Bullwhip effect measure in a seasonal supply chain

In this study, the bullwhip effect in a seasonal supply chain was quantified by considering a two echelon supply chain which consists of one supplier and one retailer. The external demand occurring at the customer was assumed to follow a SARMA (1, 0) X (0, 1) _s scheme, a seasonal autoregressive-moving average process, while the retailer employed an base-stock policy to replenish their inventory. The demand forecast was performed with a SARMA (1, 0) X (0, 1) _s using the minimum mean-square error forecasting technique. In order to develop the bullwhip effect measure in a seasonal supply chain, the lead time demand forecast, forecast error, and the optimal inventory policy at the retailer were derived in sequence. The variance of order quantity based on these results was obtained. Then, various properties were derived by analyzing the bullwhip effect measure. Specifically, it was determined that the seasonal cycle plays an important role in bullwhip effect under a seasonal supply chain. The findings also point out that the replenishment lead time must be less than the seasonal cycle in order to reduce the bullwhip effect. Therefore, the lead time needs to be reduced through collaboration between the retailer and supplier.     

The impact of information sharing and inventory control coordination on supply chain performances

The lack of coordination in supply chains can cause various inefficiencies like bullwhip effect and inventory instability. Extensive researches quantified the value of sharing and forecasting of customer demand, considering that all the supply chain partners can have access to the same information. However, only few studies devoted to identify the value of limited collaboration or information visibility, considering their impact on the overall supply chain performances for local and global service level. This paper attempts to fill this gap by investigating the interaction of collaboration and coordination in a four-echelon supply chain under different scenarios of information sharing level. The results of the simulation study show to what extent the bullwhip effect and the inventory variance increase and amplify when a periodic review order-up-to level policy applies, noting that more benefits generate when coordination starts at downstream echelons. A factorial design confirmed the importance of information sharing and quantified its interactions with inventory control parameters, proving that a poor forecasting and definition of safety stock levels have a significant contribution to the instability across the chain. These results provide useful implications for supply chain managers on how to control and drive supply chain performances.     

Quantifying and mitigating the bullwhip effect in a benchmark supply chain system by an extended prediction self-adaptive control ordering policy

An undesired observation known as the bullwhip effect in supply chain management leads to excessive oscillations of the inventory and order levels. This paper presents how to quantify and mitigate the bullwhip effect by introducing model predictive control (MPC) strategy into the ordering policy for a benchmark supply chain system. Instead of quantifying the bullwhip effect with commonly used statistical measure, we derive equivalently the expression of bullwhip metric via control-theoretic approach by applying discrete Fourier transform and (inverse) z-transform when the demand signal is stationary stochastic. A four-echelon supply chain is formulated and its dynamical features are analyzed to give the discrete model. An extended prediction self-adaptive control (EPSAC) approach to the multi-step predictor is implemented in the development of MPC formulation. The closed-form solution to MPC problem is derived by minimizing a specified objective function. The transfer function for MPC ordering policy is then obtained graphically from an equivalent representation of this closed-form solution. A numerical simulation shows that MPC ordering policy outperforms the traditional ordering policies on reducing bullwhip effect.     

A supply chain contract with flexibility as a risk-sharing mechanism for demand forecasting

Demand forecasting is one of the main causes of the bullwhip effect in a supply chain. As a countermeasure for demand uncertainty as well as a risk-sharing mechanism for demand forecasting in a supply chain, this article studies a bilateral contract with order quantity flexibility. Under the contract, the buyer places orders in advance for the predetermined horizons and makes minimum purchase commitments. The supplier, in return, provides the buyer with the flexibility to adjust the order quantities later, according to the most updated demand information. To conduct comparative simulations, four-echelon supply chain models, that employ the contracts and different forecasting techniques under dynamic market demands, are developed. The simulation outcomes show that demand fluctuation can be effectively absorbed by the contract scheme, which enables better inventory management and customer service. Furthermore, it has been verified that the contract scheme under study plays a role as an effective coordination mechanism in a decentralised supply chain.     

PI补充策略下供应链牛鞭效应的$H_infty$控制

牛鞭效应是指供应链管理订单制定环节中因信息扭曲造成的需求逐级放大的一种现象.针对供应链中的节点企业,在订货点法的基础上加以改进构建新的订单制定规则,并构建$H_\infty$控制器达到抑制牛鞭效应的目的,从而降低供应链整体成本.订单制定环节由企业订单规则和需求预测两个部分组成,为应对需求持续上升使安全库存发散的情况,在订货点法的基础上设计PI补充策略下的新订单规则,并以系统$H_\infty$范数与供应链牛鞭效应的指标定义相同为基础,引入$H_\infty$控制器代替预测函数.仿真结果表明,所设计的PI 补充策略下的$H_\infty$控制器法与传统订单制定算法相比,可有效削减牛鞭效应,并且使企业库存始终维持在一个安全稳定的状态.     

含时滞的不确定性供应链网络系统牛鞭效应控制策略及其经济性能分析

考虑需求、生产能力、供应链结构等内外不确定性因素和供应链系统运作延迟,构建了不确定环境下含时滞的供应链库存网络系统状态转移模型.针对牛鞭效应问题,提出了基于库存水平波动状态的控制策略和相应的经济性能指标;借助线性矩阵不等式方法,深入分析库存策略的参数优化设计对牛鞭效应以及经济性能的影响.仿真结果表明,在经济性能约束下,该库存策略具有较强的牛鞭效应遏制能力,从而表明了策略的有效性和实用性.     

神经网络在解决某企业供应链牛鞭效应问题中的应用

牛鞭效应是供应链运营管理中客观存在的现象。企业为了减少由实际需求和计划数量的偏差造成的生产不稳定,提高安全库存数量从而保证正常的生产活动,在此情况下需求逐级放大引发了牛鞭效应。精准预测是缓解牛鞭效应的重要手段,但是传统的时序预测在复杂的环境中并没有很好的预测效果。基于以上问题,从理论层面论证了需求预测、安全库存、牛鞭效应之间的关系,提出能够优化预测结果的ARIMA-BP模型。以某制造商企业近两年的产品订单数据为研究对象,分别用不同的预测模型对订单进行预测分析,再与该企业原预测模型下的牛鞭效应仿真结果进行对比。结果表明,ARIMA-BP的模型预测精度更高,能够有效地缓解牛鞭效应。     

The impact of replenishment parameters and information sharing on the bullwhip effect: A computational study

Demand variability amplification across the supply chain, known as the bullwhip effect, results in serious inefficiencies across the chain. Managers are expected to minimize this phenomenon in their chain in order to reduce costs and increase customer satisfaction by making critical decisions on replenishment policy. We study how specific replenishment parameters affect order variability amplification, product fill rates and inventory levels across the chain. Furthermore, we study how demand information sharing can help towards reducing order oscillations and inventory levels in upper nodes of a supply chain. A two-stage supply chain consisting of a warehouse and stores that face customer demand is modeled. Real demand data are used as the underlying customer demand during the experiments.     

System dynamics modelling for supply-chain management: A case study on a supermarket chain in the UK

This paper presents a system dynamics (SD) approach for the analysis of the demand amplification problem, also known as the bullwhip effect, which has been studied fairly extensively in the literature. The construction of an SD model is reported using a part of a supermarket chain system in the UK as an example. Based on the model, the causes of the dynamic behaviour of the system and the sources of amplification from the downstream to the upstream of the chain are investigated. The impact of information delays, demand forecasting and information sharing on the performance of the multi‐echelon supply chain is analysed. Some implementation issues are also addressed based on the simulation analysis.     

Measure of bullwhip effect in supply chains with first-order bivariate vector autoregression time-series demand model

With supply chains becoming increasingly global, the issue of bullwhip effect, a phenomenon attributable to demand fluctuation in the upstream section of the supply chains, has received greater attention from many researchers. However, most existing research studies on quantifying the bullwhip effect were conducted under the first-order autoregressive [AR(1)] incoming demand process or its variants as the fundamental demand process, thereby failing to account for the retailer demand dependency. This research work thus examined the bullwhip effect for the first-order bivariate vector autoregression [VAR(1)] demand process in a two-stage supply chain consisting of one supplier and two retailers. The impacts of the correlation parameters of the demand process, the correlation coefficient between the two error terms, and the variances of the error terms on the bullwhip effect were investigated. As such, the measure of the bullwhip effect was established using an analytical approach in which the minimum mean square error (MMSE) forecasting method and the base stock policy were applied to all members of the supply chain. Numerical experiments were then conducted to illustrate the behavior of the bullwhip effect with respect to various parameters of the demand processes to see in which situations the bullwhip effect would be absent. In addition, an evaluation of the inventory variance ratio was analyzed.     

Smoothing inventory decision rules in seasonal supply chains

A major cause of supply chain deficiencies is the bullwhip effect, which implies that demand variability amplifies as one moves upstream in supply chains. Smoothing inventory decision rules have been recognized as the most powerful approach to counteract the bullwhip effect. Although several studies have evaluated these smoothing rules with respect to several demand processes, focusing mainly on the smoothing order-up-to (OUT) replenishment rule, less attention has been devoted to investigate their effectiveness in seasonal supply chains. This research addresses this gap by investigating the impact of the smoothing OUT on the seasonal supply chain performances. A simulation study has been conducted to evaluate and compare the smoothing OUT with the traditional OUT (no smoothing), both integrated with the Holt-Winters (HW) forecasting method, in a four-echelon supply chain experiences seasonal demand modified by random variation. The results show that the smoothing OUT replenishment rule is superior to the traditional OUT, in terms of the bullwhip effect, inventory variance ratio and average fill rate, especially when the seasonal cycle is small. In addition, the sensitivity analysis reveals that employing the smoothing replenishment rules reduces the impact of the demand parameters and the poor selection of the forecasting parameters on the ordering and inventory stability. Therefore, seasonal supply chain managers are strongly recommended to adopt the smoothing replenishment rules. Further managerial implications have been derived from the results.     

闭环供应链混合切换库存控制模型研究

针对一类具有回收、再制造、再分销的闭环供应链系统,以再制造产品的补货能力为切换信号设计了基于再制造优先的混合切换库存控制策略,使得市场需求优先由再制造产品满足,并应用切换控制理论研究混合切换库存控制策略的性能特征,分析系统参数对闭环供应链系统的关键性能指标的影响。仿真分析表明,合理的切换控制策略可以有效抑制闭环供应链运作过程的波动,保证系统具有良好的“牛鞭效应”特征、平稳的库存管理成本以及较高的顾客服务水平。     

A study on inventory replenishment policies in a two-echelon supply chain system

Inventory control plays an important role in supply chain management. Properly controlled inventory can satisfy customers’ demands, smooth the production plans, and reduce the operation costs; yet failing to budget the inventory expenses may lead to serious consequences. The bullwhip effect, observed in many supply chain management cases, causes excessive inventory due to information distortion, i.e. the order amount is exaggerated while a minor demand variation occurs, and the information amplified dramatically as the supply chain moves to the upstream. In this paper, one of the main causes of bullwhip effect, order batching, is considered. A simplified two-echelon supply chain system, with one supplier and one retailer that can choose different replenishment policies, is used as a demonstration. Two types of inventory replenishment methods are considered: the traditional methods (the event-triggered and the time-triggered ordering policies), and the statistical process control (SPC) based replenishment method. The results show that the latter outperforms the traditional method in the categories of inventory variation, and in the number of backlog when the fill-rate of the prior model is set to be 99%. This research provides a different approach to inventory cost-down other than the common methods like: information sharing, order batch cutting, and lead time reduction. By choosing a suitable replenishment policy, the number of backorder and the cost of inventory can be reduced.     

基于物联网技术的供应链信息共享

文章先介绍供应链中存在的牛鞭效应现象,提出供应链结构、时间延迟、需求预测等六个对牛鞭效应的成因。然后结合新型物联网技术,对信息共享进行了分析讨论。最后得出实施信息共享可以有效减弱牛鞭效应的结论。     

An improved demand forecasting method to reduce bullwhip effect in supply chains

Accurate forecasting of demand under uncertain environment is one of the vital tasks for improving supply chain activities because order amplification or bullwhip effect (BWE) and net stock amplification (NSAmp) are directly related to the way the demand is forecasted. Improper demand forecasting results in increase in total supply chain cost including shortage cost and backorder cost. However, these issues can be resolved to some extent through a proper demand forecasting mechanism. In this study, an integrated approach of Discrete wavelet transforms (DWT) analysis and artificial neural network (ANN) denoted as DWT-ANN is proposed for demand forecasting. Initially, the proposed model is tested and validated by conducting a comparative study between Autoregressive Integrated Moving Average (ARIMA) and proposed DWT-ANN model using a data set from open literature. Further, the model is tested with demand data collected from three different manufacturing firms. The analysis indicates that the mean square error (MSE) of DWT-ANN is comparatively less than that of the ARIMA model. A better forecasting model generally results in reduction of BWE. Therefore, BWE and NSAmp values are estimated using a base-stock inventory control policy for both DWT-ANN and ARIMA models. It is observed that these parameters are comparatively less in case of DWT-ANN model.     

Reducing the negative effects of sales promotions in supply chains using genetic algorithms

This paper considers the detrimental effect of promotions on the supply chain (SC), one of the main causes of the bullwhip effect. A genetic algorithm (GA) is proposed to reduce these negative effects. In order to validate the GA, it is used to determine the optimal ordering policy in an online version of the MIT beer distribution game. Subsequently, the GA is applied in a number of experiments involving deterministic and random demand and lead times combined with sales promotions. It is shown how GAs can be used to dampen the impact of the bullwhip effect and can be used to assist supply managers in predicting reorder quantities along the supply chain.     

The impact of information sharing on ordering policies to improve supply chain performances

Bullwhip effect represents the amplification and distortion of demand variability as moving upstream in a supply chain, causing excessive inventories, insufficient capacities and high operational costs. A growing body of literature recognizes ordering policies and the lack of coordination as two main causes of the bullwhip effect, suggesting different techniques of intervention. This paper investigates the impact of information sharing on ordering policies through a comparison between a traditional (R, S) policy and a coordination mechanism based on ordering policy (a combination of (R, D) and (R, S) policies). This policy relies on a slow, easy to implement, information sharing to overcome drawbacks of the effect, in which replenishment orders are divided into two parts; the first is to inform the upstream echelons about the actual customer demand and the second is to inform about the adjustment of the inventory position, smoothing at the same time the orders of the different levels of the supply chain. A simulation model for a multi-echelon supply chain quantifies the supply chain dynamics under these different policies, identifying how information sharing succeeds to achieve an acceptable performance in terms of both bullwhip effect and inventory variance.     

基于组合预测的供应链系统建模及其鲁棒状态反馈镇定

针对上游节点未能及时获取下游节点当前订单信息的情形,提出一种利用历史订单信息预估供应链下游节点企业订货量的线 性组合预测方法,进而将供应链系统模型化为一个含有多状态时滞的线性时滞不确定性系统,给出了供应链系统时滞依赖状态反馈鲁棒镇定的充分条件和状态反馈控制器设计方法.仿真算例表明,组合预测方法以及鲁棒状态反馈控制器能有效抑制牛鞭效应,显著改善供应链系统的性能.     

A Model Predictive Control framework for robust management of multi-product,multi-echelon demand networks

Model Predictive Control (MPC) is presented as a robust, flexible decision framework for dynamically managing inventories and meeting customer requirements in demand networks (a.k.a. supply chains). As a control-oriented framework, an MPC-based planning scheme has the advantage that it can be tuned to provide acceptable performance in the presence of significant uncertainty, forecast error, and constraints on inventory levels, production and shipping capacity. The translation of the supply chain problem into a formulation amenable to MPC implementation is initially developed for a single-product, two-node example. Insights gained from this problem are used to develop a partially decentralized MPC implementation for a six-node, two-product, three-echelon demand network problem developed by Intel Corporation that consists of interconnected assembly/test, warehouse, and retailer entities. Results demonstrating the effectiveness of this Model Predictive Control solution under conditions of demand forecast error, constraints on capacity, shipping and release, and discrepancies between actual and reported production throughput times (i.e. plant-model mismatch) are presented. The Intel demand network problem is furthermore used to evaluate the relative merits of various information sharing strategies between controllers in the network. Both the two-node and Intel problems show the potential of Model Predictive Control as an integral component of a hierarchical, enterprise-wide planning tool that functions on a real-time basis, supports varying levels of information sharing and centralization/decentralization, and relies on combined feedback–feedforward control action to enhance the performance and robustness of demand networks. These capabilities ultimately mitigate the “bullwhip effect” in the supply chain while reducing safety stocks to profitable levels and improving customer satisfaction.