Bullwhip Effect In Supply Chain Research Articles

Mitigating bullwhip effect in supply chains by engaging in digital transformation: the moderating role of customer concentration

Mitigating bullwhip effect in supply chains by engaging in digital transformation: the moderating role of customer concentration

Assessing the Bullwhip effect in supply chain: trends, gaps, and overlaps

Due to operational and behavioral factors, the Bullwhip effect (BWE) arises with variations in the logistics flow, yielding uncertainty and disturbances along the supply chain (SC). Previous studies have discussed isolated approaches, underestimating the influence of behavioral aspects over operational ones, and multi-factor analysis, which helps to measure and diminish the BWE. This study systematically assesses the bodies of knowledge to identify new trends, emphasizing gaps and overlaps to underly behavioral-operational links and multi-factor scenarios through a unified frame of reference built during the paper review. The results from this research spot new BWE trends like COVID and closed-loop supply chain (CLSC) driven by disruption and return flows; the influence of behavioral causes over pricing, returning flows, production capacity, and batching; the combination of multi-factor topics like pricing, production capacity, synchronization, and order batching. This overview contributes to understanding new trends and connections in the issues, highlighting logistic challenges and opportunities to explore future studies with a broader scope. It also elucidates the BWE causes and how to handle them, which could assist in comprehending its effects and advantages on the technical elements of logistics.

Does the short-selling mechanism improve energy security? Evidence from China.

Reducing carbon emissions and improving energy efficiency are essential to respond to global climate change actively. However, improving energy security under resource and environmental constraints is the key for China to achieve high-quality and sustainable development. Thus, using the panel data at the enterprises and regional levels in China from 2013 to 2020, this research develops an energy security index to study the impact of the short-selling mechanism on energy security. The results show that China's energy security is generally on the rise under the short-selling mechanism. However, it has different degrees of impact on energy security in different energy structures, supply chains, and environments. In addition, China's energy security has a regional spillover effect under the short-selling mechanism's capital pressure and supervision governance. This paper also finds that short-selling can alleviate the supply chain's bullwhip effect and risk effect. Finally, the short-selling mechanism complements the micro-influence mechanism of energy security, and short-selling positively impacts China's energy security.

Inhibitory influence of supply chain digital transformation on bullwhip effect feedback difference

PurposeThis paper breaks through the limitations of the research on bullwhip effect in the traditional supply chain, extends the research perspective to digital supply chain and discusses the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two.Design/methodology/approachThis paper starts with the weakening mechanism of supply chain digitization on bullwhip effect, builds bullwhip effect models of traditional supply chain and digital supply chain, respectively, simulates the influence of supply chain digitization transformation on bullwhip effect by using Matlab software and analyzes the causes of bullwhip effect in supply chain led by T company and the digitization process.FindingsFirstly, digitization can reduce bullwhip effect in multi-level supply chain by reducing information feedback deviation. Second, digital transformation is conducive to improving the overall performance of the supply chain. Third, government incentives can promote the digital transformation of supply chain and inhibit bullwhip effect.Research limitations/implicationsAlthough the study considers the heterogeneous subject -- the government's incentive effect on digital transformation and information sharing – it does not include the influence of the end node in the supply chain, that is the consumer. In addition, this paper only analyzes and discusses the bullwhip effect on the amplification of demand, without considering the situation that the market contraction will lead to the reduction of demand.Practical implicationsThis paper considers the distortion degree and delay degree of information feedback, carries out quantitative analysis of bullwhip effect, builds the bullwhip effect model of traditional supply chain and digital supply chain, uses Matlab software to analyze the difference of the influence of supply chain digital transformation on bullwhip effect suppression and puts forward the corresponding control strategy.Social implicationsThe research shows that digital transformation can reduce the bullwhip effect in multi-layer supply chain by reducing the information feedback deviation, which is conducive to improving the overall supply chain performance, and government support can accelerate the digital transformation of supply chain to a certain extent.Originality/valueFirst, break through the limitations of traditional supply chain research, expand the research perspective to digital supply chain and discuss the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two. Second, quantify the bullwhip effect through information feedback bias and provide an analysis method for the weakening of the bullwhip effect. Third, the driving role of the government in the digital transformation of the supply chain is considered in the study, so that the model is more close to the actual situation of enterprise operation.

Dynamic analysis of NGO emergency relief goods supply: 2020 Hubei COVID-19 as a case.

Global emergencies cause significant damage to lives, assets, and the economy. Therefore, the supply of relief goods is essential in emergency relief contexts, which is generally the function of non-government organizations (NGOs) as they have unique relief goods supply advantages. However, few studies have explored the influencing factors on NGO relief goods supply efficiency. To systematically explore the factors affecting supply efficiency, we aim to develop a supply chain model for simulating and providing policy suggestions. Taking the 2020 Hubei COVID-19 as case study, this research developed a system dynamic (SD) model for the NGO relief supply system to evaluate and quantify the impact of factor changes on relief supplies. It was found that transportation and information delays aggravated the NGO emergency supply chain bullwhip effect and caused large supply fluctuations. The initial relief goods inventory was found to be a decisive factor in reducing shortages in disaster areas; however, government support was found to play only a limited role in reducing information and transportation delays. This study enriches NGO emergency supply chain literature and provides suggestions for guiding NGO relief goods supplies in the future.

시스템 다이내믹스를 활용한 Covid-19 물류대란에서의 채찍현상에 관한 연구

Purpose Triggered by the measures taken to manage Covid-19, the increase in supply uncertainty and demand distortion in the market caused information asymmetry that led to the bullwhip effect of supply chain. We study the factors that affect the bullwhip effect caused by Covid-19 as well as its aftermaths. Methods We designed a stock flow diagram to analyze the bullwhip effect based on a supply chain simulation using system dynamics and also perform a causal analysis on variables affecting the supply. Results The results show that the increase in safty inventory, reduction of ship delays, and information sharing can reduce the bullwhip effect while dampening the amplification of orders in the global supply chain consisting of a retailer, a RDC (Regional Distribution Center) and a manufacturer. Conclusion A research model that analyzes the bullwhip effect of the covid-19 logistic crisis using system dynamics is proposed. The model is expected to serve as a useful tool to analyze and manage potentially uncertain global supply chain risk scenarios caused by disruptive events such as Covid-19.

How Does Misinformation and Capricious Opinions Impact the Supply Chain - A Study on the Impacts During the Pandemic.

Misinformation or fake news has had multifaceted ramifications with the onset of the Covid-19 pandemic, creating widespread panic amongst people. This study investigates the impact of misinformation/ fake news (on internet platforms) on consumer buying behavior, impact of fear (created by fake news) on hoarding of essential products and consumer spending and finally impact of misinformation-induced panic buying on supply chain disruptions. It draws upon the consumer decision theory and the cognitive load theory for explaining the psychological and behavioral responses of consumers. The study follows an inductive approach towards theory building using a multi-method approach. Initially, a qualitative research method based on interviews followed by text-mining has been used followed by analysis using python for topic modelling using Latent Dirichlet Allocation (LDA). The findings revealed several prominent themes like consumer shift to online buying, two contrasting spending intentions namely financial security and compensatory consumptions, irrational panic buying, uncertainty/ambiguity of government protocol and norms, social media fraudulent practices and misinformation dissemination, personalized buying experience, reduced trust on news and marketers, logistics and transportation bottlenecks, labor shortage due to migration and plant closures, and bullwhip effect in supply chains.

Eliminating bullwhip effect in supply chain stock systems using smart controllers

Several alternative approaches have been proposed for supply chain modeling majority of which steady-state models. These models cannot adequately deal with dynamic characteristics of supply chain system affected by lead time, demand fluctuation, sale prediction and so forth. Static models in particular cannot describe, analyze and provide solutions for a key issue in supply chains called bullwhip effect. The bullwhip effect is information deviation from one end of the supply chain to the other which intensifies fluctuation and change in demand from downstream to upstream. This issue leads to major deficiencies. One of the approaches used to cope with dynamic issues is control systems approach. In present study, a predicting model controller was developed to minimize the bullwhip effect in supply chain. In addition, a prediction methodology is integrated into predicting model control framework to predict uncertainty in distorting demand behavior. Integration of a prediction methodology in predicting model control framework improved the controlling system's performance. The main feature of demand signal used in model design is its fluctuation and distortion. One of the main factors behind bullwhip effect is demand signals processing and in fact, the predicting model used.

Application of system dynamics to reveal the suitable CPFR mode and unsold goods return policy for deteriorating items

With the intensification of global competition and rapid changes in customer needs, most companies focus on rapid customer response. This has caused problems such as shortened product lifecycles, diversified and small volume demands, and shortened lead time order. As a result, suppliers at all levels in a supply chain produce too many finished products, parts, or components, and increase inventory stock. This is an undesirable situation, particularly for deteriorating items. Increased inventory of deteriorating items does not only increase inventory cost, but also the product cost when the items lose function due to long storage time. In order to solve this problem, many scholars have proposed tools to improve supply chain performance, for example, collaborative planning, forecasting and replenishment (CPFR). Therefore, this research applied system dynamics to reveal the suitable CPFR mode for deteriorating items under two types of unsold goods return policies. The first model prohibits buyer to return unsold goods to seller, while the second model allows buyer to return unsold goods to seller. By using CPFR, buyer and seller can share, change and instantly update forecast information, both on the supply and demand sides. The inventory levels can be minimized and the bullwhip effect in supply chain can be resolved by CPFR to create a win-win situation for both seller and buyer. The results show that for deteriorating items, seller receives more profit than buyer in all four CPFR scenarios under the policy that buyer cannot return goods to seller. However, if buyer can return goods to seller, the seller’s profit is less than the buyer’s but the profit gap is not as wide as the previous model. Therefore, to maintain long-term collaborative relationship, seller should allow buyer to return goods. In both models, total profit is the highest when buyer is responsible for order generation in CPFR. This study applied system dynamics to illustrate the impact of different business models on supply chain performance and then to reveal effective strategy for supply chain partners.

The bullwhip effect in supply chains: Review of recent development

The bullwhip effect happens whenever the demand order fluctuations in the supply chain (SC) escalate as they are transferred up the SC. In fact, a small change in point-of-sale demand may be interpreted by SC participants as a much bigger variability in demand. This looks like a cracking a whip, where a small flick of the wrist may yield a large motion at the end of the whip. Misstate data from one side of a SC to the other part may yield substantial sloppiness. This includes increase in inventories, shortage of cash flow, weak customer satisfaction, etc. Enterprises may efficiently reduce the bullwhip effect by completely learning its root causes. This paper presents an overview on the concept and recent development of the bullwhip effect.

Reducing the bullwhip effect in supply chain with factors affecting the customer demand forecasting

Reducing the bullwhip effect in supply chain with factors affecting the customer demand forecasting

Sensitivity analysis of the bullwhip effect in supply chains with time delay

ABSTRACT The bullwhip effect (BWE) is a major challenge engineers facing in the industry. From the time and economic point of view, it is important to determine stability regions, where the bullwhip effect does not occur. In this paper, we designed a structure for optimal regulation of the BWE with Network Inverse Data Envelopment Analysis (IDEA) approach. The results indicated that time can negatively affect the magnitude of the bullwhip effect. So, the effect of time and delays should be considered in the BWE score. There are two modes: (1) the RBWE occurs at this interval, where the proposed network was able to regulate and control the RBWE; (2) the RBWE does not occur at this interval, where the stability area was obtained to ensure that the manager can change the inputs or outputs according to his policies, in this area with no regard to the occurrence of the RBWE. Finally, two numerical examples indicated the method efficiency for managing the RBWE in the proposed network supply chain.

Knowledge diffusion of supply chain bullwhip effect: main path analysis and science mapping analysis

Knowledge diffusion of supply chain bullwhip effect: main path analysis and science mapping analysis

System Performance Implications of Capacity and Flexibility Constraints on Bullwhip Effect in Supply Chains

AbstractUtilizing behavioral experiments involving approximately one thousand subjects, we empirically evaluate the relationship between volume flexibility and the Bullwhip Effect (BWE). We not only evaluate differences in the level of BWE, but also differences in associated system performance. We find that systems with hard capacity constraints are effective at limiting BWE, but also produce very poor customer service levels. However, systems with low levels of volume flexibility generate similar performance as fully flexible systems but do so while also generating a lower BWE. In a multi‐echelon setting, we show that having partial volume flexibility at the upstream echelon helps in reducing order variance, with no significant drop in service level in the supply chain. This is particularly useful in those manufacturing environments which prefer stable orders for production planning without affecting the downstream customer service level. Counterintuitively, both experiments uncovered instances of irrational behavior where participants invoked flexibility in ways that did not always enhance system performance. In addition, these experiments also established enhancements in BWE and system performance through introduction of incremental volume flexibility into upstream echelon of a supply chain.

The analysis of bullwhip effect in supply chain based on hedging strategy compared with optimal order quantity strategy

The bullwhip effect is one of the most important problems in the supply chain management. It can cause large inefficiency in supply chain. Although there are many researches about bullwhip effect, few studies have investigated this phenomenon caused by product price fluctuation. In this paper we consider a two-period supply chain consisting of one supplier, one wholesaler and one retailer. The wholesale price may increase greatly in the beginning of second period. If this happens, a large number of end customers will go to purchase the product from retailer. For managing the end customers’ demands in the second period, we consider two ordering strategies available to the retailer including optimal order quantity strategy and hedging strategy with call option. For each strategy, we calculate the bullwhip effect ratio for two periods and compare the results. We found that the lower exercise price in hedging strategy compared with the wholesale price in the optimal order quantity strategy must not contribute to extra product purchase. The research provides new insights into how hedging strategy can reduce bullwhip effect.

Research on System Dynamics Simulation of Building Materials Supply Chain

The bullwhip effect of supply chain under non-information sharing and information sharing environment is analyzed theoretically. The model of building materials supply chain in two environments is constructed, and the system dynamics flow chart is established by Vensim PLE simulation software. The correctness of the theoretical analysis is verified by an example simulation. The sensitivity analysis of transportation delay time and manufacturer’s production cycle shows that extending transportation time can reduce manufacturer’s inventory, but raw material inventory in transit will increase; Shortening the production cycle of producers under the information sharing environment is more conducive to reducing the bullwhip effect of the supply chain.

The Design of Inverse Network DEA Model for Measuring the Bullwhip Effect in Supply Chains with Uncertain Demands

Two different bullwhip effects with equal scores may have different sensitivities and production patterns. As a result, the difference between these two seemingly equal scores has been ignored in previous methods (such as frequency response and moving average). So, the present study constructs a model of Inverse Network Data Envelopment Analysis, to introduce the relative and interval scores of the bullwhip effect magnitude, when a series of uncertain demands are made in a specific time interval. In the first stage of the proposed network, the uncertain demands and the forecasted uncertain data are regarded respectively as the model’s inputs and outputs. These output data constitute the intermediate variables and consequently the inputs of the second stage of the study model. In the second stage, after considering the ordering policies, the uncertain orders are sent. Due to utilizing both the optimistic and pessimistic perspectives, the study methodology includes an interval value for measuring the bullwhip effect with relative attitude. In the optimistic perspective, the analyzed decision making unit has the optimal status in comparison with other decision making units. In the pessimistic perspective, the analyzed decision making unit has the worst status in comparison with other decision making units. The results show that time is an unfair factor in the size of the bullwhip effect. The impact of uncertainties on the bullwhip effect in the demand forecasting stage is greater than the ordering stage. According to the research findings, cross-sectional planning is possible at different times according to different conditions. Therefore, using the results of the research, a fair score of the bullwhip effect can be obtained by considering all perspectives.

Information sharing in decentralised supply chains with partial collaboration

We explore the value of information sharing for smoothing the dynamics of supply chains when some echelons do not collaborate. To this end, we study seven information sharing structures in a four-echelon supply chain using a system dynamics approach. We find that the overall propagation of the bullwhip effect in supply chains decreases as the number of echelons sharing information grows, but it is not dependent on their position. Nonetheless, the performance of the echelons strongly relies on the degree of downstream collaboration; therefore, information sharing in the lower nodes has a higher impact on the overall supply chain costs. We also investigate the benefits of adding new members to the collaborative strategy in different lead-time scenarios. Finally, we provide managerial recommendations for decentralised supply chains.

Research on simulation of school uniform supply chain optimal model based on internet of things

In this paper, the information sharing platform based on the Internet of things is constructed to improve the efficiency of supply chain, and the flow chart based on the Internet of things of the school uniform is analyzed, integrated application of Internet of things technology in supply chain to analyze the detail of the system. By calculating Lyapunov Index, power spectrum, and correlation dimension, the bifurcation and chaos characteristics of supply chain under different production strategies are analyzed. At the same time, the periodicity and initial value sensitivity of chaos are used to explain the feasibility of manufacturer organizing flexible production and the causes of bullwhip effect in supply chain and corresponding prevention measures. And the experiment results show that bullwhip effect will occur in the chaotic supply chain system, which can be suppressed by short-term forecasting and accelerating the speed of demand information transmission. Chaos also has a favorable side for enterprises, such as making low-cost flexible production possible.

The Bullwhip Effect in Closed-Loop Supply Chains: A Comparison of Series and Divergent Networks

Supply chain network structure has been mentioned as one of the contributors to the Bullwhip Effect in the supply chain. With the incorporation of reverse logistics into most of the supply chains, the structure of the supply chain changes especially if there are more than one player in the reverse chain. In this research, systems dynamics models were developed to compare the Bullwhip Effect between serial and divergent supply chain networks with different structures in closed-loop supply chains. The scenario experimentation revealed that increasing the number of collectors in the reverse chain increased the Bullwhip suffered, Bullwhip generated and the Bullwhip slope in all supply chain configurations. Therefore, it seems to be more beneficial for a closed-loop supply chain to have one reliable collector supplying used products than more than one collector with uncertainties in the quantity of products returned. Findings also reveal that a serial network is more sensitive to changes in the reverse chain in terms of the number of parties involved compared to a divergent supply chain. This means that the serial network assumption overestimates Bullwhip measures. Whilst it may be easy to measure the Bullwhip Effect by assuming a serial supply chain structure, this value might be overestimated especially for supply chains that are involved with more than one player at each echelon. These findings provided some useful insights on modelling supply chain networks to reduce the Bullwhip Effect in closed-loop supply chains.