Bullwhip Effect Research Articles

An Intelligent Supply Chain Information Collaboration Model Based on Internet of Things and Big Data

Supply chain information collaboration refers to a relationship in which supply chain partners organically integrate, coordinate, and develop resources, business processes, and organizations in order to achieve common goals. The goal is to create and coordinate efforts in all aspects of the supply chain. The overall value of the supply chain is greater than the simple sum of the value of each link, thereby improving the competitiveness of the entire supply chain. However, information sharing in supply chain information collaboration has always had problems such as information distortion, information loss, and information delay. The effective coordination of information has become a difficult point in supply chain management. This paper mainly uses the Internet of Things and big data technology to build a simulation model of the supply chain bullwhip effect based on the mathematical model of the bullwhip effect and uses the simulation method to simulate and study the key factors in the model. The image of the simulation results objectively clarifies the value of information collaboration in the supply chain.

Using Serious Games and Simulations for Teaching Co-Operative Decision-making

Teaching students and training employees for management responsibilities, enabling acquisition of new skills, of knowledge and experiences to better respond to market and job demands remain challenging. New methodologies and pedagogical approaches need to be explored to support the design of new tools that enhance the acquisition of cooperative decision-making skills. Exploiting lessons learnt from entertainment software and encouraging the use of gaming techniques, which have the potential to maximize user immersion and interactivity, represent an opportunity. The authors examine dedicated tools that aim to support teaching and training of co-operative decision-making skills, and address issues identified within specific areas respectively the management of renewable energy parks and supply chain management. Two examples are presented: a simulation tool that enables employees to experiment collaborative decision-making scenarios associated with the management of renewable energy parks, and a game that is used for the application of methods in the strategic decision making in supply chains. The paper reports on this serious game that enable students experience the bullwhip effect in a controlled environment.

Effects of Yield and Lead-Time Uncertainty on Retailer-Managed and Vendor-Managed Inventory Management

Generally, there are various elements of uncertainty in a supply chain. In particular, uncertainties in lead time, demand, and yield are very important in the semiconductor industry. Higher uncertainty can lead to bullwhip effects that can undermine the performance of the entire supply chain. This study examines the relationship between uncertainty in the supply chain and the outcome of inventory replenishment policies. Specifically, we analyze the effects of well-known uncertainties on manufacturer production quantity and retailer order quantity decisions in a decentralized supply chain. In addition, we also analyze and compare the effects of these uncertainties for the retailer-managed inventory and the vendor-managed inventory policies. Using numerical experiments, a comparative analysis of the two alternatives is conducted to determine suitable options for improving supply chain performance. In general, the performance of vendor-managed inventory is better than that of retailer-managed inventory, but we observe from the numerical experiments that there exist circumstances under which retailer-managed inventory shows better supply chain performance.

RETRACTED ARTICLE: Design of prototype system for multi-agent supply chain information sharing benefit distribution management

As the international market environment changes more and more fierce, the potential for enterprises to improve their ability to adapt to market changes and improve their competitiveness will become smaller and smaller, and the difficulty will increase, such as product delivery speed, quality, cost and service. The level will obviously be constrained by the relationship between product manufacturers and suppliers of raw materials and components, product distributors, etc. More and more enterprises are beginning to effectively carry out internal management changes, and the traditional competitive relationship among enterprises gradually transforming into a new type of partnership. The supply chain plays an increasingly important role in the process of enterprise competition. High-level supply chain operation and management are the key to successful operation of the company. The supply chain is a complex system, which requires members to reduce the “bullwhip effect” through information sharing, to achieve decision synchronization, reduce inventory, and improve market response. Therefore, information sharing is an important part of supply chain research. The competition in the century is the competition between supply chains. The end-customer’s individualization of products, the increasing requirements for diversification, and the increasing uncertainty of the economic environment have prompted enterprises to pay more and more attention to the coordination of relations among members of the supply chain. The key to coordinate the supply chain is to realize information sharing. This paper proposes an architecture of agile supply chain management system based on the multi-agent information sharing under the guidance of self-organization theory. At the same time, the prototype of the supply chain management system was established by using web technology, distributed object technology (CORBA/DCOM) and related information resource organization and management technology.

Bullwhip effect and complexity analysis in a multi-channel supply chain considering price game with discount sensitivity

This paper constructs a multi-channel supply chain that includes a manufacturer, a dual-channel retailer and an online retailer. The dual-channel retailer has a traditional channel and an online channel, while the manufacturer cooperates with the retailers and opens a direct channel simultaneously. The horizontal and historical price discount sensitivities are taken into account to establish the price-sensitive demand functions. A price game model with the heterogeneous expectations is proposed and analysed with the methods of the stability domain, the bifurcation diagram and the maximum Lyapunov exponent. The complexity and bullwhip effect of each channel are investigated with respect to the price adjustment speed and historical price discount sensitivity. The results show that a moderate or low price discount sensitivity keeps the system stable and a high price discount sensitivity brings the system into the twofold cycle state or chaotic state. It is interesting that if both online channels increase the adjustment speeds with similar strategy the system will enter chaos through the Neimark–Sacker bifurcation. The bullwhip effect is affected by the price adjustment speed and the price discount sensitivity, especially in the double-period or chaotic state, where the bullwhip of the overall supply chain is increased significantly. The results show that controlling price discount sensitivity is useful for supply chain node companies.

Supply Chain Finance Revisited: A Critical Review with Future Prospects

Supply Chain Finance (SCF) is a relatively recent thinking in Supply Chain Management (SCM) literature. Major Interest in SCF has steadily increased since the past decades and especially during the global financial crisis of 2008. However, SCF places the focus of research on the interconnection among SCM, corporate value and financial performance, away from the myopic perspective of managing solely the cost when studying financial aspects of SCM. Despite the crisis-related research interest and the growing importance of SCF, academic contributions on the subject remain vague, while scarce research efforts have been identified toward the systematic documentation of its core concepts and the development of a “general theory” of SCF. This paper aims to redefine the term SCF by shedding light on theoretical ambiguities, provide an up-to-date systematic literature review of the SCF concept and identify research gaps. The goal is to also highlight emerging areas like the “Supply Chain Financial Bullwhip Effect” and Blockchain Technology.

Support vector regression and Adaptive neuro fuzzy to measure the Bullwhip effect in supply chain

Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy (ANFIS) are the advance statistic knowledge. They are employed to construct the forecasting signal models for a tin milk industry. Then the models are used to measure the Bullwhip Effect (BWE) in the supply chain with the beer game methods. This study tried to minimize the BWE with forecasting methods through the comparative for both methods and attempt to prove that a small Mean Square Error (MSE) of the BWE. The data is collected from the price and demand variables, then it is generated in random normal distribution. It informs that in two conclusions. ANFIS is better than SVR in case of MSE comparison. Moreover, the totals BWE of ANFIS and SVR are 209 and 1,237 respectively. The Second conclusion is the better forecast model leads to reduce the amplification across the supply chain.

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.

Supply chain relational capital and the bullwhip effect

PurposeThe purpose of this paper is to conduct a large-sample empirical investigation of how relational capital impacts bullwhip at the supplier.Design/methodology/approachThe study uses mandatory disclosures in regulatory filings of US firms to identify a supplier’s major customers and constructs empirical proxies of supply chain relational capital, i.e., length of the relationship between suppliers and customers and partner interdependence. Multivariate regression analyses are performed to examine the effects of relational capital on bullwhip at the supplier.FindingsThe findings show that bullwhip at the supplier is greater when customers are more dependent on their suppliers, but is reduced when suppliers share longer relationships with their customers. The results also provide additional insights on several firm characteristics that impact supplier bullwhip, including shocks in order backlog, selling intensity and variations in profit margins. Furthermore, the authors document that the effect of supply chain relationships on bullwhip tends to vary across industries and over time.Originality/valueThe study employs a novel data set that is constructed using firms’ financial disclosures. This large panel data set consisting of 13,993 observations over 36 years enables thorough and robust analyses to characterize supply chain relationships and gain a deeper understanding of their impact on bullwhip.

Bullwhip Effect in Supply Networks: The Impact of Network Structure

The bullwhip effect refers to the amplifcation of demand variation when moving upstream the supply chain. Bullwhip research so far has mainly focused on linear and distributor networks while integrator and more general network types have not been analyzed in detail yet. First, we derive a metric that allows us to measure the bullwhip effect in a general network structure. Second, we quantify the bullwhip effect in different network archetypes (linear, diverging, integrating and generalized networks). Third, we simulate the bullwhip effect both for network archetypes and for a large number of real supply networks with distributor, integrator and generalized structures. The bullwhip effect can be shown to decrease strongly if network settings are heterogeneous (i.e., nodes use different number of forecasting periods and order scheduling) and if horizontal complexity (i.e., number of suppliers per network tier) increases. In real supply networks, the bullwhip effect tends to be much lower (up to -97% for selected structures) than in linear supply chains. For network types that are vulnerable to the bullwhip effect, we propose strategies to reduce it.

The effect of replenishment policy on bullwhip effect considering the higher-level shipments

ABSTRACTA multi-level and multi-node supply chain multi-agent simulation model is established based on the two maximum inventory level replenishment strategies, which consider the superior shipments. The effect of these two replenishment strategies on the bullwhip effect of supply chain role level and enterprise node is discussed. This paper describes in detail how the supply chain enterprises deal with important issues such as, receipt from superior, subordinate delivery, and order to the higher level. What is more important is that how to carry out demand forecasting and replenishment using the maximum inventory level strategy when ordering to the superiors. Results show that the bullwhip effects on the other three kinds of scenarios are all different from those with a higher quantity of goods, which the bullwhip effect is not notable. The bullwhip effect significantly increases from downstream to upstream. In the development of replenishment strategy, compared to not considering the superior shipments, considering it will significantly reduce the layer bullwhip effect and node bullwhip effect of the agents in supply chain. Considering the superior shipments has brought beneficial impact to the order quantity stability of each layer and the order quantity stability of each node enterprise.

Multi-objective particle swarm optimisation approach for production-inventory control systems

PurposeThis paper aims to optimise the dynamic performance of production–inventory control systems in terms of minimisation variance ratio between the order rate and the consumption, and minimisation the integral of absolute error between the actual and the target level of inventory by incorporating the Pareto optimality into particle swarm optimisation (PSO).Design/method/approachThe production–inventory control system is modelled and optimised via control theory and simulations. The dynamics of a production–inventory control system are modelled through continuous time differential equations and Laplace transformations. The simulation design is conducted by using the state–space model of the system. The results of multi-objective particle swarm optimisation (MOPSO) are compared with published results obtained from weighted genetic algorithm (WGA) optimisation.FindingsThe results obtained from the MOPSO optimisation process ensure that the performance is systematically better than the WGA in terms of reducing the order variability (bullwhip effect) and improving the inventory responsiveness (customer service level) under the same operational conditions.Research limitations/implicationsThis research is limited to optimising the dynamics of a single product, single-retailer single-manufacturer process with zero desired inventory level.Originality/valuePSO is widely used and popular in many industrial applications. This research shows a unique application of PSO in optimising the dynamic performance of production–inventory control systems.

An Analysis on the Bullwhip Effect of Stationery Supply Chain Network in Korea

본 논문은 문구산업의 공급사슬네트워크에서 채찍효과의 원인을 실증적으로 규명하고 이를 감소시킬 수 있는 대안을 제안한 다. 기존 물류관련 문헌이 자본재 산업 중 특히 성장기 제품의 채찍효과를 수요기반 측면으로 분석하고 있는 반면, 본 논문은 보다 일반적인 소비재 산업 내 성숙기 제품의 공급사슬에서 발생하는 채찍효과의 구조적 원인을 밝히는 것을 목적으로 한다. 이를 위해 본 연구는 먼저 국내 문구산업의 공급사슬 내 기업들로부터 수집한 실제 매출 자료를 이용하여 공급사슬네트워크에서의 채찍효과를 확인하고 기존 문헌으로부터 도출한 분석틀을 적용하여 채찍효과의 4가지 원인과 대응 방안을 분석한다. 특히 본 논문은 실증적 연구방법으로써 공급사슬네트워크 내 기업의 실무 담당자들과의 심층면접을 통해 수집한 자료를 활용하여 계층적 분석 과정(AHP) 모형을 구축하고 분석함으로써, 실무자들이 고려하는 공급사슬네트워크에서 채찍효과의 중요 원인을 실증적으로 밝힌 다. 이에 따른 본 연구의 결과 소비재 산업 중 특히 성숙기 제품의 특성을 지닌 문구산업의 공급사슬네트워크에서 채찍효과의 존재를 확인할 수 있었으며, 이의 주요 원인이 기업의 정책적인 부분에 해당하는 가격변동으로 확인되었다. 이는 기존 문헌의 수요 예측의 수정이라는 연구 결과와는 대비되는 것으로 공급기반 측면에서 채찍효과를 감소시킬 수 있는 방안을 고려할 필요가 있음을 보여준다. 본 연구의 결과는 소비재 산업 특히 성숙기 제품의 공급사슬네트워크에서 공급업체의 가격 전략에 대한 새로운 가능 성을 제시하는 한편, 물류 네트워크에서 유통업체와 같이 최종 소비자와 접점을 이루는 조직이 구매 또는 판매 경로를 선택하기 위한 효과적인 방법으로 확장될 수 있다.The purpose of this study is to empirically identify the bullwhip effect on the stationery supply chain network and to propose an alternative solution to reduce it. Existing literature related to logistics analyzes the bullwhip effect of the capital goods industry, especially the products during the growth period, at the aspect of the demand base. This article, however, is focusing on structural cause of the bullwhip effect in the supply chain of more general consumer goods during the mature period. To this end, the bull whip effect in the supply chain network was confirmed using the actual sales data collected from the companies in the supply chain of the domestic stationery industry. Then the four causes and countermeasures of bullwhip effect were analyzed by applying the analysis framework derived from existing literature. In particular, this article used an empirical research method. By constructing and analyzing AHP (Analytic Hierarchy Process) model using the materials collected through in-depth interviews with working level respondents of the companies in the supply chain network, significant causes of bullwhip effect in the supply chain network were identified. As a result, it was confirmed that there was bullwhip effect in the stationery supply chain network which could be characterized as mature period goods among consumer goods. It was also confirmed that the major cause was price fluctuation falling under the division of corporate policy. The result of this study is not consistent with previous studies which addressed the demand signal processing. It showed the needs to consider the method to reduce bullwhip effect from the supply base. The results of this study suggest new possibilities for the suppliers pricing strategy for the consumer goods in the mature period in the supply chain network. It can be extended to an effective way to select a sales or marketing channel for organizations that are in contact with end consumers in the logistics network.

Measure of bullwhip effect in supply chain with price-sensitive and correlated demand

In this paper, the bullwhip effect is analytically measured in a simple two-stage supply chain with one supplier, and one retailer who experiences a compound demand of a first-order autoregressive process and a stochastic retail price. Our study demonstrated that the bullwhip effect can be, under certain conditions, stronger or weaker than the case where the price is not considered. In addition, quantitative analysis is also performed to study the behavior of the bullwhip effect. It is found that the magnitude of both lead time and autocorrelation coefficient impacts on bullwhip effect has been affected by the appearance of price and its interactions with demand.

Reducing the Bullwhip effect in a supply chain network by application of optimal control theory

Controlling the bullwhip effect and reducing the propagated inventory levels throughout the supply chain layers has an important role in reducing the total inventory costs of a supply chain. In this study, an optimal controller that considers demand as control variable is designed to dampen propagated inventory fluctuations for each node throughout the supply chain network. The model proves to be very useful in revealing the dynamic characteristics of the chain and provides a proper interface to study decisions taken into account at each node of the supply chain in different periods by decision makers (DMs). In the proposed approach, two feedback loops and online updated values of net stock quantities are used for calculation of the orders. To investigate the efficiency of the proposed approach, a real case of bicycle industry is conducted. The acquired results justify the efficiency of the proposed approach in controlling and dampening the bullwhip effect and reducing inventory levels, net stock quantities and inventory attributed costs throughout the supply chain network layers.

How to measure bullwhip effect by network data envelopment analysis?

One of the most complicated decision making problems in supply chain management is performance assessment which involves diverse criteria. One of the main criteria for evaluating supply chain performance is bullwhip effect (BWE). For supply chains’ managers, measuring bullwhip effect is of critical importance. However, BWE is measured in classical series or parallel structures of supply chains. This network structure is rarely found in real-life (Dominguez, Cannella, & Framinan, 2014; Dominguez, Framinan, & Cannella, 2014). To present an insight of BWE measure in different supply chain networks (SCNs), a novel mathematical approach is proposed. To deal with this issue, a new network data envelopment analysis (NDEA) model is developed to measure relative BWE of non-serial SCNs and their divisions. Our proposed model is based on slacks-based measure (SBM) model. Since bullwhip effect is undesirable, worst-practice frontier (WPF) approach is considered. Accordingly, a new network worst practice SBM model with undesirable outputs is developed to calculate BWE of non-serial SCNs. In addition, BWE of each division is computed. Finally, a case study in pharmaceutical industry validates applicability of the proposed model.

Downstream Demand Inference in decentralized supply chains

For many years, the main objective of studying decentralized supply chains was to demonstrate that a better inter-firm collaboration could lead to a better overall performance of the system. The literature has demonstrated that collaborating by sharing information, even in a partial way, can lead to near-optimal solutions. In this context, many researchers have studied a phenomenon called Downstream Demand Inference (DDI), which presents an effective demand management strategy to deal with forecast problems. DDI allows the upstream actor to infer the demand received by the downstream actor without the need of information sharing. Recent research showed that DDI is possible with Simple Moving Average (SMA) forecast method, and was verified for an autoregressive [AR(1)] demand process, a moving average [MA(1)] demand process, and an autoregressive moving average [ARMA(1, 1)] demand process. In this paper, we extend the strategy's results by considering causal invertible [ARMA(p, q)] demand processes. We develop Mean Squared Error and Average Inventory level expressions for [ARMA(p, q)] demand under DDI strategy, No Information Sharing (NIS) and Forecast Information Sharing (FIS) strategies. We compute the Bullwhip effect generated by employing SMA method and we simulate the resulted improvement compared to employing MMSE method. We analyze the sensibility of the three performance metrics in respect with lead-time value, SMA and ARMA(p, q) parameters. We compare DDI results with NIS and FIS strategies’ results and we show experimentally that DDI generally outperforms NIS. Finally, we provide a revenue sharing contract as a practical recommendation to incite supply chain managers to adopt DDI strategy.

Inventory and Production Dynamics in a Discrete-Time Vendor-Managed Inventory Supply Chain System

This paper presents some analytical results on production and order dynamics in the context of a discrete-time VMI supply chain system composed of one retailer and one manufacturer. We firstly derive the lower bound and upper bound on the range of inventory fluctuations for the retailer under unknown demand. We prove that the production fluctuations can be interestingly smoothed and stabilized independent of the delivery frequency of the manufacturer used to satisfy the retailer’s demand, even if the retailer subsystem is unstable. The sufficient and necessary stability condition for the whole supply chain system is obtained. To further explore the production fluctuation problem, the bullwhip effect under unknown demand is explored based on a transfer function model with the purpose of disclosing the influences of parameters on production fluctuations. Finally, simulation experiments are used to validate the theoretical results with respect to inventory and production fluctuations.

The Bullwhip Effect in Supply Networks

We offer a new network perspective on one of the central topics in Operations Management -- the bullwhip effect (BWE). The topic has both practical and scholarly implications. We start with an observation: the variability of orders placed to suppliers is larger than the variability of sales to customers for most firms, yet the aggregate demand variability felt by suppliers upstream does not amplify commensurably. We hypothesize that changes to the supplier's customer base can smooth out its aggregate demand. We test the hypothesis with a data set that tracks the evolution of supply relationships over time. We show that the effect of customer base management extends beyond the statistical benefits of aggregation. In particular, both the formation and the dissolution of customer-supplier relationships are associated with the smoothing of the aggregate demand experienced by suppliers. This provides fresh insight into how firms may leverage their customer-supplier relationships to mitigate the impact of the BWE.

The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes

Various approaches have been considered in the literature to improve demand forecasting in supply chains. Among these approaches, non-overlapping temporal aggregation has been shown to be an effective approach that can improve forecast accuracy. However, the benefit of this approach has been shown only under single exponential smoothing (when it is a non-optimal method) and no theoretical analysis has been conducted to look at the impact of this approach under optimal forecasting. This paper aims to bridge this gap by analysing the impact of temporal aggregation on supply chain demand and orders when optimal forecasting is used. To do so, we consider a two-stage supply chain (e.g. a retailer and a manufacturer) where the retailer faces an autoregressive moving average demand process of order (1,1) -ARMA(1,1)- that is forecasted by using the optimal Minimum Mean Squared Error (MMSE) forecasting method. We derive the analytical expressions of the mean squared forecast error (MSE) at the retailer and the manufacturer levels as well as the bullwhip ratio when the aggregation approach is used. We numerically show that, although the aggregation approach leads to an accuracy loss at the retailer's level, it may result in a reduction of the MSE at the manufacturer level up to 90% and a reduction of the bullwhip effect in the supply chain that can reach up to 84% for high lead-times.