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Journal of Business Research 122 (2021) 502–517
Available online 25 September 2020
0148-2963/© 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Articial intelligence in supply chain management: A systematic
literature review
Reza Toorajipour
a
, Vahid Sohrabpour
b
,
c
, Ali Nazarpour
d
, Pejvak Oghazi
e
,
*
, Maria Fischl
f
a
School of Innovation, Design and Engineering, M¨
alardalen University, Box 325, 631 05 Eskilstuna, Sweden
b
Department of Operations Management, Copenhagen Business School, Copenhagen, Denmark
c
SAVEGGY AB, Ideon Innovation, Ideon Science Park, Lund, Sweden
d
School of Business, Maynooth University, Maynooth, Co. Kildare, Ireland
e
School of Social Sciences, Sodertorn University, Alfred Nobels all´
e 7, Stockholm, Sweden
f
Siemens Gas and Power GmbH & Co. KG, Siemens Energy, Berlin, Germany
ARTICLE INFO
Keywords:
Articial intelligence
Supply chain management
Systematic literature review
ABSTRACT
This paper seeks to identify the contributions of articial intelligence (AI) to supply chain management (SCM)
through a systematic review of the existing literature. To address the current scientic gap of AI in SCM, this
study aimed to determine the current and potential AI techniques that can enhance both the study and practice of
SCM. Gaps in the literature that need to be addressed through scientic research were also identied. More
specically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the po-
tential AI techniques for employment in SCM; (3) the current AI-improved SCM subelds; and (4) the subelds
that have high potential to be enhanced by AI. A specic set of inclusion and exclusion criteria are used to
identify and examine papers from four SCM elds: logistics, marketing, supply chain and production. This paper
provides insights through systematic analysis and synthesis.
1. Introduction
The world has been moving towards a digital future over the years,
and Industry 4.0 technologies are considered to be the way of the future
(Kumar et al., 2020). One of the most prominent of these technologies
(including blockchain, IoT, cloud computing, etc.) is articial intelli-
gence (AI) (Dirican, 2015), dened as the capability of machines to
communicate with, and imitate the capabilities of, humans (Schutzer,
1990). Using AI leads to problem solving with higher accuracy, higher
speed and a larger amount of inputs. AI is neither a new subject nor a
new academic eld of study (Huin et al., 2003); however, only recently
have technological developments shown that AI has a vast set of appli-
cations (Min, 2010), making headlines by adapting processes in
numerous diverse areas (Martínez-L´
opez and Casillas, 2013; Jarrahi,
2018), including supply chain management (SCM). While some areas of
information technology are being reduced to a position of competitive
necessity, AI technology is emerging as a competitive advantage (Thow-
Yick and Huu-Phuong, 1990). In this regard, many companies are
shifting from remote monitoring to control, optimization, and nally,
advanced autonomous AI-based systems to improve their functionality
(Kohtam¨
aki et al., 2019).
Along with its rising importance in industry, AI shows an increasing
and broader presence in the scholarly discourse, and this presence has
affected many elds, such as business research, which has picked up on
the topic, and AI is now researched from a more holistic perspective (e.g.
Canhoto and Clear, 2020; Dirican, 2015; Soni et al., 2020), with SCM
being recognised as one of the elds most likely to prot from AI ap-
plications. Although interest from practitioners and researchers is thus
high (as demonstrated by the large number of studies regarding AI, e.g.
Jarrahi, 2018; Kaplan and Haenlein, 2020; Nishant et al., 2020; Rans-
botham et al., 2017), there is a need to explore the contribution of AI to
the eld of SCM. Several studies have mentioned this need (e.g. Dubey
et al., 2020; Min, 2010; Vargas Florez et al., 2015). This gap is addressed
by the current study through a systematic review and by answering the
following research question (main RQ): how does AI contribute to SCM
studies?
In order to conduct an inclusive yet practical literature review, we
focus on related subelds based on the work of Stock and Boyer (2009),
* Corresponding author.
E-mail addresses: vahid@saveggy.com (V. Sohrabpour), ali.nazarpour@mu.ie (A. Nazarpour), Pejvak.oghazi@sh.se (P. Oghazi), maria.schl@siemens.com
(M. Fischl).
Contents lists available at ScienceDirect
Journal of Business Research
journal homepage: www.elsevier.com/locate/jbusres
https://doi.org/10.1016/j.jbusres.2020.09.009
Received 5 May 2020; Received in revised form 4 September 2020; Accepted 6 September 2020
Journal of Business Research 122 (2021) 502–517
503
who cover the main aspects and fundamental keywords in their deni-
tion of SCM:
The management of a network of relationships within a rm and between
interdependent organizations and business units consisting of material
suppliers, purchasing, production facilities, logistics, marketing, and
related systems that facilitate the forward and reverse ow of materials,
services, nances and information from the original producer to nal
customer with the benets of adding value, maximizing protability
through efciencies, and achieving customer satisfaction.
From the above theoretical denition, four key words are extracted
for the search strings, including marketing, logistics, production and
SCM (material supplier and purchasing are included in the latter). This
study is structured as follows. The next section outlines the methodology
of the research, with details about how the review was conducted. Then,
the analysis and synthesis section breaks down individual studies into
constituent parts and describes the relationships between them. The
paper concludes by discussing and summarising the ndings of the
research (Denyer and Traneld, 2009).
2. Methodology
To overcome the recognised weaknesses of a narrative review
(Traneld et al., 2003) or an expert review with ad hoc literature se-
lection (Kitchenham et al., 2009), this study adopted an evidence-
informed, systematic literature review approach. We followed the ve-
step process outlined by Denyer and Traneld (2009), including a
pilot search in the rst phase to gain a deeper understanding of the
current literature, construct the criteria for literature selection and
derive the research question and the subsequent steps. Consequently,
the systematic review that we employed has ve phases, as depicted in
Fig. 1.
2.1. Pilot search and research question
2.1.1. Pilot search
As outlined above, we conducted a pilot search as part of the rst
phase in order to better our understanding of the examined eld and the
existing literature. We located the sources of literature by checking the
results of a dened search string in different publishers’ electronic
databases (refer to Table 1). In addition, we used the pilot search to
identify criteria for the inclusion and exclusion of literature following
the suggestion of Denyer and Traneld (2009), which is fully explained
in Section 2.3.
2.1.2. The research question
A proper systematic literature review is based on a well-formulated,
answerable question that guides the study (Counsell, 1997). Formu-
lating a research question is the most crucial and probably the most
difcult part of the research design, and devising a research question
leads to selecting research strategies and methods; in other words,
research is conducted on the foundation of research questions (Bryman,
Fig. 1. Research process of systematic literature review.
Table 1
Search protocol for selected literature sources.
Database Article
parts
searched
Fields searched Search string Time span
Science
direct
Title,
abstract,
keywords
All elds “articial
intelligence”
AND “keyword”
2008–2018
Emerald
insight
Title,
abstract,
keywords
All elds “articial
intelligence”
AND “keyword”
JSTOR Title,
abstract,
caption
Business, Public
Policy &
Administration,
Management &
Organisational
Behavior,
Marketing &
Advertising,
Finance
“articial
intelligence”
AND “keyword
1” NOT
“keyword 2”
NOT “keyword
3” NOT
“keyword 4”
Wiley
online
library
Title,
abstract,
keywords
All elds “articial
intelligence”
AND “keyword
1” NOT
“keyword 2”
NOT “keyword
3” NOT
“keyword 4”
Taylor &
Francis
Title,
keywords
All elds “articial
intelligence”
AND “keyword”
R. Toorajipour et al.
Journal of Business Research 122 (2021) 502–517
504
2007). Conducting a pilot search led us to the question that this research
is centred around: how does AI contribute to SCM studies?
To provide a clear answer to this question, we anatomised it into the
following four sub-research questions (SRQs): (SRQ 1) Identify the most
prevalent techniques of AI that are applied in SCM studies. (SRQ 2)
Identify the potential AI techniques that can be employed in SCM
research. (SRQ 3) Identify the subelds and tasks in SCM that have
already been improved using AI. (SRQ 4) Identify the subelds and tasks
that have high potential to be improved by AI. The aim of SRQs 1 and 3 is
to analyse the existing literature and provide deep insight into the cur-
rent state of knowledge for both researchers and practitoners. The aim of
SRQs 2 and 4 is to identify potential gaps and opportunities for research
and practical improvement and devise a guideline for future studies.
2.2. Locating the studies
In order to locate the relevant studies, we selected the search engine
(s) and the search strings. Bearing in mind that we required databases
providing broad access to a multitude of relevant literature over a spe-
cic period of time, we opted for ve databases with large coverage of
the peer-reviewed literature related to our research question; namely,
Wiley Online Library, ScienceDirect, Emerald Insight, Taylor & Francis
and JSTOR. These databases were explored using search strings specif-
ically seeking contributions relevant to the topic (see Table 1).
As suggested by Rowley and Slack (2004), it is necessary to be very
specic regarding the search strings. For this study, the search strings
included “articial intelligence” AND “keyword”. The keywords used
were “supply chain”, “production”, “marketing” and “logistics”, which
were extracted from the comprehensive denition of SCM by Boyer and
Stock (2009). While the search protocols used to explore the individual
databases were fundamentally the same, minor modications were
applied for each search engine to account for the search mechanisms of
these databases. For ScienceDirect and Emerald Insight searches, for
instance, the search string was applied to the title, abstract and key-
words sections, while for Taylor & Francis, the search string did not
include the abstract section. In order to obtain results for Wiley Online
Library and JSTOR, we had to modify the search string to “articial
intelligence” AND “keyword 1” NOT “keyword 2” NOT “keyword 3”
NOT “keyword 4”.
2.3. Study selection and evaluation
The primary search strings used were relatively broad to ensure that
papers adopting different taxonomies were identied. Considering the
inclusion and exclusion criteria from the pilot search, we identied 758
articles. The rst criterion targets the time span of the literature, which
is between 2008 and 2018, since the majority of the papers and a large
number of new trends and applications contributing to this topic have
emerged during this period. The second criterion focuses on relevance
and quality: only peer-reviewed journal and conference papers were
considered for the review, meaning book reviews, chapters, case reports,
discussions and news articles are not included; in addition, each paper
was read by two authors to ensure that the paper has the required
quality. We applied a second set of criteria to exclude irrelevant papers.
To avoid overlooking highly relevant articles and to mitigate the pos-
sibility of forming opinions that biased the relevance we attached to
certain articles (Orwin et al., 1994), we dened a bespoke article in-
clusion protocol for reviewing titles, keywords and abstracts of studies.
This additional set of selection criteria stipulate the following: (1) the
article is written in English, (2) it employs AI as the main tool/
perspective/focus, and (3) it contributes to the eld of SCM.
To achieve an acceptable level of accuracy in applying the selection
criteria, we reviewed an initial sample of 50 abstracts by two reviewers,
checking the inter-code reliability throughout the process. The selection
of articles was checked against the criteria, the results were compared
and discussed, and issues were resolved in case of disagreement (Miles
and Huberman, 1994). Application of these criteria reduced the number
of selected articles for analysis and synthesis to 64. This process is
highlighted in Table 2; numbers without parentheses show the initial
results after the database search and the application of the inclusion/
exclusion criteria from the pilot search, while numbers in parentheses
are the selected papers after applying the second set of criteria.
2.4. Analysis and synthesis
In order to analyse the 64 articles, we broke them down into con-
stituent parts based on a specic set of characteristics feeding back to
our research question. These characteristics are as follows: the SCM eld
of the study (i.e. supply chain, production, marketing and logistics); the
respective subeld(s) of the study; the AI technique(s) used; the out-
comes and ndings; and the industry that the study aims to improve. For
synthesis, we strove to identify and describe the associations of the
different characteristics.
2.5. Reporting the results
Targeting an academic audience, the results of this study are pre-
sented in the form of tabulations, statistics and discussions. Following
the suggestion of Denyer and Traneld (2009), the ndings and dis-
cussion section encompasses a summary of the reviewed literature in
terms of extracted data, highlighting what is known and what is un-
known about the research question.
3. Analysis and synthesis
After gathering the appropriate collection of relevant papers, the
data analysis and synthesis begins. Whereas the aim of the analysis is to
breakdown each study into its constituent parts and describe the overall
relationships and connections, the aim of synthesis is identify the asso-
ciations between parts of different studies (Traneld et al., 2003).
Analysis and synthesis of this study are represented through the
following subsections.
3.1. Distribution and statistics
Article type and date. Out of the 64 articles identied for review, 14
contribute to marketing, 6 to logistics, 23 to production and 21 to the
general eld of supply chain. As depicted in Fig. 2, the time span of this
review was 2008 to 2018, with the literature being sourced from peer-
reviewed journals and conference proceedings through a database
search. 25% of the literature came from conference proceedings, and
75% were journal papers (Fig. 3).
3.2. Categorical analysis of the literature
Table 3 assigns the different articles to the SCM elds of marketing,
Table 2
Search results.
Science
direct
Emerald
insight
JSTOR Wiley Taylor &
Francis
Subleds Articial Intelligence Total
Marketing 16
(10)
22
(1)
75
(1)
44
(1)
3
(2)
160
(15)
Logistics 44
(3)
5
(2)
41
(0)
13
(0)
2
(1)
105
(6)
Supply
Chain
25
(15)
5
(2)
15
(1)
1
(0)
4
(2)
50
(20)
Production 209
(14)
23
(6)
94
(1)
112
(0)
5
(2)
443
(23)
Total 294
(42)
55
(11)
225
(3)
170
(1)
14
(7)
758
(64)
R. Toorajipour et al.
Journal of Business Research 122 (2021) 502–517
505
logistics, production and supply chain. We will now summarise the
subelds and contents of each category.
A total of 14 articles can be assigned to the eld of marketing. Three
articles independently refer to sales: Lee et al. (2012) propose a system
based on an articial neural network (ANN) to forecast sales in the
convenience store industry, Ketter et al. (2012) propose a real-time
model for sales management using agent-based systems (ABSs), and
O’Donnell et al. (2009) use a genetic algorithm (GA) to present an online
system that helps sales promotion. Two articles address pricing: Shakya
et al. (2010) use various AI techniques to propose a pricing system for
diverse products and services, and Peterson and Flanagan (2009) use an
ANN to suggest a pricing model with lower errors and greater precision.
Two articles focus on segmentation: Casabay´
o et al. (2015) combine
cluster analyses and fuzzy models to propose an approach for market
segmentation, and Sarvari et al. (2016) use an ANN and k-means clus-
tering for customer segmentation. Bae and Kim (2010) and Martínez-
L´
opez and Casillas (2009) focus on consumer behaviour; the former use
association rule and tree-based models to suggest an integrative con-
sumer behaviour prediction model, whereas the latter use a genetic
fuzzy system to propose a methodology for knowledge discovery in
databases that supports consumers’ decision behaviours. Stalidis et al.
(2015) also use an ANN when proposing a marketing decision support
framework. Rekha et al. (2016) explore the use of support vector data
description to facilitate the selection of contacts. Martínez-L´
opez and
Casillas (2013) carry out a historical literature review of AI-based sys-
tems applied to marketing. Kwong et al. (2016) propose a methodology
of integrating affective design, engineering and marketing to dene the
design specications of new products using a GA and fuzzy models. And,
nally Taratukhin and Yadgarova (2018) suggest an approach for
product life-cycle management (PLM) with multi-agent systems (MASs).
Seven articles belong to the logistics eld. Two refer to container
terminal operations and management: Salido et al. (2012) employ
heuristics through a decision support system (DSS) to calculate the
number of reshufes needed to assign containers to the appropriate
places, whereas Cardoso et al. (2013) use automated planning to pro-
pose a system for container-loading problems. Wang et al. (2012) pro-
pose an intelligent system for industrial robotics in the logistics eld.
Knoll et al. (2016) adopt a predictive inbound logistics planning
approach, whereas Klumpp (2018) develops a multi-dimensional con-
ceptual framework to distinguish between better- and worse-performing
human–articial collaboration systems in logistics. Eslikizi et al. (2015)
address inter-organisational lot-sizing problems by implementing a set
of self-interested and autonomous agents. Finally, Lee et al. (2011)
examine how AI techniques and radio-frequency identication (RFID)
can enhance the responsiveness of the logistics workow.
Fig. 2. Time distribution.
Fig. 3. Paper type distribution.
Table 3
Summary of the categorisation of the literature.
Field Subeld Study
Marketing Sales forecasting Lee et al. (2012)
management Ketter et al. (2012)
promotion O’Donnell et al. (2009)
Pricing Shakya, Chin, and Owusu
(2010); Peterson and Flanagan
(2009)
Segmentation Market Casabay´
o, Agell and S´
anchez-
Hern´
andez (2015)
Customer Sarvari, Ustundag and Takci
(2016)
Consumer behaviour Bae and Kim (2010); Martínez-
L´
opez and Casillas (2009)
Marketing decision support Stalidis, Karapistolis and
Vafeiadis (2015)
Direct marketing Rekha, Abdulla and Asharaf
(2016)
Industrial marketing Martínez-L´
opez and Casillas
(2013)
New products specication design Kwong et al. (2016)
Product life-cycle management Taratukhin and Yadgarova
(2018)
Logistics Container terminal operations and
management
Salido et al. (2012); Cardoso
et al. (2013)
General Wang et al. (2012)
Inbound Logistics Processes Knoll, Prüglmeier and Reinhart
(2016)
Logistics systems automation Klumpp (2018)
Lot-sizing Eslikizi et al. (2015)
Logistics workow Lee et al. (2011)
Production Assembly lines Kucukkoc and Zhang (2015)
automation Sanders and Gegov (2013)
Production monitoring Olsson and Funk (2009)
forecasting Li, Chan and Nguyen (2013);
Gligor, Dumitru and Grif
(2018); Sheremetov et al.
(2013)
systems Küfner et al. (2018); Ennen
et al. (2016)
planning and
scheduling
Ławrynowicz (2008); Sousa
and Tavares (2013)
data Qui˜
n´
onez-G´
amez and
Camacho-Vel´
azquez (2011)
Integrated production
management
Bravo et al. (2011)
General Mayr et al. (2018)
Manufacturing systems Martinez-Barbera and Herrero-
Perez (2010); Heger et al.
(2016)
decision
support
Kasie et al. (2017)
problem-
solving
Camarillo, Ríos and Althoff
(2018)
Quality control and
improvement
Taylan and Darrab (2012)
monitoring Brandenburger et al. (2016)
Product line optimisation Tsafarakis et al. (2013)
Workow Ma, Leung and Zanon (2018)
Product-driven control Trentesaux and Thomas
(2012)
Low-volume production Munguia, Bernard and Erdal
(2011)
Supply
chain
Demand forecasting Küfner et al. (2018);
Amirkolaii et al. (2017); Bala
(2012); García, Villalba and
Portela (2012); Geem and
Roper (2009); Mobarakeh
et al. (2017);
Facility location Vargas Florez et al. (2015)
Supplier selection Ferreira and Borenstein
(2012); Vahdani et al. (2012);
Supply chain network design Zhang et al. (2017)
Supply chain risk management Tsang et al. (2018)
Inventory replenishment Sinha, Zhang and Tiwari
(2012)
(continued on next page)
R. Toorajipour et al.
Journal of Business Research 122 (2021) 502–517
506
A total of 23 articles pertain to production. Kucukkoc and Zhang
(2015) offer a GA-based model for parallel two-sided assembly line
balancing problems, whereas Sanders and Gegov (2013) review some of
the applications and examples of AI tools for assembly automation.
Olsson and Funk (2009) present a CBR-based system for production
monitoring. Three articles concentrate on production forecasting and all
of them employ ANNs. For example, Li et al. (2013) evaluate the
applicability of neural decision tree (NDT) for modelling petroleum
production data in addition to comparison of the NDT and ANN ap-
proaches for prediction of petroleum production. Gligor et al. (2018)
propose an ANN-based solution for forecasting the electricity production
of a photovoltaic power plant, and Sheremetov et al. (2013) focus on
different models, such as a feedforward neural network model and a
Gamma classier for forecasting in the time series context of petroleum
engineering. In production systems, Küfner, Uhlemann, and Ziegler
(2018) utilise decentralised data analysis for decentralised data reduc-
tion and information extraction; their model can also detect production
faults and reduces machine maintenance costs. Ennen et al. (2016)
implement a self-learning production ramp-up system. In production
planning and scheduling, Sousa and Tavares (2013) present a study of
different planning approaches, while Ławrynowicz (2008) proposes an
AI-based methodology. Qui˜
n´
onez-G´
amez and Camacho-Vel´
azquez
(2011) offer an AI-based classication methodology for validation of
production based on ANN, GA and data mining. Bravo et al. (2011)
implement a distributed AI architecture to approach the problems of
integrated production management. Mayr et al. (2018) identify and
introduce exemplary application scenarios for knowledge-based sys-
tems. Martinez-Barbera and Herrero-Perez (2010) and Heger et al.
(2016) address manufacturing systems using fuzzy logic (FL) and
Gaussian models. Kasie, Bright, & Walker (2017) approach
manufacturing decision support using case-based reasoning (CBR) and
rule-based reasoning (RBR). Camarillo et al. (2018) address
manufacturing problem-solving using CBR and a production-oriented
approach In quality control and improvement, Taylan and Darrab
(2012) demonstrate the use of AI techniques to propose an approach for
the design of fuzzy control charts. In quality monitoring, Brandenburger
et al. (2016) suggest a system for quality monitoring and data repre-
sentation. The rest of the articles target various subelds of production:
Tsafarakis et al. (2013) propose a hybrid particle swarm optimisation
approach for product line optimisation; Ma et al. (2018) propose an AI-
based workow framework for steam-assisted gravity drainage (SAGD)
reservoirs; Trentesaux and Thomas (2012) present the concept of
product-driven control; and Munguia et al. (2011) propose a tool for the
assessment and selection of rapid prototyping/manufacturing systems
for low-volume production using ANN and FL.
20 articles relate to the supply chain. A signicant portion of the
articles in this eld are concerned with forecasting. Five articles are
devoted to demand forecasting. Efendigil et al. (2009) propose an AI
forecasting mechanism modelled using ANNs and adaptive network-
based fuzzy inference system techniques to manage the fuzzy demand
with incomplete information. Amirkolaii et al. (2017) present a survey
on forecasting methods used in supply chains to select the best-
performing AI methods. Bala (2012) develop an AI forecasting model
for retailers based on customer segmentation to improve the perfor-
mance of inventory. García et al. (2012) propose an intelligent system
for time series classication using support vector machines. Geem and
Roper (2009) propose an articial neural network model to efciently
estimate the energy demand. Mobarakeh et al. (2017) investigate fore-
casting methods, their variants and articial intelligence (AI) methods
to propose best method variant that is capable of accurate demand
forecasting. Vargas Florez et al. (2015) propose an AI-based humani-
tarian facility location DSS that is able to adequately manage the
response to a disaster despite failures or inadequacies of infrastructure
and potential resources. Two articles focus on supplier selection: Fer-
reira and Borenstein (2012) suggest a fuzzy-bayesian supplier selection
model, whereas Vahdani et al. (2012) suggest a neuro-fuzzy supplier
selection model for the cosmetic industry. By means of ABSs and MASs,
Ferreira and Borenstein (2011) present a simulation framework for
supply chain planning, Zgaya et al. (2009) suggest a negotiation model,
and several studies examine and elaborate the use of RFID technology
integrated into an ICT framework (Dias et al., 2009; Parida et al., 2016;
Oghazi et al., 2018). In a study, Zhang et al. (2017) propose an efcient
bio-inspired algorithm for designing optimal supply chain networks in a
competitive oligopolistic market. Tsang et al. (2018) propose an Internet
of Things (IoT)-based risk monitoring system (RMS) specically created
with AI techniques in mind. Sinha et al. (2012) suggest an algorithm to
solve the problem of inventory replenishment in the relationship be-
tween distributed plant, warehouse and retailer. Two articles concen-
trate on the SCM process: Pino et al. (2010) focus on a multi-agent
supply chain system, and Merlino and Sproģe (2017) explore the main
technological changes and the most advanced cases of augmented sup-
ply chains. A literature review by Min (2010) explores different subelds
of AI that are suitable for solving practical problems relevant to SCM.
Chong and Bai (2014) examine the predictors of open inter-
organisational system adoption, using RosettaNet as a case study.
Finally, Regal and Pereira (2018) conceptually model intelligent main-
tenance systems and spare-parts supply chain integration as a means to
benet areas such as AI, reasoning and context-aware systems.
3.3. AI techniques
Another characteristic we analysed was the AI technique that the
articles used or revolved around. By “AI techniques”, we mean algo-
rithms, architectures, data or knowledge formalisms, and methodolog-
ical techniques, that can be described in a precise, clean manner (Bundy,
1997). To conduct the analysis, we rst identied the scientic sources
that report a comprehensive list of AI techniques in practice and scien-
tic literature. Studies by Chen et al. (2008) and Min (2010) introduce a
group of AI techniques and their application. More comprehensively,
Bundy (1997) presents a thorough catalogue of AI techniques as a
reference work available for different purposes. Other references are
mentioned independently of the source in which they are being cited.
Table 4 presents the AI techniques used in every eld of the literature,
and Table 5 presents all the AI techniques used, along with their
frequencies.
Most of the variety in terms of AI techniques can be seen in the eld
of production. Aside from the higher number of articles, this is primarily
due to the practical nature of the literature in this eld, which typically
encompasses experimental research, case studies and real-life problem-
solving studies. ANNs, GA and ABSs are the most frequently used tech-
niques in production. With 12 techniques used, marketing is second in
terms of variety, with the most frequent techniques being ANNs and GA,
with four appearances each. The third-most-diverse eld is supply chain,
with 21 articles and 11 AI techniques. ANNs, fuzzy models and GA are
more frequent in this eld. Finally, logistics has the least variety, with
eight techniques from seven articles.
Table 5 presents the total frequency of AI techniques through the
entire literature. Since some articles employed more than one AI tech-
nique, the total frequency of AI techniques is greater than the number of
articles. More precisely, 41 articles (64.1%) have a single-technique
approach, 13 (20.4%) have a double-technique approach, three (4.6%)
Table 3 (continued )
Field Subeld Study
Crisis management Zgaya et al. (2009)
Global value chains Dias et al. (2009)
Supply chain process management Pino et al. (2010);
General Min (2010)
Supply chain integration Chong and Bai (2014)
Supply chain planning Ferreira and Borenstein (2011)
Maintenance systems Regal and Pereira (2018)
Sustainability Merlino and Sproģe (2017)
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have a multi-technique approach, and seven (10.9%) articles adopt a
more generalised view on AI. It is noteworthy that studies used double or
multiple AI techniques in two manners: by combining them and making
a hybrid and by employing them in a sequential manner.
The most popular AI technique is ANN (used 15 times), which can be
seen across all the elds. The second-most-frequent technique is FL/
modelling (12 times), a technique capable of extending the simple
Boolean operators as a means to express implications Bundy (1997).
Intelligent agents, in the form of MASs and ABSs, are in third place (nine
times), perhaps due to the wide range of their applications. GA is the
next most popular technique (seven times), followed by data mining and
CBR (four times each), swarm intelligence and support vector machine
(SVM) (three times each), and simulated annealing and automated
planning (two times each). The rest of the techniques – association rule,
tree-based models, hill climbing, k-means clustering, expert systems,
heuristics, robot programming, stochastic simulation, Bayesian net-
works, RBR, decision trees and Gaussian models – are used once.
3.4. Distribution of outcomes
Another factor on which the analysed literature is based is the
outcome of the research. Like any scholastic methodology or purpose,
every study has a unique outcome (an algorithm, a system, a method-
ology, etc.). Table 6 presents the studies’ outcomes, which have been
categorised by eld.
Experimental/practical studies usually produce models, systems,
frameworks, approaches, algorithms, methods and methodologies,
whereas conceptual/philosophical studies deliver literature reviews,
examples, concepts, ontologies, comparisons, forecasts and explora-
tions. In line with this, we conclude that the selected marketing litera-
ture has an experimental/practical orientation, since the majority of
results adopt forms similar to those just outlined. For the same reason,
logistics is considered to have an experimental/practical orientation.
Furthermore, whereas approximately 25% of the production literature
Table 4
Categorisation of AI techniques based on elds.
Field AI technique
Marketing 1.Articial neural networks (4)
2. Genetic algorithm (4)
3. FL/modelling (3)
4. Agent-based/multi-agent systems (2)
5. Swarm intelligence (1)
6. Simulated annealing (1)
7.Association rule (1)
8.Tree-based models (1)
9.Support vector machines (1)
10.General forms of AI (1)
11.k-means clustering (1)
12. Hill climbing (1)
Logistics 1.Articial neural networks (1)
2.Agent-based/multi-agent systems (1)
3.Data mining (1)
4.Simulated annealing (1)
5.Automated planning (1)
6. Robot programming (1)
7. General forms of AI (1)
8. Heuristics (1)
Production 1.Articial neural networks (8)
2.FL/modelling (5)
3. Case-based reasoning (4)
4. Genetic algorithm (3)
5.Agent-based/multi-agent systems (2)
6. Data mining (2)
7.Decision trees (2)
8.General forms of AI (1)
9. Gaussian (1)
10. Rule-based reasoning (1)
11. Automated planning (1)
12.Swarm intelligence (1)
13.Expert systems (1)
Supply chain 1.Articial neural networks (5)
2.FL/modelling (4)
3. Agent-based/multi-agent systems (4)
4. General forms of AI (4)
5.Physarum model (1)
6. Bayesian networks (1)
7.Swarm intelligence (1)
8.Data mining (1)
9.Support vector machines (1)
10. Stochastic simulation (1)
Table 5
Total frequency of AI techniques used.
AI techniques Amount
Articial neural networks 18
Fuzzy logic and models 12
Multi-agent and agent-based systems 9
Genetic algorithm 7
General forms of AI 7
Data mining 4
Case-based reasoning 4
Swarm intelligence 3
Support vector machines 2
Simulated annealing 2
Automated planning 2
Decision trees 2
Association rule 1
Tree-based models 1
Hill climbing 1
k-means clustering 1
Expert systems 1
Heuristics 1
Robot programming 1
Stochastic simulation 1
Bayesian networks 1
Physarum model 1
Rule-based reasoning 1
Gaussian models 1
Table 6
Distribution of outcomes.
Field Outcome Amount %
Marketing Model 4 28.5
Approach 3 21.4
System 2 14.2
Methodology 2 14.2
Framework 1 7.1
Method 1 7.1
Literature review 1 7.1
Logistics System 4 57.1
Approach 2 28.5
Framework 1 14.2
Production Approach 6 26
System 4 17.3
Methodology 2 8.6
Framework 2 8.6
Application and comparison 1 4.3
Application scenario 1 4.3
Applications and examples 1 4.3
Architecture 1 4.3
Model 1 4.3
Concept 1 4.3
Concept and applications 1 4.3
Assessment tool 1 4.3
Comparative study 1 4.3
Supply chain Model 6 28.5
System 4 19
Method 3 14.2
Algorithm 2 9
Forecast 1 4.7
Ontology 1 4.7
Literature review 1 4.7
Exploration 1 4.7
Framework 1 4.7
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produced conceptual/philosophical outcomes, the bulk of the results in
this eld are experimental/practical. Finally, SCM – with fewer than
25% of conceptual/philosophical outcomes – is considered experi-
mental/practical.
4. Discussion
To answer the main RQ, the following four SRQs are devised to
provide a clearer and more comprehensive answer. In this part, we strive
to provide responses to each of these steps.
SRQ 1: What are the most prevalent AI techniques applied in SCM
studies?
Although many AI techniques can be applied to SCM, the results of
our study show that some are used more than others. The most prevalent
and inuential is ANNs, an information-processing technique that can be
used to nd patterns, knowledge or models from an extensive amount of
data (Aleksendri´
c and Carlone, 2015). ANNs are normally based on
mathematical regression to correlate input and output streams from and
to process units. Such models predominantly depend on a large number
of experimental data (Yang and Chen, 2015). ANNs are typically used as
the main technique in computational intelligence due to their impres-
sive versatility (Kasabov, 2019). In SCM, such applications range from
sales forecasting, marketing DSSs, pricing and customer segmentation to
production forecasting, supplier selection, demand management and
consumption forecasting. Li (1994) argues that ANNs are increasingly
popular in today’s business elds. This is mainly due to their capability
of solving data-intensive problems in which the rules or algorithms for
solving the problem are unknown or difcult to express (Chen et al.,
2008).
The second technique is FL/modelling, which represents the border
between AI and non-AI techniques (Bundy, 1997). While it has been
approximately 40 years since Zadeh (1965) rst introduced the FL
theory, it has only recently become a prominent technique for devel-
oping complicated models and systems. The reason for this rapid
development, as well as for its growing popularity, is that such an
approach addresses qualitative information perfectly in that it resembles
the manner in which humans make inferences and decisions (Kera-
mitsoglou et al., 2006).
Another group of techniques that are frequently used in SCM studies
is that of ABSs and MASs. An agent-based model is a type of computa-
tional model that simulates the actions and interactions of autonomous
agents, either collectively or individually, while considering the
assessment of their inuences on the system in general. This technique
merges elements from complex systems, game theory, computational
sociology and evolutionary programming (Grimm and Railsback, 2005).
In other words, agents are entities capable of perceiving the surrounding
environment and can act autonomously and proactively to solve specic
problems. When agents interact with one another to achieve goals, they
form MASs, i.e. a network of agents (Lesser, 1995), Functioning as a
piece of software containing code and data (Parrott et al., 2003), they
are capable of modelling, designing and implementing complex systems.
It is for this reason that since the mid-1990s, agents have been widely
employed in SCM and other elds to solve several types of problems.
Examples of applications include distributed supply chain planning
(Frayret et al., 2007), design and simulation of supply chain systems
(Barbuceanu et al., 1997), analysis of the complex behaviour of supply
chains (Avci and Selim, 2017; Wang et al., 2012) and negotiation-based
collaborative modelling (Jiao et al., 2006).
Results show that one of the most inuential AI techniques in the
SCM literature is Gas, a search technique mimicking natural selection
(Kraft et al., 1997), in which the algorithm evolves to the point at which
it has adequately solved the problem. Introduced in the 1970s, GAs are a
group of computational models inspired by evolution. These algorithms
encode a potential solution to a particular problem using a data structure
similar to chromosomes. They apply recombination operators to these
structures in such a manner as to preserve crucial information. GAs are
often regarded as function optimisers, and the range of problems to
which GAs have been applied is quite extensive (Whitley, 1994). As an
AI technique, GAs address various categories of combinatorial decision
problems. These problems encompass complicated managerial chal-
lenges regarding supply chain activities of selling, sourcing, making and
delivering goods or services. GAs have increased their role in developing
managerial decision-making processes, improving supply chain ef-
ciency as a result (Min, 2015). GAs have become a popular technique in
many SCM studies due to their wide range of applications, including
multi-objective optimisation of supply chain networks (Altiparmak
et al., 2006), partner selection in green supply chain problems (Yeh and
Chuang, 2011), multi-product supply chain networks (Altiparmak et al.,
2009) and the problem-solving approach to closed-loop supply chains
(Kannan et al., 2010).
Data mining is a new discipline that has arisen by combining several
other disciplines, stimulated mainly by the growth of gigantic databases
(Hand, 2013). The primary motivating stimulus behind data mining is
that these big databases contain information that is valuable to the
database owners in that it provides insight into decision-making and
other processes. In SCM, data mining could be used in several manners,
such as controlling and monitoring warehouses, food supply chains and
sustainability in supply chains (Ting et al., 2014; Wang and Yue, 2017),
improving knowledge management and marketing (Shaw et al., 2001),
and enhancing supply chain innovation capabilities (Tan et al., 2015).
Several studies employ CBR, a technique based on the cognitive
psychological notion that humans nd their knowledge through solving
multiple problems (Clifton and Frohnsdorff, 2001). Functioning as a
paradigm of cognitive science and AI, in which the reasoning procedure
is modelled as primarily memory-based, CBR solves new problems by
retrieving gathered and saved “cases” of analogous problem-solving
episodes and by adapting the solutions to match new needs (Leake,
2001). CBR has been used in SCM studies in a number of manners, such
as designing mechanisms for supply chains under demand uncertainties
(Kwon et al., 2007), supply chain risk management (Giannakis and
Louis, 2011), supplier performance evaluations (Humphreys et al.,
2003), agile SCM (Lou et al., 2004), and supply chain negotiations (Fang
and Wong, 2010).
Forming part of collective intelligence, swarm intelligence studies
the behaviour of social insects by determining their efciency at solving
complicated problems, such as nding the shortest route between their
nest and food source or organising their nests (Saka et al., 2013). Over
the past 20 years, such a technique has attracted considerable attention
in almost every area of engineering, science and industry (Yang and
Karamanoglu, 2013). In SCM studies, it is usually utilised for the
designing of systems for pricing, product line optimisation (Tsafarakis
et al., 2013), inventory replenishment (Sinha et al., 2012), supply chain
network architecture optimisation (Kadadevaramath et al., 2012), and
minimisation of supply chain costs (Kumar et al., 2010) and the
designing of agile supply chain networks (Bachlaus et al., 2008).
Another technique employed in the AI-SCM literature is SVMs, an
approach that uses a linear classier to classify data (Peter et al., 2019)
that is capable of deciphering subtle patterns in noisy and complex data
sets (Hongmao, 2016). SVMs were introduced in the 1990s and have
been widely used in numerous applications (Gholami and Fakhari,
2017). In terms of SCM, they have been used in numerous studies and for
different purposes, such as supply chain demand forecasting (Carbon-
neau et al., 2008), time-series classication in supply chains (García
et al., 2012), supplier selection (Guosheng and Guohong, 2008), and the
designing of systems for supply chain networks (Surana et al., 2005).
In addition to the leading AI techniques discussed thus far, there are
various other techniques that are applied to SCM studies, including
simulated annealing, automated planning, association rule, tree-based
models, hill climbing, k-means clustering, expert systems, heuristics,
robot programming, stochastic simulation, Bayesian networks, the
Physarum model, RBR, decision trees and Gaussian models.
SRQ 2: What are the potential AI techniques that can be employed in
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509
SCM research?
There are many AI techniques that have received less or even no
attention from SCM researchers, despite their appropriateness for in-
clusion in future SCM studies. One of the most promising of these is
natural language processing (NLP): the study of computer programs that
take human (natural) language as input. Applications of NLP deal with
tasks ranging from the low level (such as assigning parts of speech to
words) to the high level (such as providing specic answers for ques-
tions) (Cohen, 2014). In other words, NLP is the use of computers to
understand and then process human language in the form of text or
speech (Geman and Johnson, 2001). Machine translation (the automatic
translation of text or speech from one language to another) began with
the very earliest computers (Kay et al., 1994) and is now being widely
used in many tasks in different businesses. NLP interfaces also permit
computers to interact with humans using natural language, such as to
query databases (Geman and Johnson, 2001). This feature allows NLP to
be an important facilitator in SCM, mainly due to its potential to
enhance and simplify human–machine interactions. Text mining is an
example of NLP at its most practical: nding information in prose of
various types (Cohen, 2014) to aid production, manufacturing and lo-
gistics. Such a process can also accelerate industrial procedures and
improve the process of data generation and collection as a result of
simplied interactions between humans and machines. Most NLP sys-
tems are based on formal grammar; that is, a description of the language.
Such systems usually identify the language from the sentences and
provide descriptions by dening, for instance, the phrases of the sen-
tence, their interrelationships and certain aspects of their meanings
(Geman and Johnson, 2001). The advanced use of this process is evident
in chatting robots or “chatbots”, which are being used increasingly in
today’s world. Chatbots on social networking platforms and websites
represent a new innovation in computer-based marketing communica-
tion (Van den Broeck et al., 2019). The use of NLP in the form of chatbots
has great potential in marketing campaigns, online advertisement,
brand management, customer relationship management and data
collection. As a means for improved relationships with customers
(Letheren and Glavas, 2017), chatbots present a commercially savvy
tactic, having been introduced to Facebook messenger in 2016 as a
means for companies to accelerate and facilitate their customer service
processes (Van den Broeck et al., 2019).
Tabu search (TS) is a neighbourhood search method that avoids local
minimum traps by accepting worse (or even infeasible) solutions and by
limiting the current solution neighbourhood to the solutions’ search
history (Pi´
oro and Medhi, 2004); in other words, TS is a local search
algorithm that restricts the feasible neighbourhood by using excluded
neighbours (Edelkamp and Schr¨
odl, 2012). Since the search history is
stored as a Tabu or in a forbidden list, the attribute-based Tabu memory
helps to diversify the search by avoiding short-term cycles or sequences
of similar solutions (Pi´
oro and Medhi, 2004). The main idea of this
technique is to prevent the search from becoming stuck in local minima
by preventing backward moves (Dechter, 2003). Similar to simulated
annealing, TS is not foreign to SCM, having been employed to solve the
problem of closed-loop supply chain networks (Easwaran and Üster,
2009), as well as to redesign multi-echelon supply chain networks (Melo
et al., 2012). However, this technique has potential for wider and deeper
use in SCM. Lately, metaheuristic techniques, such as simulated
annealing, GAs and TS, have been proposed to solve certain optimisation
problems (Glover and Marti, 2006), with the evidence suggesting that TS
is often overlooked in favour of these.
In the area of robotics, a robot is dened as a system of rigid bodies or
links connected by joints. Bundy (1997) names robot dynamics and
robot programming as specic AI techniques. These systems have been
in use for a long time, and every now and then, a new application is
developed. While robot dynamics concentrates on the problems of
calculating the acceleration of a robot for simulation and control, robot
programming tells a robot (a mechanical device in conjunction with an
electronic system) what to do. Moreover, the need for a more agile
supply chain is now deemed necessary by consumers that want faster
and error-free deliveries.
A Markov decision process (MDP) is often dened as a framework
with which to model decision-making, whereby the outcome is partially
based on the input of the decision maker and partially random. Littman
(2001) argues that MDPs make models in sequential, stochastic envi-
ronments; the nature of the model is that an agent or decision maker
inhabits an environment that changes state randomly in response to
action choices made by the decision maker. The environment inuences
the instant reward obtained by the agent, in addition to the probabilities
of future state transitions. The agent’s goal is to select actions that
maximise the long-term pay-off of the reward. MDP can also be used for
planning optimisation, allowing the decision maker to determine at
what states specic actions should be taken.
Another AI technique that has not been used to its full potential in
SCM is that of expert systems; while our review shows some degree of
utilisation, this technique has a lot to offer. Expert systems are pre-
dominantly used in the elds of reasoning and decision-making in that
they emulate the decision-making abilities of humans. This technique
can be employed in DSSs, particularly in terms of lot-sizing and sup-
plier/buyer selection.
SRQ 3: What are the subelds and tasks in SCM that have already
been improved using AI?
To elaborate on the contributions of AI on SCM subelds in more
detail, we need to take a closer look at the outcomes of the studies and
the inuences of AI on the literature. In the subeld of marketing, ANNs
have a strong impact, both when used solely or in combination with
others. In terms of the latter, a combination of self-organisation map
neural networks and radial basis function neural networks has been used
to create an “Enhanced Cluster and Forecast Model”, which, in com-
parison to other similar models, is easier to build, has higher accuracy
and is suitable as a forecasting system in real-world sales campaigns.
ANNs are also used to improve marketing DSSs by increasing accuracy,
with an ANN-based marketing support framework being devised to
provide automatic classication of unknown cases, done not by per-
forming a new data analysis but rather by generalising the knowledge
derived from already-analysed examples. Furthermore, ANNs are used
to develop customer segmentation – an approach that is vital in mar-
keting campaigns – and improve customer segmentation. Fuzzy models
are the second-most-prevalent technique in the literature, having been
used to make an innovative segmentation approach that combines
cluster analyses and fuzzy learning techniques to produce higher accu-
racy. Pricing, one of the components of the marketing mix, is enhanced
by a multi-level ANN-based model, which generates signicantly lower
pricing errors, has greater pricing precision out-of-sample and extrap-
olates better from more volatile pricing environments compared to he-
donic models (which typically utilise large numbers of dummy
variables). Another pricing system, which employs a combination of a
GA, swarm intelligence, simulated annealing and hill-climbing tech-
niques, has been developed for a range of products and services to
optimise price and production policies. A particular advantage of such a
system is the signicant reduction in the cost of implementing, and the
increased expressiveness of, the revenue management model.
Consumer behaviour has been extensively studied in the manage-
ment science and operations management community (Wei and Zhang,
2018), with AI being a particular inuence. On a related note, it is worth
acknowledging that current AI approaches are developed by combining
various AI techniques, rather than by employing a singular AI technique.
Target marketing strategies often combine the association rule and tree-
based models within an integrated model to predict whether a customer
buys a specic product, with the results demonstrating good perfor-
mance compared to other models. Moreover, GA and fuzzy models have
been combined to devise a brand-new complete methodology for
knowledge discovery in databases, to be applied in marketing causal
modelling and with utilities to be used as a marketing management
decision support tool for consumer behaviour. Development of
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technologies can result in effective and efcient sales management
(Madhavaram and McDonald, 2010), and the current digitisation shift
will have profound implications for personal selling and sales manage-
ment functions (Syam and Sharma, 2018).
In sales, GAs have been used to devise an online system for reducing
the bullwhip effect along supply chains, from which an optimal ordering
policy for each member of the supply chain can be determined, which in
turn reduces both costs and the bullwhip effect. Moreover, ABSs have
been employed to create a real-time sales management model with the
ability to predict future economic conditions to make tactical sales de-
cisions. This AI-based model outperforms more traditional short- and
long-term predictive modelling approaches. The concept of direct mar-
keting has gained popularity in recent years (Łady˙
zy´
nski et al., 2019),
allowing producers to customise their product properties for individual
customers in a unique manner, thus increasing campaign efciency and
reducing costs (Hossein Javaheri et al., 2014). To improve the process of
direct marketing, SVMs have been used to develop a method that is able
to predict with higher accuracy compared to existing methods.
The eld of industrial marketing has evolved profoundly since it was
rst introduced in 1971, with part of this evolution being the inclusion
of research in SCM (Ellram and Ueltschy Mureld, 2019). As a revolu-
tionary factor of Industry 4.0, AI-based systems applied to industrial
marketing have been an inuential and important part of the current
literature. Another eld that has been improved by AI is PLM. A multi-
agent approach for PLM, which integrates business and engineering
knowledge during the whole product life cycle, has yielded improved
performance. Finally, GA and fuzzy models have been merged to
develop a methodology in which affective design, engineering and
marketing are integrated simultaneously as a means to dene the design
specications of new products.
According to the literature, the focus in logistics is predominantly on
two subelds: rst, container terminal operations and management and
second, lot-sizing. Within these subelds, it is common to see DSSs used
to assist terminal operators in nding the most appropriate solution in
each particular case. To this end, a heuristic planner (that calculates the
number of reshufes needed to assign the containers to the appropriate
places) and a greedy randomized adaptive search procedure (that gen-
erates an optimised order of vessels to be served according to existing
berth constraints) are combined to solve the berth allocation problem
and the quay crane assignment problem. Metaheuristics are general
high-level processes that coordinate simple rules and heuristics to nd
appropriate or optimal approximate solutions to computationally dif-
cult combinatorial optimisation problems (Aiex et al., 2002). GRASP
(Feo and Resende, 1995) is a multi-start or iterative metaheuristic in
which every iteration comprises two phases: construction and local
search. The former creates a solution; if it is not feasible, then a repair
procedure should be applied.
Automated planning is an AI technique that studies a deliberation
process that chooses and organises actions by anticipating their expected
outcomes computationally (Ghallab et al., 2004). It is often employed to
create a fully automated system, capable of carrying out the entire
loading process through the information relating to the products, the
industrial plant and the transporting devices. In lot-sizing, ABSs are used
to develop a simulation framework for supply chain planning, which is
able to capture all aspects of the sample supply chain and to model the
regulations not only by highlighting their simple external constraints but
also by incorporating them in the decision-making process of each agent.
As such, the agents can quickly respond to norms and adapt their
behaviour to nd more adequate strategies with which to plan the
operation of the supply chain. In addition to ABSs, simulated annealing
has been utilised to respond to an inter-organisational lot-sizing problem
in a more efcient, cost-effective manner. The inbound logistics process
is another related subeld that has been improved by using an approach
based on data-mining techniques. AI has been used to develop a multi-
dimensional conceptual framework that aids in the planning of
human–articial collaboration systems in logistics systems automation
by distinguishing between the performances of such systems ex ante for
investment decision purposes. Finally, robot programming is used to
improve real-life strategies in the form of an intelligent system for in-
dustrial robotics in logistics.
In the eld of production, 18 subelds have been addressed using AI
techniques. Assembly lines and automation are addressed by GAs, fuzzy
models, ANNs, and CBR, which has led to more sufcient algorithms and
improved performances. Production forecasting has received focus from
several studies employing decision trees and ANNs to devise models and
approaches that are more efcient and demonstrate competitive per-
formance. The three subelds of manufacturing – systems, decision
support, and problem-solving – are inuenced by Gaussian models and
FL, CBR and RBR, and CBR, respectively. This has led to an improved
dynamic approach for parameter adjustment of dispatching rules, an
enhanced automated guided vehicle system, a less complex decision
support framework and a novel approach for solving manufacturing
problems. Quality monitoring, control and improvement has been
addressed by data mining and FL, leading to the creation of a precise
system for quality monitoring and data representation and a systematic
approach for designing fuzzy quality control tools. Production planning
and scheduling are inuenced by expert systems, GA and automated
planning, which has led to improved methodologies and approaches.
Production monitoring, systems and data are addressed by CBR, ANNs,
and data mining, ANNs and FL, respectively. This has resulted in high-
performing monitoring systems, a decentralised data analysis integra-
tion concept with higher accuracy and an adaptable AI-based classi-
cation methodology for validation of production data. MASs and ABSs
are utilised to enhance subelds of integrated production management
and production systems, leading to highly effective architecture and a
more stable self-learning production ramp-up system. Swarm intelli-
gence – the collective behaviour of decentralised, self-organised systems
(Beni, 2009) – has been utilised to address product line optimisation by
constructing an approach for optimal industrial product lines. The
concept and applications of product-driven control are addressed by
MASs, with the expected advantages and the related problems also being
discussed. Being the most prevalent AI technique in the literature, ANNs
have been employed to enhance production workow by creating a
workow framework that identies associated shale heterogeneities in
SAGD reservoirs based on features extracted from production time-series
data. ANNs, in combination with FL, have been used to build a more
effective tool for assessing and selecting rapid prototyping/
manufacturing systems for low-volume production.
Supply chain demand forecasting and management has remained the
centre of focus for SCM, with ANNs, fuzzy models, data mining and
SVMs leading to forecasting methods with higher accuracy, improved
performances and more effective approaches. Supplier selection is
another important topic that has attracted attention from researchers. In
this subeld, Bayesian networks, ANNs and FL are utilised, resulting in
supplier selection models with greater effectiveness and performance. In
supply chain, MASs have been employed to simulate a SCM process
management network and propose a standard one. In other subelds,
the use of stochastic simulation has led to the creation of a new hu-
manitarian facility location DSS, Physarum models being used to in-
crease the efciency and practicality of supply chain network designs,
and FL being utilised to enhance the effectiveness of risk management
and monitoring systems. A co-evolutionary immuno-particle swarm
optimisation algorithm has addressed the problem of inventory replen-
ishment in distributed plants, warehouses and retailers by using an al-
gorithm that is superior to conventional ones in terms of cost-
effectiveness. For supply chain planning, ABSs have been employed to
design a framework that quickly reacts to norms and adapts to nd
better strategies. Crisis management has become an important issue due
to globalisation and outsourcing, and supply chains have become more
exposed to disruptive external incidents (Ponis and Ntalla, 2016). AI
researchers have addressed this issue using MASs and have developed an
architecture characterised by independent agent zones sharing
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511
information that focuses on provision balancing in order to avoid stock-
out conditions and effective resource balancing to reduce risk and the
bullwhip effect. As a growing engineering discipline, intelligent main-
tenance relates to the analysis of multivariate data from multiple sources
and provides users with information about systems, products and ma-
chines (Lapira et al., 2013), from which a conceptual model of intelli-
gent maintenance systems and spare-parts supply chain integration has
been devised. Consumption forecasting is addressed through SVMs and
ANNs, which have been used to develop an improved oil consumption
forecasting model. Exploration of the main technological changes in
sustainable supply chains has been performed from an AI perspective,
ranging from material handling (Martinez-Barbera and Herrero-Perez,
2010) to production and distribution (Merlino and Sproģe, 2017).
Global value chains and supply chain integration are addressed through
the use of MASs and ANNs, respectively, which has led to improved
models and forecasts.
It is noteworthy that the degree of focus on different topics varies,
with some topics not receiving any attention at all, deemed here to be
gaps in the literature. In general, in marketing, the subelds of sales and
segmentation have received the most attention, while pricing, consumer
behaviour, marketing decision support, direct marketing, industrial
marketing and PLM have received less, despite all employing AI tech-
niques. In logistics, container terminal operations and management
received more attention than the other subelds of inbound logistics
processes, logistics systems automation, lot-sizing and logistics work-
ow. In production, the subelds of assembly, quality, manufacturing
and forecasting were more frequently cited compared to product line
optimisation, workow, product-driven control and low-volume pro-
duction. In supply chain, the subeld of demand forecasting was the
most commonly cited, while supply chain integration, supply chain
planning, supplier selection, supply chain network design and supply
chain risk management received less attention.
SRQ 4: What are the subelds and tasks that are likely to be improved
by AI?
AI is an industry 4.0 technology that is capable of revolutionising
many industries and elds (Kearney et al., 2018; Townsend and Hunt,
2019). As such, almost all the elds of SCM, as well as its subelds, are
prone to being inuenced by AI.
Discussing marketing mix, Kotler (1982) looks at price, place, pro-
motion and product. Our results show that most marketing articles that
focus on AI revolve around price, sales and segmentation, with less
attention given to promotion, product and place. Overall, sales promo-
tion, advertising, inventory, sales force, public relations and direct
marketing are the subelds that can be improved dramatically through
the use of AI.
Taking logistics as an example, distribution and transportation, lo-
gistics hub management, healthcare logistics and logistics risk man-
agement are likely to be improved with AI due to both their applicative
potential and the lack of research in this eld. In supply chain, the
subelds of buyer selection, nancial SCM, automated replenishment,
smart warehousing and green supply chain are those in need of more
attention from an AI perspective. Finally, in production, subelds such
as mega-projects management and advanced project process manage-
ment are important topics for consideration.
5. Conclusion
Recent breakthroughs in computing power have enabled the growth
and complexity of AI applications. Building on this further, the aim of
the current research was to clarify how AI contributes to SCM studies
based on a systematic review of the literature. We examined 64 articles
published that were identied through ve phases. Our ndings suggest
that among several different AI techniques available, some have been
employed in a wider range in comparison to others. Our results indicate
that the most prevalent AI technique is ANNs, which are usually used to
nd complex patterns that humans cannot nd. ANNs can be applied to
several categories of problems, including pattern classication,
approximation, optimisation, clustering, function, prediction, retrieval
by content and process control. The second-most-commonly used tech-
nique is FL, which is a form of multiple-valued logic that handles the
concept of partial truth. As Bundy (1997) argues, FL extends the simple
Boolean operators by providing and presenting a series of implications.
In contrast with conventional set theory, in which an object is either a
member of a set or not, a fuzzy set takes any value between 0 and 1. As
such, fuzzy models can describe vague statements through natural lan-
guage (Chen et al., 2008). Results show that FL is widely used as a
modelling tool and is also a popular technique for creating hybrid
intelligent systems. The third technique is ABS/MAS, which has a broad
application in SCM. This prevailing technique works by perceiving the
surrounding environment, followed by acting autonomously and pro-
actively to solve a specic problem. Agents have been extensively used
in SCM to solve several types of problems in supply chain planning,
design and simulation of supply chain systems, analysis of the complex
behaviour of supply chains and negotiation-based collaborative
modelling.
Other major techniques that can be considered prevalent in the
literature are GAs, a type of search technique that mimics natural se-
lection that is capable of tackling various categories of combinatorial
decision problems; data mining, which can be employed to provide in-
sights and make decisions from big data sets; CBR, a cognitive
psychological-based technique that solves new problems by retrieving
gathered and saved cases of analogous problem-solving episodes and
adapting the solutions to match new requirements; swarm intelligence,
which mimics behaviour of social insects to solve complicated problems;
and SVMs, which use a linear classier to classify data to decipher subtle
patterns in chaotic data sets. Other less prevailing AI techniques used in
SCM studies include simulated annealing, automated planning, associ-
ation rule, tree-based models, hill climbing, k-means clustering, expert
systems, heuristics, robot programming, stochastic simulation, Bayesian
networks, the Physarum model, RBR, decision trees and Gaussian
models. These techniques have been used in SCM studies but not as
frequently as ANNs, FL, ABSs, GA, data mining, CBR, swarm intelligence
or SVMs, which make for an interesting gap that should be addressed in
future research. In addition, our ndings reveal a number of AI tech-
niques in need of further research and industrial adoption, such as NLP
(machine–human interactions), TS (optimisation, robot dynamics, and
programming that focuses on creating intelligent robots) and MDP (a
framework for modelling the decision-making process).
Furthermore, we nd that the network-based nature of SCM and
logistics provides a natural framework with which to implement AI. A
network of suppliers, for instance, generates large amounts of data and
requires agile decision-making. As such, using AI tools for big data
analysis and DSSs is highly recommended. In addition, SCM companies
depend on physical and digital networks that must function harmoni-
ously amidst large volumes, lean asset allocation, low margins and time-
sensitive deadlines. AI facilitates optimisation and improving network
orchestration in an efcient manner, which cannot be achieved by
humans. Therefore, research on interactive decision-making systems
promotes a deeper understanding of AI solutions and accordingly im-
proves the capabilities of such solutions. Using such systems allows AI to
help this industry redene today’s practices by transitioning operations
from reactive to proactive, processes from manual to autonomous, ser-
vices from standardised to personalised and production planning from
forecasting to prediction. Improvements in computer chip technology
are an essential part of the widespread use of AI. Since logistics is con-
cerned with transportation, employing computer chips for tracking is a
vital step. Since tracking generates large amounts of data that can be
analysed and interpreted for many different purposes, research on such
processes for the outcome of these technologies is necessary. As an
important part of marketing, automation of customer interactions is a
new yet promising area. Voice or chatbots represent a new generation of
customer service, with high productivity levels and acceptable returns
R. Toorajipour et al.
Journal of Business Research 122 (2021) 502–517
512
on investment. Since these virtual assistants are developed to allow
more complex dialogues with customers, their use can be profoundly
effective for automation of customer service enquiries.
6. Limitations and implications
Like any other study, this research has limitations. Conducting a
literature review in order to identify the research gaps and evaluate the
current knowledge in the eld can lead to a broad perspective. Since we
aimed to cover a wide-reaching body of knowledge with several sub-
elds, we were unable to cover the details of every study. Therefore, a
more focused and single-technique evaluation is highly recommended.
This study has both managerial and theoretical implications that can
be applied to research and practice in SCM. First, this study contributes
to theory by analysing and discussing the state-of-the-art of AI in SCM. In
this regard, the most prevalent AI techniques applied in SCM studies are
covered in order to provide a comprehensive perspective on the existing
literature on the topic. Answering the second SRQ, the potential AI
techniques that can be employed in SCM research are discussed. The aim
here was to offer a wider range of less-popular techniques that might
become more inuential in future research. The third SRQ addressed the
subelds and tasks in SCM that have already been improved using AI.
The aim of this discussion was to analyse the existing literature that can
be the foundation of future studies. Moreover, it allows researchers to
identify what has been done already. Answering the nal SRQ, the
subelds and tasks that are likely to be improved by AI are discussed to
provide insight for future studies in the eld. In addition to these four
parts, this study explains the existing gaps and future research oppor-
tunities that can improve the body of the literature. Not only can this
pave the way for future researchers but also it can act as a structured
guideline that prevents repetition and bias in conducting AI-SCM
studies.
The results of our study have some implications for managers and
practitioners as well. Here, we suggest the following notions and
guidelines:
GAs have a vast set of applications that not only contribute to sci-
entic research but also have increased its role in developing managerial
decision-making processes and improving supply chain efciency (Min,
2015). GAs have become a popular technique due to its applicability to
multi-objective optimisation of supply chain networks (Altiparmak
et al., 2006), partner selection in green supply chain problems (Yeh and
Chuang, 2011), multi-product supply chain networks (Altiparmak et al.,
2009) and the problem-solving approach towards closed-loop supply
chains (Kannan et al., 2010). Hence, we suggest that managers employ
GA-based solutions in the form of bespoke software to address the
existing supply chain problems.
Another AI technique that has the potential to revolutionize the
future of SCM is NLP. Various applications of this technique are already
available; however, the full potential of NLP is yet to be discovered.
Currently, NLP is employed in the form of chatbots used in marketing
campaigns, online advertisement, brand management, customer rela-
tionship management and data collection.
Glover et al. (2008) highlight a wide range of applications for TS in
terms of SCM, including workforce planning, machine scheduling,
transport network design, network design for services, exible
manufacturing, just-in-time production, multi-item inventory planning,
volume discount acquisition, project portfolio optimisation, vehicle
routing and multi-mode routing. This paper therefore sees TS as another
potential technique for further utilisation for supply chain researchers,
managers and practitioners.
Robots have various applications, as seen in manufacturing and
warehouse logistics (e.g. Amazon Robotics). While robot dynamics and
robot programming are not new in SCM (particularly in terms of the
practical aspects of manufacturing, production and warehousing), the
vast potentials of these areas have not been fully utilised. Within the
broad subelds of SCM, our study reveals limited use of these
techniques, such as packaging and container terminal operations, which
are likely to see further robotic inuence going forward. Therefore, not
only can researchers conduct exhaustive literature reviews or empirical
studies on automatic robots but also managers can benet from the vast
applications of intelligent robots in logistics, warehousing,
manufacturing and production.
Like other decision-making models, managers can utilize MDPs in
SCM processes and tasks; although this technique has a limited use, it
has robust potential for utilisation in SCM. MDP is mainly applicable to
planning optimisation, allowing the decision maker to determine at
what states specic actions should be taken.
Expert systems can assist SCM managers in the form of DSSs,
particularly in dealing with lot-sizing and supplier/buyer selection is-
sues. Furthermore, other planning-oriented techniques of AI, such as
distributed problem-solving and hierarchical planning, are also useful
for SCM managers. Due to the forward-looking nature of supply chain
planning, such techniques are potentially applicable to this eld as well.
7. Further research
Although some studies provided insights into the use of AI in SCM,
there are still many scientic gaps. Scrutinising the collected literature,
we identied several research suggestions that were either made by
authors or elaborated on through the literature analysis and synthesis
itself. For instance, we found that while supply chain integration has
been addressed by AI researchers (Regal and Pereira, 2018), there is still
a need for more research on this topic in order to uncover more detail
and to improve the scientic evidence. Based on this aforementioned
scientic gap, effort could be made in the form of using ABSs with
advanced complexity management capabilities for solving problems in
supply chain integration. Such ABSs could also be applied to supply
chain risk or disaster management issues, both of which are recognised
gaps (Min, 2010).
Real-time pricing (RTP) is an important demand-side management
factor for adjusting the load curve to achieve peak load shifting, and as
stated in the literature (e.g. Min, 2010), it has the potential to be covered
on a deeper level using AI. It is also recognised that research on RTP is
partially country-oriented, with the majority of studies focusing pre-
dominantly on China (Jiang et al., 2019; Sun et al., 2018; Wang et al.,
2018). Hence, we suggest more studies with a focus on non-Chinese
markets.
Reverse auctions, in which sellers bid for the prices at which they are
willing to sell their products, as mentioned by Min (2010), are a major
gap in the literature, particularly from an AI perspective. We suggest
more research on reverse auctioning involving supply chain partners
using AI techniques, such as heuristic pricing methods.
Answering the second SRQ (identifying the AI techniques that have
the potential for employment in SCM studies), we suggest utilising the
following AI techniques in future research to ameliorate the current
knowledge gap. First, NLP is an interesting and promising eld with
potential to signicantly affect the SCM literature and practice by
merging with other Industry 4.0 technologies such as IoT and block-
chain. Second, due to the optimisation algorithm that can control an
embedded heuristic approach, we believe that TS deserves more scien-
tic focus in SCM studies in the future. Third, due their great applica-
bility in manufacturing, production, warehousing and logistics, robot
dynamics and programming provide a profound scientic basis for
future research. Finally, automated planning, which has signicant roles
ranging from controlling space vehicles to programming robots, requires
further attention to make new opportunities for synergy between theory
and practice.
Rule-based expert systems, which are primarily based on sets of “if-
then” statements, can be developed in the form of an assistant in logistics
for outsourcing or manufacturing contract decisions, and such a process
(Min, 2010) warrants further research.
While studies on transportation and mobility are not limited (e.g.
R. Toorajipour et al.
Journal of Business Research 122 (2021) 502–517
513
Dimitrakopoulos et al., 2020; Franklin and Paez, 2018; Redding and
Turner, 2015), our literature review implies a gap in terms of the use of
AI techniques in this area. Such a gap could be remedied by devising, for
example, a hybrid meta-heuristic to integrate the AI traits of GAs with
those of ant colony or other similar optimisation techniques.
Logistics and supply chain optimisation are new topics, but their use
of AI techniques is considered important if the knowledge base of the
eld is to be enhanced. Another topic, arguably even more important, is
supply chain and logistics cost management and optimisation (Min,
2010). Moreover, although some work has been done on facility location
(e.g. Vargas Florez et al., 2015), this topic can be profoundly enhanced
by applying novel AI techniques. Based on our results, we suggest the
use of the FL approach integrated with GAs or ANNs to address this
issue. Other AI techniques that are potentially apt for further research in
SCM include expert systems and the Markov decision process.
Regarding the application of AI techniques in various industries, our
research shows that the majority (76.5%) of the literature does not
target or address a specic industry. Of those that do, the focus is pre-
dominantly on industries of convenience stores, the energy market,
tourism, real estate, container terminals, oil production, electric drives,
carpet, cosmetics and aircraft spare parts. While not being industry-
specic results in a general perspective that can be applied to several
industries, specic AI-SCM studies that focus on particular industries are
also needed in order to create a more specialised and profound scientic
background. More specically, the literature exhibits a need for expert
systems to be used to improve airline revenue management (Min, 2015).
Other industries, such as heavy vehicles, drones and ying robots, the
postal service, manufacturing, automobile and tourism, are potential
avenues for more academic research. Moreover, due to the synergy and
the complementary nature of hybrid AI techniques, it is more likely that
future research trends will move towards these methods.
The results also show that many studies focus on creating and
developing models, frameworks, approaches, solutions, etc., with few
studies testing their usability, application or generalisability. This gap
could be addressed through the use of real-world practical data to test
the proposed items. Moreover, using AI for complex problems/sce-
narios, multiple case study designs and empirical comparison of studies
on the same topic holds the potential to enhance the existing literature.
In general, AI offers the ability to optimise and improve network
orchestration with a level of efciency that cannot be achieved with
human thinking alone. Hence, we encourage research on the interactive
decision-making systems to promote a deeper understanding, and thus
improve the capabilities, of AI tools.
As a result of the discussions and following the recent trends in
research (e.g. Byun et al., 2020; Kotler et al., 2019; Mahroof, 2019;
Verma and Gustafsson, 2020), this study further recommends the
following research propositions to be explored in future research. Each
proposition corresponds to one or more relevant SRQs:
Proposition 1:.ANNs, FL and ABS/MAS are the most prevailing AI
techniques in SCM, and they have affected this eld the most. (SRQ 1)
Proposition 2:.ANNs, FL and ABS/MAS are studied the most in the SCM
literature; therefore, they have affected SCM practice more than other AI
techniques. (SRQ 1)
Proposition 3:.The employment of an AI technique in both research and
practice is dependent on the availability of relevant software/applications
regarding that technique. (SRQ 1,2)
Proposition 4:.Using AI in the eld of SCM happens through using
appropriate AI-based software (on the AI side) and well-dened SCM prob-
lems that can make use of such software (on the SCM side). Therefore, to
enhance the use of AI in SCM, both researchers and practitioners require
purposefully designed software and well-structured problems. (SRQ 1,3)
Proposition 5:.The empirical studies that produce AI-based models,
systems and frameworks have positive and direct impacts on practical use of
AI. (SRQ 1,3,4)
Proposition 6:.Improvement of less-prevailing AI techniques and dis-
covery of their novel applications can enhance the SCM subelds that have
received less attention from researchers. (SRQ 2,4)
Funding
This research did not receive any specic grant from funding
agencies in the public, commercial, or not-for-prot sectors.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
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Reza Toorajipour holds MSc in international business administration at Shahid Beheshti
University. He works at Shiraz Oil Rening Company where he serves as business expert.
His research interests include business model innovation, supply chain management and
industry 4.0 technologies.
Dr. Vahid Sohrabpour has a multidisciplinary approach in research. He is passionate
about the research on Industry 4.0 and the use of technologies such as Internet of Things
(IoT), Cloud Computing, Block Chain, Big Data and Articial Intelligence in Supply Chain
Management. These technologies lead to establish Smart Supply Chain, Smart
Manufacturing and Smart Product which are related to his background in Packaging Lo-
gistics, Supply Chain Management, Operations Management, Quality Management and
Computer Engineering
Dr. Ali Nazarpour is Assistant Professor in Management at Maynooth University School of
Business. Ali has been awarded PhD in Operations and Supply Chain Management from
University College Dublin, Smurt School of Business. He earned his BSc degree in In-
dustrial Engineering and Master’s degree in Business Administration from Iran University
of Science and Technology. Prior to his PhD studies, he worked in the construction sector
and in the automotive industry where he served as Sales Supervisor, Marketing and Sales
Planning Chief, and Inventory Management Project Manager.
R. Toorajipour et al.
Journal of Business Research 122 (2021) 502–517
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Dr. Pejvak Oghazi is Professor in Business Studies and head of Department at School of
Social Sciences, Sodertorn University. He holds an MSc in Industrial and Management
Engineering in addition to a PhD in Industrial Marketing. Prior to his current position,
Professor Oghazi worked as an industrial manager at national and international level.
Professor Oghazi’s current research interests revolve around topics in Business studies.
Maria Fischl Research Associate and Project Manager at University Of St Gallen Hsg
R. Toorajipour et al.
