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This 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.

The 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.

The 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.

This 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.

The 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.

Digital 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.

The 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.

This 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.

This 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.

Firstly, 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.

Although 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.

This 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.

The 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.

First, 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.

Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.

Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.

The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.

By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.

This study attempts to determine the relative contribution of each of the causes of the bullwhip effect and to identify which causes of the bullwhip effect have relatively significant impacts on the variability of orders in supply chains.

Computer simulation models are developed. A fractional factorial design is used in collecting data from the simulation models. Statistical analyses are conducted to address the research objectives.

When all of the nine possible causes of the bullwhip effect are present in the simulation models, the following six factors are statistically significant: demand forecast updating, order batching, material delays, information delays, purchasing delays and level of echelons. Among these six factors, demand forecast updating, level of echelons, and price variations are the three most significant ones.

Simulation models for the beer distribution game are developed to represent supply chains. Different supply chain structures can be constructed to examine the causes of the bullwhip effect.

In order to mitigate the bullwhip effect, supply chain managers need to share actual demand information and coordinate production and distribution activities with their partners.

This study measures the relative contribution of each of the causes of the bullwhip effect and provides evidence that transparent and accurate information flow and supply chain coordination could be a key to reduce the amplification of demand in supply chains.

This study aims to answer the question of how firms can deal with the great bullwhip effects caused by COVID-19?

An exploratory research method has been adopted and evidence was collected based on 41 online interviews.

The study finds that the bullwhip effect is caused by the sudden changes in customers purchasing behaviour during the pandemic and the businesses’ inaccurate anticipation of the situation. Managing the bullwhip effects caused by COVID-19 requires situation awareness, localisation and an intelligent supply chain. Situation awareness is a vital concept in emergency response, knowing what is going to figure out what should be done. Furthermore, reducing the geographical distances between the firm and other parties in the supply chain, which equates to supply chain localisation, enforces just-in-time inventory. Finally, supply chain digitalisation is no longer an option; implementing such a solution enables end-to-end visibility, collaboration, flexibility and optimisation of orchestration of the supply chain.

This study presents indicators explaining how organisations can deal with the great bullwhip effects caused by COVID-19.

The ongoing outbreak of the COVID-19 pandemic has brought about significant challenges for supply chain management, and this study contributes to the body of knowledge and proposes a model of reducing the bullwhip effects.

The purpose of this paper is to explore whether and how the bullwhip effect, as found in product supply chains, might also manifest itself in services, as well as what policies can be successful for mitigating it.

A combination of analytic methods was used – inductive case analysis and analysis of data from two service supply chains in the telecom industry.

Empirical evidence from two cases was examined and provides support for the presence of a service bullwhip effect. Quantitative and qualitative case data were used to explore how this effect manifests itself in services, the distinctive drivers of the bullwhip effect in services, and the managerial actions that can either trigger or mitigate these bullwhip effects. In total, eight propositions are developed and three types of characteristics that potentially make the bullwhip effect worse in services than in manufacturing are identified: the destabilizing effects of manual rework in otherwise automated service processes; the omission of accurate and timely data on rework volumes upstream in the chain, pointing at future bullwhip effects downstream; and the lack of a supply‐chain mindset within the various departments jointly responsible for delivering the service, leading to longer delays in reacting to service bullwhips as they develop over time.

The research is based on two cases within a single industry, limiting generalizability. The propositions developed need testing in a wider set of contexts, including hybrid service and product supply chains.

The implications of this research can help organizations prevent or reduce the negative impact of planned and unplanned fluctuations in their service supply chains.

This paper explores an area that has been well researched in manufacturing, but not in services, and it contributes to both the theory and practice of service supply chains.

Using a phenomenon known as the bullwhip effect, the authors explore why additional uncertainty in the marketplace can create severe disruptions in global supply chains (GSCs). The purpose of this paper is to analyze related risks in regional vs GSCs during low and high levels of uncertainty. The authors propose and discuss a number of potential implications alongside some tactics that may help mitigate disruptions in some cases before they become terminal problems for the supply chain sustainability.

Monte Carlo simulation is used to generate the conditions of uncertainty and various scenarios that may emerge to challenge GSCs. Vensim software is utilized as a tool for simulation purposes. The authors considered scenarios applicable to manufacturing and retail sectors specifically because of storability property of goods.

Regional supply chains, as opposed to global ones, are more stable and reliable (less risk of disruption) during low and high levels of uncertainty. During uncertain times, upstream suppliers are at greater risk in GSCs. Firms must make strategic decisions that will secure its supply chain functionality and assess the likelihood of such events since many firms entered emerging markets.

Building on internalization theory, it shows that risk and survival are components of decision making that are further complicated by supply chains now operating globally in emerging markets. The paper demonstrates with simulation that GSCs are riskier than regional supply chains in low and high levels of uncertainty, particular as it relates to the bullwhip effect. It also provides recommendations about supply chain restructure and investments in communication improvements to reduce the bullwhip effect in the supply chain.

This paper seeks to describe a conceptualisation of the multiple facets of the bullwhip effect between stocking levels within and between value chains and value systems.

The paper provides a conceptual discussion of the bullwhip effect. It is refined and re‐defined.

The bullwhip effect has usually been explored between inter‐organisational stocking levels. Recently, it has also been explored within intra‐organisational stocking levels. A broader descriptive framework is introduced, one that positions the bullwhip effect construct in intra‐ and inter‐organisational, as well as intra‐ and inter‐channel, stocking levels in and between value chains and value systems.

A research agenda is provided that goes beyond current definitional boundaries and state‐of‐the‐art research of the bullwhip effect.

The refined and re‐defined bullwhip effect is of interest to practitioners. It considers inter‐organisational and intra‐organisational stocking levels. In addition, it considers intra‐ and inter‐channel stocking levels. It is of great concern to achieve best practices in business.

The principal contributions are – a dynamics model of the bullwhip effect construct; a principle of stocking level variability; a typology of stocking level variability; a framework that describes different levels of analysis of the bullwhip effect; and a re‐definition of the bullwhip effect construct – within or between value chains and value systems.

The operational aspects of supply chain, when handled correctly, results in diminishing the impact of the bullwhip effect. The purpose of this study is to analyze the impact of operational and financial variables on the bullwhip effect. Various operational factors that contribute to the bullwhip effect in a supply chain are identified and their impact on variability in production is measured at manufacturer’s end in the supply chain.

Ten different sectors of the Indian economy are identified and analyzed on the basis of bullwhip effect. The ratio of change in production with respect to change in demand is taken as a metric to measure the bullwhip effect. Initially, the impact of identified variables on bullwhip effect is analyzed using the linear regression analysis and then to gain more insights, the threshold regression model is applied according to the change in bullwhip ratio.

The study identifies four threshold regions in which bullwhip ratio is changing its slope considerably. The operational and financial variables impacting bullwhip effect differently in these four regions provide useful insights about how the variables are impacting the bullwhip effect.

Past 11 years of observations on identified operational and financial variables are studied for ten different sectors. The operational and financial variables are identified on basis of available literature but may not be exhaustive in nature.

The present study implies that the emphasis must be given to the magnitude of the bullwhip ratio. Strategies must be adopted that result in mitigation of bullwhip effect. Such mitigation strategies must not only be restricted on the basis of type of product or sector, perhaps they must be on the basis of threshold region of bullwhip ratio.

The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of threshold regression considering the bullwhip ratio as a threshold variable.

A plethora of studies focused on the cause and solutions for the bullwhip effect, and consequently many have successfully experimented to dampen the effect. However, the feasibility of such studies and the actual contribution for supply chain performance are yet up for debate. This paper aims to fill this gap by providing a holistic system-based perspective and proposes a fuzzy logic decision-making implementation for a single-product, three-echelon and multi-period supply chain system to mitigate such effect.

This study uses system dynamics (SD) as the central modeling method for which Vensim® is used as a tool for hybrid simulation. Further, the authors used MATLAB for undertaking fuzzy logic modeling and constructing a fuzzy inference system that is later on incorporated into SD model for interaction with the main supply chain structure.

This research illustrated the usefulness of fuzzy estimations based on experts’ linguistically and logically defined parameters instead of relying merely on the traditional demand forecasting based on time series. Despite the increased complexity of the calculations and structure of the fuzzy model, the bullwhip effect has been considerably decreased resulting in an improved supply chain performance.

This dynamic modeling approach is not only useful in supply chain management but also the model developed for this study can be integrated into a corporate financial planning model. Further, this model enables optimization for an automated system in a company, where decision-makers can adjust the fuzzy variables according to various situations and inventory policies.

This study presents a systemic approach to deal with uncertainty and vagueness in dynamic models, which might be a major cause in generating the bullwhip effect. For this purpose, the combination between fuzzy set theory and system dynamics is a significant step forward.