Revisiting the bullwhip effect: how can AI smoothen the bullwhip phenomenon?

Abstract

Purpose
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.

Design/methodology/approach
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.

Findings
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.

Originality/value
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.

Full text

Revisiting the bullwhip effect:
how can AI smoothen
the bullwhip phenomenon?
Eric Weisz
Institute for Transport and Logistics Management,
Vienna University of Economics and Business, Vienna, Austria
David M. Herold
Australian Centre for Entrepreneurship Research, School of Management,
Queensland University of Technology, Brisbane, Australia and
Institute for Transport and Logistics Management,
Vienna University of Economics and Business, Vienna, Austria, and
Sebastian Kummer
School of Management, Jilin University, Changchun, China
Abstract
Purpose –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.
Design/methodology/approach –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.
Findings –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.
Originality/value –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.
Keywords Bullwhip effect, Supply chain, Artificial intelligence, Literature review
Paper type Literature review
1. Introduction
The bullwhip effect and its implications on supply chains have been studied by numerous
academics, but it still represents one of the most contemporary operational and logistics
problems in supply chain research (Forrester, 1961;Wang and Disney, 2016). In simple terms,
the bullwhip effect refers to amplifications in orders along the supply chain, meaning that
small variances in customer demand lead to increasing oscillations in the supply chain when
moving upstream (Yang et al., 2021). In other words, a bullwhip effect occurs due to a lack of
coordination along the supply chain, as members pursue individual strategies instead of
seeing the supply chain as a single unit, which not only leads to a “distortion”(Lee et al.,
1997b) of demand and related orders, but also results in significant negative operational and
financial impacts for businesses and organizations (Pournader et al., 2021).
However, as businesses rely on global and increasingly complex supply chains to satisfy
their customers, they regard the supply chain as “an integrated process which includes all
AI
smoothening
the bullwhip
effect
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0957-4093.htm
Received 24 February 2022
Revised 17 May 2022
7 July 2022
9 November 2022
12 December 2022
Accepted 19 December 2022
The International Journal of
Logistics Management
© Emerald Publishing Limited
0957-4093
DOI 10.1108/IJLM-02-2022-0078

activities associated with the flow and transformation of goods from raw materials stage
through to the end user”(O’Donnell et al., 2006, p. 1523). In the past, the respective supply
chain stages and its related operational processes were often managed independently in their
own organizational structures, but technological shifts and the use of artificial intelligence
(AI) question the self-sufficient approach and reinforce the interdependence along the supply
chain. We argue that the use of AI, which refers to systems capable to perform tasks usually
associated with human intelligence such as learning or problem solving (Nilsson, 1971;
Pournader et al., 2021), has a significant impact on the way how information, goods and
financial flows are managed in an integrated supply chain (Herold et al., 2021a;Pournader
et al., 2021;Raisch and Krakowski, 2021).
While initial AI applications focused on the automation of routine tasks, the technological
advances in new machine learning techniques, big data availability and the exponential
increase of computational power allow organizations today to apply AI-based solutions to
complex management problems that was originally reserved for human intelligence
(Brynjolfsson and Mcafee, 2017). Scholars argue that the management of AI is unlike
information technology (IT) management in the past and the associated machine learning
technologies “have greater autonomy, deeper learning capacity, and are more inscrutable than
any of the intelligent IT artifacts that have come before”(Berente et al., 2021, p. 1433).
In particular, current AI technologies, including facial recognition, autonomous vehicles, robots
and natural language processing are developed and adopted in and for various problem
domains with more than half of firms implementing these new technologies in some form in
their supply chain processes (Balakrishnan et al.,2020). As a consequence of this fusion and
interconnection between data and knowledge in the cyber- and physical space, the “information
environment surrounding AI development has changed profoundly”(Pan, 2016, p. 410).
In fact, studies show that managers are increasingly using AI to tackle operational
problems along the supply chain (Baryannis et al., 2019;Sharma et al., 2022). For example, the
use of AI leads to more flexible, reliable and accurate forecasts compared to classic forecast
techniques, in particular in the area of demand forecasting which can help to reduce demand
variability and thus the bullwhip effect (Jaipuria and Mahapatra, 2014;Prakash and Pandey,
2014). In other words, AI can be seen as enabler to enhance current forecasting techniques
and to better react to changing environmental conditions by integrating multiple sources
such as vacations, weather, life cycle phases or seasonality (e.g. Helo and Hao, 2021;Kiefer
et al., 2019;Singh and Challa, 2016).
AI and its methods of machine learning and deep learning in combination with big data
analysis techniques provides an opportunity to smoothen the bullwhip effect in supply chains
in ways that is different to former technologies. In particular, AI in and for supply chains
differentiates itself from known and existing technologies in three ways: First, the information
environment has changed, i.e. sensing and wearable devices enable networks and data
transfer among groups and individuals resulting in aggregated knowledge and capabilities in
a ternary space [cyber, physics and human society or (CPH)] (Balakrishnan et al., 2020;Pan,
2016). Second, combing machines and humans knowledge, i.e. instead of using a computer to
simulate human intelligence, AI builds hybrid intelligence systems by to optimize data flows
for greater collaboration along the supply chain (Pournader et al., 2021). Third, the change in
data resources can be observed, i.e. the data-driven algorithms are incorporating big data sets,
networks as well as intra- and interorganizational information flows (Bresciani et al., 2021).
In contrast to the traditionally fragmented and thus not “intelligent”enough IT solutions, AI
provides an opportunity to establish effective business systems as a response to the
increasing dynamic nature of supply chains (Helo and Hao, 2021).
But although AI and AI applications have become an increasingly prevalent research
topic in management research, supply chain scholars have been very shy to tackle the
bullwhip effect from an AI perspective and so far, have neglected to outline a research
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agenda. In other words, the trigger for our study was the observation that current AI
literature in supply chain seems to be focusing on quantitative modeling, thus highlighting
the need for a comprehensive and structured management perspective that can guide supply
chain and logistics scholars and managers to expand their understanding of the future scope
of research how AI can smoothen the bullwhip effect.
In an attempt to addressing this topic, we develop the Bullwhip-Smoothing-Framework
(BSF) based on key management tasks and functions to analyze the bullwhip phenomenon in
the supply chain from AI perspective. We argue that it provides a wealth of opportunities for
supply chain management scholars to advance knowledge in this critical area, better
understand processes and disseminate knowledge between academic and managers. It needs
to be emphasized that this paper neither presents and addresses the analytical and modeling
perspective, nor discusses specific forecast techniques. Rather, this review of the literature
focuses on the management perspective, asking the research question: “How can AI smoothen
the bullwhip effect in the supply chain?”
With AI and its link to the bullwhip effect still in its infancy, this paper conducts a
systematic literature review to provide an overview about the research published to date.
Hence, this paper’s contribution is threefold. First, we review the scope and characteristics of
the bullwhip effect and AI and discuss its implications to date. In doing so, we propose the
new BSF framework that is based on key management pillars that can help to analyze the
bullwhip phenomenon in the supply chain from an AI perspective. Third, we use key
management pillars of the framework to provide an integrated perspective of AI and the
bullwhip effect and to highlight future research directions that will result in further debate
and investigation into this important but neglected area of study. By proposing a new
framework using a structured management approach that examines 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.
The remainder of this paper is organized as follows: The next chapter describes the scope
and characteristics of AI and the bullwhip effect and outlines the key management pillars of
the proposed framework. This is followed by a description of the methodology we used for
our systematic literature review. Next, the main findings of the literature review are presented
by summarizing the literature on AI and the bullwhip effect. Finally, a research agenda is
proposed based on the current gaps in the literature and we provide directions for future
research.
2. AI and the bullwhip effect: scope and characteristics
The following section defines the scope of study by highlighting the key elements of the
bullwhip effect, discussing the capabilities of AI and presenting the key management pillars
that contribute to our BSF framework. According to Durach et al. (2017), a framework reveals
the scope of the systematic literature review by specifying the unit of analysis, the study
context and the definition of the constructs used. Our BSF framework can be used as a tool to
analyze the bullwhip effect and its implications in the supply chain and uses AI as a construct
to extract meaning and highlight future research opportunities. We firstly highlight the
elements of the bullwhip effect in the next section, followed by the key management pillars
that are most relevant to manage the bullwhip effect and an outline of relevant AI capabilities.
2.1 Bullwhip effect
The main argument behind the bullwhip effect is that the information between supply chain
layers is only available to few selected participants and restricted to members further up and
down the supply chain (Lee et al., 1997a). As a consequence, companies have thus no data
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about the actual consumer demand which results in distorted information by the companies’
own forecasts and transactions and a lack of coordination along the supply chain (O’Donnell
et al., 2006). This results in an amplified change of demand (e.g. when variances of orders may
be larger than sales), which causes multiple problems along the supply chain, such as an
increase in inventory costs, manufacturing costs, replenishment times and/or transportation
costs (Wiedenmann and Gr€
oßler, 2019).
To categorize the bullwhip effect, Lee et al. (1997a) identified four main causes: Demand
forecast, order batching, rationing and shortage gaming and price variations. Concerning
demand forecast, companies usually base their orders on data from previous orders received
by their customers, not on the actual demand. The fact that most companies are untrusting
and are not willing to share information leads to various forecast methods within the supply
chain, potentially causing a bullwhip effect either by using wrong data or imperfect forecast
techniques. With regard to order batching, companies may order a larger quantity of a
product to reduce transportation costs or to receive a discount for larger quantities. Although
this benefits the company, it may cause problems along the supply chain as the order is not
based on actual consumer demand, leading to a distortion of the “true”demand.
Rationing and shortage gaming can also cause a bullwhip effect, as manufactures may
ration their product when demand exceeds supply, leading to an exaggeration of orders.
However, the exaggeration of orders may result in lower demand and cancellation of orders,
which leaves manufacturers with excess inventory and uncertainty of demand, although
consumer demand is unchanged. Price variations may also cause a bullwhip effect: for
example, sales promotions usually result in large spikes in demand in consumer demand and
may lead to distortions downstream and upstream in the supply chain due to inefficiencies
such as excessive inventory, overtime costs, missed production schedules, poor customer
service and/or quality problems.
2.2 Management pillars for AI
The scope of the bullwhip effect comprises the typical structure of distribution logistics,
i.e. we focus on the flow between (1) the retailer, (2) the wholesaler or the distribution center
and (3) the manufacturer (see Figure 1). In a typical structure, information often flows
Figure 1.
The BSF
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upstream only between two consecutive members of the supply chain; however, our
framework argues that AI is a tool that can be used to improve the information flow between
all relevant members, further supporting an integrative approach.
Drawing from this integrative approach (Kurniawan et al., 2017;Lee et al., 1997b) and in
addition to the previous discussed causes, the following three distinct but interrelated key
management pillars form a crucial part of our BSF framework.
First, collaboration between members of the supply chain is a key pillar to smoothen the
bullwhip effect, mainly referring to the willingness among supply chain members to share
data. Studies show collaboration along the supply chain is of mutually benefit as aligns
supply and demand and can significantly improve performance (Herold et al., 2021b). The
fundamental aspect of collaboration can be attributed to information sharing (Barratt, 2004),
and although today’s technology and technological standards would allow a “seamless”
supply chain, exchanging data between members along the supply chain is still challenging
(Bailey and Francis, 2008;Herold et al., 2021c;Mikl et al., 2021). Often, intermediation leads to
information asymmetry and constitutes a non-value adding activity (Roeck et al., 2020) and
the lack of trust between members in the supply chain also results in increasing costs and
limited efficiency (Panahifar et al., 2018). As such, technological advancements and the use of
AI to anonymize data (e.g. federated machine learning) may present a potential solution to
tackle the information sharing and trust challenges.
Second, leadership can also be considered one of the key pillars to smoothen the bullwhip
effect by using AI. This happens mainly through commitment and execution, both referring
to the ability of the organization to invest in relevant resources to drive AI as well as to the
willingness to commit to potentially time- and cost-intensive adaption processes (Hsu et al.,
2019;Mikl et al., 2020). However, AI applications often rely on augmentation, i.e. “humans
collaborate closely with machines to perform a task”(Raisch and Krakowski, 2021, p. 193),
which includes resource-intensive repetitive tasks of human-machine learning. As a
consequence of the human involvement and its associated human biases, every initiated
augmentation can be regarded as a new learning effort which may end up as a failure.
Organizations can react to these failures either by stopping AI applications due to internal
pressures; however, they may also further commit to invest in AI applications and continuing
failures until the AI application provides value (Sabherwal and Jeyaraj, 2015). As such, it is
not clear whether companies and their managers are willing to provide a long-term
investment of resources to implement AI applications.
Third, digital skills are considered to be one of the key pillars in AI, mainly referring to the
ability to share, process and manage data (Petropoulos, 2018). However, it seems that
companies face problems to find rightly skilled labor and often companies and their
managers drive automation in the organization to remain competitive, thereby potentially
losing the human skills to change and adapt technological processes (Endsley and Kiris,
1995). Studies show that the increasing automation within organizations not only lead to a
loss of human expertise, but can also deskill staff and results in limited organizations’choices
(Lindebaum et al., 2020). As such, organizations may face a digital skills shortage when
implementing AI applications along the supply chain.
2.3 Artificial intelligence capabilities
The common perception is that AI is one of the most prominent technologies that seem to have
the ability to influence supply chain management and transform global supply chains (Raisch
and Krakowski, 2021). Stemming originally from the field of computer science, AI comprises
the development of systems that are capable to fulfill tasks that are associated with human
intelligence (Brynjolfsson and Mcafee, 2017). The phenomenon of AI has been studied by
numerous researchers over the last decades; however, technological advancements have only
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recently shown that AI started delivering its promised value in supply chain management
(Min, 2010).
The main difference of AI applications to former IT application is the role of decision-
making in organizations (Shrestha et al.,2019). While making decisions in computing has
been a core aspect throughout its history, “AI is fundamentally about making decisions
autonomously”(Berente et al., 2021, p. 1437). In other words, AI involves automating
decision-making through exhibiting “goal directed”intelligence (Russell, 2022)with
current applications achieving a high performance that humans are not capable of
achieving (Pournader et al., 2021). In fact, AI as tool to enhance productivity or improve
efficiency has generated a momentum in both in industry and academia, which is not only
proven by the investments from tech-companies such as Meta, Microsoft or Amazon
(Markoff and Lohr, 2016), but mainly because today’s AI technology is based on three
specific, but interrelated capabilities that can create a competitive advantage along
the supply chain, namely (1) the development of machine learning and deep learning, (2) the
ability to analyze big datasets to “train”algorithms and (3) using predictive analytics for
accurate demand forecasting.
First, machine learning tries to mimic the human way of thinking and examines pathways
how to translate knowledge directly from data in order to solve problems (Nayal et al., 2021).
In combination with deep learning, which extends machine learning by neural networks,
analytics use multiple layers of interplay between algorithms that feed and process numerous
data flows simultaneously to produce more accurate insights (Carbonneau et al., 2008).
Second, scholars found that there is an interdependence between AI and big data sets,
arguing that big data has empowered AI, but in turn, AI need to maintain a constant income
stream of big data sets to make correlative predictions (O’Leary, 2013). These data sets
provide, e.g. data from multiple sources to forecast the material flows and the consumer
demand along the supply chain (Wang et al., 2016). Third, as a consequence of the first two
capabilities, AI has the ability to predicative forecasting by using numerous layers of
algorithms to better understand the past, resulting in a more accurate forecast of initially
distorted information along the supply chain to potentially reduce the bullwhip effect (Riahi
et al., 2021;Seyedan and Mafakheri, 2020).
However, although interest in AI has led to broader presence and scholarly discourse
within academia, research using AI to examine supply chains is still limited, in particular with
regard to the bullwhip effect. The next section presents the research design, which will help
us to better understand how AI can be used in the supply chain to smoothen the bullwhip
effect.
3. Methodology
This study follows the systematic literature review approach developed by Durach et al.
(2017). According to Tranfield et al. (2003), systematic literature reviews offer high-quality
knowledge by using repeatable, transparent and rigorous processes that synthesize scientific
evidence. To avoid research bias, our review comprises two databases and involves three
researchers from different countries. Following Durach et al. (2017), a six-step procedure is
applied (see Figure 2) and is outlined below.
3.1 Define research aim
As the aim of this paper is to identify how AI can smoothen the bullwhip effect in the supply
chain, we use our proposed BSF framework to analyze and synthesize existing supply chain
management literature in order to highlight avenues for future research for supply chain
management scholars.
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3.2 Craft inclusion and/or exclusion criteria
We adopted a rigorous methodological approach by developing a list of inclusion criteria that
was agreed upon by all authors (see Table 1). We decided not to limit the search to specific
journals (Briner et al., 2009) and research methods (Durach et al., 2017). However, our search
focused on peer-reviewed articles because peer-reviewed articles are regarded to be of higher
quality than non-peer-reviewed articles (see Light and Pillemer, 1984). In addition, we selected
only articles from 2011 to February 2022, since 2011 was the year in which Industry 4.0 was
introduced (Yang and Gu, 2021). We need to emphasize that our review refers to content
related to management literature regarding the bullwhip effect and AI. However, as AI can be
regarded as an extremely fragmented field (Pournader et al., 2021), we extended our search to
Source(s): Adapted from Durach et al. (2017)
Step 1: Define rese arch aim
Justify review and highlight contribution
Step 2: Craft inclusion and/or exclusion criteria
Determine required characteristics of primary studies
Step 3: Retrieve ' baseline sample'
Determine search procedures and keywords
Step 4: Select pertinent literature ('synthesis sample')
Apply inclusion and/or exclusion criteria
St e p 5: Sy nthe siz e lit e rat ure
Apply codi ng schemes to extrac t per tinent information
Step 6: Report the results
Provide descriptive over view and discuss findings
Inclusion criteria Rationale
Peer-reviewed articles Published peer-reviewed articles increase the quality
of the manuscript (Denyer and Tranfield, 2009) and
enhance the quality control (Light and Pillemer, 1984)
Selection of papers published 2011 to Feb-2022 The year 2011 was selected as a starting point due to
the introduction of Industry 4.0 (Yang and Gu, 2021)
Summary must address a management aspect (as
identified in the framework) within the context of AI
and/or bullwhip
The aim of the review is to analyze and synthesize the
different features of AI and the bullwhip effect to
improve conceptual clarity and understanding
Different type of article considered (e.g. empirical,
conceptual)
The focus of the study is to evaluate and synthesize
the various topics approaches to the concept of AI and
the bullwhip effect
Article must be written in English English is the dominant research language in the field
of SCM, AI and management
Figure 2.
Steps for conducting a
systematic literature
review
Table 1.
Inclusion criteria
AI
smoothening
the bullwhip
effect

technological adoptions and innovations within the supply chains to reduce the bullwhip
effect. Consequently, articles had to demonstrate a relevant and narrow focus how
management can use AI and related technologies to smoothen the bullwhip effect according
to our key management pillars identified in our framework. Peer-reviewed articles that did
not fulfill these criteria were excluded.
3.3 Retrieve “baseline sample”
In the third step, potentially relevant articles were compiled a “baseline sample”. Two
databases were selected for the literature search to reduce bias: EBSCO Business Source
Complete and the Scopus database. Both databases represent large repositories of
management and business articles (see Sandberg and Aarikka-Stenroos, 2014) and offer a
wide range of research with high impact within the business research community. The search
string based on the research aim and the inclusion criteria was validated by a team of three
academics who specialized in AI and supply chain management. After the validation of the
initial search string, incremental keywords and associated synonyms were developed to
cover all relevant topics.
Similar to other systematic literature reviews, we searched first for supply chain
management articles in both databases entering the keyword “bullwhip”in combination with
“artificial intelligence”,“technology”or “innovation”(see Table 2). The search string was
amended for each database based on respective search guidelines and entered into the search
field. To ensure that the selection covered all relevant scientific manuscripts dealing with the
AI and/or the bullwhip effect, we additionally conducted citation searches and manually
added missing papers to the pool of articles. The search was conducted in February 2022.
3.4 Select pertinent literature
The fourth step comprised “synthesizing”the sample, i.e. we included relevant publications
while at the same time excluding irrelevant ones (Durach et al., 2017). In the initial search we
identified 458 articles from EBSCO and 295 articles from Scopus. Redundant articles were
eliminated by two authors who subsequently examined the abstracts based on the inclusion
criteria. Abstracts were analyzed independently blindly by the two researchers for validity
purposes. This procedure reduced the articles for the subsequent analysis to 112. Following
(Durach et al., 2017), the two researchers split and read the full manuscripts as a next step to
assess true relevance. This step identified 43 additional articles through cross-referencing,
but at the same time, the sample was refined by excluding manuscripts that did not address
relevant AI topics. This resulted in a final sample of 97 articles. The process of article
selection is shown in Figure 3.
3.5 Synthesize literature
This final sample of 97 articles was subsequently synthesized with the goal of providing a
consolidated overview of articles that address how the bullwhip effect can be smoothed by
AI. In addition, the content of the manuscripts will be categorized using our BSF framework
Construct Original search string Databases
AI/bullwhip/
management
(AB (“bullwhip”)) AND (AB (“artificial intelligence”)ORAB
(“SCM”)ORAB(“supply chain”)ORAB(“technology”)ORAB
(“skills”)) AND NOT AB (“leadership”)ORAB(“collaboration”)
Business Source
Complete
Scopus
Note(s): AB 5Abstract
Table 2.
Keywords and search
string
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and, in particular, the key management pillars identified above. We followed the interpretive
synthesis approach from Rousseau et al. (2008) using themes by the authors as well as open
coding schemes. As a result, nine critical sub-topics were identified within the three major
management pillars in the 97 articles. Each sub-topic represents a unique characteristic
identified in the key management pillars. Each of these themes represents a unique
characteristic or dimension within the management pillars. Thus, the mapping of articles to
the key management pillars and sub-topics provides a solid basis for identifying gaps and
suggestions for future research.
3.6 Report the results
This step provides the results of all selected studies, their relationship to each other and,
according to Denyer and Tranfield (2009),“what is known and not known”(p. 672). As such,
the analysis can be regarded as an “informed interpretation”(Rousseau et al., 2008) of the
scientific findings in relation to the research objective and the gaps identified during the
review procedure. The next section presents these findings on the current state of the art and
explains how AI can smoothen the bullwhip effect.
4. Results
In presenting our study results, we first provide a snapshot of the 97 papers from our review
based on the three identified key management pillars collaboration, leadership and digital skills
and its nine identified sub-topics (Table 3). Given the extremely fragmented dimensions of AI
and its interaction with other areas such as human resource management, contemporary
supply chain issues, leadership characteristics and even broader organizational change, the
findings will focus on how AI can smoothen the bullwhip effect. The following section includes
sub-consequently the most significant findings under each of the pillars and sub-topics.
4.1 Collaboration
Collaboration with members and along the supply chain is commonly seen as the key aspect
to smoothen the bullwhip effect in current literature with 53% of all papers (51 out of 97). We
found that collaboration can play a pivotal role in order to reduce the bullwhip effect through
a cluster of three topics comprising (1) the importance of quality information sharing and
trust, (2) use of other technologies for planning and operations and (3) use of forecasting/
predictive models.
With 16% of all papers (16 out of 97), a strong focus is placed on the sharing of quality and
transparent data and information along the supply chain. Most of the papers see joint
decisions from sharing as a competitive advantage (Jain et al., 2021); however, B€
uy€
uk€
ozkan
and G€
oçer (2018) argue that often information about supply chains operations is held locally,
which undermines effective and efficient collaboration among members along the supply
chain. As a response, Soosay and Hyland (2015) emphasize that members along the
supply chain have to establish an appropriate level of trust and integrate supply chain
79211357
Elimin ating
duplicate articles
and studying
abstracts as per
inclusion criteria
Reading entire
articles and
cross-
referencing
Database
search in
Business
Source
Complete and
Scopus
Figure 3.
Article selection
process
AI
smoothening
the bullwhip
effect

processes to achieve performance improvements through collaboration. Scholars claim that
the transfer of sensitive data by a central actor requires a high degree of trust in its discretion
as well as sense of responsibility and competence (Singh and Teng, 2016); however, by
“chaining”the immutable transaction history as well as the decentralized control function of
the network, blockchain is considered a technology that could promote security,
transparency and collaboration in the supply chain (Dobrovnik et al., 2018;Ghode et al.,
2021;Hribernik et al., 2020;Kummer et al., 2020).
Apart from classical collaboration concepts such as Collaborative Planning, Forecast and
Replenishment (CPFR), Just-in-Time or Just-in-Sequence, collaboration is also distinguished
Key pillar Topic Authors
Collaboration Information sharing and trust Dai et al. (2016),Jain et al. (2021),Soosay and Hyland (2015),
De Almeida et al. (2017),Singh and Teng (2016),Ghode et al.
(2021),Xue et al. (2021),Ran et al. (2020),Badraoui et al.
(2020),Costantino et al. (2014),Jeong and Hong (2019),
Rodrigues et al. (2015),Jiang (2019),Jiang and Ke (2019),
Rached et al. (2016) and B€
uy€
uk€
ozkan and G€
oçer (2018)
Technologies for planning and
operations
Hill et al. (2018),Yuan and Zhu (2016),Qin et al. (2017),Tian
(2016),Pournader et al. (2021),Prakash and Pandey (2014),
Preil and Krapp (2022),Raisch and Krakowski (2021),
Wiedenmann and Gr€
oßler (2019),Baryannis et al. (2019),
Sharma et al. (2022),Toorajipour et al. (2021),Min (2010),
Dash et al. (2019),Klumpp (2018),Dhamija and Bag (2020)
and Dubey et al. (2020)
Use of Forecasting/Predictive
Models
Ilie-Zudor et al. (2015),He et al. (2017),Gunasekaran et al.
(2017),Lee et al. (2019),Seyedan and Mafakheri (2020),
Souza (2014),Waller and Fawcett (2013),Brynjolfsson and
Mcafee (2017),Rahwan et al. (2019),Jaipuria and
Mahapatra (2014),Prakash and Pandey (2014),Pournader
et al. (2021),(Helo and Hao, 2021), Kiefer et al. (2019),Singh
and Challa (2016),Wiedenmann and Gr€
oßler (2019),Balan
et al. (2007),Moghadam and Zarandi (2022),Poornikoo and
Qureshi (2019) and Aggarwal and Dav
e (2018)
Leadership Top-level commitment and AI
strategy execution
Fountaine et al. (2019),Bakker and Budde (2012),Stone
et al. (2020),Baryannis et al. (2019),Brock and Von
Wangenheim (2019),Duan et al. (2019),Berente et al. (2021),
Davenport (2018),Wang et al. (2016),Henke et al. (2018),
Smith and Green (2018) and Wijayati et al. (2022)
Implementation of a centralized
budget
Beltagui et al. (2020),Frick et al. (2021) and Kruhse-
Lehtonen and Hofmann (2020)
Establishment of quick wins Belhadi et al. (2021),Lichtenthaler (2019),Campion et al.
(2020),Polak et al. (2020) and Kankanhalli et al. (2019)
Digital skills Human–machine interaction
for decision-making
Binns (2020),Arslan et al. (2021),Ashta and Herrmann
(2021),Hanelt et al. (2021),Bankins (2021),Jarrahi (2018),
Keding and Meissner (2021),de Fine Licht and de Fine
Licht (2020),Felzmann et al. (2020),Sj€
odin et al. (2021),
De Cremer (2019) and Tambe et al. (2019)
War for talent/digital skills
shortage
Chamorro-Premuzic et al. (2019),Karacay (2018),Lutz
(2019),Istomina et al. (2020),Wang and Ha-Brookshire
(2018),Pillai and Sivathanu (2020) and Klett and Wang
(2013)
Targeted upskilling Black and van Esch (2021),Ng (2016),Jaiswal et al. (2021),
Foroughi (2020),Frankiewicz and Chamorro-Premuzic
(2020),Elliott (2018),Vallor (2015) and Budhwar et al. (2022)
Table 3.
Topics by author
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between planning support and management support (Hill et al., 2018). While planning
support (e.g. CPFR) focuses on aspects of intensive partnership-based planning and
coordination, management support focuses on operational aspects such as the organization
of cross-company data transfer. Moreover, some studies also highlight technologies that can
help to overcome collaboration issues between members of the supply chain (Yuan and Zhu,
2016). For example, the trend to use sensor technology for monitoring and data collection is
regarded as a potential solution as well as radio frequency identification (RFID) provides also
an opportunity to capture current and product-related data (Bottani et al., 2010).
From an AI perspective, 17 studies mention AI or machine learning in the context of
collaboration management, mainly in the context of predictive models or forecasting. For
example, machine learning is also used in conjunction with predictive maintenance or
predictive quality (Ilie-Zudor et al., 2015). The main argument is that by using historical data,
a system learns to evaluate current data in such a way that it can make a prediction about
future developments (Gunasekaran et al., 2017;Lee et al., 2019). Furthermore, similar
predictive models can also be used for forecasting along the entire supply chain (Seyedan and
Mafakheri, 2020). The authors argue that with the help of machine learning, more precise
statements can be made about inventory, orders, deliveries and demand. As a consequence,
the utilization of warehouses and transport service providers can thus be estimated more
accurately, leading to reduced costs and more efficiency.
4.2 Leadership
Our analysis revealed the importance of the leadership for a successful use of AI along the
supply chain. However, we couldn’t identify a study that particularly includes the
bullwhip effect in their examinations. Nevertheless, we identified 19 papers (20% of all
paper) that discuss the role of leadership in the context of AI, consisting of the three
leadership topics (1) top-level commitment and AI strategy execution, (2) implementation
of a centralized budget and (3) the establishment of quick wins. In general, leadership is
seen as crucial as it up to management to make the key decisions about AI, to oversee AI
projects, allocate resources, govern the organization and coordinate the members along
the supply chain.
Studies show that companies still have problems to identify business or use case for AI in
the organizations and often struggle to design interfaces and determine when and how
machines and humans need to interact (Fountaine et al., 2019). As a result of this uncertainty,
companies may become disillusioned how to build AI applications and managers may be
disappointed by the pace of progress (Bakker and Budde, 2012), leading to growing
impatience and a potential stop of all AI activities. Scholars found that the use of AI along the
supply chain can be regarded as a long-term investment for companies and top-level
commitment and its associated determination and persistence are key factors for the
implementation (Brock and Von Wangenheim, 2019;Duan et al., 2019). In particular, the
authors stress that a fully committed leadership, from the board over to C-suite to the senior
managers, is required and managers need to be highly involved in all execution aspects of the
company’s AI strategy and data initiatives.
Companies need to be prepared to develop, test, and deploy the AI technologies internally
and need to integrate AI in all of decision-making on all business levels from strategy to
operations along the supply chain. Baryannis et al. (2019) argues that for AI to work, it needs
to satisfy the two characteristics of autonomously deciding on a course of action that leads to
success in supply chain-related objectives and do so under a partially unknown supply chain
environment. Supply chain academics stress that the implementation of AI calls for
commitment and execution to create an effective business environment for AI, instead of
relying on IT specialists, coding and data scientists (Wang et al., 2016).
AI
smoothening
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effect

For the implementation, leadership should be able consider where to deploy AI first.
Belhadi et al. (2021) argues that using AI for marketing and sales is preferable as it results in
quick wins, as does it with process optimization business cases. The main argument is here
that cost savings opportunities are easier to justify than building new business opportunities
for novel revenue streams, which in turn leads to quick(er) demonstration of AI benefits and
to a subsequent further buy-in and commitment from management. In order to push AI
applications, studies show that a centralization of budgets is useful as the separate
organizations’units and functions are often not prepared to carry the expenses for the whole
organizations’AI capability building. This leads not only to an integrated AI approach, but
also frees up resources that might be needed for a scaling up (Beltagui et al., 2020).
4.3 Digital skills
Our analysis reveals that digital skills or competencies are seen as a central factor for the
successful digital transformation as well as for the exploitation of the potential of AI.
In particular, literature about digital skills in a broader AI context comprised 27 papers with a
cluster of three topics, where management needs to (1) define human–machine interaction for
assisted decision-making, (2) address the war for talent, i.e. digital skills shortage and (3) offer
targeted upskilling to shape the digital transformation.
However, as the topic of skills is strongly related to the human resources discipline,
scholars discuss and highlight the implications for managers that need to be considered when
using AI in the supply chain (Binns, 2020). For example, scholars stress that while it is
essential to invest massively in digital education and skills, information literacy and critical
thinking should be taught at the same time to prepare for tasks beyond the reach of machines
(Ashta and Herrmann, 2021). Other scholars emphasize that although qualification programs
and training courses should be “enriched”with AI content, the digital transformation and the
use of AI will lead to demand for numerous new job profiles that cannot be clearly described
at the moment (Hanelt et al., 2021).
Another important aspect is the ethical aspect when using AI in the supply chain.
According to Bankins (2021), managers need to acknowledge that AI should not lead to a
conflict of “machines vs employees”, but rather to identify pathways how these two can-
coexist. Keding and Meissner (2021) show that the more technology is involved in the
automation of decision-making processes, the more human judgment is needed; thus, humans
and AI are needed to achieve “assisted decision-making.”Other scholars go further and see
the use of AI technologies to drive democratize decision-making and thus enable users to act
in an informed way by providing transparency and linking people, information and
knowledge (Sj€
odin et al., 2021).
From a pure bullwhip perspective, no study specifically investigates digital skills
regarding to the bullwhip effect, and only a handful of studies examine digital skills for AI
and its implications along the supply chain. However, some studies that examine digital skills
in and for AI, see the skill market situation rather as “dramatic”due to a potential digital skills
gap (i.e. the gap between existing and required digital skills) that seems to be growing
(Chamorro-Premuzic et al., 2019). As a consequence, the “war for talent”–for digital talent –is
supposed to intensify; thus, companies not only need to provide the right incentives to attract
such talent, but need to invest in targeted upskilling to build further competencies, which will
also help retain talent long term and subsequently shape the organization’s digital
transformation (e.g. Foroughi, 2020).
5. Identified gaps and directions for future research
In this study, we examined the academic literature on AI and the bullwhip effect from a
management perspective, not only to provide a framework that characterizes the field and
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stimulates scholarly discussion, but also to provide explicit insights and concrete
recommendations for an emerging research agenda. Overall, our literature review reveals
that several topics related to AI and the bullwhip phenomenon from a management view are
severely underrepresented. Therefore, we would like to suggest the following
recommendations for future research.
5.1 Little attention has been given to how AI can smoothen the bullwhip effect in the supply
chain from a management perspective
Although a number of papers have addressed AI and its impact on the bullwhip effect (data
transfer, coordination, etc.), the management perspective and its processes and requirements
to smoothen the bullwhip phenomenon have not been addressed properly in previous
research. The lack of research in this area can be partially explained by the need for
interdisciplinary research, as the field of AI and the bullwhip effect is not only extremely
fragmented, but also interacts with other disciplines and areas such as human resource
management, contemporary supply chain issues, leadership characteristics, or even broader
organizational change. We believe that our BSF framework developed in this paper can
provide a foundation for management-related directions of future research in two ways. First,
our framework can provide the backbone for management analyses of supply chain
processes in terms of the three key management pillars to identify success factors for
management by acknowledging the idiosyncrasies of AI. Such analyses may also help to
validate or further advance the framework as an appropriate management tool in supply
chain management. Second, we encourage researchers to contribute with incremental
concepts and frameworks on AI and the bullwhip effect, either developed from scratch or
based on the various conceptual contributions from the information management and supply
chain literature to date.
5.2 The link between AI applications and collaboration appears to be under-examined, in
particular regarding the topic of trust between members along the supply chain
Although our review identified several important papers investigating collaboration along
the supply chain in the context of a bullwhip effect, papers examining specifically AI to
enhance collaboration efforts are still limited. The majority of these papers consist of
literature reviews, which discuss only the potential of AI applications or only briefly touch the
collaboration issue as part of a broader review. This is somewhat surprising, given that the
collaborations are considered to be one of the key factors not only to reduce the bullwhip
effect, but also as an area of improvements by AI and its applications. Moreover, the
collaboration literature so far lacks also an examination of AI and its applications regarding
the trust issue between supply chain members. Most of the articles we found present
blockchain as a tool to decentralize the data, but fail to make clear recommendations how to
specifically integrate and manage the trust issue with members –and often conflicting
interests –along the supply chain. Furthermore, we also couldn’t identify any articles that
discuss collaboration and the integration of digital start-ups that offering AI applications
along the supply chain. Given that AI applications are increasingly created by start-ups,
future research may not only examine the specifics of AI for collaboration along the supply
chain, but also how digital start-ups offering AI solutions can reduce the bullwhip effect.
5.3 Ways how AI can influence collaboration efforts in practice to reduce the bullwhip effect
have so far been neglected
We argue that the practical part how management can collaborate using AI along the supply
chain has been heavily neglected. So far, the vast majority of papers examined for this study
AI
smoothening
the bullwhip
effect

presenting or extending predictive models or specific forecast techniques are of theoretical
nature. In particular, scholars use either the beer-game or other games to illustrate the
implications of the bullwhip phenomenon, or they use experiments and complex modeling to
demonstrate the effects on the supply chain. So far, concrete investigations how AI can
smoothen the bullwhip effect from a practical management perspective is quite limited.
Future research could, therefore, explore specific or interrelated management issue that can
managers in the supply chain sphere to better understand how to overcome the challenges of
AI and the bullwhip effect.
5.4 Specific leadership and digital skills that need to be built and to implement AI applications
in the supply chain have only been partially explored
Our review suggests that the human resource-related issues regarding the digital
transformation and the potential use of AI have been quite well researched. Interestingly,
most of studies discuss the human–machine interaction and conclude that AI still needs
humans to assist them in decision-making. Apart from these human resources research, only
few studies mention the digital shortage and needed upskilling for a successful
implementation of AI application. In particular, from a management perspective, there is
lack of studies investigating what kind of specific skills are needed to implement AI, how
organizational structures should be changed and how to address the digital skills gap. Hence,
the challenge for future research in the area of digital skills it to find and develop realistic use
cases and approaches within companies to identify specific leadership and skill demands.
6. Implications for theory and practice
6.1 Theoretical implications
Our paper employed a systematic literature review to examine how AI can smoothen the
bullwhip effect. More specifically, our study is one of the first papers that specifically examine
the link between AI and the bullwhip from a managerial perspective. As such, the paper
extends the theory’s propositions that the tasks and goals of a supply chain in implementing
and using AI systems are better satisfied when the management involves collaboration
among supply chain participants, strong leadership from the top as well as a focus on the
development of digital skills. Our findings also respond to the call of Berente et al. (2021) to
understand the “approaches to the communication, leadership, coordination, and control of
AI soon”(p. 31).
So far, most managers and academics have rather overpraised the usage of AI systems,
but neglected the challenges associated with the implementation of AI to smoothen the
bullwhip effect or failed to appreciate the consequences from a management perspective.
Thus, we examined existing literature and identified three interrelated key management
pillars and provided insights into the specifics behind collaboration, leadership and digital
skills. In other words, we shed light on the relationship between AI and the bullwhip
phenomena, thereby analyzing its impacts on the business environment and expanding the
different theoretical perspectives.
In addition, we present the BSF framework that can be used as a tool to analyze the
bullwhip effect and its implications in the supply chain. The BSF start with the question what
influences the bullwhip effect from an AI view, thereby presenting a context-aware approach
how to manage the demand variability in the supply chain. As a result, our framework offers
a foundation for the advancement of theorizing how to smoothen the bullwhip effect with AI
by providing a structural management approach.
As its core, it assumes that AI provides a powerful tool to smoothen then bullwhip
phenomena depending on the extent how the three key management pillars and their inherent
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challenges can be overcome. However, we need to point out that there is difference between
smoothing and eliminating the bullwhip effect: although AI has the capability to greatly
influence demand variability along the supply chain, the bullwhip effect will still represent a
significant future management challenge for supply chain managers and scholars.
6.2 Practical implications
AI has had a dramatic impact on organizations in recent years. Initial AI applications were
rather used as tools for complex calculations and automation, but the current application of
AI is seen as transformative tool to enhance the productivity for organizations and
individuals. However, so far, the productivity promise of AI applications is often not fulfilled,
in particular along the supply chain with regard to the bullwhip effect. Our results reveal and
identify the reasons behind these unfulfilled promises and present the key pillars and their
challenges that need to be addressed to smoothen the bullwhip phenomena. In particular, this
study emphasizes the pressing need for managers to further develop AI in their organizations
and to deal with the inherent management challenges in the organization and along the
supply chain.
Our paper offers interesting insights into the practical implications for smoothing the
bullwhip effect by using AI systems that are supported by strong collaboration, leadership
and digital skills. With AI rapidly becoming a preferred tool to enhance the organizations’
ambitions, it is important that managers are aware of the opportunities and the challenges
associated with the implementation of AI along the supply chain. Thus, managers are urged
to look at how AI can influence the supply chain collaboration, in particular with regards to
trust and the use of forecasting/predictive models to smoothen the bullwhip effect. Our
findings also revealed that not only collaboration is a crucial element, but also that “softer”
factors such as leadership and addressing the digital skills gap are necessary for successful
AI systems.
Our framework also points to the need to find a balance between the performance of AI
and its accountability. In particular, one the one hand, managers need to find a way to develop
metrics and standards to quantify the performance, but, on the other hand, need also create
ethical frameworks that provide transparency within and beyond their organizations. The
inherent complexity behind the use of AI along the supply chain thus represents an ongoing
challenge to address the demands from society and the market. Our framework and its
structured management approach present a first step to analyze AI and its implications on
the bullwhip effect along the supply chain.
7. Summary and conclusion
In this study, we set out to achieve three interrelated goals. First, we reviewed management
literature specifically focused on how AI can help to smoothen the bullwhip effect in supply
chain and discussed its implications to date. Second, we proposed the new BSF that is based
on key management aspects and characteristic that can help to analyze the bullwhip
phenomenon in the supply chain from an AI perspective. And third, we used the BSF as the
backdrop to our systematic literature review on AI and the bullwhip effect to synthesize the
research that has been published to date. By categorizing this research into key management
pillars, we were also able to identify gaps and propose future research directions that will
contribute to further debate and investigation into this important yet neglected field of study.
The identification of the scope and characteristics shows that AI and the bullwhip effect
are defined by three core elements (Figure 1 above). First, the bullwhip effect can be
categorized around four causes: demand forecast, order batching, rationing and shortage
gaming as well as price variations. Second, the management of AI and the bullwhip effect
AI
smoothening
the bullwhip
effect

are embedded around three pillars, comprising collaboration, leadership and digital skills.
Based in these findings, we also provided a definition for the supply chain in the context
of the bullwhip phenomenon. Third, the scope of AI is defined by the three types of
capabilities, namely the exponential increase in computational power, the development of
machine learning and deep learning and the ability to analyze big datasets to “train”these
algorithms.
One central contribution of this paper is to shed light on the current state how
management can use AI to smoothen the bullwhip effect. More specifically, 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. Although the
literature provided sufficient knowledge for a first definitional framework, our systematic
review reveals that the AI and the bullwhip aspect from comprehensive management
perspective to date have rather been neglected by academic scholars. In other words, research
of AI and its implications on the bullwhip effect is in its infancy and provides plenty of
opportunities for further research in each of the three key management pillars. Our study
provides, therefore, a critical first step.
Even though AI is increasingly prominent topic among academics and managers,
management literature dealing how to use AI in organizations with its operational and
strategic horizons has yet to resonate in the minds of AI and supply chain scholars. We hope
that both the gaps and challenges presented in this contribution will spark ideas, discussions
and projects on how to fill this largely open canvas.
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Corresponding author
David M. Herold can be contacted at: d.herold@qut.edu.au
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AI
smoothening
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