Bullwhip Effect Research Articles

Bullwhip Effect – Logistic Stability Examination in Serial and Arborescent Topologies with Demand Uncertainty and Delay

The paper analyzes the formation of the bullwhip effect in logistic systems as a significant threat to preserving stability in the face of non-negligible goods transport delay and uncertainty of demand and stock records. The popular order-up-to policy is selected as the method governing the goods flow. A dynamic model of entity interaction is constructed and examined, first, analytically, then in numerical tests for various scenarios of practical significance, e.g., a supply chain with external and local demand signals or real-world European goods distribution system. It has been found that the order-up-to policy does not trigger the bullwhip effect despite the delays in the goods delivery in the nominal operating conditions in supply chains. However, in networked environments, even the basic configuration triggers the bullwhip effect.

Consumer behavior during the COVID-19 pandemic: An analysis of food purchasing and management behaviors in U.S. households through the lens of food system resilience

The COVID-19 pandemic has stimulated considerable interest in the resilience of the U.S. food system. Less attention has been paid to the resiliency characteristics of the final link in the food system – individual households. We use national survey data from July 2020 to understand the food acquisition, preparation, and management strategies that households implemented in response to the pandemic. We find a substantial increase in the amount of food prepared and consumed at home which scales with respondents’ time availability, perceived risks of dining out, and pandemic-induced income disruption. We then identify several household responses to support this increase in home food consumption that are in line with practices suggested to enhance resiliency at other links in the food supply chain, including increased cold storage capacity and enhanced in-house capability via improved cooking and food management skills. We discuss how responses such as improved food skills can reduce the propagation of shocks through the supply chain by allowing greater flexibility and less waste, while actions such as increased home cold storage capacity could undermine system resilience by exacerbating bullwhip effects, i.e., amplifying consumer demand shocks that are propagated to upstream food supply chain actors.

Measuring and eliminating the bullwhip in closed loop supply chains using control theory and Internet of Things

Closed-loop supply chains are complex systems as they involve the seamless backward and forward flow of products and information. With the advent of e-commerce and online shopping, there has been a growing interest in product returns and the associated impact on inventory variance and the bullwhip effect. In this paper, a novel four-echelon closed-loop supply chain model is presented, where base-stock replenishment policies are modelled by means of a proportional controller. A stochastic state-space model is implemented, initially to capture the supply chain dynamics while the model is analysed under stationarity conditions with the aid of a covariance matrix. This allows the bullwhip effect to be expressed as a function of replenishment policies and product return rates. Next, an optimisation method is introduced to study the impact of the Internet of Things on inventory variance and the bullwhip effect. The results show that the Internet of Things can reduce costs associated with inventory fluctuations and eliminate the bullwhip effect in closed-loop supply chains.

An Un-Supervised Approach for Backorder Prediction Using Deep Autoencoder

Background: Backorders are an accepted abnormality affecting accumulation alternation and logistics, sales, chump service, and manufacturing, which generally leads to low sales and low chump satisfaction. A predictive archetypal can analyze which articles are best acceptable to acquaintance backorders giving the alignment advice and time to adjust, thereby demography accomplishes to aerate their profit. Objective: To address the issue of predicting backorders, this paper has proposed an un-supervised approach to backorder prediction using Deep Autoencoder. Methods: In this paper, artificial intelligence paradigms are researched in order to introduce a predictive model for the present unbalanced data issues, where the number of products going on backorder is rare. Results: Un-supervised anomaly detection using deep auto encoders has shown better Area under the Receiver Operating Characteristic and precision-recall curves than supervised classification techniques employed with resampling techniques for imbalanced data problems. Conclusion: We demonstrated that Un-supervised anomaly detection methods specifically deep auto- encoders can be used to learn a good representation of the data. The method can be used as a predictive model for inventory management and help to reduce the bullwhip effect, raise customer satisfaction as well as improve operational management in the organization. This technology is expected to create the sentient supply chain of the future - able to feel, perceive and react to situations at an extraordinarily granular level.

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.

Research on Bullwhip Effect Management in Supply Chain Based on System Dynamics

Since the development of the Beer games in the last century, beer games and the bullwhip effect they reflect have attracted extensive attention and discussion from scholars. This paper will explore the causes of bullwhip effect, includes oscillation, amplification effect, and phase lag in supply chain management. With the help of system dynamics, the beer game model is constructed and the bullwhip effect is visually observed. From the perspective of sup-ply chain structure and management, this paper makes a comparative analysis of the inventory of supply chain management strategies, thus finding the direction of supply chain management optimization-accelerating flow, and helping supply chain managers and practitioners to develop-mental models.

Bullwhip Effect Reduction Using Vendor Managed Inventory (VMI) Method in Supply Chain of Manufacturing Company

Prediction of the amount of production can be done by forecasting demand and using appropriate methods. The supply chain studied at ABC company consists of manufacturing (vendors) and sales offices. Initially forecasting is done at each level of the supply chain with different forecasting methods. Therefore, uniform forecasting methods are needed for each supply chain actor. Based on testing the forecasting method conducted, the Winter’s Method. The model is used to link forecasting and implementation using the Vendor Managed Inventory (VMI) approach. Inventory planning in the supply chain cannot be done individually and must be thought of as a coordinated system. Inventory control is done by using the optimal lot calculation, namely Economic Order Quantity (EOQ). Based on demand forecasting and optimal lot determination, it can be calculated the value of the Bullwhip effect that occurs after the use of VMI in the supply chain has changed from 1.359 to 0.514 at the Manufacturing level.

ANALISIS MODEL MANAJEMEN PERMINTAAN SCM NETWORK DAN PERAMALAN PERMINTAAN PADA PENJUALAN BUSANA MUSLIM MENGGUNAKAN METODE LINEAR REGRESSION

Supply chain management is very important for a company because it will affect supply performance in the company. Doing business in this era has many challenges that must be faced, especially in the Muslim clothing business. The way to stabilize the demand diagram of the Muslim clothing business, retailers are required to manage the supply chain so that they can meet the total demand. The object of this research is Rabbani Cirebon which was obtained from a literature study published in a journal entitled "Trend of Muslim Lifestyle Changes" from Banjarmasin State Polytechnic. The journal has sales data based on product types from monthly in 2016. From this data will be processed and analyzed using data analysis techniques. This data analysis technique uses time series forecasting data analysis techniques. From this time series method, this research uses moving average and linear regression. After modeling the data, the forecast error is measured using MAD, MAPE, RMSE, and MSE. The overall MSE results were 103731.8 and RMSE 322.0743. The benefit of demand forecasting is to reduce the Bullwhip Effect, plan future resources, for example, such as stock management, place control, product distribution, and demand for raw materials so as to make the right decisions. The results showed that the linear regression method has better forecasting than the moving average because linear regression has a smaller error rate than the moving average. But even so, the error rate of this study is still very large, so it is necessary to do more research to minimize the error rate.

Nonlinear analysis and active management of production-distribution in nonlinear supply chain model using sliding mode control theory

This paper deals with system dynamics approach for dynamical behaviors and control synthesis of supply chain system by utilizing three-stage production-distribution model. The presented approach offers systematic tools for determining fundamental relationships between multi-echelons in the supply chain dynamics by using eigenvalues, bifurcation, and time history investigation. By exploring system dynamics on time series analysis, it is found that system performance has suffered severely from the bullwhip effect under impacts of model uncertainties and perturbed demand. The novel fractional-order sliding mode control algorithm has been presented based on adaptation mechanism, ensuring that the shipment flows are robustly stable in supply chain networks against disruptions. This is a smarter way of getting sufficient strength to sustain existing competitive market for mitigating the risks and improving the supply chain performance. The system stability has been thoroughly analyzed by using Routh-Hurwitz criterion and Lyapunov theory. Extensive numerical simulations have been conducted to obtain insights into the system behaviors and to validate effectiveness of active control policies by matching the shipment sent to customer demand, ensuring supply chains resilience. Finally, it is found that the presented approach can help decision-makers develop more efficient supply chain management system against severe market disruptions.

Measuring the bullwhip effect with market competition among retailers: A simulation study

By considering the market competition among retailers, the object of this study is to measure the bullwhip effect in a two-stage supply chain with one supplier and multiple retailers. A model is detailed for measuring the bullwhip effect in which multiple retailers exhibit AR(1) demand processes and the degree of market competition is captured with copula. Through the model developed in this paper, we use simulation methods to study the influence of market competition on the bullwhip effect. The simulation results show that the market competition has a significant impact on bullwhip effect. The influence of the types of copulas on the bullwhip effect remains stable as the lead time increases. The supply chain structure has an impact on the bullwhip effect. The results of the empirical application show that a suitable copula helps to improve prediction accuracy for the bullwhip effect. Moreover, we discuss how our study can be applied to bullwhip effect management.

Suboptimal sliding manifold For nonlinear supply chain with time delay

In this paper, a basic dynamic model of the supply chain system is constructed in which the oscillation problems caused by the time delay in remanufacturing, ordering, the disturbance of the system parameters are considered along with the customers’ demand estimation. Time delay in production systems affects the efficiency of supply chain and lead to the bullwhip effect. This paper proposes a new method according to the model structure for controlling the bullwhip effect based on state dependent Riccati equation (ESDRE) and designing suboptimal sliding manifolds for a nonlinear supply chain in the presence of input and state delays. A switching control scheme is obtained based on the designed suboptimal sliding manifold. It is proved that this control scheme can guarantee that the nonlinear supply chain system is asymptotically stable and understand soft switching among subsystems of the nonlinear supply chain to mitigate fluctuations in the system variables. The efficiency of suboptimal sliding manifold method was examined by comparing LQR method in the presence of various time delays. Also, simulation investigation in real supply chain proved this efficiency.

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.

Artificial intelligence-based inventory management: a Monte Carlo tree search approach

The coordination of order policies constitutes a great challenge in supply chain inventory management as various stochastic factors increase its complexity. Therefore, analytical approaches to determine a policy that minimises overall inventory costs are only suitable to a limited extent. In contrast, we adopt a heuristic approach, from the domain of artificial intelligence (AI), namely, Monte Carlo tree search (MCTS). To the best of our knowledge, MCTS has neither been applied to supply chain inventory management before nor is it yet widely disseminated in other branches of operations research. We develop an offline model as well as an online model which bases decisions on real-time data. For demonstration purposes, we consider a supply chain structure similar to the classical beer game with four actors and both stochastic demand and lead times. We demonstrate that both the offline and the online MCTS models perform better than other previously adopted AI-based approaches. Furthermore, we provide evidence that a dynamic order policy determined by MCTS eliminates the bullwhip effect.

Empirical Assessment of Bullwhip Effect in Supply Networks

Previous empirical studies on bullwhip effects treat each industry or firm as isolated from its supply chain network. In this paper, the authors are interested in the role played by supply chain relational connection in moderating how demand variability signal is transmitted upstream. The paper conducts an empirical study based on a panel data of 55 manufacturing industries and 9 wholesale industries. The regression analysis shows that demand variability is propagated through supply chain upward and the transmission is influenced by the structural relationship between suppliers and customers, which is measured by customer-base concentration and customer interconnectedness. On the other hand, customer demand variability has a greater impact on industries with less concentrated customer base or with less interconnected customers.

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 impact of information visibility on ordering dynamics in a supply chain: a behavioral perspective

AbstractPrevious research on the Bullwhip Effect shows that information visibility—Point‐Of‐Sale (POS) data or supply‐chain partner‐inventory data—can reduce the amplification of orders in a supply chain. This study compiles and analyzes the data from two previous experiments with the beer game (Croson and Donohue, 2003, 2006) to gain insight on the specific mechanisms that decrease order amplification. By structuring the data as a panel and using a fixed‐effects estimation model, we find that additional supply‐chain‐level information (e.g., POS and inventory data) leads subjects to react less aggressively to changes in their own inventory and to pay more attention to the supply line of orders placed. Furthermore, our analysis shows that such effects are more pronounced for upstream subjects. Our findings provide insight into the role that additional supply‐chain information play on subjects' orders in the beer game. © 2021 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

A system dynamics model for the impact of capacity limits on the Bullwhip effect (BWE) in a closed-loop system with remanufacturing

Every organisation has an upper limit to the number of orders or products that it can manufacture or remanufacture per unit time. In reverse logistics operations, capacity limits can lead to inefficiencies in the remanufacturing process. In this paper, comparisons were made of the Bullwhip effect (BWE) in closed-loop systems that have collection and remanufacturing capacity limits and those that do not. Collection and remanufacturing capacity limits were introduced for a system where a company had to collect ‘enough’ products before remanufacturing can begin. This introduced collection backlogs, remanufacturing backlogs and remanufacturing downtimes to the closed loop supply chain (CLSC). By adopting a systems dynamics approach, the research performed ‘what-if’ analyses of the closed-loop system under different levels of the factors under investigation. Two case studies were investigated: one remanufacturing electric vehicle batteries (low demand, slow moving item) and the other remanufacturing kitchen appliances (high demand, fast moving item). Firstly, introducing collection and remanufacturing capacity limits in the reverse chain increased the BWE to a level higher than the reverse chain without any capacity limits, but not to the level of the forward chain without any product returns. Secondly, introducing collection and remanufacturing capacity limits for a closed-loop system where a company had to collect ‘enough’ products before remanufacturing begins had different impacts depending on the product demand size and speed. The presence of external returns by other parties not regulated by an organisation had an impact of lowering the BWE in the closed-loop system and it also impacted how the other factors under investigation affected the Bullwhip effect. These findings were used to provide managerial insights for organisations venturing into reverse logistics.

Coping with the Bullwhip Effect Using Inventory or Cash: An Environment-Behavior-Performance Paradigm

The bullwhip effect is widely found in business and exerts adverse effects on business activities. To investigate the influence of the bullwhip effect on firms’ performance and their responses, this study proposed an environment-behavior-performance analysis framework and offered a new perspective for studying the bullwhip effect. Using data collected from 1,734 listed manufacturers in China from 2002 to 2017, we adopted regression models to test the proposed model and conducted a series of robustness tests. We find that the bullwhip effect is positively related to operating risk, inventory, and cash holdings, and the moderate levels of inventory and cash are negatively associated with operating risk. Specifically, inventory and cash play different roles and work together to alleviate operating risk induced by the bullwhip effect. However, excess resource holdings are positively associated with operating risk. Therefore, firms with different levels of resources should hold suitable levels of inventory, cash, or both as contingent responses to the bullwhip effect.

Evolutionary Game Analysis of Information Sharing in Fresh Product Supply Chain

Fresh produce has increasingly become an important part of people’s diet. However, the loss of fresh produce in the supply chain has existed for a long time and is difficult to overcome. Some companies use their own information management systems or use information systems built by other companies to release and manage fresh agricultural product information in a timely manner, thereby reducing product loss caused by the “bullwhip effect.” However, this will also bring pressure on construction costs and the risk of information leakage. Based on the evolutionary game model, this paper conducts process modeling and analysis on the behavior of enterprise groups participating in information sharing. It is concluded that the greater the difference between the income obtained through information sharing and the cost of building information system, the higher the likelihood of enterprises participating in information sharing. In addition, the greater the profit from the construction of information platform, the smaller the profit of “free rider,” and the smaller the risk of information leakage, the greater the enthusiasm of enterprises to participate in information sharing. Finally, some suggestions are proposed from the perspective of maximizing supply chain benefits.

Optimization and Simulation of Dynamic Performance of Production–Inventory Systems with Multivariable Controls

The production–inventory system is a problem of multivariable input and multivariant output in mathematics. Selecting the best system control parameters is a crucial managerial decision to achieve and dynamically maintain an optimal performance in terms of balancing the order rate and stock level under dynamic influence of many factors affecting the system operations. The dynamic performance of the popular APIOBPCS model and the newly modified 2APIOBPCS model for optimal control of production–inventory systems is examined in the study. This examination is based on the leveled ground with a new simulation scheme that incorporates a designated multi-objective particle swarm optimization (MOPSO) algorithm into the simulation, which enables the optimal set of system control parameters to be selected for achieving the situational best possible performance of the production–inventory system under study. The dynamic performance is measured by the variance ratio between the order rate and the sales rate related to the bullwhip effect, and the integral of absolute error related to the inventory responsiveness in response to a random customer demand. Our simulation indicates that the 2APIOBPCS model performed better than or at least no worse than, and more robust than the APIOBPCS model under different conditions.