠道Stackelberg模型

本文研究了在均值-标准差方法控制下的一个具有风险偏好(风险规避、风险中性、风险喜好)零售商与两个风险中性供应商之间供应链渠道协作和竞争。首先,在考虑零售商风险偏好下,提出了供应商联盟与非联盟两种情况中的各方处于Stackelberg-leader或Stackelberg-follower不同权利地位时各决策模式及其对应决策模型;然后,通过对比分析各决策模式最优解及深入分析零售商风险偏好对各渠道成员最优决策影响,得到了基于零售商风险偏好下的供应链渠道各成员的领导者地位将较大影响各方期望效用,而对供应链渠道整体期望效用最大化并无影响,同时,零售商过度喜好风险或者规避风险都将会对供应链整体期望效用造成致命伤害;最后,通过数值分析进一步验证了前面结论。     

A recovery mechanism for a two echelon supply chain system under supply disruption

Supply chains are becoming increasingly competitive in order to meet customer demands. The task of optimizing highly evolved supply chains is not easy, especially when it is particularly sensitive to unexpected disruption. This paper presents a newly developed real-time recovery mechanism for a two stage serial supply chain system, consisting of one manufacturer and one retailer, where the production is disrupted for a given period of time during the production up time. The model is capable of determining the recovery schedule for the manufacturer and the retailer, and ensuring that the total relevant costs are minimized, while seeking to recover the original schedule by the end of the recovery time window. The model was solved using an efficient heuristic developed in this paper, which performed well in giving quality solutions within reasonable time. It can be shown that the optimal recovery schedule is dependent on the shortage cost parameters, as well as on the extent of the disruption. The presented model is useful to assist decision makers to take a pro-active approach for maintaining business continuity in the event of a disruption in the supply chain system.     

Time-varying persistence in US inflation

The persistence property of inflation is an important issue not only for economists, but especially for central banks, given that the degree of inflation persistence determines the extent to which central banks can control inflation. Further, not only is it the level of inflation persistence that is important in economic analyses, but also the question of whether the persistence varies over time, for instance, across business cycle phases, is equally pertinent, since assuming constant persistence across states of the economy is sure to lead to misguided policy decisions. Against this backdrop, we extend the literature on long-memory models of inflation persistence for the US economy over the monthly period of 1920:1–2014:5, by developing an autoregressive fractionally integrated moving-average-generalized autoregressive conditional heteroskedastic model with a time-varying memory coefficient which varies across expansions and recessions. In sum, we find that inflation persistence does vary across recessions and expansions, with it being significantly higher in the former than in the latter. As an aside, we also show that persistence of inflation volatility is higher during expansions than in recessions. Understandably, our results have important policy implications.     

宏观经济管理中的牛鞭效应及其实证

牛鞭效应是供应链中上下游企业订货量波动的一种固有规律,它不仅存在于单个供应健中,而且存在于整个宏观经济管理中。本文在定量分析牛鞭效应的形成机理的基础上,实证分析了它在宏观经济产、供、销上的存在性,有关结论对科学地进行宏观经济管理有一定指导意义。     

微观生产函数与总量生产函数的矛盾——技术再转换

根据“替代原理”和利润最大化的假定,新古典学派得出命题:利润率与人均资本量之间存在着反向单调关系。该命题在单一产品或同比例要素模型中无疑是正确的,然而一进入异质品模型就会面临“技术再转换”的悖论。一个重要的原因在于异质品模型中,在统一利润率的假设下,收入分配会影响相对价格,从而技术不能按人均资本量排序。“技术再转换”的悖论使新古典的总量生产函数受到质疑。     

Pricing decisions in a dual channels system with different power structures

This paper investigates the effect of product substitutability and relative channel status on pricing decisions under different power structures of a dual exclusive channel system where each manufacturer distributes its goods through a single exclusive retailer but two goods are substitute. A linear demand based on the utility function of a representative consumer is assumed, and three game scenarios(Manufacturer Stackelberg, Retailer Stackelbeg and Vertical Nash) are examined under symmetric and asymmetric related channel status. It is shown that no power structure is always the best for the entire supply chain though all members on supply chain have incentive to lead the Stackelberg game. Meanwhile, the vertical Nash game is an equilibrium for the members, however, a Prisoner's dilemma necessarily incurs for the entire supply chain because the Retailer Stackelberg or the Manufacturer Stackelberg can gain the better performance than that in vertical Nash for the entire supply chain when the product substitutability is moderate or higher and the asymmetric relative channel status is moderate, while consumers always get the most welfare from the vertical Nash game.     

A multivariate time series approach to modelling macroeconomic sequences

In this paper we discuss a multivariate generalization of autoregressive integrated moving average models. A methodology for constructing multivariate time series models is developed and the derivation of forecasts from such models is considered. A bivariate model for Austrian macroeconomic sequences is constructed. Furthermore it is discussed whether multivariate time series methods can be expected to lead to a significant increase in prediction accuracy when forecasting macroeconomic series.