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Supply Chain Analytics 6 (2024) 100063
Available online 19 March 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 review of decision support systems in the internet of things and supply
chain and logistics using web content mining
Vahid Kayvanfar
a
,
*
, Adel Elomri
a
, Laoucine Kerbache
a
, Hadi Rezaei Vandchali
b
,
Abdelfatteh El Omri
c
a
Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
b
Australian Maritime College, University of Tasmania, Launceston, Australia
c
Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha 3050, Qatar
ARTICLE INFO
Keywords:
Supply chain 5.0
Internet of Things
Decision Support System
Blockchain supply chains
Big data analytics
Web content mining
ABSTRACT
The Internet of Things (IoT) has attracted the attention of researchers and practitioners in supply chains and
logistics (LSCs). IoT improves the monitoring, controlling, optimizing, and planning of LSCs. Several researchers
have reviewed the IoT-based LSCs publications indexed by academic journals focusing on decision-making.
Decision support systems (DSS) are in the infancy stage in IoT-based LSCs. This paper reviews the IoT-LSCs
from the DSS perspective. We propose a new framework for helping decision-makers implement IoT based on
the decisions that need to be made by describing a transition scheme from simple, if-then decisions to analytical
decision-making approaches in IoT-LSCs. The IoT Adopter II is an extension of the IoT Adopter framework, in
which a new layer called ‘decision’ has been added to enable decision-makers implementing IoT to improve the
list of predened decision-making processes in LSCs. Although academic literature review analysis provides
valuable insights, a wide range of related information is available online. This study also utilizes a web content
mining approach for the rst time to analyze the IoT-LSCs in the decision-making context. The results show that
the IoT-LSC eld involves two emerging themes, blockchain supply chains and supply chain 5.0, and two
mainstream themes, i.e., big data analytics and supply chain management.
Introduction and motivations
Supply chains are becoming signicantly more complex nowadays
and are also vulnerable to global risks since they extend over broad
geographical areas. Furthermore, due to economic, social, and natural
factors, the external environment of supply chains is very dynamic, and
having the exibility to deal with these constant changes is necessary to
remain competitive [10]. Current and traditional supply chains cannot
meet future business requirements. While processes in traditional supply
chains are discrete and separate, digital supply chains (DSCs) break the
walls in between, creating an integrated and continuous system. This
growing evolution of supply chains through digital transformations can
be attributed to the fourth industrial revolution or Industry 4.0 (M.
[64]).
Industry 4.0 follows solutions for connecting traditional industries
internally and digitalizing effectively [73,72]. It is a complete trans-
formation of the manufacturing industry through the introduction of
digitization along with the Internet. The result of this is a signicant and
revolutionary improvement in the manufacturing processes of products
and systems [82]. Such improvement is supported by equipping fac-
tories with cutting-edge technologies such as the Internet of Things
(IoT), Cyber-physical Systems (CPSs), and Cloud Computing (CC) [7,
97]. Following the transformations that Industry 4.0 has brought to
supply chains, the term Supply Chain 4.0 has recently become a common
term in academia and the industry [6], another term being Digital
Supply Chain (DSC) [56]. Both researchers and practitioners believe IoT
is a key technology for digitalizing supply chains and logistics, and IoT is
a crucial component for transforming traditional supply chains into
DCSs [7].
The development and spread of IoT offer new possibilities for inno-
vation in modern supply chains. Supply chain operations can be
improved by using technologies, devices, and sensors that are connected
to IoT [78]. IoT is a data-driven technology comprising a set of inter-
connected objects, enabling logistics and supply chains (LSCs) to sense
and monitor the environment remotely [7,66]. Management can
dynamically optimize supply chains, monitor logistics processes
* 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.2024.100063
Received 17 September 2023; Received in revised form 10 February 2024; Accepted 15 March 2024
Supply Chain Analytics 6 (2024) 100063
2
remotely, and execute plans via the IoT [53]. Fig. 1 shows an overview
of IoT-based LSCs tasks.
With the adoption of the IoT, rms can upgrade their operational
efciencies, do activities more conveniently, and better keep up with
their competitors [65]. Digital supply chain technologies are divided
into three main categories: data-driven, knowledge-based, and deci-
sion-oriented. Data-driven technologies such as IoT and CPS collect
related data, store it in a physical (e.g., databases) or virtual place (e.g.,
clouds), and then exchange it between involved objects. The generated
data is meaningless and cannot be used without knowing it.
Knowledge-based technologies receive the data and analyze it to extract
useful knowledge. Although the extracted knowledge can be useful for
helping managers to make appropriate decisions, decision-oriented
technologies (e.g., decision support systems (DSSs)) can be designed
to optimize the decision-making approaches in supply chains [24,7]. To
the best of the authors’ knowledge, there is no serious effort to inves-
tigate these decision-oriented approaches in IoT-LSCs. In this paper, we
provide a comprehensive literature review of the decision-oriented
viewpoint of IoT technology in LSCs and then propose an integrated
framework for handling decision-making processes. Also, due to the lack
of academic publications, we use a text-mining approach to analyze the
web content and extract insights from hidden knowledge of related
websites and blogs. The main contributions of this paper could be
summarized as follows:
i) One of the very rst attempts to review the role of decision-
making in the IoT-LSCs.
ii) Discussing the role of DSS in the IoT-LSCs domain.
iii) Describing the transition scheme from simple if-then decisions to
analytical decision-making approaches in IoT-LSCs.
iv) Developing the newly proposed framework named IoT Adopter for
embedding DSS technologies in IoT-LSCs.
v) Employing a text mining approach for analyzing the IoT LSCs
from the viewpoint of decision-making based on the crawled web
contents.
Methodology
In this paper, a systematic literature review is conducted to analyze
IoT-based LSCs from the perspective of DSSs. Google Scholar is the most
comprehensive academic search engine [31], and therefore, we used it
to collect related publications. The initial list of keywords is divided into
three classes: the rst category relates to the ‘supply chains and logistics’
area, the second group refers to the ‘IoT’ area, and the third one refers to
‘decision-making’ approaches.
We applied a related search query for extracting the most relevant
academic publications from the Google Scholar database as follows:
(TS=(internet of things) OR TS=(IoT)) AND (TS=(supply chain*) OR TS=
(SCM) OR TS=(logistics) OR TS=(LSCM)) AND (TS=(decision support
system*) OR TS=(DSS) OR TS=(decision*)). The research gap investi-
gated in this research is shown with grey lines in Fig. 2.
We searched in such a way that there was at least one keyword from
each group in the abstract, title, or keywords of the indexed publica-
tions. Searching was conducted only between the titles, abstracts, and
keywords in the English language for collecting the most relevant pub-
lications during the last ve years until October 15, 2022. A total of 38
articles with the most topical relevance were selected. The number of
Fig. 1. An overview of IoT-based LSCs (Baziyad, Kayvanfar, en Kinra 2022).
Fig. 2. The investigated research gap of the current study (the shaded area).
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
3
related publications from 2008 to 2022 is presented in Fig. 3. The
number of printed articles is increasing on average, and it could be ex-
pected that this eld will receive the attention of more researchers in the
coming years.
Based on the list of keywords provided by the authors, the most
frequently used are listed in Table 1. As can be seen, the Internet of
Things (IoT), Decision Support Systems (DSS), big data, Cyber-Physical
Systems (CPS), and Industry 4.0 are the ve most frequent keywords. To
better understand, the word cloud of the keywords has been illustrated
in Fig. 4.
In the provided word cloud picture, the more frequent keywords
have been indicated bigger than the less frequent ones. For example, the
term Internet of Things has been depicted as bigger than other key-
words. With a glance at the word cloud, a wide range of keywords
related to industry 4.0 and digital supply chains are shown, such as big
data, digital twins, cloud computing, and cyber-physical systems. It
could be interpreted as decision support in digital supply chains
depending upon novel technologies.
The most active journals in publishing in the eld of decision support
in the era of IoT-based supply chains and logistics are listed in Table 2.
The table shows the publishers, Impact Factor (IF), and h-index of
various journals based on the Scimago Journal Rank (SJR). The journal
Sustainability, with three papers, is the most active journal. Also, ve
journals with two publications lie in second place: Sensors, International
Journal of Production Economics (IJPE), Future Generation Computer
Systems (FGCS), Applied Sciences, and IEEE Access. Since this eld is in
its infancy, the number of publications in different journals could be
higher. So, one may expect that the journals will focus on this area more
in the near future.
Taxonomy of IoT-LSCs in the context of decision-making
The primary component of DSSs is decision-making [39]. Therefore,
understanding the way a decision is made in IoT-LSCs is the prelude of
DSSs. This section concentrates on the concepts of decision, decision
support, and decision support systems. A brief taxonomy of investigated
publications can be seen in Fig. 6.
Pre-implementation evaluation
Before implementing IoT in LSCs, an analysis of the possibility of
applying IoT is needed. The current publications related to imple-
mentation evaluation are divided into four classes: technology adoption,
protability analysis, business model designing, and architecture
designing.
Technology adoption
For more than three decades, technology adoption has been
considered one of the main areas of Information Systems (IS) [84,85]. In
technology adoption, researchers seek to discover, describe, and predict
variables that affect adoption behavior regarding accepting technology
innovations at both the organizational and individual levels [25].
Among technology adoption as decision-making about the adoption
or rejection of IoT in LSCs, a wide range of factors such as trust, social
inuence, and technology readiness should be analyzed. However, some
publications only concentrated on requirements [11,32]. Therefore, to
make a comprehensive decision about IoT adoption in LSCs, key factors
and enablers of IoT should be identied and analyzed.
Yadav et al. [92] proposed a framework for discovering the key en-
ablers of effective coordination development in IoT-based agri-food
supply chains (AFSC). Also, using an interpretive structure model (ISM),
the framework was developed to analyze the interrelationships between
the 30 enablers under seven categories in strategic and operational
decision-making goals. They found that top management support has
the most inuence on adopting IoT to improve coordination in the AFSC
for operational and strategic decision-making processes. Although
identifying the IoT enablers in LSCs helps related managers make better
decisions about adopting the technology, without prioritization, man-
agers may be confused when conicting enablers exist. Therefore, uti-
lizing appropriate methods for ranking the enablers helps managers
decide about considering the enablers for IoT adoption based on their
degree of importance. Pimsakul et al. [61] used a two-phase approach
for analyzing the IoT key enablers in the sustainable supply chain
management (SSCM) context. The rst phase, focuses on identifying the
enablers, and in the second phase using grey relational analysis, the
Fig. 3. Number of papers published in years from 2008 to 2022.
Table 1
The most frequent keywords.
Row Keywords Frequency
1 Internet of Things (IoT) 26
2 Decision Support Systems (DSS) 9
3 Big Data 7
4 Cyber-Physical Systems (CPS) 6
5 Industry 4.0 6
6 Supply Chains 5
7 Digital twins 5
8 Digital Supply Chains 5
9 Cloud Computing 4
10 Radio Frequency Identication (RFID) 4
11 Industrial Internet of Things (IIoT) 3
12 Supply Chain Management (SCM) 3
13 IoT Supply Chains 3
14 Healthcare 3
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
4
identied enablers are prioritized. They found that system integration
and IoT infrastructure are the most inuential factors for adopting IoT in
SSCM.
Protability
Technology adoption publications focused on discovering the inu-
ential factors of implementing IoT technologies in LSCs. However,
introduced technology adoption approaches do not consider the possi-
bility of implementation from costs and benets. Therefore, introducing
methods for the estimation of costs and benets is required. Decker et al.
[19] developed a quantication cost model for evaluating smart items,
such as IoT components, from suppliers’, customers’, and shippers’
perspectives. The proposed method considers cost-related parameters
such as technology prices, xed costs, and benets such as utility.
Finally, due to the real applicability of the model, some guidelines have
been proposed for estimating the cost-benet parameters.
Business model
After nding that IoT can be used in a logistics or supply chain, a
transparent business model is needed for future implementation. The
business model refers to the rm’s logical roadmap for work to create
value [51,52] designed an ontological business model for circular supply
chain management enabled by IoT. Then, based on the designed busi-
ness model, a DSS was built to make the resource economy more ef-
cient. The embedded DSS was used to suggest repairing,
remanufacturing, recycling, and reusing decisions.
Architectures
Although business models provide a logical road map for working,
they cannot specify the technical specications of an IoT platform in
LSCs; therefore, designing architecture is required. An architecture de-
termines the IoT platforms’ layers and the way that layers interact with
each other. Primary architectures, such as three-layer, four-layer, and
ve-layer, do not support complex decision-making processes [7].
Accordingly, designing architectures enabled by a separate decision
Fig. 4. Word cloud of the involved
Table 2
The most active journals in the eld of decision support in the era of IoT-based
supply chains and logistics.
Row Journal Publisher IF H-
index
No. of
publications
1 Sustainability MDPI 4.39 136 3
2 Sensors MDPI 4.417 219 2
3 International Journal
of Production
Economics (IJPE)
Elsevier 13.494 214 2
4 Future Generation
Computer Systems
(FGCS)
Elsevier 9.166 151 2
5 Applied Sciences MDPI 3.095 101 2
6 IEEE Access IEEE 4.825 204 2
Among the most active journals, MDPI, with seven publications, is on the front
line of decision support in IoT supply chains and logistics. Indeed, MDPI covers
54% of the most active journals. Elsevier and IEEE, with 31% and 15% place
second and third, respectively. Briey, the mentioned statistics are shown in
Fig. 5.
Fig. 5. Most active publishers among the most active journals.
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
5
layer may be helpful for handling complex decision-making approaches
in IoT-LSCs. However, for complex decision-making processes in
IoT-LSCs, the current architectures are divided into two groups: a) the
primary architectures try to embed decision-making approaches [55],
and b) new architectures that add decision layers [67,86].
Implementation and improvement
Performance evaluation
Here, performance evaluation helps managers nd the status of IoT-
LSCs in terms of some parameters and then plan to improve them. Ac-
cording to the existing publications in the literature, the current per-
formance metrics of IoT-LSCs are divided into three main categories:
quality, security, and sustainability.
Quality (Products/ services, and information quality). By tracing quality-
related metrics in supply chains, appropriate decisions can be made,
particularly in supply chains with perishable goods [22] such as vac-
cines [40,41], and foods [89]. For instance, when temperature
measuring sensors of a food container show a warm temperature, a
signal is sent to the driver, who decides to turn the fan on. Therefore,
temperature violations will be controlled, and customer satisfaction will
be improved with the food received. Such systems, called food safety
pre-warning systems, help managers discover food safety risks and make
appropriate decisions for maintaining the products’ quality and safety
[88]. However, pre-warning food safety systems cannot predict the
future of perishable products in supply chains. Predicting the future
status of products may lead to better planning and decision-making.
Bogataj et al. [13] introduced a system for estimating the remaining
shelf life in IoT supply chains. The introduced system matches the
remaining shelf life with a dynamic real-time routing to minimize the
risk of perishable products. However, most current publications focused
on quality-related metrics, while some secondary factors, such as data
ow and security, may inuence safety and quality.
An appropriate data ow helps IoT supply chains and logistics to be
more integrated, effective, and responsive in complex environments
[48]. Having reviewed the related publications, Xu [90] found that an
appropriate information architecture is required for effective quality
management in IoT SCM. Inaccurate and unreliable information in LSCs
is another challenge that leads to wrong analytical outputs and misleads
managers about product quality and, nally, decision-making. There-
fore, utilizing approaches for dealing with data counterfeiting is essen-
tial. To solve the counterfeit data problem in perishable products of
IoT-LSCs, Tsang et al. [83] introduced a blockchain—IoT-based food
tractability system (BIFTS). They found that BIFTS is essential in
providing reliable and accurate data and analysis in IoT LSCs, enabling
related managers to make reliable decisions. Recently, researchers have
found that incorporating blockchain and IoT brings many benets to
digital supply chains, such as transparency, visibility, and trust [96,59].
Sustainability. Here, environmental quality refers to the quality of en-
vironments affected by production in supply chains or delivery through
logistics networks. Based on the publications, waste generation, and gas
emissions are the two factors inuencing the environmental quality of
LSCs. Waste management includes the collection, processing, trans-
portation, and disposal of waste. Traditional waste management ap-
proaches were costly and inefcient [71]. Today, waste management
has been regarded as one of the main goals of supply chains. Because of
the growing population, waste generation has increased remarkably,
leading to socioeconomic side effects [43]. Employing IoT technologies
enables supply chains to collect waste-related data from supply chains in
real-time. Analyzing the collected data helps managers make appro-
priate decisions [29]. Analyzing the data collected from IoT sensors in
supply chains can be used to minimize waste or process non-reusable
and non-recyclable wastes to generate energy for supply chains and
logistics [87]. Environmental pollution from LSCs is not limited to waste
generation. With the increased workload of factories and machines,
toxic gas (e.g., CO
2
) emissions in supply chains and logistics are also
increased. Accordingly, the polluted air in workshops injures workers’
health. They presented a smart closed-loop system in which air quality
measures were collected from CO
2
sensors and visualized after their
analysis using big data techniques. Managers could select an appropriate
Fig. 6. A taxonomy of IoT-LSC publications in the era of decision-making.
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
6
strategy for keeping workers safe by monitoring air conditions [54].
Production planning and control (PPC)
Schedule optimization. The PPC-related reviewed publications mainly
focused on the scheduling task. Yao et al. [93] presented a DSS for
adjusting the autonomous guided vehicles (AGVs) and machine sched-
ules alongside the support of shop-oor decision-making. In their pro-
posed DSS, near-optimal production schedules are found using
non-linear mixed integer programming (NLMIP). They presented a de-
cision support tool for delivery schedule optimization. The proposed
system could reduce the managers’ workload and improve the use of
vehicles by up to 10% [1]. In a comprehensive approach, a logistics
network can benet from scheduling decision-making tasks, which im-
proves average delivery speed, shortens the average transportation
distance, and minimizes the transmission process’s time consumption
[46].
Process improvement. Supply-demand management is a key task in
various supply chains [38,37,74]. The embedded sensor in IoT-LSCs
collects data from different sectors and provides more visibility for
existing processes. For instance, an analytical tool can be used for de-
mand forecasting after demand data collection. Balancing production
lines can be optimized by employing an effective process conguration
method that considers the forecasted demands [16]. However, in some
cases, real data may not be accessible. In such cases, simulation ap-
proaches are helpful. Tamas et al. [80] proposed a decision-support
simulation method for process conguration in logistics in which
changes in processes are tested and evaluated. Therefore, unnecessary
planning failures are found and disposed of from the possible solutions.
The Colored Petri Net (CPN) is one of the simulating modeling ap-
proaches successfully used by [23] for the process conguration of
cotton transportation. Decision support tools can be designed to
construct and evaluate different process models and guide managers in
selecting the best one for implementation in LSCs [44,49]. Process
management is a continuous task, and one cannot expect that when
processes are congured, LSCs do not need more improvement in their
processes. Therefore, process re-engineering is essential in modeling,
evaluating, and nally changing the processes for improvement goals
[17].
Decision-making drivers
Previously, embedded sensors in the IoT-LSCs collected related data
and sent them to the processor to check the conditions and make a
simple decision. Such decisions are called If-This-Then-That (IFTT). For
example, if the processor nds out that the received temperature ex-
ceeds 32 Celsius, it orders the conditioner to turn on [27]. In IFTT de-
cisions, processors decide based on limited constant rules that may not
work under complex environments. Because in a complex environment,
a wide range of variables inuences the nal decisions that, in many
cases, conict with each other. Therefore, utilizing new analysis ap-
proaches such as big data, data mining, and machine learning have
emerged. We call them analysis-based decision-making drivers, enabling
IoT-LSCs to make appropriate decisions in complex environments. The
transition from IFTT decisions to analysis-based decisions in IoT-LSCs is
shown in Fig. 7.
Big data analysis. Big data refers to the massive amount of data that is
becoming impossible to store, process, and analyze with traditional
database mechanisms [75]. Big data is described based on ten basic el-
ements, namely volume (data size), variety (diversity of different data
types), velocity (data generation speed), veracity (data understandabil-
ity), value (benets provided by big data), validity (data precise and
accuracy), variability (context of data), viscosity (latency or lag of data
transmitting between source and destination), virility (data transmission
speed between multiple sources), and visualization (data interpretation
by symbolizing data in a complete view way) [26,50]. After checking the
elements, if it is determined that generated data in an IoT-LSC follows
big data characteristics, data mining, and machine learning approaches
cannot be used for analyzing the data, and big data techniques should be
employed. Indeed, the utilized big data technique must meet the
mentioned 10Vs.
Increasing digitalization between involved actors and constantly
changing logistics locations are some big data generation sources that
recent publications concentrated on. [15] developed an integrated DSS
using big data technologies to determine underground logistics system
(ULS) hub locations in complicated logistics environments. Their pro-
posed DSS works on a big data platform with six layers: the data source,
data pipeline, data processing, data storage, data application, and data
service layers. According to the proposed platform, data is collected
from sensors, QR codes, and GPS. Then, passing from a
high-performance pipeline technology (e.g., Kafka), collected data are
sent to big data processing technologies such as Spark and Hadoop.
Here, Hadoop is utilized for processing a large amount of static data
collected and stored over time, while Spark is embedded to process the
dynamic data for real-time reaction to receiving data. Indeed, Hadoop
was used for analyzing historical data analysis goals such as ULS hub
location selection. Spark is employed for dynamic analysis applications
such as route optimization for driving vehicles. Finally, the static and
dynamic analysis outputs are transferred to corresponding applications
for users’ utilization.
Communicating with involved actors in a supply chain or logistics
network requires a successful digital transformation. Digital trans-
formation suffers from two critical issues: incompatibility within
different data layers of the production value chain and a signicant
increase in data processes. In order to deal with the issues, Sorger et al.
[77] developed the Reference Architecture Model Industry 4.0 frame-
work (RAMI 4.0; a standardizing technical communication tool) for
connecting the supply chain stakeholders in a big data environment. In
this case, big data was used to optimize the digital transformation within
Fig. 7. Transition from simple decision-making to complex decision-making.
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
7
and between the actors involved in IoT supply chains and, consequently,
enhance the real-time decision-making processes.
Edge computing (ED) is a new technology that, rather than pro-
cessing data directly on the cloud, preprocesses the collected data from
IoT devices at the broader of the network before sending them to the
remote centralized or distributed servers deployed in the cloud [76].
Indeed, ED architectures utilize the capabilities of local processors
embedded in the physical layer of the IoT for some primary pre-
processing tasks, which leads to a decrease in the processes of central-
ized and decentralized servers. Therefore, employing ED architectures in
IoT systems minimizes latency and reduces bandwidth costs and energy
consumption [76]. ED is an alternative to processing big data from
massive IoT sensors [69]. Dobrescu et al. [20] introduced an architec-
ture for implementing ED technology in manufacturing supply chains,
proving dual communication on both vertical (between the network
layers) and horizontal (between similar devices deployed at the same
Edge level) levels. The local processing idea behind the ED architecture
facilitated real-time processing, leading to faster decision-making.
The existing software has not been designed to process big data from
a wide range of IoT sensors, and software should be equipped with
complex event processing (CEP) engines to process such big data.
Therefore, designing CEP-based software is another solution used by
IoT-LSCs for dealing with big data processing, particularly in dynamic
environments where data are generated in real-time [47,57,70]. Briey,
solutions for handling big data processing in decision-making in
IoT-LSCs are shown in Fig. 8.
Information systems (IS). Information systems (ISs) are formal, socio-
technical organizational systems responsible for collecting, processing,
storing, and distributing information [60]. Successful management of
supply chains requires operational and performance analysis of pro-
vided information of different processes such as inventory and delivery
schedules and lead times [18,42]. Therefore, to provide helpful infor-
mation about the assessment of specic goals in supply chains, adopting
an information system is needed [63]. Indeed, an information system is
the prerequisite of analytical and decision-making processes in the
supply chain because IS provides the required information for analytical
and decision-making models in supply chains and logistics. Chen [14]
introduced a new supply chain information system based on IoT tech-
nology. The designed supply chain information system shares relative
information with an operational decision model for an appropriate de-
cision about a specic issue. Finally, web service technology is utilized
to improve the interpretability between Internet applications.
Digital twins (DT). Digital Twins (DT) refer to the dynamic virtual rep-
resentation of a real system by describing and simulating physical en-
tities’ attributes, behaviors, and rules [94,28]. Briey, DT replicates the
physical objects or processes over a period of time by virtual represen-
tation [2]. The collected data from IoT sensors are fed to the DT simu-
lator platform, and based on the embedded virtual model, a digital copy
of physical entities is provided. Finally, an analytical approach, such as
machine learning techniques, is used to analyze the digital copy and
then make appropriate decision-making [81]. Supporting
decision-making in different processes of IoT-LSCs is one of the earlier
goals of DT-based IoT-LSCs [58]. Hauge et al. [33] discussed the role of
DT in supporting decision-making processes of the proper component
selection for a specic task in production logistics operations. They
explained the requirements of implementing DT to support
decision-making processes under two applications: workstation
designing and automatic guided vehicle route planning. In order to
support decision-making processes in DT-based IoT-LSCs, a decision
model is embedded in the provided DT platform.
Before embedding a decision layer in DT-based IoT-LSCs, a
comprehensive understanding of how a DT can connect decision-making
in IoT-LSCs is essential. Therefore, some types of architectures have been
introduced: technical architectures [30,35], process architectures [35,4],
and hardware architectures [28,5]. Here, technical architectures have
holistic approaches explaining the relationships between different layers
of the technology. However, technical architectures do not pay attention
to the details of the processes. Therefore, process architectures for the
structural design of processes are required. Finally, in order to imple-
ment the technology, hardware specications must be determined
through a hardware architecture. Three main DT architectures sup-
porting decision-making tasks in IoT-LSCs are shown in Fig. 9.
IoT Adopter II: a new framework for implementing decision-
driven IoT-LSCs
Although IoT-LSCs support decision-making, most focus is on simple
If-Then decision-making. At the same time, large data generated by
embedded sensors in different locations need more sophisticated
analytical approaches. The concentration of existing publications is on
simple decision-making that does not need state-of-the-art analytical
tools and techniques. Also, different data analysis techniques have been
Fig. 8. Big data solutions for handling decision-making in IoT-LSCs.
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
8
introduced recently, and some specic ones may be appropriate for
guiding managers about each decision-making. Therefore, introducing a
framework for decision-driven IoT implementation in LSCs is essential.
Although the IoT Adopter introduced by Baziyad et al. [7] guides users
in implementing IoT in supply chains, it does not consider
decision-making processes. So, in this paper, we propose a new frame-
work —the IoT Adopter II— that enables users to implement IoT tech-
nology in logistics and supply chains alongside the decision-making
processes. The proposed IoT Adopter II is presented in Fig. 10.
According to the proposed framework, a list of decisions that can be
made in LSC is provided. After that, the importance of decisions should
be determined from the point of view of LSC experts. The less important
ones are removed. By consulting with the related experts, those de-
cisions that cannot be supported by IoT technology are removed. For
those decisions that IoT can support, a process is built. Now, we deal
with the decision-making process rather than a simple decision. By
choosing an appropriate technology adoption model, inuential factors
on acceptance of IoT for designed decision-making processes are gained.
A quantitative model is used to calculate the IoT adoption rate (AR) by
knowing the effectiveness level of discovered factors. If the AR is lower
than the experts’ expectancy level (TH), the decision-making process
should be redesigned; otherwise, the protability of the process is
calculated. If employing IoT for the related decision-making process is
found protable, some architectures are designed, and the best one is
selected for implementing IoT technology. If the implemented IoT sys-
tem improves the decision-making processes of the LSC, practically, we
nd that the IoT is useful; otherwise, the decision-making process should
be redesigned. The IoT Adopter evaluated the IoT implementation
without considering its applications in the LSC. At the same time, IoT
Adopter II tries to investigate IoT technology in a specic context
determined by decisions.
Discussion
Based on the reviewed papers and the IoT Adopter framework, we
designed IoT Adopter II to help logistics and supply chain decision-
makers evaluate the IoT implementation based on the specic
decision-making goals that must be considered. We discuss IoT Adopter
II’s managerial, theoretical, and practical implementations as follows.
Managerial implications
The IoT Adopter II can be used as a road map for managers wanting
to implement IoT in their supply chains and logistics. Indeed, IoT
Adopter II provides some guidelines, making the path clearer for man-
agers and related decision-makers. The IoT-Adopter II also prevents the
Fig. 9. Digital Twin-based IoT-LSCs architectures.
Fig. 10. IoT Adopter II: a proposal for decision-oriented IoT implementation in LSCs.
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
9
implementation of projects that fail and subsequently prevents the loss
of companies. Moreover, in cases where the designed process for uti-
lizing IoT for a specic decision may not be implementable, the pro-
posed framework guides managers in redesigning the process.
Theoretical implications
DSSs revolve around the decisions that must be made. Before
implementing a DSS, it should be clear what decisions should be sup-
ported and what decisions can be supported. Responding to these
questions paves the way for designing a DSS.
However, there must be a framework for guiding IoT implementation
in LSCs under decisions that must be made. Therefore, this paper de-
velops the IoT Adopter to IoT Adopter II for adding a decision-clarifying
layer. The new layer not only lists the possible decisions that can be
made in LSCs but also removes those that the IoT cannot support. Also,
in comparison to the previous version, IoT Adopter II takes more details
into account. The third important change is related to considering the
process of re-engineering. Indeed, the IoT Adopter II assumes that when
the acceptance rate for IoT implication is low, rejecting the IoT imple-
mentation is not a good solution, and the decision-making process
should be redesigned.
Practical implications
Although the IoT Adopter II framework has been designed to guide
managers in implementing IoT in LSCs, it can be customized for utili-
zation in further scopes. From a practical point of view, the IoT Adopter
II forces technical teams to design the best probable architecture they
can.
It provides a solution for simulating different architectures and
comparing them to select the best one. By the embedded architecture,
IoT Adopter II prevents implementing an IoT platform with poor tech-
nical characteristics. As a result, redesigned costs are decreased.
Analyzing the decision making in the context of IoT-LSCs through
web content mining
Web content mining can help discover the topics, themes, senti-
ments, and trends of web data and classify, cluster, and summarize web
pages according to their contents.
In previous sections, we reviewed and classied the academic pub-
lications related to decision-making in IoT-LSCs. In contrast, a huge
amount of information is available on websites. Also, due to the time-
consuming review process, academic publications are less up-to-date
than web content [8]. Besides, due to the vast textual data on the
web, traditional reviewing is not possible in a reasonable time. We adopt
a web content mining approach, which helps us discover the topics and
themes of web data.
Here, we use a text-mining approach to analyze the related web
content. First, according to the FeedSpot ranking, the six best LSC blogs
and websites with the most related content in the era of IoT-LSCs have
been selected. Then, related textual data, including decision-making and
IoT concepts, are screened and crawled. The statistics of collected data
are shown in Table 3. Overall, 168 links were collected, and after
manual scanning, we found 128 of them relevant to our research goals.
Co-word analysis is one of the practical techniques for analyzing
textual data and extracting valuable insights. However, most co-word
analysis approaches work based on documents, including pre-dened
keywords. While analyzing the website information, we need another
step to extract the keywords. In this context, the Rapid Automatic
Keyword Extraction (RAKE, [68]) is used. The RAKE algorithm is suit-
able for individual document analysis rather than corpus analysis and
does not require any prior knowledge or training data. Then, extracted
keywords are preprocessed based on the introduced steps by Hosseini
et al. [36] and Pourhatami et al. [62]: 1) standardization of singular and
plural forms (e.g., supply chain and supply chains), 2) combination of
acronyms (e.g., supply chain management and SCM), and 3) elimination
of general keywords (e.g., computers).
To construct a co-word network, two steps are performed: First, a
dictionary that counts the number of co-occurrences of each pair of
keywords is created. The keywords are selected from a feature selection
phase, which identies the most relevant words for the topic of interest.
Then, a co-occurrence matrix that shows the frequency of co-occurrence
of each pair of keywords in the dictionary is generated. The co-
occurrence matrix is input into the Gephi 0.9.2 software, and a co-
word network is generated. The nodes represent the keywords, and
the edges indicate the frequency of co-occurrence of each pair of
keywords.
Then, Louvain community detection [12] is executed to disclose the
involved themes. Community detection is a fundamental problem in
network analysis that aims to nd groups of nodes that are more similar
to each other than to the others. Finally, the given co-word network is
visualized by the Gephi 0.9.2 software (see Fig. 11), in which nodes in a
specic cluster have the same color.
The constructed co-word network comprises four main themes that
we label based on their most weighted degree centrality: SCM (red
theme), Supply Chain 5.0 (yellow theme), Big Data Analytics (green
theme), and Blockchain Supply Chains (purple theme). Here, the
weighted degree centrality of a given keyword K refers to the summation
of frequencies that the keyword K appears in a website link with other
network words. Keywords with the most weighted degree centralities
are listed in Table 4.
Cluster 1 (SCM)
In the context of IoT, SCM is the process of using interconnected
devices and sensors to monitor, control, and optimize the ow of ma-
terials, information, and services from the source to the customer. SCM
in IoT can improve the visibility, efciency, and responsiveness of the
supply chain, as well as enable new capabilities and opportunities, such
as real-time tracking, quality management, automation, and
sustainability.
Cluster 2 (Supply chain 5.0)
Supply Chain 5.0 is a term that refers to the integration of human
creativity and machine efciency in supply chain management. Supply
Chain 5.0 aims to cater to the hyper-personalization and hyper-
customization of customer needs, which requires the use of technolo-
gies such as collaborative robots, articial intelligence, big data ana-
lytics, and edge computing. Supply Chain 5.0 also seeks to balance the
economic, social, and environmental aspects of the supply chain by
enhancing transparency, traceability, automation, and sustainability.
Table 3
Statistics of collected data from websites and blogs.
Row Web address Total number of
links
Total number of
related links
1 https://www.supplychain
brain.com
110 80
2 https://www.logisticsb
ureau.com
13 7
3 https://letstalksupplychain.
com
5 2
4 https://www.allth
ingssupplychain.com
10 9
5 https://www.chainalytics.
com
10 6
6 https://www.supplycha
in247.com
20 17
7 Total 168 121
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
10
Cluster 3 (Big data analytics)
Big data can provide valuable insights into the patterns, trends, and
behaviors of the supply chain actors, such as suppliers, customers, and
logistics providers. Big data can enable data-driven and evidence-based
decisions that can improve the performance, efciency, and resilience of
the supply chain. Big data can help to monitor and track the status and
location of the products, assets, and vehicles in real-time, analyze and
predict the demand and supply of the products, optimize and automate
the production, distribution, and delivery of the products, and enhance
and innovate the products, services, and business models.
Cluster 4 (Blockchain supply chains)
Blockchain supply chains are the application of blockchain tech-
nology to the management of the ow of materials, information, and
services from the source to the customer. Blockchain supply chains can
increase the transparency, traceability, and security of the transactions
and data among the supply chain partners, such as suppliers, customers,
carriers, and banks.
Co-word analysis outputs disclose the themes of the documents
involved without providing information about the status of the disclosed
themes. Thus, a strategic diagram is applied to investigate the status of
themes from the context of development and applicability. A strategic
diagram utilizes two criteria called centrality and density. Centrality is
an external validator criterion that measures a theme’s relationships
with other themes; in contrast, density is an internal validator that
measures the coherence of words’ relationships in a specic theme. A
high-density score for theme T indicates that T has developed theoreti-
cally well. Also, the high centrality score for them, T, indicates that T has
connected strongly with other themes and has good potential for
providing new applications.
According to the density and centrality measures, a strategic diagram
is divided into four quadrants: Themes of Quadrant I (QI) have high
centrality and density; therefore, they have theoretically developed well
and have been applied successfully in other involved themes. Here,
supply chain management and big data analytics lie in QI. Indeed, in the
era of decision-making of IoT LSCs, SCM and big data could be devel-
oped from theoretical and practical points of view. In contrast, themes of
QIII have low density and low centrality. The involved themes in QIII are
emerging themes or declining themes. Here, blockchain supply chains
and supply chain 5.0 lie in QIII are emerging themes that must be
Fig. 11. The co-word network of web contents.
Table 4
Keywords with the most weighted degree centrality.
Row Word Weighted
degree
Row Word Weighted
degree
1 supply chain
5.0
110 7 supply chain
industry
69
2 SCM 108 8 global supply
chains
55
3 blockchain
supply chains
95 9 supply chain
leaders
51
4 supply chain
4.0
88 10 entire supply
chain
45
5 beverage
supply chains
81 11 robotic process
automation
45
6 big data
analytics
74 12 supply chain
visibility
30
V. Kayvanfar et al.
Supply Chain Analytics 6 (2024) 100063
11
improved from the context of theory and application. The current stra-
tegic diagram is depicted in Fig. 12.
Conclusions, limitations, and future research
A Decision support system (DSS) is a computer-based decision-
making system equipped with a knowledge-based model extracted from
analyzing the received data and is used for discovering and analyzing a
problem in a specic area of information and management system [79].
DSSs have been applied in different areas such as transportation [34],
drug [97], and task-technology t [23]. However, traditional DSSs did
not utilize data mining approaches; therefore, they could not disclose
the hidden knowledge of data. Indeed, traditional DSSs could only
process and summarize the original data without the ability to convert
raw data to practical knowledge (Y. [30]). By reviewing the collected
publications, we found that although IoT-LSCs tried to improve
decision-making processes, they needed to concentrate on DSS concepts.
Also, there is little evidence about utilizing advanced analytical ap-
proaches for shifting from traditional DSSs to advanced ones in
IoT-LSCs. Therefore, embedding state-of-the-art analytical approaches
in DSSs of IoT-LSCs is required from a theoretical implication. Conse-
quently, from practical implications, developing DSSs in the form of web
applications and software is another gap in the reviewed publications
that should be considered in future works. From managerial implications,
a web-based DSS can enable managers to trace and track the supply
chain and logistics in real-time and make appropriate decisions based on
the situation.
This paper makes the following original contributions to the litera-
ture: (i) it provides one of the rst comprehensive reviews of the role of
decision-making in the IoT-LSCs context; (ii) it explores the potential
and challenges of applying decision support systems (DSSs) to support
various aspects of IoT-LSCs management and optimization; (iii) it pro-
poses a transition scheme that guides the evolution of decision-making
processes in IoT-LSCs from simple rule-based methods to more sophis-
ticated analytical techniques: (iv) it introduces a novel framework called
IoT Adopter II that integrates DSS technologies into IoT-LSCs design and
implementation; (v) it demonstrates a text mining approach that ex-
tracts and analyzes decision-making insights from web contents related
to IoT-LSCs.
We found that decision-making in big data environments is one of the
challenges in IoT-LSCs. The extremely high data generation speed and
the complex nature of the data coming from heterogeneous IoT sensors
are one of the most critical dilemmas that DSSs may face, particularly
when real-time decision-making is needed [79]. In such cases, applying
big data techniques is a good solution for enabling DSSs to process
massive amounts of data and extract knowledge for decision-making in a
short time [45]. Employing local processing technologies such as edge
computing (ED) can enable DSSs for real-time data processing collected
from IoT-LSCs sensors. However, a comprehensive review of potential
solutions for designing big data-based DSSs for IoT-LSCs is still needed.
Additionally, designing a decision support framework for analyzing the
different solutions for managing the big data generated in IoT-LSCs DSSs
and recommending the best solutions can reduce DSS designers’ chal-
lenges when building their DSS.
Although reviewing methodology provides insights into the publi-
cations, it cannot disclose the hidden knowledge of publications [62,9].
Additionally, the literature on IoT and its related domains grows
continuously; therefore, more human effort is needed to cover many
publications in a feasible time horizon. Accordingly, text mining ap-
proaches such as topic modeling [3], Natural Language Processing (NLP;
[95]), and co-word analysis [8] are useful to overcome the mentioned
challenges. Despite the lack of academic publications in the era of
decision-making in IoT LSCs, a wide range of textual data is available on
the web. Thus, we used a text mining approach to extract hidden
knowledge from related websites and blogs and provide deeper insights.
We found that IoT LSC, from the context of decision-making, comprises
two emerging themes: Big Data Analytics and SCM, as well as two
mainstream themes: Big Data Analytics and Supply Chain 5.0.
Both review analysis and the utilized co-word analysis analyzed the
decision-making in the current state of IoT LSCs. Both approaches focus
on descriptive analysis and do not provide predictive insights. There-
fore, employing a predictive analysis helps researchers to determine the
future state of a eld. Link prediction is one of the predictive approaches
that can be utilized to investigate the future status of IoT LSCs from the
context of decision-making. Link prediction calculates the probability
that two specic keywords will be used in the same paper. Indeed, it can
be used to determine what sciences and different areas will converge in
the future. Overall, employing link prediction can explain how the dis-
closed four themes can converge with each other in the future, and
therefore, researchers can nd ideas for work efciently.
CRediT authorship contribution statement
Vahid Kayvanfar: Conceptualization, Data curation, Methodology,
Project administration, Software, Supervision, Validation, Writing –
original draft, Writing – review & editing. Adel Elomri: Conceptuali-
zation, Investigation, Supervision. Laoucine Kerbache: Supervision,
Validation. Abdelfatteh El Omri: Investigation, Resources. Hadi
Rezaei Vandchali: Resources, Validation, Writing – review & editing.
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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