Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters

A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems

In a supply chain, the distorted demand information when it goes upstream is commonly known as the bullwhip effect. In this paper, the impact of demand substitution on the bullwhip effect in a two-stage supply chain is investigated. In our model, a single retailer observes inventory levels for two products, among which product 1 can be used to substitute product 2. The retailer places orders to a single manufacturer following an order-up-to inventory policy and uses a simple moving average forecasting method to estimate the lead-time demand. The customers' demands are modeled by an autoregressive process. By analyzing the bullwhip effect in such settings, quantitative relations between the bullwhip effect and the forecasting method, lead time, demand process, and the product substitution are obtained. Numerical results show that demand substitution can reduce the bullwhip effect in most cases.

In this paper, the bullwhip effect in a multistage supply chain has been analyzed using double moving average and double exponential smoothing forecasts. We find out that double exponential smoothing and double moving average forecasts can sometimes have a variance reducing effect on the supply chain. In particular, the bullwhip effect exists for positively correlated or independent and identically distributed (i.i.d) demands. However, for negatively correlated demands, bullwhip effect does not necessarily exist. In addition, our analysis shows that the order variance based on simple moving average forecast is always larger than the order variance based on double moving average forecast, and the order variance based on simple exponential smoothing forecast is always larger than the order variance based on double exponential smoothing forecast. For the independent and identically distributed (i.i.d) demands , it can be shown that the order quantity fluctuation range for double exponential smoothing is greater than that for double moving average.

Superior supply chains are one of the best ways to compete in today's marketplaces.In Supply Chain Management, overall supply chain evaluation needs to include an important logistical effect known as the Bullwhip Effect.It shows how small changes at the demand end of a supply chain are progressively amplified up the supply chain.Production plans are based on demand forecasting and suppliers not only react on changed demand, they adapt the level of safety stock (variation of stocks and orders increases).In this paper two special situations in a four-stage supply chain are studied: i) stable demand with a single 5 % change in demand (with application of four different stock keeping policies), and ii) changing demand with alternating 5 % changes in demand (up and down, with another three stock keeping policies).The results of spreadsheet simulations are shown in tables and charts.Increasing variability of production orders and stock levels up the supply chain is evident.The Bullwhip Effect is measured by the standard deviation of orders.The comparison of the results shows that the Bullwhip Effect can be partially reduced by appropriate stock keeping policy.

A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment

Measure of bullwhip effect in supply chains with first-order bivariate vector autoregression time-series demand model

The impact of customers’ MD&A sentiment on the bullwhip effect: A dyadic buyer-supplier perspective

Quantifying the bullwhip effect in supply chains

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.

Reducing the bullwhip effect is of the main challenges in the field of supply chain management (SCM). One of the solutions is to implement SCM software for collaborating within every upstream and downstream supply chain members effectively. However, there is a lack of measurement models to show the relationship between changes of the bullwhip effect and the implementation of SCM software. Therefore, this study proposed an innovative model for measuring the bullwhip effect under the implementation of supply chain management software. Moreover, an empirical four-stage supply chain case in Taiwan is taken for numerical analysis. The results show that a higher implementation rate of supply chain management software in the supply chain, the more improvements of the bullwhip effect will make. Furthermore, it also found that under the same software implementation rate in better performed and worse performed supply chains, the worse one can make more notable improvements for the bullwhip effect.

PurposeThis study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.Design/methodology/approachThe methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.FindingsThe study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.Research limitations/implicationsThis study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.Practical implicationsThe study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.Social implicationsDigital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.Originality/valueThe study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.

The Bullwhip Effect (BWE) occurs when orders made to suppliers have a bigger variation than sales to the buyer. This is a major concern that businesses are working on eliminating, as it has numerous side effects, such as excessive inventory, stock-outs, insufficient production, increased costs, etc. The new study adds to the literature by demonstrating how to quantify and assess the bullwhip impact on any supply chain. The results show that when consumer demand is unstable, the BWE is magnified. This was achieved by using the proposed formula which was based on an explanation and graph of the traditional BWE. A stochastic simulation based on a case study that replicates the behavior of a generic supply chain in a real-world market was used to evaluate the formula.

The supply chain design plays a key role in product availability in normal operations. The process of availability of stock level in running a business is necessary to attract and retain customers. The components or the channel partners in supply chain are responsible for calculating forecasted demand for the products. The overestimation as well as underestimation of the forecasted demand by each channel partners in the supply chain may result in the phenomenon of bullwhip effect in the supply chain. Vegetable, fish, and meat supply chains, which are the highly perishable products of daily life use, are to some extent different from that of the non-perishable products’ supply chains. Thus, the channel partners involved in the supply chain of vegetable, fish, and meat also forecast the different stock levels of the respective categories. In some cases, there are excess or shortfall in the availability of such items. This study attempts to comprehend and emphasize the phenomenon of the bullwhip effect in the category of fish and vegetable supply chain. The study further assesses the underlying factors responsible for the bullwhip effect. The study concludes with major suggestions for eliminating such bullwhip effect.

The competition in the twenty-first century is not the competition between enterprises, but the competition in the supply chain. “Bullwhip effect” is a common phenomenon in supply chain management. With the development of economy and the intensification of competition, the structure of supply chain is increasingly complex. Therefore, how to eliminate and effectively prevent the harm of “Bullwhip effect” has become a problem that people pay special attention to. The objective of this study is to provide a new way of using agility to prevent bullwhip effect. Through the combination and application of a variety of analytical methods, the key criteria of agility are ranked, so that enterprises can control these factors in priority, and formulate strategies in advance, so as to improve the agility of enterprises to prevent the negative impact brought by the bullwhip effect, and find the optimal solution to the “bullwhip effect.” Taking a Guangdong Yinglian Technology Company as an example, this study found that the best way to reduce the “bullwhip effect” of the supply chain is to improve customer satisfaction, and secondly to improve employees’ work capabilities.KeywordsAgilityBullwhip effectQFD

In this paper, the supply chain of manufacturing enterprises under service transformation is analyzed first in detail. Second, the supply chain model is constructed by means of system dynamics simulation. Then, the characteristics of bullwhip effect of supply chain in service transformation are studied through simulation. Finally, on the basis of simulation analysis, this paper further proposed the countermeasures of bullwhip effect in supply chain under service transformation.