What are causes of cash flow bullwhip effect in centralized and decentralized supply chains?

Bullwhip effect in supply chain is a phenomenon which can emerge in both inventory levels and replenishment orders. Bullwhip effect causes variations in cash conversion cycle (CCC) across cash flow of supply chain. As a result, it can lead to inefficiencies such as cash flow bullwhip (CFB). Due to negative impact of CFB on cash flow of supply chain, it can lead to a decrease in efficiency of supply chain management (SCM). That is why supply chain modeling is a proper start point for effective management and control of the CFB. This paper aims to analyze concurrent impact of causes of inventory bullwhip effect and effect of their interactions on CFB based on generalized OUT policy from aspect of CCC variance. To this end, first we develop system dynamics structure of beer distribution game as simulation model which includes multi-stage supply chain under both centralized and decentralized supply chains. Then, in order to develop CFB function, we design experiments in developed simulation model using response surface methodology (RSM). Results demonstrate that if each chain member uses generalized OUT policy as replenishment model, there still exists CFB in both chains and CFB largely stems from rationing and shortage gaming in both centralized and decentralized supply chain. In addition, when information on ordering parameters are not shared among members, parameters of downstream stage (i.e. retailer) are more important than parameters of upstream stage (i.e. manufacturer) in reducing CFB function.     

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.     

Optimizing of bullwhip effect and net stock amplification in three-echelon supply chains using evolutionary multi-objective metaheuristics

In this article, we intend to model and optimize the bullwhip effect (BWE) and net stock amplification (NSA) in a three-stage supply chain consisting of a retailer, a wholesaler, and a manufacturer under both centralized and decentralized scenarios. In this regard, firstly, the causes of BWE and NSA are mathematically formulated using response surface methodology (RSM) as a multi-objective optimization model that aims to minimize the BWE and NSA on both chains. The simultaneous analysis of the BWE and NSA is considered as the main novelty of this paper. To tackle the addressed problem, we propose a novel multi-objective hybrid evolutionary approach called MOHES; MOHES is a hybrid of two known multi-objective algorithms i.e. multi-objective electro magnetism mechanism algorithm (MOEMA) and population-based multi-objective simulated annealing (PBMOSA). We applied a co-evolutionary strategy for this purpose with eligibility of both algorithms. Proposed MOHES is compared with three common and popular algorithms (i.e. NRGA, NSGAII, and MOPSO). Since the utilized algorithms are very sensitive to parameter values, RSM with the multi-objective decision making (MODM) approach is employed to tune the parameters. Finally, the hybrid algorithm and the singular approaches are compared together in terms of some performance measures. The results indicate that the hybrid approach achieves better solutions when compared with the others, and also the results show that in a decentralized chain, the order batching factor and the demand signal processing in wholesaler are the most important factors on BWE. Conversely, in a centralized chain, factors such as rationing, shortage gaming, and lead time are the most effective at reducing the BWE.     

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.     

A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment

Supply chain management is important for companies and organizations to improve their business and enhance competitiveness in the global marketplace. The bullwhip effect problem of supply chain systems with vendor order placement lead time delays in an uncertain environment is addressed in this paper. Among the numerous causes of bullwhip effect, we focus on uncertainties with respect to demand, production process, supply chain structure, inventory policy implementation and especially vendor order placement lead time delays. Minimizing the negative effect of these uncertainties in inducing bullwhip effect creates a need for developing dynamical inventory policy that increases responsiveness to demand and decreases volatility in inventory replenishment. First, a dynamic model of supply chain with above uncertainties is developed. Then, a novel uncertainty-dependent robust inventory control method using inventory position information is proposed. Additionally, the maximum allowable vendor order placement lead time delay that ensures the stability of supply chains and the suppression of bullwhip effect under the proposed inventory control policy is explored and measured. We find that vendor order placement lead time delays do play important role in supply chain dynamics and contribute to its turbulence and volatility. The effectiveness and flexibility of proposed method is verified through simulation study.     

Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches

In this paper, a two-degrees-of-freedom Internal Model Control structure is incorporated in production inventory control for a supply chain system. This scheme presents an intuitive and simple parametrization of controllers, where inventory target tracking and disturbance (demand) rejection in the inventory level problems are treated separately. Moreover, considering that the lead times are known, this scheme presents a perfect compensation of the delay making the stabilization problem easier to handle. This control structure is formulated for a serial supply chain in two ways (by using a centralized and a decentralized control approach). The behavior of these inventory control strategies is analyzed in the entire supply chain. Analytical tuning rules for bullwhip effect avoidance are developed for both strategies. The results of controller evaluations demonstrate that centralized control approach enhances the behavior with respect to the inventory target tracking, demand rejection and bullwhip effect in the supply chain systems.     

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.     

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.     

Nonlinear modeling and performance analysis of a closed-loop supply chain in the presence of stochastic noise

ABSTRACT

We study four-echelon supply chains consisting of manufacturer, wholesaler, retailer and customer with recovery center as hybrid recycling channels. In order to gain a larger market share, the retailer often takes the sales as a decision-making variable. For this purpose, in this supply chain, the retailer limits the forecast of market demand in future periods with expected logic. It also manages demand by leveraging prices and choosing market. In this paper, first, we investigate the state-space model of this supply chain system and examine the effect of complex dynamic and stochastic noise on the bullwhip effect. We analytically prove that this factor leads to the bullwhip effect. So, first, we filtered the information between nodes with extended Kalman filter after which we regulated the destructive effects of the bullwhip phenomenon by designing a non-linear quadratic Gaussian optimal controller. Eventually, the simulation results indicate the efficiency of the proposed method.     

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.     

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.     

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.     

Taming the bullwhip effect whilst watching customer service in a single supply chain echelon

We study a generalised order-up-to policy that has highly desirable properties in terms of order and inventory variance and customer service levels it generates. We quantify exactly the variance amplification in replenishment orders, i.e. the bullwhip effect, and the variance of inventory levels over time, for i.i.d. and the weakly stationary auto regressive (AR), moving average (MA) and auto regressive moving average (ARMA) demand processes. We demonstrate that high customer service as measured by fill-rate, and smooth replenishments need not increase inventory cost substantially. We observe that in some instances of the ARMA demand pattern this comes at the expense of a relatively small increase in safety stock, whilst in other instances inventory levels can actually be reduced.     

An integrated production and inventory model to dampen upstream demand variability in the supply chain

We consider a two-echelon supply chain: a single retailer holds a finished goods inventory to meet an i.i.d. customer demand, and a single manufacturer produces the retailer’s replenishment orders on a make-to-order basis. In this setting the retailer’s order decision has a direct impact on the manufacturer’s production. It is a well known phenomenon that inventory control policies at the retailer level often propagate customer demand variability towards the manufacturer, sometimes even in an amplified form (known as the bullwhip effect). The manufacturer, however, prefers to smooth production, and thus he prefers a smooth order pattern from the retailer. At first sight a decrease in order variability comes at the cost of an increased variance of the retailer’s inventory levels, inflating the retailer’s safety stock requirements. However, integrating the impact of the retailer’s order decision on the manufacturer’s production leads to new insights. A smooth order pattern generates shorter and less variable (production/replenishment) lead times, introducing a compensating effect on the retailer’s safety stock. We show that by including the impact of the order decision on lead times, the order pattern can be smoothed to a considerable extent without increasing stock levels. This leads to a situation where both parties are better off.     

Metrics for bullwhip effect analysis

A bullwhip measurement system based on a two-criterion assessment—‘internal process efficiency’ and ‘customer service level’—is developed in this paper. The framework is designed to assess both individual (single member) and systemic (whole supply chain) performances. Data collection and calculation methods, update and monitoring mechanisms, as well as related procedures for each metric used, are detailed. A comparative analysis with a recent work by Barlas and Gunduz is performed, showing that the adoption of the proposed performance measurement system can help academics and practitioners to better understand, study and avoid the bullwhip effect. Such analysis also provides evidence on the relevance of considering when analysing the bullwhip effect in supply chains, the ‘customer importance’ aspect that is often forgotten in the published literature.     

Goodwill,inventory penalty,and adaptive supply chain management

Information visibility is generally useful for decision makers distributed across supply chains. Availability of information on inventory levels, price, lead times, demand, etc. can help reduce uncertainties as well as alleviate problems associated with bullwhip effect. A majority of extant literature in this area assume a static supply chain network configuration. While this was sufficient a few decades ago, advances in e-commerce and the ease with which order processing can be performed over the Internet necessitates appropriate dynamic (re)configuration of supply chains over time. Each node in the supply chain is modeled as an actor who makes independent decisions based on information gathered from the next level upstream. A knowledge-based framework is used for dynamic supply chain configuration and to consider the effects of inventory constraints and ‘goodwill,’ as well as their effects on the performance dynamics of supply chains. Preliminary results indicate that neither static nor dynamic configurations are consistently dominant. Scenarios where static configurations perform better than the modeled system are identified.     

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。     

时滞变质品供应链的数量折扣协调模型

在由单个批发商与单个零售商组成的两级时滞变质品供应链中,分别建立分散决策和集中决策下供应链系统的单位时间利润函数,通过比较得到集中决策优于分散决策的条件。引入数量折扣契约对时滞变质品供应链进行协调,促使整个供应链系统实现集中决策下的最优状态。设计收益共享合同来合理分配协调后的利润增量,保证批发商和零售商的单位时间利润都比协调前有所增加,从而实现供应链系统的帕累托改善。最后,通过数值算例演示了两种决策模式的单位时间利润情况,并分析了关键参数对供应链系统及各成员企业单位时间利润水平的影响。     

Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters

In less than half a century, the supply chain management (SCM) imposed itself as a strategic expertise. Today, it lands a new era, more complex, and must be the synonymous of competitive advantage. The supply chain has essentially served as a link between customers and products, producers and suppliers. The generation of the new supply chain (SC) should be evolutionary, and should be adjusted quickly to the rise or the decrease of the various customers’ demands. Several problems of the supply chain are superimposed such as the amplification of the demand, also called the bullwhip effect (BWE). This latter is a distortion in the market demand when this demand propagates from enterprise to enterprise. Finally, at the end of the chain, the supplier of raw materials receives completely uncertain commands. Our research aims to reduce, or even eliminate, the bullwhip effect in two respects-namely increase of the stock level and reduction of the service given back to customers. The solution that we propose to the bullwhip rests on, firstly, the use of the preference functions based on a statistical chronological series analysis (Box and Jenkins method) in order to construct the different models such as demand, stock level, and the order quantity. Secondly, the integration of the decision maker preference in the demand forecast and inventory management processes.     

Analyzing the behavior of the bullwhip effect considering different distribution systems

Recently, researchers have shown increased interest in quantifying the bullwhip effect, and several attempts have been made to alleviate this phenomenon within supply chain management; however, absent from the current literature surrounding this topic is an in-depth analysis of the impact of different distribution systems, particularly cross-docking systems, upon the behavior of the bullwhip effect. This research aims to investigate the measure of the bullwhip effect in three different supply-chains; (I) with a central warehouse, (II) with a cross-docking system, and (III) without any distribution systems. These three different supply chains are subsequently analyzed to discover which supply chain helps reduce the bullwhip effect more. In doing so, the reasoning here is based on the premise that the demand process follows a mixed autoregressive-moving average model and all the stages employ the base stock policy for inventory replenishment, if necessary. In addition, the above mentioned supply chains are assumed to have two members in the retailer stage, with a different market share of the customer demand. It was found that factors such as lead time, market share of each retailer, autoregressive coefficient and moving average parameter contribute to the selection of the most effective distribution system.