A bibliometric analysis of data-driven technologies in digital supply chains

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Supply Chain Analytics 6 (2024) 100067
Available online 21 May 2024
2949-8635/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
A bibliometric analysis of data-driven technologies in digital supply chains
Hamed Baziyad
a
, Vahid Kayvanfar
b
,
*
, Aseem Kinra
c
,
d
a
Department of Information Technology, Tarbiat Modares University (TMU), Tehran, Iran
b
Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
c
Department of Global Supply Chain Management, University of Bremen, Bremen, Germany
d
Institute for Shipping Economics and Logistics (ISL), Universit¨
atsallee 11 - 1328359, Bremen, Germany
ARTICLE INFO
Keywords:
Digital Supply Chains
Logistics
Internet of Things
Cyber-Physical System
Industry 4.0
Text Mining
ABSTRACT
Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the core components of data-driven technologies
of Industry 4.0, attracting much attention in digital supply chains and leading to a growing tide of academic
publications. This study conducts a bibliometric analysis of data-driven technologies in digital supply chains.
Additionally, some bibliometric methods, such as co-word analysis, are utilized to study the intellectual structure
of the eld and present a big picture. The co-word analysis maps data-driven technologies’ intellectual structure
in digital supply chains and logistics. 3887 publications from the Web of Science (WoS) and Scopus between
2010 and 2021 were collected and analyzed. Then, a strategic diagram is employed on the co-occurrence
network, indicating each theme’s current situation from two aspects of applicability and theory development.
The study reveals that IoT and CPS technologies are in their infancy in digital supply chains and logistics, and
additional studies are needed to ll the research gaps in this eld.
1. Introduction
In the era of Industry 4.0, traditional supply chains are undergoing a
transformative shift to digital supply chains (DSCs), capturing the in-
terest of researchers and industry leaders alike [34,103]. Digital Supply
Chain (DSC) technologies are categorized into three pivotal types:
data-driven, knowledge-based, and decision-oriented. Data-driven
technologies are responsible for the collection, storage, and exchange of
data across various entities. Knowledge-based technologies delve into
this data to distill valuable insights and knowledge. Finally,
decision-oriented technologies leverage this accrued knowledge to
empower managers in making informed and strategic decisions [146,
17]. Data-driven technologies form the foundational layer upon which
knowledge-based and decision-oriented technologies operate; thus,
without data-driven technology, the other two cannot work efciently.
Thus, we concentrate on the data-driven technology aspect of DSCs in
the current research.
Internet of Things (IoT) and Cyber-Physical Systems (CPS) are among
the most critical data-driven technologies in DSCs [17]. These technol-
ogies are considered by many researchers to be the driving force behind
Industry 4.0, fueling its growth and innovation [124,42]. CPSs enable
physical environments to interact with humans by integrating
computational and physical abilities [14]. The employment of emerging
IoT technologies has opened up novel opportunities for CPSs, enabling
the connection of cyber-physical devices via internet-based platforms.
Integrating IoT and CPS shifts the focus of internet applications from
mere digital data transformation to the active control of physical envi-
ronments through cyber-physical devices [99,37]. From a network
perspective, IoT is a set of heterogeneous and unique addressable
cyber-physical objects such as Radio Frequency Identication (RFID),
sensors, and smart devices connected through a worldwide network in a
standard communication protocol [15]. The expansion of communica-
tion and control through CPS over the internet is known as the IoT [23].
Indeed, these two concepts are interrelated and cannot be considered in
isolation. Those publications that only focused on one of these tech-
nologies to review DSCs cannot provide a comprehensive overview of
the data-driven dimension. The current paper is one of the few studies
that address this gap by examining IoT and CPS concurrently in the
context of DSCs. Fig. 1 presents a schematic representation of how these
leading technologies are integrated within DSCs.
Both IoT and CPS are recognized as underlying technologies that
facilitate compatibility among new technological developments, inno-
vative ideas, and frameworks. They also lay the groundwork for ongoing
research that propels the domain’s continued progression [99]. In the
* Corresponding author.
E-mail address: valikayvanfar@hbku.edu.qa (V. Kayvanfar).
Contents lists available at ScienceDirect
Supply Chain Analytics
journal homepage: www.sciencedirect.com/journal/supply-chain-analytics
https://doi.org/10.1016/j.sca.2024.100067
Received 21 March 2024; Received in revised form 13 May 2024; Accepted 16 May 2024

Supply Chain Analytics 6 (2024) 100067
2
context of digital supply chains and Industry 4.0, underlying technolo-
gies could include the hardware, software, networks, and protocols that
form the backbone of IoT and CPS, allowing them to operate and
communicate effectively. Therefore, by concentrating our research on
IoT and CPS, we expect our study to cover various related data-driven
technologies within DSCs. While there are various data-driven tech-
nologies, they do not serve as foundational elements in DSCs to the same
extent as IoT and CPS. Including these other technologies in an analysis
could skew the results, as they are not as essential to the core structure
and functioning of DSCs as IoT and CPS.
The increasing volume of research on IoT and CPS technologies un-
derscores the need for both scholars and practitioners to have a broad-
scale understanding of these areas. Due to the insufcient capability of
review analysis approaches to cover a wide range of publications, there
is no comprehensive overview of data-driven technologies within the
archipelago of conducted reviews. In contrast, bibliometric analysis
serves as a suitable alternative, offering the capacity to effectively
analyze an extensive corpus of publications [66]. It can be concluded
that bibliometric analysis excels in managing extensive datasets, offer-
ing a macro-level perspective on a research domain. Conversely, review
analysis delivers an in-depth and meticulous consolidation of ndings
tailored to a specic narrow subject matter (e.g., Vallaster et al. [134]).
The advocate for bibliometric analysis emphasizes its capacity for
autonomously producing research themes, which are likely to be less
subjective than those derived through manual compilation in systematic
literature reviews [80]. Bibliometric analysis serves as a powerful tool
for researchers to uncover the intricate structure and evolutionary
trends within a eld, as noted by Pourhatami et al. [114].
While bibliometric methodologies have been applied to scrutinize
sectors such as IoT and CPS, the focus on supply chains and logistics
remained scant (e.g., [30,53,107,122,128,143]). Baziyad et al. [17]
bridged this void by probing into IoT and CPS within DSCs through a
literature review analysis. Nevertheless, the eld still awaits the
completion of a thorough bibliometric analysis. Building upon this
premise, the present paper employs bibliometric analysis to unravel the
synergy between the vanguard technologies of Industry
4.0—specically, IoT and CPS—and the digital supply chain sphere.
Such an analysis reveals practical implications for implementing IoT and
CPS in DSCs, offering businesses a roadmap for integration and adop-
tion. It also contributes to the theoretical understanding of digital supply
chains, providing a foundation for future research and development in
this area.
Although a few researchers have tried to employ bibliometric anal-
ysis for IoT in DSCs, they have ignored the essential role of CPSs [118,
30]. The current paper represents a novel bibliometric analysis that
examines the IoT and CPS technologies, offering a novel perspective on
the inuence of data-driven technologies within DSCs. Previous studies
have revealed various themes within the research eld but have not
examined the developmental progress of each theme. Indeed, the themes
should be explained, how they can be applied in real applications, and
how they can be developed. As a result, this paper employed a strategic
diagram for analyzing the situations of different themes of IoT and CPS
technologies in the context of DSCs, from the viewpoints of applicability
and development.
Previous studies have often examined IoT and CPS in isolation,
failing to provide a comprehensive view of their combined role in DSCs.
This research addresses this gap by concurrently analyzing both IoT and
CPS within the context of DScs. We pioneer bibliometric analysis in
DSCs, focusing on CPS and the IoT. It presents a strategic diagram to
evaluate IoT and CPS’s current state and future potential in DSCs. This
paper offers an exhaustive analysis of the roles of IoT and CPS in DSCs by
examining a vast array of papers from Web of Science (WoS) and
Elsevier’s Scopus databases, spanning 2010–2021, using co-word anal-
ysis. This technique unveils the latent knowledge within the eld,
allowing for a detailed understanding of the current research landscape
and future trajectories. The paper aims to extract key insights from these
core data-driven technologies and poses relevant research questions as
follows: (a) What are the principal research themes and the latest ad-
vancements within each domain that are situated at the intersection of
fundamental data-centric technologies in digital supply chains? (b) How
has research on the topic evolved? (c) What are the specic enabling
technologies that have been incorporated, and in what ways have they
been merged with the recognized themes?
Overall, the current paper distinguishes itself with several key con-
tributions to the eld: (a) This study is one of the early efforts to
investigate IoT and CPS technologies as fundamental elements of data-
driven Industry 4.0 technologies, specically within the realm of digi-
tal supply chains; (b) By focusing on IoT and CPS, the study aims to
encompass a broad spectrum of related data-driven technologies within
DSCs, recognizing their pivotal role compared to other technologies; (c)
It introduces a strategic diagram to assess the applicability and devel-
opment of various themes related to IoT and CPS technologies in DSCs;
(d) Diverging from the traditional, time-intensive approach of manually
standardizing keywords, this manuscript introduces a groundbreaking
framework that utilizes string similarity, streamlining the keyword
standardization process for extensive datasets. This paper’s remainder is
structured as follows: Section 2 provides a literature review of biblio-
metric and co-word analysis. Section 3 introduces the utilized method-
ology. Section 4 depicts the obtained results, and Section 5 explains the
conclusion, limitations, and future directions.
2. Leading data-driven technologies of DSCs
Industry 4.0, representing a holistic approach to digitalization,
stands at the forefront of technological exploration conducted through
bibliometric analysis in the realm of digital supply chains. For instance,
Motallebi et al., [108] provided an extensive survey of the architecture,
progression, and movement of Industry 4.0 within supply chain
research, utilizing analysis of academic social networks. Majiwala and
Kant [104] conducted an exhaustive bibliometric analysis, encompass-
ing 1554 articles from the Scopus database, spanning the years
2011–2022. They aimed to pinpoint research hotspots, thematic areas,
trending topics, and the foundational knowledge base of Industry 4.0 as
it intertwines with supply chain management. Hoang et al. [77] pre-
sented a comprehensive overview and organizational structure of
knowledge on social media’s role in supply chain management. It
employed bibliometric analysis of 354 articles from the Web of Science
database, spanning 2008–2022, using co-citation and co-word analysis
techniques. The study identied ve key ways social media is applied in
supply chains: (1) improving sustainability and business transformation,
(2) utilizing social media analytics to derive business insights, (3)
Fig. 1. Different technologies of DSCs.
H. Baziyad et al.

Supply Chain Analytics 6 (2024) 100067
3
enhancing communication and coordination within the network, (4)
fostering social engagement and customer relations, and (5) advancing
supply chain relationship management strategies. Jetty and Afshan [84]
provided a comprehensive bibliometric analysis and a systematic review
of the literature on Industry 4.0 within the supply chain sector, aiming to
discern the current research trends. In certain instances, bibliometric
researchers honed in on more specialized areas of study within digital
supply chains. Gharaibeh et al., [56] assessed the advancement of
digitalization within construction supply chains through a thematic re-
view of literature and identication of research trajectories. It offered a
conceptual framework that assists researchers in examining the pre-
vailing trends of Supply Chain 4.0, particularly its implementation in the
wood construction sector relative to other industries. They also pro-
posed potential pathways for future investigations in the domain of
Supply Chain 4.0 within the wood construction industry. As digital
technologies gain prominence in the Industry 4.0 landscape, biblio-
metric researchers have increasingly concentrated on particular tech-
nologies such as, articial intelligence [115], blockchain [69], digital
twin [96]. In alignment with contemporary research objectives, this has
led to a detailed examination of the IoT and CPS, which are pivotal in
driving the digital transformation of supply chains.
2.1. Cyber-physical systems (CPSs)
CPS was introduced by Helen Gill in 2006 at the National Science
Foundation in the United States [98]. All in all, CPSs refer to a set of
transformative technologies increasing the interactions between phys-
ical properties and computation systems for managing the inter-
connected systems [14] that have penetrated various domains such as
smart grids, smart cities, smart manufacturing, and intelligent trans-
portation (D. [150]). CPSs are regarded as physical and engineered
systems integrating computational and physical capabilities and enable
humans to interact with physical environments by enabling technologies
such as monitoring and controlling systems (e.g., [70,93]).
2.2. Internet of things (IoT)
IoT was introduced by Kevin Ashton in 1999 [9], comprising a set of
interconnected objects equipped with intelligence characteristics
everywhere and every day [141]. In other words, physical and virtual
environments can be connected and communicated through IoT tech-
nologies facilitating information-sharing processes [11]. Also, every
object in an IoT-based platform is addressable, traceable, and identi-
able among some sensors collecting the environmental data [10] under a
common infrastructure [62]. In a comprehensive denition, IoT is a
collection of smart objects armed with integrated and collaborated
forms of sensors, networking, and processing technologies delivering
smart services to the ultimate customers [8]. IoT is a transformation
system converting the physical environment into a virtual one to provide
an appropriate environment for a device to device communication (e.g.,
[119,142]).
By using bibliometric analysis, Bouzembrak et al. [30] could map out
the landscape of IoT technology in food safety, identifying key con-
tributors, inuential regions, and the main topics of interest within this
specic area of study. This method provides a structured way to visu-
alize the complex web of academic work and to understand the collec-
tive direction of research efforts in the eld. Kaya et al. [87] utilized
bibliometric analysis to delve into the impact of the IoT on supply chain
management within the context of Industry 4.0. In a same way, Through
bibliometric analysis, Katoch [86] were able to systematically map out
the landscape of IoT in SCM and logistics, offering a structured overview
of the eld’s evolution and current state. This method allowed for a
data-driven approach to identify trends, gaps, and opportunities for
further research and practical application. Kayvanfar et al. [88] con-
ducted a thorough co-word analysis of web-based content to present an
extensive overview of the contemporary landscape of IoT in supply
chains and logistics, underscoring the pivotal inuence of IoT technol-
ogy in advancing supply chain processes.
2.3. Digital supply chains (DSCs) in the era of leading data-driven
technologies
While CPS and IoT are closely related elds that cannot be separated
clearly, many researchers have not considered them together in their
review research. Several research papers addressed IoT technologies in
supply chains ([125,21,72]; Yangke [47]), while others emphasized
CPSs [133,82]. The review of digital supply chains rarely considered
both CPS and IoT technologies [105]. Consequently, considering IoT and
CPS technologies in DSCs remains challenging, limiting researchers’ and
practitioners’ access to an overview of these two leading technologies.
Using bibliometric analysis, Bouzembrak et al., [30] analyzed 48
academic publications which covered IoT applications in food safety. It
cannot cover different supply chains, as it is limited to the food sector. In
order to extend the analytical domain, Rejeb et al., [118] applied bib-
liometric analysis to a broader range of publications (807 papers) for
investigating IoT technologies in supply chains. However, there is still
an issue. Aside from IoT, neither study considered CPS. In Table 1,
related works are briey summarized.
While some studies have examined the integration of IoT and CPS
within supply chain queries, the inclusion of an extensive array of non-
relevant keywords has diluted their focus, diverting it from a strictly
data-driven approach (e.g., [80,104]). For example, Iftikhar et al. [80]
expanded their search criteria to include not only IoT and CPS but also
blockchain, big data, cloud computing, and other cutting-edge tech-
nologies to gather comprehensive data for their analysis. This issue is
also prevalent in the expansive domain analyses such as Industry 4.0
within supply chains, as outlined at the start of Section 2. The current
study is at the forefront of bibliometric research, addressing a critical
gap by concentrating exclusively on IoT and CPS within supply
chains—a subject that, until now, has not been thoroughly explored. By
pioneering this focused approach, our analysis offers unprecedented
insights into the integration and inuence of these technologies in DSCs.
The ndings have the potential to shape future research directions and
inform industry practices, marking a signicant advancement in the
eld.
The present work focuses solely on revealing the various clusters of
investigated publications, without analyzing their relative status. The
most popular methodology for analyzing cluster status is the strategic
diagram, which is not present in papers introduced by Bouzembrak
et al., [30] and [118].Using a strategic diagram, we can measure how
each eld is being developed, as well as how it can be utilized in various
applications. Besides showing the different clusters of IoT and CPS
within DSCs, the status of each cluster is also determined by a strategic
diagram.
3. Materials and methods
Bibliometric analysis employs mathematical and statistical tech-
niques to scrutinize written communication and literature. Essentially, it
is a quantitative approach that allows for the identication of prevailing
trends within a research eld [43]. Among the innovative tools in bib-
liometrics, text mining stands out for its ability to unearth the latent
knowledge within scholarly articles [147]. Furthermore, co-word anal-
ysis, a subset of text mining [97], has been effectively used in the
literature for bibliometric studies [135] to delineate the intellectual
contours across various disciplines [114,20]. Co-word analysis is a
quantitative technique that reveals the connections and interplay among
concepts within a eld (Q.-R. [149]). As a graph-based method, co-word
analysis focuses on dividing a co-word network into sub-themes and
sub-graphs, thereby mapping out the diverse sectors within the domain
under study [40]. The synergy between bibliometric and co-word ana-
lyses is depicted in Fig. 2, underscoring their role as pivotal tools in
H. Baziyad et al.

Supply Chain Analytics 6 (2024) 100067
4
bibliometric research.
Overall, co-word analysis is particularly valuable in elds with large
volumes of literature, as it can efciently process and analyze extensive
datasets to provide a macro-level understanding of the research land-
scape. It’s a powerful tool for researchers, policymakers, and institutions
aiming to grasp the complexities and dynamics of scientic knowledge.
Thus, this research uses a co-word analysis to disclose the hidden
knowledge of IoT and CPS-based supply chain publications. Different
themes and sub-themes of the surveyed eld are revealed as the rst
extracted knowledge. Finally, themes are analyzed from applicability
and theory development perspectives by applying a strategic diagram.
Hosseini et al., [78] introduced a comprehensive framework for co-word
analysis applied in this paper. According to their framework, a co-word
analysis comprises four main phases; data collection, keyword selection,
matrix calculation, and results.
3.1. Data collection
WoS and Scopus are two central databases that researchers mainly
refer to and have enough coverage of interdisciplinary publications
(Archambault et al., 2006). After the combination of the search queries
extracted from the literature review (see Table 1), the main query used
in this paper is TITLE-ABS-KEY ("cyber-physical system*" OR "cyber
physical system*" OR "smart system*" OR "cyberphysical system*" OR
"CPS*" OR "Internet of Things" OR "Internet-of-Things" OR "IoT" OR
"Internet of Everything*" OR "Industrial Internet" OR "Web of things" OR
"Web-of-Things" OR "WoT") AND ("supply chain*" OR "logistics" OR
"Supply Chain Management" OR "Logistics Management" OR "Digital
Supply Chain*" OR "digital logistics" OR "Smart Supply Chain*" OR
"Intelligent Supply Chain*" OR "Smart Logistics" OR "Intelligent Logis-
tics"). The search query, spanning from 2010 to March 2021, targeted
titles, keywords, and abstracts of various scholarly works, including
journal articles, conference papers, reviews, book chapters, and edito-
rials. Only papers published in English were included. This could be due
to the researchers’ language prociency or the wider accessibility of
English-language research. The search targeted a variety of scholarly
documents, including journal articles, conference papers, reviews, book
chapters, and editorials. This broad scope allows for a comprehensive
understanding of the eld. Publications that were irrelevant or off-topic
were excluded. This means that only papers directly related to the
research question or topic were considered. Publication slacking key-
words and abstracts were omitted. Keywords and abstracts are essential
for understanding the focus and content of a paper without having to
read the full text.
The initial search yielded 2070 publications from the Web of Science
(WoS) and 3961 from Scopus. After deduplication, the total was
consolidated to 4514 entries. Subsequent ltering removed 308 publi-
cations without keywords and seven lacking abstracts. An additional
Table 1
Brief overview of related works in comparison to the present study.
References Area Methodology
CPSs IoT Logistics Supply Chains Review Bibliometric
Ivanov, Sokolov [82] √ √ √
Tonelli et al., [133] √ √ √
Li [100] √ √ √
Sun [131] √ √
Verdouw et al., [136] √ √ √ √
Manavalan, Jayakrishna [105] √ √ √ √ √
Aryal et al., [7] √ √ √
Birkel, Hartmann [26] √ √ √
Ben-Daya et al., [21] √ √ √ √
Rejeb et al., [117] √ √ √ √
Shah, Ververi [125] √ √ √
He et al., [72] √ √ √
Kaya et al., [87] √ √ √
Ben-Daya et al., [22] √ √
Ding et al., [47] √ √
Baziyad et al., [17] √ √ √ √
Bouzembrak et al., [30] √ √ √
Rejeb et al., [118] √ √ √ √
Katoch [86] √ √ √
Abbasi, Ahmadi Choukolaei [1] √ √
Bouchenine, Abdel-Aal [28] √ √
Emrouznejad et al., [49] √ √
Aljuneidi et al., [4] √ √
Abosuliman [2] √ √ √
Canonico, Sperlì [36] √ √
Singh et al., [128] √ √ √
Kayvanfar et al., [88] √ √ √ √
Alvarez-Alvarado et al., [5] √ √
Hasan et al., [71] √ √
Our Paper √ √ √ √ √
Fig. 2. The position of co-word analysis and text mining tools in biblio-
metric analysis.
H. Baziyad et al.

Supply Chain Analytics 6 (2024) 100067
5
312 publications were deemed irrelevant and excluded. Fig. 3 details the
data renement process, and Fig. 4 highlights the marked growth in
publication volume between 2010 and 2021, emphasizing a pronounced
escalation commencing in 2016.
3.2. Keyword selection
Selecting the right keywords is pivotal in co-word analysis, as it has a
direct impact on the quality of the results. By choosing the most perti-
nent keywords for constructing the co-word network, one can extract
more meaningful and in-depth insights from the co-occurrence network.
Frequency is a key criterion in this selection process, with an emphasis
on identifying and incorporating the most commonly occurring key-
words into the co-word graph [78]. After extracting the primary list of
keywords, a ltering process is needed to integrate and clean the
collected data. Keyword ltering comprises three main phases, i.e., a)
standardizing the similar keywords (e.g., supply chain and supply chains
or, e.g., radio frequency identication and radio-frequency identication);
b) integrating acrimonies (e.g., Internet of Things and IoT); c) removing
general keywords (e.g., algorithms).
3.2.1. Standardizing the similar keywords
While previous research often relied on manual standardization of
keywords—a laborious task with large datasets—this paper presents an
innovative framework that leverages string similarity for efcient
keyword standardization. We employ six key similarity metrics: Ham-
ming [68], Levenshtein [60], Jaro Winkler [140], Jaccard [83], Sor-
ensen [46], and Ratcliff Obershelp [116] facilitated by the ’textdistance
4.2.1
′
package in Python 3.6. This approach ensures that keywords with
high similarity scores, such as ’supply chain’ and ’supply chains,’ are
accurately consolidated. The entire standardization process is detailed
in Fig. 5.
3.2.2. Integrating acrimonies
After consolidating similar keywords, acronyms were identied and
substituted with their full terms to enhance clarity (for example, ’SCM’
was expanded to ’supply chain management’). Additionally, for the sake
of graph legibility, acronyms were employed to represent lengthy terms.
3.2.3. Removing general keywords
In the context of a co-word network, general keywords are those that
are common and may not provide specic insights into a particular
research domain. They are often too broad to be of interest to domain
experts and may not discriminate between the nuanced topics of a eld.
For example, words like "data," "analysis," or "results" could be consid-
ered general because they are widely used across various research areas
and do not pinpoint a unique aspect of a study. Consequently, we
exclude general keywords to rene the analysis process.
3.3. Matrix calculation
The co-occurrence matrix is fundamental to co-word analysis, as it
records the frequency of keyword pairings. Before constructing the co-
word network, it is essential to normalize the matrix. Various normali-
zation methods have been adopted in literature, including the Jaccard
Index, Association Strength, Cosine, and Inclusion Index [48], which are
detailed in Table 2 and in terms of Eqs. (1−4). Association Strength,
which excels in modularity—a key measure of community detection
quality—outperforms other normalization methods. Consequently, it is
the preferred choice for normalizing the co-word network.
Where Cij represents the number of co-occurrences of keywords i and
j. Besides, Ci and Cj indicate the frequency of keywords i and j, respec-
tively. According to the normalized co-occurrence matrix, the co-word
network can be visualized in which each node is a keyword, and edges
are the normalized co-occurrences.
3.4. Content analysis
In order to nd the involved themes and topics of the research eld,
clustering and community detection methods are applied on normalized
co-word matrices [19,78]. Also, a strategic diagram is utilized to eval-
uate and compare the positioning of each identied theme relative to
others. The integration of co-word analysis with strategic diagrams en-
riches the bibliometric analysis by providing a multi-dimensional
perspective that is more informative and actionable for understanding
and navigating the complexities of scientic research.
3.4.1. Themes identication
Community detection algorithms are a type of clustering method
concentering on graph-based datasets such as co-occurrence networks.
The Louvain algorithm [27] is one of the most used community detec-
tion algorithms applied in this paper for clustering the co-word network
among Gephi 0.9.2 software. Also, community detection is evaluated by
the modularity criterion.
Modularity is an appropriate measurement for discovering commu-
nity detection algorithms. More modularity values indicate that the
provided themes have the lowest similarity with others and items in a
community are very similar [110]. The modularity formula is presented
in Eq. (5) [110].
Q=1
4∑ij(Aij −kikj
2m)sisj(5)
m=1
2∑iki
Where ki and kj represent the node’s degree and m indicates a total
number of edges. Aij introduces presented edges between node i and j.
Also, the expected number of edges between nodes i and j can be
calculated as kikj
2m.
3.4.2. Strategic diagram
A strategic diagram maps science clusters in a two-dimensional
matrix, determining their maturity [90]. A strategic diagram com-
prises two main measurements, i.e., density and centrality (or degree
centrality). Density is an appropriate criterion for measuring a theme’s
internal cohesion, and centrality measurement represent how each
theme can play a central role in the research eld. According to Hosseini
et al., [78], the metrics of density and centrality are determined using
Eq. (6) and Eq. (7). The strategic diagram, detailed in Fig. 6, is divided
Fig. 3. Search query results from WoS and Scopus databases.
H. Baziyad et al.

Supply Chain Analytics 6 (2024) 100067
6
into four principal quadrants [58].
CL=∑i∈L∑j∈MWij.eij (6)
In Eq. (6), CL is known as the centrality of community (theme) L, and
i indicates the nodes (words) of community L. M refers to other themes
except community L, and j shows the existed nodes of these commu-
nities. eij is a binary variable indicating the existence or non-existence of
a link between nodes i and j. Finally, Wij refers to the weight of the
existed link between nodes i and j.
DL=2E
N(N−1)(7)
In Eq. (7), DL indicates the density of cluster L, E represents the total
number of edges in theme L, and nally, N is the total number of nodes in
cluster L. The strategic map is divided into four main sections, including
Mainstreams (quadrant I), Ivory Tower (quadrant II), Bandwagon
Fig. 4. Number of publications between 2010 and 2021 (IoT and CPS- based supply chains).
Fig. 5. Standardizing similar keywords
Table 2
Search queries of IoT, CPSs, and supply chains from the literature.
Domain Related Search Query Paper
CPSs "cyber-physical system*" OR "cyber physical
system*" OR "smart system*" OR
"cyberphysical system" OR "CPS"
Mohamed et al.,
[107]
IoT Internet of Things OR Internet-of-Things OR
IoT OR Internet of Everything OR Industrial
Internet OR Web of things OR Web-of-Things
OR WoT
Koot et al., [94]
Supply
chains
"supply chain*" OR
"Supply Chain Management" OR "Logistics
Management" OR
(Nakamura et al., n.
d.; [50,118,94])
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(quadrant III), and Chaos/Unstructured (quadrant IV). Emerging or
declining themes are not developed enough and cannot be combined
with other areas resulting in the undeveloped and peripheral eld. Over
time, the situation of an emerging or declining theme can be changed in
two ways:
a) Further development and move towards Ivory Tower: When an emerging
or declining theme is developed, it moves to the Ivory Tower quad-
rant. Although a eld has developed in the ivory tower section, it is
still in an isolated environment that does not connect enough with
other themes. After moving towards the ivory tower, connecting to
other elds, and nding more theme applications, it shifts to the
mainstream theme. Themes in the mainstream quadrant have
developed enough and linked intensely to other elds, also known as
‘motor themes’.
b) Further cohesion and move towards Bandwagon: When an emerging or
declining theme links to other themes and shows its applications in
other concepts, it gains more centrality and moves to the bandwagon
theme. Bandwagon theme includes basic and transversal themes
having low density but high centrality. In other words, despite being
a beginner have found many fans in a wide range of applications and
domains. Like the ivory tower quadrant, bandwagon themes can be
shifted to the mainstream theme, but with improvements in their
density, no centrality.
4. Result
4.1. Different areas of CPS & IoT-based supply chains
The Louvain community detection algorithm was applied for theme
identication of CPS & IoT-based supply chains domain leading to
disclose nine main themes: ‘‘project management’’, ‘‘Enabling Tech-
nologies’’, ‘‘Location-based Services (LBSs)’’, ‘‘Agri-Food Supply
Chains’’, ‘‘Smart Grid Challenges’’, ‘‘Industry 4.0’’, ‘‘Intelligent
Computing’’, ‘‘Marketing, and ‘‘Data Science’’. The co-word network,
depicted in Fig. 7, is visually organized by color-coding, with each
distinct color denoting a separate theme. In the co-word network visu-
alization (Fig. 7), keywords that appear more frequently are displayed in
larger fonts for emphasis. For detailed analysis, community detection is
conducted on sub-themes comprising over 40 nodes. The preliminary
ndings of this re-community detection, including the number of nodes,
edges, and sub-themes for each primary theme, are summarized in
Table 3 and illustrated in Fig. 8. Also, the modularity criterion is
calculated for each clustering. Each theme of the main co-word network
is explained (Subsections 4.2.1 to 4.2.9). According to several studies,
modularity should be greater than 0.2 in a successful community
detection (e.g., [55,3,127]). After applying the Louvain algorithm to the
main graph and its sub-graphs, we found that all modularity criteria
exceeded the threshold of 0.2, as illustrated in Table 3. Hence, we can
say that the discovered communities are of high quality. In other words,
clusters’ internal members are similar and different from the members of
other clusters.
Overall, the main themes and sub-themes of CPS and IoT-based
supply chains and logistics domains over the last decade are depicted
in Fig. 7.
4.1.1. Project management (C1)
The successful deployment of Industry 4.0 technologies within sup-
ply chains necessitates sophisticated skills and methodologies. A robust
project management framework is essential to effectively orchestrate
these competencies. Despite its critical role, project management has not
been adequately emphasized in the context of Industry 4.0 and, by
extension, Supply Chain 4.0—which leverages these advanced tech-
nologies. Recognizing project management as a fundamental element is
vital for the effective integration of technologies like IoT and CPS into
supply chain operations [52]. Prior to the adoption of IoT and CPS
technologies in supply chains, it is imperative to address several foun-
dational prerequisites during the project management phase. Key proj-
ect management tasks in the development of IoT and CPS-enabled
supply chains include designing technological architectures, standard-
izing supply chain operations, simulating process workows, and con-
ducting statistical analysis of the simulated data.
Computer simulations simulate different standards and architectures
during the project management phase to implement IoT and CPS tech-
nologies in supply chains. Simulations are run, then the results are
analyzed statistically, and then the best standards and architectures are
selected. Fig. 9 shows an overview of project management tasks for
implementing IoT and CPS technologies in supply chains.
It is important to remember that the project management process
does not end with the mentioned tasks, and the provided gure is based
on the analyzed literature. The project management community with 11
nodes and 21 edges can be seen in Fig. 10.
Fig. 6. Strategic diagram based on density and centrality measurements (adapted from Giannakos et al. [58].
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4.1.2. Enabling technologies (C2)
Many technologies, such as IoT and CPS, are not standalone tech-
nologies. However, instead, they require other technologies or enabling
technologies to work. In other words, enabling technologies facilitate
efcient utilization of main technologies. C2, with 138 nodes and 3803
edges, primarily focused on enabling technologies. After executing the
Louvain algorithm on this theme, seven sub-themes are disclosed, as
shown in Fig. 11.
4.1.2.1. Information management (C2_1): Blue Sub-theme. In informa-
tion management, the ow of information is controlled, from how it is
created, gathered, organized, and disseminated to how it is used. People
and organizations use information management to access, process, and
utilize information efciently and effectively [44]. Based on the
reviewed papers and prepared graph in Fig. 11, it can be found that
information management tasks (data creation, collection, organization,
dissemination, utilization) of IoT and CPS-based supply chains can be
handled by different enabling technologies. Through technologies such
as RFID, data can be created and collected by sensors embedded in
supply chains and then disseminated via Internet-based and cloud
computing technologies for storing in a database. The data are fed into
information systems for virtualizing supply chains so that supply chains
can be visualized. Indeed, information systems enable supply chains to
represent physical objects virtually. C2_1 focuses on information man-
agement in supply chains based on IoT and CPS, showing different in-
formation management tasks.
Fig. 7. The co-word network of CPS & IoT-based supply chains.
Table 3
Co-word normalization methods.
Normalization Modularity Eq.
Association Strength(i,j) = Cij
Ci×Cj
0.252 1
Cosin(i,j) = Cij

Ci×Cj
√
0.205 2
Jaccard(i,j) = Cij
Ci+Cj−Cij
0.212 3
Inclusion Index(i,j) = Cij
min(Ci,Cj)
0.175 4
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4.1.2.2. Wireless sensor networks (WSNs) (C2_2): Green Sub-theme.
Wireless Sensor Networks (WSN) are fundamental to the operation of
IoT and CPS technologies. While WSNs can function independently of
IoT and CPS, the latter technologies are inherently reliant on the prin-
ciples of WSNs for their existence [45,89]. A WSN is a set of wireless
sensors (nodes) which are interconnected to form a network and have
three main capabilities: I) environmental data collection, II) data pro-
cessing and III) wireless communication. In a WSN, data is collected
from the environment and sent over wireless links to clients and com-
mands. The received data can be computed for specic purposes [67]. In
Fig. 11, it is evident that WSN technologies of IoT and CPS-based supply
chains can be described based on sensation, communication, and
computation perspectives.
The focus of the sensation is mainly on two concepts: physical objects
and energy consumption. A physical object is something such as a
vehicle or container in logistics and transportation systems that can be
equipped with sensors. Since embedded sensor nodes have limited
resources, energy efciency has become a critical issue for SCs. In order
to implement simple computation processes and aggregation functions
(MEAN, MEDIAN, MAX, and MIN), the collected data from embedded
sensors should be transferred to other nodes. Network protocols such as
ZigBee and MQTT carry out transformation tasks. The sensor nodes of
WSNs can handle basic analysis approaches, but due to the limited
computational power, they cannot handle advanced analysis methods.
To perform more sophisticated analyses, they connect to external net-
works such as Universal Mobile Telecommunication Systems (UMTSs)
and other types of telecommunication networks via gateways.
4.1.2.3. Logistics information systems (LISs) (C2_3): Red Sub-theme. In
summary, LIS is a supportive decision-making tool responsible for
monitoring and controlling the supply chain’s ows, including infor-
mation, material, and nancial ones. The main objective of LIS is to
deliver the right amount of products at the right time and place [74,75].
C2_3 explains managing the modern logistics working based on Logistics
Fig. 8. The main themes of CPS and IoT-based supply chains and logistics.
Fig. 9. Project management in the context of IoT and CPS implementation in supply chains.
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Information Systems (LISs). LIS-based logistics help to manage smart
objects and materials among controlling the information ow. Indeed,
LIS enables logistics industries to implement machine-to-machine
(M2M) interactions via the internet, disclosing the intelligent logistics
concept.
4.1.2.4. Web services (C2_4): Yellow Sub-theme. Web services are reus-
able software modules that connect customers with internet services and
then provide related services for meeting customers’ needs [112]. Using
Service-Oriented Architectures (SOAs) as one of the main tools of web
services, a software can be divided into multiple sub-applications that
run on multiple remote servers, all of which can provide services to
clients [85]. Even though SOA operates on a reactive approach, where
clients’ requests are processed and responded to, they are not proactive
and cannot react to events. Thus, an Event-Driven Architecture (EDI)
was developed to sense environmental events and respond to them in
real-time [32]. Web services provide a wide range of services to end
users but require more automation to handle tasks like search, choice,
enhancement, and implementation of appropriate services. Adding
machine-processable capabilities to web services is necessary for such
automation. In this way, the semantic web is born [51]. The C2_4 il-
lustrates the IoT and CPS technologies in the supply chain through the
web service dimension. Web services in IoT and CPS-based supply chains
have been studied from two perspectives: architecture and performance
analysis. Architectures such as SOA and EDI relate to the structure of
web services where the technological layers and how they interact to
provide appropriate services are determined. In a more advanced mode,
the semantic web employs ontology-based architectures to provide
appropriate services. As part of the performance analysis of web ser-
vices, key performance indicators (KPIs) such as reliability are studied to
improve service provision.
4.1.2.5. Knowledge management (KM) (C2_5): Purple Sub-theme. KM
consists of selecting, collecting, storing, organizing, packaging, and
communicating the essential business information of a rm to enhance
its competitive advantages by making the best decisions [24]. Based on
the C2_5, it can be understood that IoT and CPS-based supply chains use
knowledge management systems to analyze structured information of
Warehouse Management Systems (WMSs) to extract relevant knowledge
that may be useful in decision-making processes. Accordingly, KM aims
to make IoT and CPS-based supply chains more efcient in environ-
mental management, asset management, and quality and safety
problem-solving.
4.1.2.6. Smart logistics (C2_6): Light-blue Sub-theme. Smart logistics
harness Industry 4.0 technologies, notably the IoT and CPS, to foster
integration of information, intelligence, and systematic operations
within logistics distribution networks ([16]; N. [148,132]). Ports and
harbors represent key areas of interest for the implementation of smart
logistics, categorized within the C2_6 cluster. Presently, the Web of
Things (WoT), serving as an application platform for IoT, is recognized
as a pivotal technology in the realm of smart logistics.
4.1.2.7. Electronic Product Code (EPC) (C2_7): Orange Sub-theme. EPC is
a tag data standard for identifying physical objects by employing a
unique identier developed by the Auto-ID Center at MIT. The EPC-
global Network is comprised of four parts, namely I) Object Name Ser-
vice (ONS); II) EPC Information Services (EPCIS); III) EPC Discovery
Service (EPCDS); IV) EPC Security Services (EPCSS) [31,81]. C2_7 refers
to EPCIS, which enables logistics and supply chains to identify physical
objects based on unique codes. The EPCIS is a crucial component of
electronic tag-based technologies such as RFID to construct the EPC
Network Architecture. An EPC Network Architecture as an information
service provides a user interface that allows clients to trace and control
data from different sources of EPC data.
4.1.3. Location-based services (LBSs) (C3)
The LBS mainly deals with location-based data gathered from (mo-
bile) devices and users [79]. LBSs integrate location-based data from
mobile devices and objects (e.g., users’) information to offer
value-added services for meeting users’ needs over wireless networks
[57]. Two main types of LBS exist user-requested and condition-based
LBSs. The position data in a user-request scenario is retrieved once by
the user and used for subsequent requests to location-dependent ser-
vices. Navigation and routing are some of the critical user-requested LBS
services. Condition-based LBSs, on the other hand, retrieve objects’
positions once predened conditions have been met. Emergency services
can be considered condition-based LBSs, which automatically receive a
person’s location from the mobile network when they call the center
[41]. Since different objects in a logistics system move between places,
nding their position among supply chains is crucial. Various LBS ser-
vices, such as location-based order allocation, navigation, and
location-based pricing, are used today to improve logistics supply chains
([139]; Xu [138,76]).
As indicated by the dark green color in Fig. 7, C3, which has 34
vertices and 214 linkages, concentrated on the LBS concept of IoT and
CPS technologies among logistics supply chains. In brief, C3 demon-
strated four technology types of location-aware technologies (e.g., Global
Positioning Systems (GPS), Indoor Positioning Systems (IPS), and Online
Social Networks (OSNs)), data collection technologies (e.g., Arduino), data
transfer technologies (e.g., Bluetooth, antennas, 5 G mobile communica-
tion systems, and wireless communication), data processing technologies
(e.g., mobile applications, smartphones, mobile computation). LBSs
combine object data collected from data technologies with location-
based data gathered from location-aware technologies and transfer
them using data transfer technologies to data processing technologies
for service delivery (See Fig. 12). A variety of LBS applications in lo-
gistics supply chains have been developed in various domains, such as
agricultural robots, eet operations, emergency services, and accident
prevention.
The related network of the C3 theme is illustrated in Fig. 13.
4.1.4. Agri-food supply chains (C4)
The signicance of agriculture and food within supply chains is
Fig. 10. Project management theme.
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Supply Chain Analytics 6 (2024) 100067
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paramount, especially in today’s global economy [126]. C4, with 43
nodes and 347 edges, is referred to as the Agri-food supply chain. After
running the Louvain algorithm on this community, four sub-themes are
revealed, as shown in Fig. 14.
4.1.4.1. Food quality and safety tracing (C4_1): Green Sub-theme. As a
result of globalization, foods and agricultural products travel long dis-
tances from farm to fork. Therefore, quality assurance and safety
assurance are critical issues for supply chains during travel. As part of
Agri-Food supply chains, traceability is essential for meeting food safety
and quality and increasing consumers’ condence [13]. C4_1 focuses on
the tractability duty of Agri-food supply chains in which real-time
monitoring systems trace fresh agricultural products’ safety and quality.
4.1.4.2. From precession agriculture to smart farming(C4_2): Red Sub-
theme. As a management strategy, precision agriculture (PA) utilizes
information technologies for data collection from multiple sources,
which can be applied as a decision support tool for crop-related pro-
ductions. As a result of atomization processes, PA facilitates the collec-
tion and analysis of data, thereby allowing for quick decision-making
and management process implementation [39]. With PA systems, data is
stored in local databases without the possibility of sharing it with other
farmers and suppliers. As emerging technologies such as the IoT
emerged, PAs role faded, and a new concept called smart farming (SF)
emerged. Food supply chains are equipped with IoT-based systems,
which allow farmers to collect, share, and analyze data in real-time and
make decisions faster [120]. However, some researchers consider both
SF and PA to have the same meaning and use them interchangeably.
Overall, SF and PA are dened as using modern technologies, such as IoT
and drones, to manage farms to increase food production and yield while
reducing the need for labs, land, and inputs [144]. C4_2 explains SF and
PA concepts and their enabling technologies, such as Geographic In-
formation Systems (GIS), knowledge-based systems, virtual reality (VR),
environmental technologies, and drones.
4.1.4.3. 3D printing (C4_3): Yellow Sub-theme. Additive manufacturing
(AM), also known as 3D printing, builds objects by joining materials
based on 3D model data [35]. In other words, AM is a technology that
can create physical models from virtual solid model data. In an AM
technology, collections of 2D cross-sections with nite thicknesses are
generated from the data. Physical objects are formed by adding 2D
cross-sections together in a layer-by-layer sequence [59]. 3D printing
plays a crucial role in food safety and quality by reducing waste,
increasing variety, and controlling food quality. As a result of 3D
Fig. 11. Enabling technologies.
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Supply Chain Analytics 6 (2024) 100067
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printing, supply chains can design food structures based on customer
preferences of color and size [137]. 3D printing eliminates long-distance
transportation of products in cold chains by using locally-sourced raw
materials in a kitchen. This way, food becomes less susceptible to
pathogens, and security increases. 3D printing could be a key technology
in pandemic prevention because of the improving food security, espe-
cially in the case of the COVID-19 virus [151,91] pandemic. Specically,
C4_3 addressed using 3D printing technology to improve food security,
particularly during pandemics such as COVID-19.
4.1.4.4. Digital food storage (C4_4): Blue Sub-theme. Improving food
storage in supply chains is one way to reduce food waste [121,130].
Digitalization is improving different parts of the supply chain, such as
food storage, so we can reach our food waste reduction goals [6]. Digital
platforms, like websites, can store perishable agri-food products, such as
fruits, reducing food waste associated with supply chains. Food digital
storage for waste management is described briey in C4_4.
4.1.5. Smart grid challenges (C5)
C5, with 68 nodes and 733 links, describes the challenges of the
smart grid in supply chains. After the re-community detection process,
the cyber-security area is divided into ve main sections, as indicated in
Fig. 15.
4.1.5.1. Smart power grids (C5_1): Red Sub-theme. With a smart grid,
three components are integrated into existing power grids: modern
sensing technologies, advanced communications, and control method-
ologies [113]. Smart grids utilize automated control and communication
techniques to enhance the efciency and reliability of electric power
transmission networks [65]. In a smart grid, electricity is produced by
different electricity producers using different energy sources. The
generated electricity is then transmitted and distributed to the end users.
Fig. 12. Enabling technologies of LBSs.
Fig. 13. Wireless communications.
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The concept of intelligence applies to load balancing and smart elec-
tricity generation in a smart grid concept [102]. C5_1 introduces smart
power grids in intelligent electric power transmission for logistical and
supply chain sectors, including intelligent vehicle highway systems
(IVHS). Electric power transmission in logistics and supply chains is
analyzed and controlled by analyzing the data provided by SCADA
(Supervisory control and data acquisition) systems.
4.1.5.2. Smart Grids’ Vulnerabilities (C5_2): Blue Sub-community. As a
type of CPS, smart grids integrate intelligent devices into a communi-
cable setting to make power systems easier to control. However, this
integration can pose severe cyber threats, which also present vulnera-
bilities for smart grids [64]. C5_2 explains the security challenges of
DSCs under launching smart power grids. Malware, cyber-attacks such
as Denial-of-service attacks (DoS), computer crime, and hardware se-
curity are the main components of security issues. Also, anomaly
detection and intrusion detection are two standard methodologies
aiming to improve the supply chains’ smart grid security indicated in the
presented sub-graph.
4.1.5.3. Authentication (C5_3): Orange Sub-community. Smart Grid’s
success depends mainly on how well it can defend itself against remote
network-based attacks. The rst and most effective defense against at-
tacks is user authentication for smart grid access [54]. Authentication
and authentication protocols are the main concepts of C5_3 introducing
anti-counterfeiting methods for improving the security issues of smart
grids.
4.1.5.4. Supply chain risk management (SCRM) (C5_4): Yellow Sub-
community. Cyber-security risk assessment is critical for businesses,
which aims to identify vulnerabilities and threats of smart grids. Smart
grid security requirements and security controls should be specied
using the risks assessed in the risk assessment. Hecht et al., [73]. C5_4
concentrates on implementing risk assessment approaches for identi-
fying cyber-security risks of smart grids in supply chains. The risks
associated with supply chains can be managed and controlled through a
successful risk assessment.
4.1.5.5. Blockchain-based smart grids (C5_5): Green Sub-theme. As a
decentralized transaction and data management technology, Blockchain
is one of the Distributed Ledger Technologies (DLTs), working based on
peer-to-peer (P2P) systems, where transactions are executed in a
distributed environment. Blockchains’ ability to provide secure data
exchange and management conditions has led many to launch
blockchain-based systems [145]. Smart grids have been moving towards
blockchain-based smart grids that are more secure and protect them-
selves from attacks and vulnerabilities [106,123]. C5_2’s main focus is
on blockchain-based smart grids, improving data privacy, and making
supply chains and logistics more secure.
4.1.6. Industry 4.0 (C6)
Industry 4.0 is mainly composed of IoT and CPS, as previously
mentioned. C6 with 95 nodes and 1861 edges explains Industry 4.0-
related concepts. After the re-community detection phase, seven
themes are revealed, as shown in Fig. 16.
4.1.6.1. DSCs (C6_1): Purple Sub-them. Emerging technologies have
brought competitive advantages to supply chains, introducing a new
concept called DSC, which has resulted in cost reductions [95], product
development [33] and performance improvements [92]. C6_1 explains
the future directions of supply chains that incorporate emerging tech-
nologies and digitalization concepts as competitive advantages. Based
Fig. 14. Agri-food supply chains.
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Supply Chain Analytics 6 (2024) 100067
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on the provided sub-theme, digitalization is considered the primary
purpose of future supply chains.
4.1.6.2. Smart manufacturing (C6_2): Green Sub-theme. Smart
manufacturing involves ve main concepts, namely: a) smart designing:
As part of a smart design process, end users can work with manufac-
turers to develop their desired products; b) smart monitoring: with smart
monitoring, the status of manufacturing systems can be accessed
remotely, and desirable data can be collected and analyzed for specic
purposes; c) smart machining: it refers primarily to the internet of things-
based manufacturing, where automated systems are integrated into a
networked system; d) smart controlling: is related to managing smart
machines involved in the manufacturing process; e) smart scheduling: this
refers to the planning of the smart manufacturing processes [111,61].
C6_2 focuses on smart manufacturing processes. Nowadays,
manufacturing companies apply CPSs and embedded systems to direct
manufacturing processes towards smartness. Accordingly, all of the
involved implementations of product life-cycle (PLC), such as designing,
planning, and controlling, can be executed by smart manufacturing
systems.
4.1.6.3. Cloud manufacturing (C6_3): Yellow Sub-theme. Cloud
manufacturing combines cloud computing, IoT, and service-oriented
technologies to create a collaborative environment between enter-
prises, even across long distances. Cloud manufacturing has changed
how industrial resources are used via internet-based services [29]. Cloud
manufacturing is a technology that virtualizes the manufacturing
resources to provide ubiquitous demand meeting [101]. C6_3 refers to
an approach involving cloud manufacturing in supply chains, where
certain services, such as distributed computer systems, are accessible
from the cloud.
4.1.6.4. Augmented reality (AR) (C6_4): Red Sub-theme. The goal of AR
is to "augment" the physical world with virtual objects. A concept of
augmented reality is the overlaying of digital information of physical
objects (e.g., geographical places) over computer-created (virtual) set-
tings to improve customer experiences [25]. C6_4 represents the
augmented reality technology that uses virtual objects to augment the
physical supply chain.
4.1.6.5. Digital twins (DT) (C6_5): Orange Sub-theme. C6_5 refers to the
DT technology. A digital twin [12] is a virtual representation of a
physical object [63], simulating the real world through collected data
from smart devices and sensors. Using mathematical modeling and other
practical approaches, DT-based environments can optimize physical
environments [109,38]. Although IoT and CPSs enable supply chains
and logistics to collect and integrate sensors’ data, digital twins capture
the collected data and create a copy of the considered supply chain or
logistics. Digital twins aid managers trace the system’s current situation
and enable them to predict the future state of the systems in a simulated
environment.
4.1.6.6. Enterprise resource planning and decision support systems (C6_6):
Light-blue Sub-theme. Enterprise Resource Planning (ERP) is one of the
Fig. 15. Cyber-security.
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Supply Chain Analytics 6 (2024) 100067
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main crucial elements of organizations and supply chains aiming for
efcient resource management and allocation processes. With the
advent of innovative technologies, intelligent ERPs emerged, working
based on intelligent agents such as intelligent robots providing auton-
omous cooperation. Decision Support Systems (DSS) is another tech-
nology improving the ERPs’ performance through analytical tasks.
4.1.6.7. IIoT (C6_7): Dark blue Sub-theme. The IIoT, as a concept of
Industry 4.0, is limited to applications of IoT in industries for different
goals, such as improving production processes [129]. In C6_7, the IIoT is
primarily used to provide industrial services in logistics and supply
chain management. Using IIoT technologies, a wealth of data is gener-
ated in supply chains and logistics, which can be analyzed to improve
managerial tasks.
4.1.7. Intelligent computing (C7)
Evolutionary algorithms as a type of intelligent computing are
applied in SCM problems such as energy conservation and sustainability
issues. Due to the NP-hard nature of evolutionary algorithms such as
Ant-Colony Optimization (ACO) and Particle Swarm Optimization
(PSO), novel technologies such as fog computing and edge computing
are utilized to increase the performance and speed of the algorithms.
This community can be found in Fig. 17.
4.1.8. Marketing (C8)
C8, with 41 nodes and 376 links, explains the marketing aspect of the
considered domain. After running community detection on this theme,
four main sub-themes are disclosed, as shown in Fig. 18.
4.1.8.1. Behavioral analysis (C8_1): Orange Sub-theme. Big data enables
Fig. 16. Industry 4.0.
Fig. 17. Fog-based intelligent computing.
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marketers and public relations to analyze customers’ behavioral char-
acteristics for designing appropriate value chains and services, leading
to better operations management and mass customization.
4.1.8.2. Adoption (C8_2): Green Sub-theme. C8_2 mainly focuses on
adopting IoT, CPSs, and future technologies in supply chains in which
challenges and opportunities are surveyed for implementation pro-
cesses. Business modeling is one of the effective methods for meeting
such an aim.
4.1.8.3. Reverse logistics (C8_3): Blue Sub-theme. Reverse logistics refers
to all processes of re-using, re-manufacturing, recycling products, and
improving the circular economy of the entire supply chain. Cost and
Benet Analysis (CBA) is one of the exciting investigations executed by
researchers in IoT and CPSs-based reverse logistics.
4.1.8.4. Customer satisfaction (C8_4): Red Sub-theme. The quality and
performance of the IoT and CPS-based supply chains and logistics are the
main factors inuencing customer satisfaction. Besides, analytical tools
such as prediction models provide relevant insights about customers,
guiding managers to select the best strategies to improve customer
satisfaction.
4.1.9. Data science (C9)
A massive amount of generated data by embedded systems and
sensors of IoT and CPSs, provide conditions for data analysis processes.
Data mining, ML, AI domains utilizing different algorithms such as deep
learning, neural networks, Support Vector Machine (SVM), logistic
regression, Support Vector Regression (SVR), and Random Forest for
various goals such as pattern recognition, classication, feature extrac-
tion, and forecasting. This theme can be seen in Fig. 19.
4.2. Strategic diagram of IoT and CPS-based supply chains and logistics
Although co-word analysis gives an appropriate overview of the
investigated topics by disclosing the involved clusters, it cannot plot the
status of found communities or clusters. Co-word analysis without
employing a strategic diagram would not be benecial, and one cannot
extract practical implications. As a result, a strategic diagram is
employed in this research to determine the status of each theme. As
explained in Subsection 3.4.2, the strategic diagram works based on the
centrality and density measures calculated based on the co-word graph.
Fig. 18. Feasibility.
Fig. 19. Data Science.
H. Baziyad et al.

Supply Chain Analytics 6 (2024) 100067
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The centrality and density of each theme are computed and indicated in
Table 4. Overall, the strategic diagram of the revealed communities is
indicated in Fig. 20. As shown in Fig. 20, QI is empty. QII (Ivory Tower)
comprises three themes of “Intelligent Computing C7”, “Marketing C8”,
and “data science C9” which are well structured internally but without
solid external linkage.
Connecting these themes with the other eight communities extends
and moves them to the QI area. QIII, with four themes, has the greatest
number of areas, namely “Project Management C1”, “Wireless
Communication C3”, “Agri-Food Supply Chains C4”, and “Smart Grid
Challenges C5”. Indeed, a tremendous amount of studies and in-
vestigations are in their initial stages. C1 and C3 are closer to QII, while
C4 and C5 are closer to QIV. Accordingly, C1 and C3 need more in-
vestigations, and C4 and C5 require more combinations with other
themes for moving to QIV.
Generally speaking, QIV includes two central themes: “enabling
technologies” and “Industry 4.0”. Because of the high-density measure
of the themes, little effort on these themes transfer theme into QI.
5. Discussion and insights
Regarding the strategic diagram, the ideal point for each theme lies
in QI, in which themes are developed enough and connected to other
related themes intensely. In other words, the closer the strategic dia-
gram’s top-right corner, the more mature theme. The nature of well-
developed and external-connected themes of QI has comprehensive ef-
fects on other scopes, guiding them to executing novel research. Here-
upon, they play a motor role for other domains and are known by the
’mainstream’ metaphor. Fig. 20 reveals an absence of themes within
Quadrant I, which may suggest that the maturity of IoT and CPS-based
supply chains is still in development. This could potentially lead to an
intensied focus among researchers on this topic, thereby increasing the
volume of related publications. It is recommended that themes from
other quadrants be directed towards Quadrant I to enhance their level of
maturity.
5.1. QII: Ivory tower
Many researchers have well investigated and developed some themes
but without enough combination with other related domains, which
gives rise to staying on QII, such as C7, C8, and C9. For this reason,
themes of QII are called ‘developed but isolated themes’. Indeed, despite
enough growth in academic aspects of QII, they do not have much us-
ability in real-world conditions. Ivory Tower is an appropriate metaphor
applied for QII, referring to ‘science without application’.
C7, or Intelligent Computing, has theoretical and technical
complexity, preventing researchers of different elds from investigating
it. Also, examining such technologies needs to be implemented in many
cases that reduce the research speed and make it complicated. Accord-
ingly, researchers in other elds have been less interested in studying
this area. The initialization of online fog-based services with easy-to-use
ability enables researchers to investigate similar domains effectively.
C8 or marketing is another isolated eld that could grow internally
well, concentrating on the consumers’ sides. Overall, marketing could
grow internally in different themes, i.e., customers’ behavioral analysis,
technology adoption, customer satisfaction, and reverse logistics, while
it could not penetrate other domains. Indeed, a wide range of domains
affects companies’ choices to apply IoT and CPSs in their supply chains
which were neglected in the C8, making this eld more isolated. For
instance, the two main concerns of supply chains are cyber-security and
health elds that directly affect technology adoption in market research.
According to C9, data science techniques are applied to the gener-
ated data by CPSs and IoT technologies for analyzing the supply chain
sections. Although data analytic tools were utilized chiey, they were
employed in a few applications because of the low amount of accessible
data in this eld. The security issue is one of the main reasons preventing
data sharing in this eld. Launching simulation-based systems can
provide related data for various analytical applications in different
domains.
5.2. QIII: Emerging or declining themes
QIII is the opposite of QI, where both maturity criteria (development
and connection) are low. Overall, themes of this quadrant lie in two
sections: a) emerging themes and b) declining themes. Due to the
newness of the areas of this section, many irregularities can be seen in it,
indicating an undeveloped sphere. Therefore, the ‘Chaos/undeveloped’
metaphor is used for QII. Because of the short life of IoT and CPS-based
supply chains, themes of QIII are considered emerging. C1, C3, C4, and
C5 are the components of QIII.
Since IoT and CPS-based supply chains are new, particularly in the
real world, researchers faced a lack of standard models for imple-
mentation. Accordingly, an endeavor was made to develop simulation-
based methods for virtualizing different states of technology imple-
mentation. Using such methods speeds up research processes without
any time-consuming implementation.
C3 or Location-based Services has mainly concentrated on applicable
hardware of IoT and CPSs in supply chains. IoT and CPSs have been
applied for a short time in supply chains, resulting in low testing of
related technologies. Accordingly, researchers may investigate and
compare the more comprehensive range of hardware devices in supply
chains.
Due to the many benets of IoT and CPS technology in perishable
supply chains, some of them, such as agri-food supply chains (C4), could
be emerged as an independent clusters in the studied network. The high
risk of agri-food supply chains for human health, particularly in the
COVID-19 crisis, is another reason that has forced researchers to
investigate IoT and CPS technologies in them. Most applications of IoT
and CPS-based technologies in agri-food supply chains refer to real-time
tracking tasks in which product locations and conditions (such as hu-
midity and temperature) are traced everywhere and every time. Con-
necting collected data from tracking systems with analytical tools can
add other benecial tasks to supply chains, such as predicting tools. A
prediction-based system enables supply chains to predict critical factors
such as delays, spoiling time, supply, and demands.
Although cyber-security is one of the main problems of IoT and CPS-
based systems, it is in its infancy due to its new applications in supply
chains. One of the leading solutions for cyber-security problems is
implementing Blockchain technology utilizing distributed computing,
resulting in a safe IoT and CPS-based supply chain. It can be predicted
that applying Blockchain technologies in IoT and CPS-based supply
chains will soon change into a hot topic domain.
Table 4
Re-community detection results.
Id Cluster Color No.
Nodes
No.
Edges
Modularity No. Sub-
themes
C1 Project
Management
Pink 11 21 - 1
C2 Enabling
Technologies
Light-
green
138 3803 0.250 7
C3 LBSs Dark
green
34 214 - 1
C4 Agri-Food
Supply Chains
Dark
blue
43 347 0.339 4
C5 Smart Grid
Challenges
Light
blue
68 733 0.416 5
C6 Industry 4.0 yellow 95 1861 0.260 7
C7 Intelligent
Computing
Dark
Red
12 37 - 1
C8 Marketing Orange 41 376 0.309 4
C9 Data Science Light
red
28 292 - 1
Total - 470 27423 0.252 9
H. Baziyad et al.

Supply Chain Analytics 6 (2024) 100067
18
5.3. QIV: Basic and transversal
The QIV theme refers to basic themes that cannot be developed fully
theoretically but are applied across many applications. Though a theme
may be developed well, only its basic knowledge is applied to the eld
under investigation. Perhaps industry 4.0 is well developed, but in the
context of IoT- and CPS-based supply chains, only the basics of the
concept are used. The IoT- and CPS-based supply chains are not well-
developed in industry 4.0, but their fundamental concepts have
considerable applications. They are considered ‘bandwagons’, employed
by other domains. C2 and C6 are categorized in QIV.
The concept of C2 refers to the enabling technologies of IoT and CPS
implemented in several aspects of supply chains. Technology-enabled
supply chains and logistics are considered the foundation of the IoT
and CPS. While related technologies are advancing rapidly, learning
about and implementing them in supply chains takes time. Therefore,
the investigation of new IoT and CPS technologies from the DSC
perspective has remained in the early stages. Research and Development
(R&D) organizations that survey novel technologies used in supply
chains may encourage researchers to include novel technologies in
different aspects of DSCs.
The focus of C6 or Industry 4.0 is heavily on the manufacturing as-
pects of IoT- and CPS-based supply chains connected to the IIoT. C6 is
interpreted very similarly to C2, in which a wide range of related
technologies has been investigated in manufacturing supply chains.
Moreover, research on the technological aspects of each technology was
neglected, and utilizing related technical experts can give a better
theoretical overview to audiences.
In the dynamic domain of digital supply chains, it is imperative to
bridge the gap between theoretical advancements and practical appli-
cations. The domains of Intelligent Computing, Marketing, and Data
Science have reached a level of theoretical maturity that positions them
as prime candidates for pioneering application-driven research. These
areas are ripe for innovation, offering a fertile ground for the develop-
ment of cutting-edge features that can revolutionize data-driven tech-
nologies. Conversely, the domains of enabling technologies and Industry
4.0 have seen widespread practical application across various sectors
within digital supply chains. Despite this, there exists a signicant op-
portunity to bolster their theoretical foundations. By deepening the
theoretical understanding, we can unlock new dimensions of these
technologies and enhance their efcacy and scope of application.
Furthermore, emerging themes such as Project Management, Location-
Based Services (LBSs), Agri-Food Supply Chains, and Smart Grid Chal-
lenges present a dual frontier for development. These areas require both
theoretical expansion and empirical exploration to uncover novel ap-
plications. It is essential to foster a symbiotic relationship between
theory and practice, ensuring that each informs and enriches the other.
6. Conclusions, limitations, and future research
This paper concentrated on disclosing the main themes of CPS and
IoT-based supply chains and logistics. A co-word analysis, one of the
signicant bibliometric methods, was applied to related publications of
Scopus and web of science (WoS) databases between 2010 and 2021.
Fig. 20. Strategic diagram.
Table 5
The strategic diagram measurement.
Q ID Top Keywords Centrality Density
QI: Motor - - - -
QII: Ivory
Towers
C7 Intelligent computing, fog
computing, edge computing,
ACO, PSO
336 0.5606
C8 Big data, behavioral research,
CBA, customer satisfaction,
value chains
1101 0.4585
C9 Data mining, ML, AI, pattern
recognition, deep learning,
learning systems
1728 0.7725
QIII:
Emerging/
Declining
C1 Simulation model, computer
simulation, software, standards
457 0.3818
C3 Arduino, GPS, antennas,
LoRaWAN, IPS, Bluetooth
655 0.3815
C4 Agricultural products, food SCs,
cold chain logistics, food waste,
precision agriculture
1591 0.3843
C5 Cyber-attacks, blockchain,
computer crime, security
challenges, authentication,
privacy, network security
1786 0.3218
QIV:
Bandwagon
C2 IoT, RFID, WSN, Zigbee, LIS,
WoT, semantic web, M2M, CC,
ICT
9315 0.4023
C6 Industry 4.0, IIoT, AR, ERP,
embedded systems, digital
twins, smart manufacturing,
DSC
2760 0.4168
H. Baziyad et al.

Supply Chain Analytics 6 (2024) 100067
19
Generally, nine central clusters were revealed, including 1) “Project
Management”, 2) “Enabling Technologies”, 3) “Location-based Ser-
vices”, 4) “Agri-Food Supply Chains”, 5) “Smart Grid Challenges”, 6)
“Industry 4.0”, 7) “Intelligent Computing”, 8) “Marketing”, and 9) “Data
Science”.
Upon identifying the core themes, we utilized a strategic diagram to
assess the present status of each topic within IoT- and CPS-driven supply
chains. The analysis revealed an absence of a mainstream theme—a
position signifying extensive development and widespread application
across various domains. Consequently, the goal for each theme is to
achieve enhanced development and increased applicability, propelling
it into a mainstream status. Themes C7, C8, and C9 reside in the Ivory
Tower quadrant, indicating they are underdeveloped. To transition into
mainstream topics, they need to be integrated with other relevant
themes. Themes C1, C3, C4, and C5 are positioned in QIII, signifying
they are either emerging or declining themes that are peripheral and not
fully developed. These themes exhibit numerous research gaps, neces-
sitating focused attention for comprehensive internal and external
exploration. It’s imperative that each theme undergoes thorough
investigation and interdisciplinary collaboration. Themes C2 and C6,
located in the bandwagon quadrant, are more central yet less developed.
Intensied internal research efforts could propel them towards the
motor quadrant, which is indicative of well-developed and widely
applied themes.
Although this paper investigated CPS- and IoT-based supply chains
successfully, it dealt with some critical limitations, which could be
considered as future streams, as follows:
6.1. Time-consuming
Co-word matrix calculation is time-consuming, mainly when the
number of investigated publications and keywords is high. Utilizing
parallel computing and big data approaches can improve this issue
effectively.
6.2. Prediction model
The proposed co-word analysis describes the current situation of the
publications without prediction ability. Combining methods such as link
prediction with co-word analysis gives a comprehensive overview of
future research.
6.3. Web mining
The rise of blogs and OSNs has signicantly facilitated the accessi-
bility of data [18,88]. The present research was conducted on academic
databases while there is a massive amount of data about CPS- and
IoT-based supply chains on websites and OSNs. Besides, due to the
relatively long review, published papers’ contents in WoS and Scopus
databases are older than websites, blogs, and OSNs. Accordingly,
implementing the co-word analysis on these sources may extract another
aspect of knowledge. It must be considered that this process needs an
appropriate keyword extraction algorithm for running co-word analysis
on web-based data because web-based datasets are unstructured and
have no keyword section, such as publications of WoS and Scopus.
CRediT authorship contribution statement
Aseem Kinra: Supervision, Project administration, Methodology,
Investigation, Conceptualization. Vahid Kayvanfar: Writing – original
draft, Supervision, Project administration, Methodology, Conceptuali-
zation. Hamed Baziyad: Writing – original draft, Visualization, Vali-
dation, Software, Methodology, Data curation, Conceptualization.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
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