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.     

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.     

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.     

基于混合需求的供应链多级库存协同订货模型

针对供应链多级库存系统存在混合需求的情况,建立了基于混合需求的多级库存协同订货模型。该模型假设在供应链中只有最下游节点面临的需求是独立需求,而其他上游节点面临的需求都是与之相关的相关需求。由于相关需求是一种块状需求,其库存成本构成与独立需求明显不同。因此,通过对多级库存系统的库存成本构成进行重新分析,分别给出了需求确定时不允许缺货和允许缺货的协同订货模型。另外,还通过对安全库存的分析给出了需求不确定时的协同订货模型。最后,给出了模型求解的遗传算法,并进行了实例仿真分析,展示了这种协同订货模型在混合需求的供应链中的实用性。     

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.     

Supply chain modeling in uncertain environment with bi-objective approach

Supply chain is viewed as a large-scale system that consists of production and inventory units, organized in a serial structure. Uncertainty is the main attribute in managing the supply chains. Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships among various entities exist in the SC. Uncertainty may result from customer’s demand variability or unreliability in external suppliers. In this paper we develop an inventory model for an assembly supply chain network (each unit has at most one immediate successor, but any number of immediate predecessors) which fuzzy demand for single product in one hand and fuzzy reliability of external suppliers in other hand affect on determination of inventory policy in SCM. External supplier’s reliability has determined using a fuzzy expert system. Also the performance of supply chain is assessed by two criteria including total cost and fill rate. To solve this bi-criteria model, hybridization of multi-objective particle swarm optimization and simulation optimization is considered. Results indicate the efficiency of proposed approach in performance measurement.     

A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand

We study a two-stage, multi-item inventory system where stochastic demand occurs at stage 1, and nodes at stage 1 replenish their inventory from stage 2. Due to the complexity of stochastic inventory optimization in multi-echelon system, few analytical models and effective algorithms exist. In this paper, we establish exact stochastic optimization models by proposing a well-defined supply–demand process analysis and provide an efficient hybrid genetic algorithm (HGA) by introducing a heuristic search technique based on the tradeoff between the inventory cost and setup cost and improving the initial solution. Monte Carlo method is also introduced to simulate the actual demand and thus to approximate the long-run average cost. By numerical experiments, we compare the widely used installation policy and echelon policy and show that when variance of stochastic demand increase, echelon policy outperforms installation policy and, furthermore, the proposed heuristic search technique greatly enhances the search capacity of HGA.     

An integrated approach for logistic and vendor managed inventory in supply chain

In this paper, a quaternary policy system towards integrated logistics and inventory aspect of the supply chain has been proposed. A system of multi retailers and distributors, with each distributor following a unique policy, will be analysed. The first policy is continuous time replenishment policy where the retailers’ inventory is replenished in every time interval. In the next three policies, inventory of the retailers will be replenished by some definite policy factors. The vendor managed inventory (VMI) system is used for updating the inventory of the retailers. An order-up-to policy (q, Q) is used for updating the inventory of distributors. Total erstwhile demands to the retailer will be used to determine the amount of inventory acclivity. Furthermore, the distributors will be sending the delivery vehicles to few fellow retailers who are shortlisted according to the policy, followed by the retailers and associated distributors. On the basis of random demand that the retailers are facing from end customers and the total demand that has incurred in the supply chain, products are unloaded to the selected retailers from the delivery vehicle. The path of the delivery vehicle is retrieved by dynamic ant colony optimization. In addition, a framework has been developed to measure the end-customer satisfaction level and total supply chain cost incorporating the inventory holding cost, ordering cost and the transportation cost. The framework has been numerically moulded with different settings to compare the performance of the quadruplet policies.     

A production base-stock policy for recycling supply chain management in the presence of uncertainty

This study focuses on solving master planning problems for a recycling supply chain with uncertain supply and demand. A recycling supply chain network includes collectors, disassemblers, remanufacturers, and redistributors working from the collection of returned goods to the distribution of recovered products to the market. The objective of this study is to maximize the total profit of the entire recycling supply chain. Considering the stochastic property of the recycling supply chain, this study institutes stocking and processing policies for each member of the recycling supply chain to better respond to unknown future demand. We propose a heuristic algorithm called stochastic recycling process planning algorithm (SRPPA) to address master planning problems in the recycling supply chain and its supply and demand uncertainties. The main SRPPA process consists of three phases. In the leader determination phase, SRPPA identifies the most important node as the leader of the recycling supply chain. In the candidate policy set generation phase, SRPPA defines the search range for the inventory policy and forms the candidate policy sets based on the characteristics of the leader. In the best policy set selection phase, SRPPA constructs the simulation process for each inventory policy candidate to identify the best policy set. A scenario analysis is then presented to show the effectiveness and efficiency of SRPPA.     

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.     

Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.     

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.     

The replenishment policy of agri-products with stochastic demand in integrated agricultural supply chains

Market demand of agri-products is influenced by uncertain factors, such as weather, temperature, and customer preferences. In integrated agricultural supply chains, traditional inventory models are useless because of the stochastic demand and deteriorative characteristic of agri-products. This paper provides a method to determine the optimal replenishment policy of integrated agricultural supply chains with stochastic demand. In these EOQ/EPQ models, shortages are allowed and are backlogged if market demand is stochastic. The objective function is to minimize the total cost of the supply chain in the planning horizon. The total cost includes the ordering cost, the holding cost, the shortage cost and the purchasing cost. Thinking of the nonlinear relationship and dynamic forces in models, a system dynamic (SD) simulation model is constructed to find the optimal lot size and replenishment interval. Finally, an example is given to make a sensitivity analysis of the simulation model. Compared to traditional methods (such as equalize stochastic demand), the total cost decreases by 16.27% if the supply chains adopt the new replenishment policy. The results illustrated that the new replenishment policy (with intelligent method) is beneficial to help supply chain make decision scientifically. Moreover, the intelligent method can simulate stochastic demand perfectly, and it is effectively for solving the complicated and mathematically intractable replenishment problem.     

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。     

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 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.     

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.     

A model measurement system for collaborative supply chain partners

The purpose of this paper is to understand business performance in the context of an electronic component company. This paper developed a system dynamics model that describes supply chain process structure and examines scenarios, as well. Thus, this study adopted the signal-to-noise (SN) ratio defined by the Taguchi method to evaluate the robustness of a specific supply chain behavior. Resulting in poor inventory cost performance with uncertainty demand, this paper shows how the factor delivery time and lead time of an order can improve performance. Finally, this paper serves as a guideline for decisions that require different inventory strategies.     

Agent-based web service for the design of a dynamic coordination mechanism in supply networks

The theory of network coordination presents an effective approach to improve the business processes within supply networks. The automation of the negotiation process among buyers and suppliers has become an important policy in the transactional networks. This leads to assessing the roles of both quantifiable and non-quantifiable parameters in coordination mechanisms with the aim of achieving higher performance. Here, we develop an e-based supply chain multi-agent model for the design of mass-customized on-line services. The model addresses the bullwhip effect in multi-stage supply chain and also clarifies the evaluation of inventory policies in various supply and demand uncertainties. To illustrate the feasibility of the approach, we implement a prototype system and evaluate its performance by simulation using Colored Petri Nets (CPNs). The validation results reveal the model efficiency in providing a more realistic optimization process that takes the dynamic information flow in uncertainty environments into consideration.     

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.