An extension to fuzzy estimations and system dynamics for improving supply chains

Campuzano, Mula, and Peidro (“Fuzzy estimations and system dynamics for improving supply chains.” Fuzzy Sets and Systems 161: 1530–1542) evaluate the behaviour of fuzzy estimations of demand instead of exponential smoothing for demand forecasts in a two-level (manufacturer and end customer) supply chain and demonstrate how the bullwhip effect and the amplification of inventory variance (NSAmp) can be effectively reduced. In this note, we extend and test the previous model to a three-level supply chain which consists of an end consumer, a retailer and a manufacturer. Here, the model is tested by using both Gaussian and autoregressive demand patterns. We show that the bullwhip effect and NSAmp also reduce at the level where fuzzy orders exist with good fill rate values.     

Dynamic analysis and design of a semiconductor supply chain: a control engineering approach

The combined make-to-stock and make-to-order (MTS-MTO) supply chain is well-recognised in the semiconductor industry in order to find a competitive balance between agility, including customer responsiveness and minimum reasonable inventory, to achieve cost efficiency while maintaining customer service levels. Such a hybrid MTS-MTO supply chain may suffer from the bullwhip effect, but few researchers have attempted to understand the dynamic properties of such a hybrid system. We utilise a model of the Intel supply chain to analytically explore the underlying mechanisms of bullwhip generation and compare its dynamic performance to the well-known Inventory and Order-Based Production Control System (IOBPCS) archetype. Adopting a control engineering approach, we find that the feedforward forecasting compensation in the MTO element plays a major role in the degree of bullwhip and the Customer Order Decoupling Point (CODP) profoundly impacts both the bullwhip effect and the inventory variance in the MTS part. Thus, managers should carefully tune the CODP inventory correction and balance the benefit between CODP inventory and bullwhip costs in hybrid MTS-MTO supply chains.     

The bullwhip effect on product orders and inventory: a perspective of demand forecasting techniques

Demand forecasting is one of the key causes of the bullwhip effect on product orders. Although this aspect of order oscillation is not ignored, the current study focuses on another critical aspect of oscillation: the bullwhip effect on inventory, i.e. the net inventory variance amplification. In particular, this paper studies a two-level supply chain in which the demand is price sensitive, while the price follows a first-order autoregressive pricing process. We derive the analytical expressions of the bullwhip effect on product orders and inventory using minimum mean-squared error, moving average and exponential smoothing forecasting techniques. We also propose the conditions under which the three forecasting techniques would be chosen by the retailer to minimise the sum of the bullwhip effect on product orders and inventory under different weightings. These observations are used to develop managerial insights regarding choosing an appropriate forecasting technique after considering certain distinct characteristics of the product.     

基于LSSC的供应商选择与订单分配

面对物流服务供应链中不规则的客户需求,物流服务集成商如何有效分配各物流供应商的物流订单是一大难题。考虑集成商对不同分销点的多个物流供应商分配订单的情形,建立基于物流供应商现有手头订单、物流能力可获得性、配送效率、缺损率与成本的供应商选择与订单分配组合优化模型,并采用相应算法进行算例分析。结果表明:该模型及算法能够为物流服务供应链的运作确定合适数量的供应商及进行订单分配的优化;在确保满足客户期望服务水平的同时,降低供应商的运作成本,并促使其为获取更大的竞争优势而改进自身的物流服务能力。     

Bullwhip effect and inventory oscillations analysis using the beer game model

This work examines the bullwhip effect generated and suffered by each level of a four-stage beer game supply chain when different demand scenarios are considered. The paper shows that the actors who generate lower bullwhip are those who suffer more from its effects. Moreover, a new definition of an inventory oscillations measure based on bullwhip definition is introduced. Finally the paper verifies that the new measure of inventory oscillations provides more information on supply chain performance than the bullwhip measure.     

Bayesian network modelling for supply chain risk propagation

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.     

On the risk-averse optimization of service level in a supply chain under disruption risks

The worst-case optimization of service level in the presence of supply chain disruption risks is considered for the two different service levels measures: the expected worst-case demand fulfillment rate and the expected worst-case order fulfillment rate. The optimization problem is formulated as a joint selection of suppliers and stochastic scheduling of customer orders under random disruptions of supplies. The suppliers are located in different geographic regions and the supplies are subject to random local and regional disruptions. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional service-at-risk as a worst-case service level measure. The risk-averse solutions that optimize the worst-case performance of a supply chain are compared for the two service level measures. In addition, to demonstrate the impact on the cost in the process of optimizing the worst-case service level, a joint optimization of expected cost and conditional service-at-risk using a weighted-sum approach is considered and illustrated with numerical examples. The findings indicate that the worst-case order fulfillment rate shows a higher service performance than the worst-case demand fulfillment rate. Maximization of the expected worst-case fraction of fulfilled customer orders better mitigates the impact of disruption risks. The supply portfolio is more diversified and the expected worst-case fraction of fulfilled orders is greater for most confidence levels. Finally, the results clearly show that worst-case service level is in opposition to cost.     

Effective bullwhip metrics for multi-echelon distribution systems under order batching policies with cyclic demand

A large number of problems in a distribution supply chain require that decisions are made in the presence of the bullwhip effect phenomenon. The impact of the order batching policies on the bullwhip effect is analysed in this paper, when cycle demand on a multi-echelon supply chain operating is considered. While investigating which bullwhip effect metrics are more adequate to measure the bullwhip effect in these type of systems, the optimal reordering plan that minimises the operation costs of the overall system is calculated. A Mixed Integer Linear Programming (MILP) model is developed that takes into account an inventory and distribution system formed by multiple warehouses and retailers with lateral transshipments. The bullwhip effect is measured through four metrics: the echelon average inventory; the echelon inventory variance ratio; the echelon average order; and the echelon order rate variance ratio. As conclusion the inventory metrics suggest that (i) using batching policy reduces instability; (ii) batching may reduce in general order variance if using larger batches and (iii) cycle demand length has no major impact in the bullwhip effect. A motivational example and a real word case study are used and tested.     

The impact of consumer price forecasting behaviour on the bullwhip effect

As prices fluctuate over time, a strategic consumer may buy more in advance to reduce his or her future needs in anticipation of higher prices in the future, or may choose to postpone a purchase in anticipation of lower prices in the future. We investigate the bullwhip effect from a consumer price forecasting behavioural perspective in the context of a simple two-level supply chain composed of a supplier and a retailer. We consider two different forms for the demand function – linear and iso-elastic demand functions, both depending on the prices in multiple periods. Assuming that the retailer employs an order-up-to inventory policy with exponential smoothing forecasting technology, we derive analytical expressions for the bullwhip effect under the two demand functions, and extend the results to the multiple-retailer case. We find that consumer forecasting behaviour can reduce the bullwhip effect, most significantly when the consumer sensitivity to price changes is medium (approximately 0.5) for both the demand forms. In addition, for iso-elastic demand, the mitigation of the bullwhip effect induced by consumer price forecasting behaviour becomes more significant as the product price sensitivity coefficient and standard deviation of the price decrease. These findings are applicable to the development of managerial strategies by supply chain members that are conducive to bullwhip effect reduction through customer behaviour.     

Inventory performance of some supply chain inventory policies under impulse demands

This paper attempts to study the impact of impulsive demand disturbances on the inventory-based performance of some inventory control policies. The supply chain is modelled as a network of autonomous supply chain nodes. The customer places a constant demand except for a brief period of sudden and steep change in demand (called demand impulse). Under this setting, the behaviour of each inventory policy is analysed for inventory performance of each node. It is found that the independent decision-making by each node leads to a bullwhip effect in the supply chain whereby demand information is amplified and distorted. However, under a scenario where the retailer places a constant order irrespective of the end customer demand, the inventory variance was actually found to decrease along the supply chain. The variance of the inventory remained constant along the chain when only the actual demands are transmitted by each node. The results also showed that the inventory policy which is best for one supply chain node is generally less efficient from a supply chain perspective. Moreover, the policy which performs poorly for one node can be most efficient for the supply chain. In a way, our results also provide a case for coordinated inventory management in the supply chain where all members prepare a joint inventory management policy that is beneficial for all the supply chain nodes. The results have significant industrial implications.     

The bullwhip effect in hybrid supply chain

As the service is becoming the strategic preference in the manufacturing industry, more and more manufacturing companies provide customers with total solutions by integrating services into their core product offerings, which is usually implemented in so-called hybrid supply chain (HSC). The HSC is formed as a chain by a group of manufacturers and service providers who work together to offer total solutions to customers. In HSC, customers’ responses to variable service levels and the interaction between inventory strategy and service capacity strategy have a great impact on supply chain instability, which represents an important concern in supply chain research. To tackle this problem, we establish a system dynamics model, by taking the HSC for an elevator company in China as an example. Firstly, we analyzed the oscillation characteristics of service flow and product flow. Then, we proposed the performance metrics of bullwhip effect in HSC. Finally, based on the model, we find that the bullwhip effect of HSC could be smoothened by incorporating service capacity adjustment strategy into the inventory replenishment policy.     

Bullwhip and inventory variance in a closed loop supply chain

A simple dynamic model of a hybrid manufacturing/remanufacturing system is investigated. In particular we study an infinite horizon, continuous time, APIOBPCS (Automatic Pipeline Inventory and Order Based Production Control System) model. We use Åström’s method to quantify variance ratios in the closed loop supply chain. Specifically we highlight the effect of a combined “in-use” and remanufacturing lead-time and the return rate on the inventory variance and bullwhip produced by the ordering policy. Our results clearly show that a larger return rate leads to less bullwhip and less inventory variance in the plant producing new components. Thus returns can be used to absorb demand fluctuations to some extent. Longer remanufacturing and “in-use” lead-times have less impact at reducing inventory variance and bullwhip than shorter lead-times. We find that, within our specified system, that inventory variance and bullwhip is always less in supply chains with returns than supply chains without returns. We conclude by separating out the remanufacturing lead-time from the “in-use” lead-time and investigating its impact on our findings. We find that short remanufacturing lead-times do not qualitatively change our results.     

制造企业库存长鞭效应影响因子分析及控制方法

长鞭效应是供应链管理中由于供应链合作伙伴之间缺乏合作与协调的结果。从理论上刻画了长鞭效应的内在本质,分析了产生长鞭效应的原因,以一个跨国公司的库存管理为例,详细分析了影响长鞭效应三个方面的因子：（1）集中式与分散式管理;（2）需求与供应特征;（3）产品类型与库存再订货点。还从供需合作关系的角度提出了改善长鞭效应的几个控制方法。     

基于柔性组合策略的订单配置模型

随着社会消费量的不断增加,资源变得越发紧缺,对客户订单的柔性承诺以实现企业效益的最大化问题已成为供应链中每个企业考虑的关键问题.通过提出替代和二次交付相组合的订单配置策略,建立面向按订单装配型供应链(Assemble-to-Order Supply Chain)的柔性订单配置模型,研究多工厂多客户的供需环境下如何快速履...     

Measure of the bullwhip effect considering the market competition between two retailers

In order to consider the market competition, a new supply chain with one supplier and two retailers is established in this paper. Two retailers employ different AR(1) demand processes, respectively, and an order-up-to inventory policy characterises the inventory decisions. The bullwhip effect in this supply chain is measured under the moving average forecasting technique. We investigate the effects of the lead time, the span of forecast, market competition and the consistency of demand volatility on the bullwhip effect using the algebraic analysis and numerical simulation. Conclusions indicate that different factors lead to the bullwhip effect following different patterns in the supply chain. Moreover, some suggestions are present to help managers to control parameters that yield the lower bullwhip effect.     

Analysis of the bullwhip effect in multi-product,multi-stage supply chain systems–a simulation approach

This research aims to develop a simulation approach based on system dynamics modelling (SDM) and adaptive network based fuzzy inference system (ANFIS) for quantifying and reducing the bullwhip effect in a multi-product, multi-stage supply chain. The proposed model is comprised of three groups of variables influencing the bullwhip effect, namely the structure of a supply chain network, supply chain contributions (ordering process in regular situation or when a supplier has a promotion or shortage gaming) and supply chain performances (the number of defects and ordering lead time). As a result, a two layer simulation model is developed with three generic models. The flexibility of this proposed approach is its ability to model various types of ordering policies which are basic inventory policies, material requirement planning (MRP) system and just in time (JIT) approach. The supply chain of a beverage company was selected to validate and demonstrate the flexibility of the proposed model. The findings of the proposed simulation model are consistent with the results obtained from the case study. The error magnitude of the bullwhip effect level varied between 0 and 9% resulting in bullwhip effect reductions of up to 92%. Accordingly, the bullwhip effect levels are significantly decreased by using the proposed simulation model.     

Supply chain design to guarantee quoted lead time and inventory replenishment: model and insights

Do lead time constraints only lead to re-think and re-optimise the inventory positioning along the supply chain or can they impact on the design of the supply chain itself? To answer such a question, we integrate the lead time constraints in a multi-echelon supply chain design model and challenge the difficulty of combining in the same model the long-term decisions (facility location, supplier selection) with the midterm decisions (inventory placement and replenishment, delivery lead time). The model guarantees the respect of the quoted lead time associated with each customer order and the replenishment of the different stocks (raw materials, intermediate and final products) in the different stages of the supply chain between any pair of consecutive orders. We use the model to investigate the impact of the quoted lead time and customer’s order frequency on supply chain design decisions and costs. Some of our results indicate that the lead time constraints can lead to bringing the sites of manufacturing and distribution close to the demand zone and to select local suppliers in spite of their higher cost.     

平衡记分法在供应链绩效评价中的应用研究

较之传统绩效评价，供应链评价注重组织的未来发展和前馈性，注意力更多地放在对外部增值链的测控。本文在一般平衡记分法的基础上，提出了平衡供应链记分法，从客户角度、供应链内部流程角度、未来发展性角度和财务价值角度研究了对供应链绩效评价的问题，并提出相关的参考指标。     

两种库存管理方法下的牛鞭效应比较分析

建立了ARIMA供应链需求模型,利用订货量方差和需求方差的比值,分别对Order-up库存管理方法和基于MRP的库存管理方法下的牛鞭效应进行了量化.比值大于1则牛鞭效应存在且与比值成正比,反之则牛鞭效应不存在.模拟试验对两种库存管理方法作了对比,结果表明基于MRP的库存管理方法在多数情况下产生了较小的牛鞭效应.