Impact of cross-docking on the bullwhip effect

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.

An important phenomenon in supply chain management, known as the bullwhip effect, suggests that demand uncertainty and variability increases as one moves up a supply chain. The bullwhip effect can greatly decrease the efficiency and profits within a supply chain. So it is very important to reduce or eliminate such phenomenon in supply chain management. There can be much dispute that the bullwhip effect is an phenomenon of importance in supply chain in the field of management research, yet there have been few literature reviews on this topic. This research review addresses supply chain management issues specific to bullwhip effect under uncertainty environment, reviewed and summarized the reasons of producing the bullwhip effect in a supply chain. According to the causes of producing the bullwhip effect and quantifying analysis, some measures for coping with the bullwhip were introduced. So the review provides a systematic overview of bullwhip effect. Our objective is to twofold, namely to enhance the understanding of bullwhip effect by documenting the current state of affairs, and to inspire fruitful future research by describing bullwhip effect phenomenon between relevant managerial issues and available academic literature.

Operations in today’s demand chain management framework

The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.

Bullwhip effect is a crucial problem in a supply chain. Bullwhip effect is distortion information between inventory and demand in the whole supply chain stages. This problem has not solved in the business recently, although there are many studies discussed this issue. Bullwhip effect gives a negative impact on the performance of the inventory system. This impact can reduce using Centralized Demand Information (CDI). This research aims to analyze the bullwhip effect that happens in between a manufacturer, distribution, and retailers to minimize inventory cost. This research aim is achieved by: i) Calculating the amount of bullwhip effect of PT. X that occurs in the bottled drinking water distribution system; ii) Implement the centralized demand information method to reduce the bullwhip effect on the supply chain water supply network; iii) Comparing inventory costs before and after cutting the bullwhip effect on the supply chain. The data such as time series, data in 12 periods (monthly), data on raw material procurement, holding cost and stockout costs. The reason for using CDI for calculating the bullwhip effect and Economic Order Quantity (EOQ) to calculate the inventory cost. Research findings show that using CDI will decrease the bullwhip effect and the impact is reducing inventory cost.

Purpose: The purpose of the article is to report on research that was completed to explore the concept of the bullwhip effect in supply chains and to illustrate empirically the presence of the bullwhip effect in automotive supply chains in South Africa. Problem Investigated: This article investigates the presence of the bullwhip effect - which was identified through an empirical study - and its causes and implications for supply chain management in the South African automotive component industry. Methodology: A literature study was conducted on the causes and implications of the bullwhip effect phenomenon. This was followed by an empirical study in the form of a survey among South African automotive component manufacturers. Descriptive and inferential statistics were used to determine the significant supply chain problems relating to the bullwhip effect in automotive supply chains. Findings and Implications: The results indicate that automotive component manufacturers are dependent on demand-forecasting information from their customers. They experience long lead times, fluctuating orders, cancellation of orders, excess and slow moving inventory and a lack of integration with suppliers and customers. There are also signs of relationship problems and a possible silo mentality. The mentioned results indicate the presence of the bullwhip effect in South African automotive supply chains. Since the bullwhip effect can have a major impact on organisations' costs, knowing where to invest effort and resources should be a high priority for supply chain managers. Value of the Research: Since the field of supply chain management is extremely dynamic, this article contributes to the body of knowledge and provides new insight into the bullwhip effect phenomenon. The results included in this article could assist parties in automotive supply chains to focus their attention on problems that might be within their control and if solved could lead to improved competitiveness. Furthermore, there is little empirical research on this topic in the South African automotive component industry. Conclusion: It is possible that the bullwhip effect is responsible for inefficiencies in automotive supply chains. Knowledge of the indicators of the bullwhip effect can enable supply chain managers to identify it at an early stage and thus be proactive in preventing its costly influence on the efficiency of the supply chain. The bullwhip effect can be experienced by any industry. This is possibly the case in the automotive component industry in South Africa.

Every supply chain whether services, durable goods or customer goods, is drifting more and more towards E-supply chain using Internet Technology (IT).Such supply chains make use of internet for selecting items, placing their orders and making payments too. Now days the E-supply chain for serving food items is getting very popular because of ease and facilities provided by Food Delivery Apps (FDAs) like Swiggy, Zomato and Uber Eats etc. Use of FDAs has caused variation in demand of food items and subsequently has caused an impact on bullwhip effect. Bullwhip effect is a supply chain phenomenon, which is defined as amplification of demand, as information in form of orders moves from lower echelon to higher echelon of supply chain. This paper attempts to explore the influence of E-supply chain on demand of food items and hence on the bullwhip effect, in food industry, in Indian context. In practice, E-supply chain causes price variations and results in poor forecasts and hence enhances bullwhip effect. But in present scenario, it has been demonstrated that the application of IT will contribute to the reduction of bullwhip effect in long run. Through qualitative and quantitative analysis, it has been proved that due to better information sharing the bullwhip effect has been reduced which generally increases in case of E-supply chain.

Supply chain literature reveals that study of resilient supply chains and bullwhip effect (BE) have been receiving special attention during pandemic for supply chains with seasonal as well as nonseasonal demand components. The BE phenomenon has been detected in various industries and sectors, and causes multiple inefficiencies such as higher costs of producing more than needed, wastage and transportation costs. As a result, BE forecast is of great importance for academics and supply chain managers. Despite the multitude of studies that have emerged addressing this issue, the impact of the quality of dynamic forecasts on the BE has received limited coverage in the literature. Optimal dynamic forecasts of the demand could allow managers to mitigate the upstream amplification of orders (and thus the BE), as well as reduce unnecessary inventory costs. Order quantity and BE in a supply chain depend on the forecast of the future demand. Usually minimum mean square error (MMSE) forecasts of the future demand are obtained by fitting an appropriate seasonal auto-regressive moving average (ARMA) time series model. However, a major drawback of the MMSE forecasting method is that it does not provide the associated risk forecasts. In this paper, a simple yet effective machine learning demand forecasting approach without fitting any time series model is presented.Specifically, a novel data driven machine learning algorithm that bypasses traditional forecasting steps and allows forecast weights to be optimized by minimizing the one-step ahead forecast error sum of squares (FESS) is proposed. A novel stability metric of a supply chain is proposed as the risk adjusted forecast of the future demand. It is shown that the risk adjusted forecasts can be used to check whether a given supply chain is resilient. In order to be more resilient and competitive in the current market, business leaders around the world agree that it is necessary to modernize and make major changes to their supply chain strategies. Demand risk forecasts obtained by the proposed machine learning approach allow supply chain managers to enhance the forecasting power of the order quantity and construct more resilient supply chains. The performance of proposed approach is evaluated through numerical experiments using simulated data and weekly demand data of two products. The results show that the performance of the proposed forecasts and risk adjusted forecasts of the future demand are better than the commonly used MMSE forecasts of the future demand.

An important phenomenon in supply chain management which is known as the bullwhip effect suggests that demand variability increases as one moves up a supply chain. This paper contrasts the bullwhip effect for a two-stage supply chain consisting of one supplier and two retailers under three forecasting methods based on the market share. We can quantify the correlation coefficient between the two retailers clearly, in consideration of market share. The two retailers both employ the order-up-to inventory policy for replenishments. The bullwhip effect is measured, respectively, under the minimum mean squared error (MMSE), moving average (MA), and exponential smoothing (ES) forecasting methods. The effect of autoregressive coefficient, lead time, and the market share on a bullwhip effect measure is investigated by using algebraic analysis and numerical simulation. And the comparison of the bullwhip effect under three forecasting methods is conducted. The conclusion suggests that different forecasting methods and various parameters lead to different bullwhip effects. Hence, the corresponding forecasting method should be chosen by the managers under different parameters in practice.

The main goal of our research is to analyze and display causes of a bullwhip effect formation within a supply chain, as well as to provide the appropriate solutions to limit the occurrence of the bullwhip effect by using the proper information flow and partners’ cooperation within the supply chain. The bullwhip effect is one of the most important issues in the supply chain management and it is present in many companies. It preserves a character of invisibility because there are lots of causes for its formation and they are usually difficult to discern. The bullwhip effect is a phenomenon of an increase in the order variability within a supply chain. The higher we are within the supply chain, the higher is the order variability. The company encountered with the whip effect can successfully reduce its impact by improving the information flow, as well as improving partners’ cooperation within the supply chain. In this way the company can limit its negative repercussions and increase the profit. The article focuses on the overview of the bullwhip effect within a distribution chain, from its causes to suggestions and measures how to ease its negative repercussions on the organisation. Part of the causes could be found in the market demand variability and in the lack of communication about the actual marked demand within the supply chain. The rest of the causes are related to obstacles that emerge among different partners within the supply chain (role of culture). A qualitative analysis is applied on the basis of the selected cognitions from the supply chain management. The quantitative analysis is based on the theoretical research of the effective flow of information among the participants and its contribution to the reduction of the bullwhip impact. The article discusses two research questions: 1) The correct information flow within the supply chain and the improvement of the communication among partners can lead to the bullwhip effect reduction, and 2) A reduction of the bullwhip influence can lead to the increase of cooperation among partners. The results of the analysis can be used for further research.

Purpose The purpose of the study is to analyze and measure the impact of disruption in demand which causes the bullwhip effect. The bullwhip effect impacts the performance of firm. Just like everything else, covid has had an impact on the disruption of supply chain too leading to the need of measuring the bullwhip effect of select Indian sectors. The comparison on bullwhip effect is drawn in pre- and during covid era in major sectors. The study helps to understand, analyze and measure the impact of covid and its challenges to supply chain. Design/methodology/approach The empirical study is carried out on five major select Indian sectors which have the largest market capitalization in Indian economy, namely, FMCG (fast-moving consumer goods), automobile, utility, consumer durable and IT (information technology). The disruption in the supply chain is measured in terms of bullwhip effect. The novel metric ratio of bullwhip effect is computed which is based on demand–supply mismatch and analyzed based on 10 years of observations. The data is analyzed twice, first from 2011 to 2019 (pre-covid era) and second from 2019 to 2021 (during covid era). Each time, Bombay Stock Exchange (BSE) sectoral indices are used to compute the bullwhip ratio, and empirical data is collected using Prowess. The firms listed in BSE represent most of the sector. Such panel data helps us to analyze inter- and intraindustry bullwhip effect. The changes in the bullwhip effect for various BSE listed firms are analyzed pre- and during covid era. These changes are specifically studied at the manufacturer end of the supply chain. Later regression analysis is performed to study the changes required in production based on the demand. The various strategies that cause or mitigate the impact of covid in intraindustry can be derived from the study. The disruption in production is analyzed based on the disruption in demand and profit before interest and tax (PBIT). Findings In pre-covid era, the percentage of demand disruption was low in select sectors but not exactly zero. Covid caused the disruptions in supply chain across the globe which resulted in bullwhip effect in Indian sectors too. Yet some of the sectors were able to cope better with the situation as compared to others. In the present study, same is analyzed statistically, and results are derived for practical significance. Research limitations/implications The empirical data is having the observations of past 10 years to analyze the pattern of demand disruption in the firms and hence the sectors. The impact of covid is studied on performance, which is analyzed in terms of PBIT. The impact of other factors (political, social, marketing policies, etc.) that may cause disruption in the supply chain of a firm is not considered in the study. Originality/value Study is unique, as it measures disruption and provides a peerless way to study the inter- and intrasectors. To analyze the impact of bullwhip effect on sector performance, it is very much required to first measure the bullwhip; this measure of bullwhip as a ratio of the slopes of demand and supply is a novel approach. The study emphasizes that the impact of covid is not the same among the firms, and hence among the sectors. Also, it is found that the impact of such adversities can be mitigated, and performance of firm can remain intact in turbulent times too.

The bullwhip effect in closed-loop supply chains: A systematic literature review

In today’s world, all enterprises in a supply chain are attempting to increase both their and the supply chain’s efficiency and effectiveness. Therefore, identification and consideration of factors that prevent enterprises to attain their expected/desired levels of effectiveness are very important. Since bullwhip effect is one of these main factors, being aware of its reasons help enterprises decrease the severity of bullwhip effect by opting proper decisions. Now that forecasting method is one of the most important factors in increasing or decreasing the bullwhip effect, this chapter considers and compares the effects of various forecasting methods on the bullwhip effect. In fact, in this chapter, the effects of various forecasting methods, such as Moving Average, Exponential Smoothing, and Regression, in terms of their associated bullwhip effect, in a four echelon supply chain- including retailer, wholesaler, manufacturer, and supplier- are considered. Then, the bullwhip effect measure is utilized to compare the ineffectiveness of various forecasting methods. Owing to this, the authors generate two sets of demands in the two cases where the demand is constant (no trend) and has an increasing trend, respectively. Then, the chapter ranks the forecasting methods in these two cases and utilizes a statistical method to ascertain the significance of differences among the effects of various methods.

Trust is essential in supply chain management, and it maintains relationships among members in a supply chain. Moreover, trustworthiness influences an individual’s behavior, which is related to trust-building. Therefore, this study aims to investigate trust and trustworthiness in a three-echelon supply chain based on the bullwhip effect, trust diffusion, and suppliers’ production adjustment. This study conducts experiment involving 36 participants. Two tasks are performed with unknown and known (high/low) partners’ trustworthiness levels. The results present three findings: a) The bullwhip effect could be controlled to a lower level when partners in a supply chain were with high trustworthiness but not those with low trustworthiness. b) Trust diffusion is observed in both high and low trustworthiness cases, which could be useful for developing long-term trust relationships. c) Suppliers are more likely to distrust low trustworthiness partners and adjust their production strategies relative to the high trustworthiness partners. To sum up, this study establishes a connection between trust and the bullwhip effect through experimental results. Additionally, this suggests that trustworthiness is vital information in a supply chain for enhancing performances, especially for upstream members, which is one of the critical elements for information sharing in a supply chain.

PurposePresent study deals with the most discussed rather than addressed yet still an unsolved problem of supply chain known as the bullwhip effect. Operational variables affecting the bullwhip effect are identified and their role in causing the bullwhip effect has been explored using artificial neural networks. The purpose of this study is to analyze the impact of identified operational reasons that affect the bullwhip effect and to analyze the bunch of variables that are more prominent in explaining the phenomenon of the bullwhip effect.Design/methodology/approachTen major sectors of the Indian economy are analyzed for the bullwhip effect in the present study, and the operational variables affecting the bullwhip effect in these sectors are identified. The bullwhip metric is developed as the ratio of variance in production to the variance in the demand. The impact of identified operation variables on the bullwhip effect has been discussed using the artificial neural network technique known as multilayer perceptron. The classification is also performed using neural network, logistic regression and discriminant analysis.FindingsThe operation variables are found to be varying with respect to sectors. The study emphasizes that analyzing the right set of operation variables with respect to the sector is required to deal with the complex problem, the bullwhip effect. The operational variables affecting the bullwhip effect are identified. The classification result of the neural network is compared with those of the logistic regression and discriminant analysis, and it is found that the dynamism present in the bullwhip effect is better classified by neural network.Research limitations/implicationsThe study used 11 years of observations to analyze the bullwhip effect on the basis of operational variables. The bullwhip effect is a complex phenomenon, and it is explained on the basis of an extensive set of operational variables which is not exhaustive. Further, the behavioral aspect (bullwhip because of decision-making) is not explored in the present study.Practical implicationsThe operational aspect plays a gigantic role to explain and deal with the bullwhip effect. Strategies to mitigate the bullwhip effect must be in accordance with the operational variables impacting the sector.Originality/valueThe study suggests a novel approach to study the bullwhip effect in supply chain management using the application of neural networks in which operational variables are taken as predictor variables.