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.     

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.     

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.     

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.     

供应商管理库存对牛鞭效应的影响

采用基于z变换的离散传递函数和测量牛鞭效应的控制工程方法,计算了一个由一个供应商和一个用户组成的、使用指数平滑预测的供应商管理库存供应链和传统供应链的牛鞭效应,并比较了这两种供应链对牛鞭效应的影响.通过仿真证实,应用供应商管理库存策略对供应链的物理过程进行再造是一种有效的减少牛鞭效应的方法.     

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。     

The value of information sharing in a supply chain with a seasonal demand process

This paper considers a two echelon seasonal supply chain model that consists of one supplier and one retailer, with the assumption that external demand from the customer follows a seasonal autoregressive moving average (SARMA) process, including marketing actions that cannot be deduced from the other parameters of the demand process. In our model, the supplier and the retailer employ order-up-to policy to replenish their inventory. In order to evaluate the value of information sharing in a two echelon seasonal supply chain, three levels of information sharing proposed by Yu, Yan, and Cheng (2002) are used. The results for optimal inventory policies under these three levels of information sharing are derived. We show that the seasonal effect has an important impact on optimal inventory policies of the supplier under the three levels of information sharing. Our findings also demonstrate that the replenishment of lead time must be less than the seasonal period in order to benefit from information sharing. Thus, this result provides managers with managerial insights to improve supply chain performance through information sharing integration partnerships.     

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.     

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.     

Fuzzy knowledge-based token-ordering policies for bullwhip effect management in supply chains

The “Bullwhip Effect” is a well-known example of supply chain inefficiencies and refers to demand amplification as moving up toward upstream echelons in a supply chain. This paper concentrates on representing a robust token-based ordering policy to facilitate information sharing in supply chains in order to manage the bullwhip effect. Takagi–Sugeno–Kang and hybrid multiple-input single-output fuzzy models are proposed to model the mechanism of token ordering in the token-based ordering policy. The main advantage of proposed fuzzy models is that they eliminate the exogenous and constant variables from the procedure of obtaining the optimal amount of tokens which should be ordered in every period. These fuzzy approaches model the mentioned mechanism through a push–pull policy. A four-echelon SC with fuzzy lead time and unlimited production capacity and inventory is considered to survey the outcomes. Numerical experiments confirm the effectiveness of proposed policies in alleviating BWE, inventory costs and variations.     

An assessment model for RFID impacts on prevention and visibility of inventory inaccuracy presence

Bullwhip effect has been considered as one of major research topics in supply chain management. Most of the studies disregarded the mismatch between the recorded inventory and the reality. However, it is shown that the inventory inaccuracy under uncertainty is a widespread phenomenon in both retail and distribution centers. Due to the propagation of information distortion along the supply chain, the financial impacts of inventory inaccuracy include not only the cost of direct inventory loss but also the increasing holding and shortage cost at each stage. The emergence of RFID technology offers a possible solution to alleviate the growing cost of inventory inaccuracy. By making full use of RFID technology, this paper attempts to compare the inventory inaccuracy impact on bullwhip effect in terms of order variance amplification and supply chain performance under two scenarios: (1) all members are aware of the inaccuracy and optimize their operations; (2) all members deploy RFID technology to reduce inventory inaccuracy. Informed order policy is used as benchmark to capture the true RFID value and differentiate two types of RFID impacts, prevention and visibility, to provide more manageable insight. In particular, the incentive of sharing information in supply chain is also provided by comparing the cost of two supply chain settings.     

Optimizing replenishment polices using Genetic Algorithm for single-warehouse multi-retailer system

Coordinating inventory and transportation policies can lead to substantial cost savings and improved service levels especially when the companies relay on third-party logistics providers to transport the products across the supply chain. In this paper, therefore focus has been given on a supply chain system of multi-supplier, single warehouse and multi-retailer with backlogging and transportation capacity. The paper aims to suggest replenishment policies that can minimize system-wide cost by taking advantage of quantity discounts in the transportation cost structures. The problem considered in this paper has been formulated as an integer programming model. The supply chain problem is usually complex and involves massive calculations hence it is difficult to obtain an optimal solution. Therefore, to overcome this issue a Genetic Algorithm (GA) based approach has been suggested to resolve the problem. The computational results demonstrate the robustness and efficacy of the GA in optimizing replenishment policies.     

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.     

Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning

Post-seismic inventory and logistics planning under incomplete and fuzzy information is an important yet understudied area in supply chain risk management. The goal of this paper is to propose a system dynamics model to analyze the behaviors of disrupted disaster relief supply chain by simulating the uncertainties associated with predicting post-seismic road network and delayed information. The simulation results indicate: (1) information delay has different influences over the relief head-quarter (the upstream) and the disaster-affected areas (the downstream); and (2) the change of road conditions and shipment schedules have impact on the on-time transportation rate in supply chain management. Furthermore, this paper defined and tested supplies' replenishment solutions combined with three inventory planning strategies and four forecasting methods under different lead time uncertainties. The results show that: (1) a strategy that considers information from both the post-seismic management center and the affected areas can provide a better logistic plan than an one takes information from one side; (2) the smooth-the-trend forecasting method is suitable for inventory and logistic planning when the post-seismic situations are volatile, while the quick-response forecasting method has good performance in stable environments. In addition, this paper proposes decision tree to help decision makers choose the appropriate stocking strategies.     

The governing dynamics of supply chains: The impact of altruistic behaviour

This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenishment orders. To control the dynamics of the supply chain, a proportional controller is incorporated into the OUT policy, which we call a generalised OUT policy. A two-echelon supply chain with this generalised OUT policy achieves over 10% inventory related cost reduction. To enjoy this cost saving, the attitude of first echelon player to cost increases is an essential factor. This attitude also reduces the bullwhip effect. An important insight revealed herein is that a significant amount of benefit comes from the player doing what is the best for the overall supply chain, rather than what is the best for local cost minimisation.     

供应链牛鞭效应的随机控制

牛鞭效应是一个衡量供应链管理运作是否良好的重要指标参数．本文采用随机控制 理论方法，针对在文[1]基础上建立的具有多分销中心的供应链动态库存模型，以供应链 系统中的订货作为控制变量抑制牛鞭效应．同时以一个石油分销系统为对象进行了供应链牛 鞭效应的随机控制仿真实验，实验表明牛鞭效应受到了抑制．     

时滞非线性供应链系统的复杂动态行为仿真分析

为较全面的理解供应链系统动态的形成演化机理,建立供应链库存系统模型,模型中同时考虑订货量非负约束和不同提前期两个因素,使模型更符合实际,但同时使系统成为带有时滞的非线性系统,具有复杂的动力学行为。利用求解最大李雅普诺夫指数的方法分析系统动态特性。通过仿真实验给出不同提前期下能够保持系统稳定的库存策略区域;针对一类常用库存策略的系统动态行为研究发现,虽然库存策略参数对系统的动态行为有较大影响,但对于确定库存策略,仍然有一定数量的库存参数设置能够保证系统稳定运行。     

Improving inventory effectiveness in RFID-enabled global supply chain with Grey forecasting model

A RFID-enabled global TFT–LCD supply chain associated with Grey forecasting model (GM) of Company A has been simulated and analyzed in this research. Three key performance indicates (KPI) including total inventory cost, inventory turnover and bullwhip effect are analyzed in the simulation experiments in order to compare the effectiveness of five different supply chain inventory models. The effectiveness of integrated system which is composed of supply chain operation, Grey short-term forecasting model and RFID system has been examined by aforementioned three KPIs. According to the result of Taguchi experiments, RFID-enabled R-SCI_GM supply chain model which integrates the GM(1,1) forecasting model based on (s, Q) pull-based replenishment policy reduces 43.36% of the total inventory cost compared with that of the non-RFID SCI_GM model. It apparently shows that a great improving effectiveness of supply chain inventory cost can be conducted while RFID system is incorporated with the GM(1,1) forecasting model.     

Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis

In this work, a discrete time series model of a supply chain system is derived using material balances and information flow. Transfer functions for each unit in the supply chain are obtained by z-transform. The entire chain can be modeled by combining these transfer functions into a close loop transfer function for the network. The model proves to be very useful in revealing the dynamics characteristic of the system. The system can be viewed as a linear discrete system with lead time and operating constraints. The stability of the system can be analyzed using the characteristic equation. Controllers are designed using frequency analysis. The bullwhip effect, i.e. magnification of amplitudes of demand perturbations from the tail to upstream levels of the supply chain, is a very important phenomenon for supply chain systems. We proved that intuitive operation of a supply chain system with demand forecasting will cause bullwhip. Moreover, lead time alone would not cause bullwhip. It does so only when accompanied by demand forecasting. Furthermore, we show that by implementing a proportional intergral or a cascade inventory position control and properly synthesizing the controller parameters, we can effectively suppress the bullwhip effect. Moreover, the cascade control structure is superior in meeting customer demand due to its better tracking of long term trends of customer demand.     

信息共享受限条件下的供应链网络系统牛鞭效应控制策略

研究在信息共享受限条件下供应链网络库存系统的牛鞭效应控制问题,建立了包括市场需求、信息可获得性、信息及时性等不确定性因素的库存网络系统状态转移模型,从系统内部动力学机制的角度分析了牛鞭效应的成因,提出了动态库存控制策略,并给出了策略参数设计的线性矩阵不等式组算法.运用系统稳定性理论,深入分析了信息共享对牛鞭效应的影响,并通过仿真结果验证了库存控制策略的有效性和实用性.