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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.

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 accurate measurement of demand variability amplification across different nodes in the supply chain, or “bullwhip effect,” is critical for firms to achieve more efficient inventory, production, and ordering planning processes. Building on recent analytical research that suggests that data aggregation tends to mask the bullwhip effect in the retail industry, the purpose of this paper is to empirically investigate whether different patterns of data aggregation influence its measurement.

Utilizing weekly, product-level order and sales data from three product categories of a consumer packaged goods manufacturer, the study uses hierarchical linear modeling to empirically test the effects of data aggregation on different measures of bullwhip.

The authors findings lend strong support to the masking effect of aggregating sales and order data along product-location and temporal dimensions, as well as the dampening effect of seasonality on the measurement of the bullwhip effect.

These findings indicate that inconsistencies found in the literature may be due to measurement aggregation and statistical techniques, both of which should be applied with care by academics and practitioners in order to preserve the fidelity of their analyses.

Using product-weekly level data that cover both seasonal and non-seasonal demand, this study is the first, to the author’s knowledge, to systematically aggregate data up to category and monthly levels to empirically examine the impact of data aggregation and seasonality on bullwhip measurement.

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.

The purpose of this paper is to conduct a large-sample empirical investigation of how relational capital impacts bullwhip at the supplier.

The study uses mandatory disclosures in regulatory filings of US firms to identify a supplier’s major customers and constructs empirical proxies of supply chain relational capital, i.e., length of the relationship between suppliers and customers and partner interdependence. Multivariate regression analyses are performed to examine the effects of relational capital on bullwhip at the supplier.

The findings show that bullwhip at the supplier is greater when customers are more dependent on their suppliers, but is reduced when suppliers share longer relationships with their customers. The results also provide additional insights on several firm characteristics that impact supplier bullwhip, including shocks in order backlog, selling intensity and variations in profit margins. Furthermore, the authors document that the effect of supply chain relationships on bullwhip tends to vary across industries and over time.

The study employs a novel data set that is constructed using firms’ financial disclosures. This large panel data set consisting of 13,993 observations over 36 years enables thorough and robust analyses to characterize supply chain relationships and gain a deeper understanding of their impact on bullwhip.

This research applies the construct of bullwhip effect in a non‐traditional context. It is explored in intra‐organisational echelons. It is argued that the bullwhip effect in a company's inventory management of inbound and outbound logistics flows depends in part upon the gap between the degree of speculation and postponement of business activities. It is also argued that the bullwhip effect is caused by the value adding of business activities in supply chains. The study shows that there is a potential bullwhip effect between companies’ inbound and outbound logistics flows, i.e. two internal stocking levels. A see‐saw model of the bullwhip effect, and a typology of the bullwhip effect in intra‐organisational echelons, are introduced. The term “reversed bullwhip effect” is also introduced. Finally, a model of the bullwhip effect‐scenarios in a dynamic business environment positions these contributions in a wider theoretical and managerial context.

Compares the bullwhip properties of a vendor managed inventory (VMI) supply chain with those of a traditional “serially‐linked” supply chain. The emphasis of this investigation is the comparative impact the two structures have on the “bullwhip effect” generated. Particular attention is paid to the manufacturer's production ordering activities as demonstrated using a simulation model based on difference equations. Documents and considers each of the four important sources of the bullwhip effect in turn. The analysis shows that with VMI implementation two sources of the bullwhip effect may be completely eliminated, i.e. rationing and gaming or the Houlihan effect, and the order batching effect or the Burbidge effect. VMI is also significantly better at responding to rogue changes in demand due to the promotion effect or to price induced variations. However, the effect of VMI on demand signal processing induced bullwhip or the Forrester effect is less clear cut. Concludes that on balance VMI offers a significant opportunity to reduce the bullwhip effect in real‐world supply chains.

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 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.

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).

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.

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.

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.

Information distortion results in demand variance amplification in upstream supply chain members, known as the bullwhip effect, and inventory inaccuracy in the inventory records. As inventory inaccuracy contributes to the bullwhip effect, the purpose of this paper is to investigate the impact of inventory inaccuracy on the bullwhip effect in radio-frequency identification (RFID)-enabled supply chains and, in this context, to evaluate supply chain performance because of the RFID technology.

A simulation modeling method based on hierarchical timed colored petri nets is presented to model inventory management in multi-stage serial supply chains subject to inventory inaccuracy for various traditional and information sharing configurations in the presence and absence of RFID. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results.

The bullwhip effect is increased in RFID-enabled multi-stage serial supply chains subject to inventory inaccuracy. The information sharing supply chain is more sensitive to the impact of inventory inaccuracy.

Information sharing involves collaboration in market demand and inventory inaccuracy, whereas RFID is implemented by all echelons. To obtain the full benefits of RFID adoption and collaboration, different collaboration strategies should be investigated.

Colored petri nets simulation modeling of the inventory management process is a novel approach to study supply chain dynamics. In the context of inventory errors, information on RFID impact on the dynamic behavior of multi-stage serial supply chains is provided.