Content uploaded by Shahbaz Khan
Author content
All content in this area was uploaded by Shahbaz Khan on Nov 11, 2022
Content may be subject to copyright.
Adoption of modern technologies
for implementing industry 4.0:
an integrated MCDM approach
Mohd Javaid
Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
Shahbaz Khan
Institute of Business Management, GLA University, Mathura, India
Abid Haleem
Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India, and
Shanay Rab
Department of Mechanical Engineering, National Institute of Technology, Delhi, India
Abstract
Purpose –Modern technologies are seen as an essential component of the fourth industrial revolution
(industry 4.0) and their adoption is vital to transform the existing manufacturing system into industry 4.0-
based manufacturing system. Therefore, the primary objective of this research explores the barriers of modern
technology adoption and their mitigating solutions in order to align with Industry 4.0 objectives.
Design/methodology/approach –Barriers to adopting modern technologies and respective mitigating
solutions are identified from the available literature. Further, these barriers are ranked with the help of expert
opinions by using the BWM method appropriately. The identified solutions are ranked using the combined
compromise solution (CoCoSo) method.
Findings –Several modern technologies and their capabilities are recognised to support the industry 4.0-
based manufacturing systems. This study identifies 22 barriers to the effective adoption of modern
technologies in manufacturing and 14 solutions to overcome these barriers. Change management, the high
initial cost of technology and appropriate support infrastructure are the most significant barriers. The most
prominent solutions to overcome the most considerable barriers are ‘supportive research, development and
commercialisation environment’,‘updated policy and effective implementation’and ‘capacity building through
training’that are the top three solutions that need to be addressed.
Research limitations/implications –The barriers andsolutions of modern technologyadoption are obtained
through a comprehensive literature review, so there is a chance to ignore some significant barriers and their
solutions. Furthermore, ranking barriers and solutions is done with expert opinion, which is not free from biases.
Practical implications –This identification and prioritisation of barriers will help managers to understand
the barriers so they can better prepare themselves. Furthermore, the suggested solutions to overcome these
barriers are helpful for the managers and could be strategically adopted through optimal resource utilisation.
Originality/value –This study proposes a framework to identify and analyse the significant barriers and
solutions to adopting modern technologies in the manufacturing system. It might be helpful for manufacturing
organisations that are willing to transform their manufacturing system into industry 4.0.
Keywords Barriers, Modern technologies, BWM, CoCoSo, Industry 4.0, Solutions
Paper type Research paper
1. Introduction
Human civilisations with evolution are getting more and more dependent upon technologies. In
the case of manufacturing technological evolution, this started with the industrial revolutions.
Working conditions and customer requirements have primarily changed from the first industrial
revolution to the fourth industrial revolution. Modern technologies are the backbone of industry
4.0, which in turn creates significant advancements in manufacturing and other fields (Bag et al.,
2018;Bongomin et al., 2020;Luthra et al., 2020;Chiarini, 2020;Rajput and Singh, 2019).
The first industrial revolution began in the 18th century when steam power was used for the
mechanisation of production, especially textiles (Kolberg and Z€
uhlke, 2015;Lu, 2017;Dalenogare
Modern
technologies
for industry 4.0
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1463-5771.htm
Received 9 January 2021
Revised 4 July 2022
11 October 2022
Accepted 16 October 2022
Benchmarking: An International
Journal
© Emerald Publishing Limited
1463-5771
DOI 10.1108/BIJ-01-2021-0017
et al., 2018;Bag et al., 2018). This revolution marked a significant turning point in history and had
a profound impact on nearly every aspect of daily life. The second industrial revolution began in
the 19th century with the discovery of electricity and assembly line. The second industrial
revolution’s major turning point was development of the internal combustion engine that started
to reach its full potential (Zheng et al., 2018;Mittal et al., 2019;Qian et al., 2020). In the second half
of the 20th century, the world entered into the third industrial revolution, which brought forth
the riseof electronics, telecommunications and computers. The third revolution opened the doors
to space expeditions, medical research biotechnology, etc. The two major turning point of this
revolution is the inventions of programmable logic controllers and robots that helped to give rise
to an era ofhigh-level automation. Now,advanced countries areimplementing the 4th industrial
revolution, intending to characterise by applying advanced technologies, especially information
and communication technologies to industrialisation (Chen et al.,2017;Xu et al., 2018;Frank
et al., 2019).
In the industry 4.0 environment, the adoption of information and communication
technology has enabled the factory to incorporate “smartness”and provide new tools for a
predictive production strategy, which is the heart of this industrial revolution. Through
information technology and industrial automation such innovations have the potential to
improve the flexibility and scalability of manufacturing systems (Papadopoulos et al., 2021).
Different technologies are attributed to industry 4.0 by scholars and practitioners (Haleem
et al., 2022). From the current perspective, there is an essential requirement to create
innovation and increase manufacturing efficiency for customised products. Thus, modern
technologies such as additive manufacturing (AM), IoT, IIoT and big data are introduced to
improve the quality of planning, operations management, proper management system and
resources (Dubey et al., 2017;Khan et al., 2022a).
The adoption of these modern technologies is extremely challenging. The modern
technologies adoption in developing countries is limited due to several issues ranging from
strategical to operational level. Several barriers exist to adoption of modern technologies,
specifically in emerging countries (Javaid et al., 2021). For instance, poor infrastructure, the
high cost of technology and fear of unemployment become major barriers to modern
technologies adoption. In addition to this, the scarcity of shared knowledge of the barriers
that affect the adoption of modern technologies may partially explain the resistance of
organisations towards industry 4.0 transformation (Ghobakhloo et al., 2022;Ghobakhloo and
Iranmanesh, 2021). Therefore, when organisations struggle to comprehend the barriers to
modern technology adoption and how they operate, and they often increase their aversion to
upgrading their technologies (Ingaldi and Ulewicz, 2019). Some studies have addressed these
issues and highlighted barriers to the adoption of modern technologies (Senna et al., 2022;Raj
et al., 2020;Calabrese et al., 2020). However, these studies do not focus on the solutions to
overcoming these barriers. In addition, existing studies aimed at prioritising adoption
barriers have typically centred on specific technologies, such as the IoT (Kamble et al., 2018;
Singh and Bhanot, 2019) or blockchain technology (Mathivathanan et al., 2021), and have not
considered the other modern technologies. Previous studies have put little emphasis on
barriers to modern technologies in the context of developing countries. Considering this, there
is a knowledge gap existing in the literature related to the adoption of modern technologies.
To address this knowledge gap, there is a need for research to identify and analyse the
significant barriers to the adoption of modern technologies. In light of the above discussions,
the followings research questions are addressed through this study:
RQ1. What are the major barriers to the adoption of modern technologies in the context of
developing countries?
RQ2. How are these barriers evaluated to overcome them in an optimal manner?
BIJ
RQ3. What are the solutions and their importance to overcome the barriers of modern
technology adoption?
In order to answer the above research questions, the following research objectives are
formulated:
RO1. To identify the substantial barriers to the adoption of modern technologies
RO2. To identify the solutions to overcome these barriers towards the adoption of modern
technologies
RO3. To prioritise the identified barriers and solutions towards the adoption of modern
technologies
RO4. To provide policy recommendations for the deployment of solutions
In order to achieve the objectives mentioned above, a systematic literature review is
conducted to identify the major barriers to adopting modern technologies and their solutions.
Furthermore, the Best Worst Method (BWM), a well-known Multi Criteria Decision Making
(MCDM) method, is applied to prioritise the barriers based on their importance. To effectively
mitigate this barrier, the identified solutions are prioritised using the combined compromise
solution (CoCoSo) method by considering the barriers as a criterion.
The remaining paper is structured as follows: section 2 provides the background of the
study by providing an overview of modern technologies and their capabilities; section 3 deals
with the proposed methodology, including the steps of BWM and CoCoSo method; section 4
analyses the data and provide the result; the findings of the study are discussed in section 5;
section 6 highlights the implications of the study; and finally, section 7 conclude the research
and provide the limitation and future scope of the study.
2. Background of the study
2.1 Modern technologies
Various innovative and advanced technologies are being made available to achieve
significant success in manufacturing (Ershadi et al., 2019;Babatunde, 2020;Bouranta, 2020).
Industries create substantial innovations through the applications of modern technologies.
Thus, it is vital to develop a technology platform that enhances the capabilities of the existing
manufacturing system and services (Boyce, 2016;Haleem et al., 2020a,b;Hultcrantz et al.,
2020;Bag and Pretorius, 2020). Modern technologies provide advancements to create high-
quality products and services. Figure 1 shows the significance of modern technologies.
2.1.1 Additive manufacturing (AM). AM uses a layer-by-layer technique to manufacture
customised products according to customers’requirements. Sometimes human error can be
too costly, and this technology helps to reduce human error. This technological advancement
can potentially have a massive impact on the manufacturing industries. Nowadays, AM
applications are limitless, and parts fabricated through AM are used in almost every area,
such as aircraft, dental restorations, medical implants, automobiles, even fashion products,
tissue engineering, etc. (Strong et al., 2018;Haleem et al., 2020a,b).
2.1.2 Big data in manufacturing. Big data is a buzzword that generates a great deal of
discussion, particularly in the manufacturing industry, where, if used correctly, it may
transform a company’s decision-making from predictive to prescriptive (Br€
uggemann and
Gr€
uning, 2008;Liaw et al., 2014;Vostrovsk
yet al., 2020). It applies in almost every industry,
such as healthcare, financial, retail, etc. With data analytics, manufacturing organisations
may enhance production, personalise product design, increase quality assurance, manage the
supply chain and assess the potential risks (Dubey et al., 2019a,b;O’donovan et al., 2015;
Qi and Tao, 2018;Cui et al., 2020;Khan, 2021).
Modern
technologies
for industry 4.0
2.1.3 Artificial intelligence and machine learning in manufacturing. Manufacturing is one of
the key industries that can maximise the use of artificial intelligence (AI) and machine
learning (ML) technology. Using AI and ML in manufacturing industries significantly
reduces downtime and improves product design. AI is at the core of industry 4.0, bringing
increased productivity with enhanced efficiency and transition times (Wuest et al., 2016;
Lee et al., 2018;Haleem and Javaid, 2019).
2.1.4 Internet of things (IoT). IoT is an emerging technology that connects all machines
and devices through the Internet to communicate with each other. This technology provides
timely information regarding on-going industrial processes (Gubbi et al., 2013;Ni
zeti
cet al.,
2020;Dubey et al., 2021). Further, the industrial Internet of Things (IIoT) refers to an
industrial framework in which a large number of devices or machines are connected and
synchronised using software tools and third-platform technologies in a machine-to-machine
and IoT environment (Hossain and Muhammad, 2016;Arnold et al., 2016;Khalil and
Saeed, 2020).
2.1.5 Robotics. Robots are no longer used as automatic machines on the plant floor. It is
currently a force in the industry, causing a revolution of change in the manufacturing sector.
Robotics has changed the manufacturing world with several advantages: less costly, safer,
Figure 1.
Modern technologies
for Industry 4.0
BIJ
more efficient, better in quality, enabling flexible manufacturing, etc (Kim et al., 2013;Taylor
et al., 2020).
2.1.6 Cloud computing in manufacturing. The virtualisation of computing resources and
services is the defining feature of cloud computing and innovation in computer architecture.
This technology opens new windows of opportunity for manufacturers to innovate and grow
globally (H€
uner et al., 2009;Kim et al., 2019;Javaid et al., 2020). The manufacturing cost can be
significantly down with a cloud-based computing system. It also improves the on-demand
delivery system because cloud-generated forecasts, which vary throughout the year, can help
determine demand and capacity planning (Tao et al., 2014;Esposito et al., 2016;Ooi
et al., 2018).
2.1.7 Augmented reality and virtual reality in manufacturing. Augmented reality and
virtual reality are reshaping the manufacturing domain. These technologies can be used in
manufacturing assembly and quality control and assist employees in assembling the product
with almost 100% accuracy. These technologies are also beneficial in training and
maintenance during the manufacturing process. It will also reduce human errors, execution
time and downtime and increase productivity and speed (Dangelmaier et al., 2005;Novak-
Marcincin et al., 2013;Dallasega et al., 2020).
2.1.8 Blockchain technology in manufacturing. Blockchain technology has enormous
potential to revolutionise manufacturing processes (Dubey et al.,2022). Blockchain
technology can enhance transparency throughout the value chain, from raw materials to
finished products (Khan et al., 2021). It has several benefits, such as supply-chain monitoring
for improved transparency; materials provenance, detection of fraud, design engineering,
complex products, quality assurance, asset tracking, identity management and regulatory
compliance (Ko et al., 2018;Mondragon et al., 2018;Lee et al., 2019;Tiwari, 2020).
2.1.9 5G and 6G technologies in manufacturing. The on-going industrial revolution
(industry 4.0) of cyber-physical systems is closely connected today with a high-speed network
like 5G often referred to as the fifth generation of mobile networks, respectively. 5G is a primary
enabler for the industrial transformation to industry 4.0 by offering wirelessconnectivity within
and around the manufacturing unit based on a global standard. These technologies can connect
various industrial equipment with diverse service requirements, such as sensors, video cameras,
sophisticated control panels with augmented reality integration, etc. These can also provide the
communication required to bring wireless connectivity to industrial equipment like industrial
controls and actuators (Cheng et al., 2018;Saad et al.,2019).
2.2 Major capabilities of modern technologies
Technologies are continuously updating to create innovation in industries. Scientists have
focussed more on research and development to improve existing technologies (Khattab et al.,
2019;Neethirajan, 2020;Tahir et al., 2020;Javaid et al., 2022). These industry 4.0 technologies
provide several developments by using industrial automation. It creates innovation using
various software and hardware. These technologies help achieve smart factories that enable
rapid change in industries (Sidhu et al., 2019;Hultcrantz et al., 2020;Akram et al., 2020). These
technologies can improve several processes ranging from planning to execution. Some of the
major capabilities of modern technologies are discussed in subsequent sections. These
modern technologies are centred on collecting, processing and analysing large volumes of
data created by the information sciences. These developments promise substantial social and
economic benefits and greater productivity and efficiency in various industries.
2.2.1 Decision making. AI can make decisions like humans with the help of programs fed to
systems. It performs complex tasks such as speech recognition and forecasting regarding
manufacturing, market demand and environmental conditions (Sidewall and Forsyth, 2020;
Ali et al., 2020).
Modern
technologies
for industry 4.0
2.2.2 Customisation. 3D printing is an emerging technology that can be manufactured
layer-by-layer with the use of Computer Aided Design (CAD) digital file input. This
technology is famous due to its capability for customisation. This helps in manufacturing
various lightweight parts for the automobile and aerospace industries (Bongomin et al., 2020;
De Corato, 2020;Bag et al., 2020a,b).
2.2.3 Sensing of complicated data. Technology, like data science, is used to sense complex
data for various industries. It can sense structured and unstructured data (Stepinac and
Ga
sparovi
c, 2020;Salk and Kennedy, 2020).
2.2.4 Data sharing. IoT is the network of devices connected to share data. It provides a
connection between different devices and assesses the performance of machines and
manufacturing systems (Salam, 2019). The data can be shared amongst users through the
Internet (Stojanovic et al., 2016;Majeed et al., 2020).
2.2.5 Job automation. Robots are used for repetitive, hazardous jobs and can be well-
automated. It can easily take up the challenge of a repetitive job efficiently (Aumi et al., 2013;
Corbett and Mhaskar, 2016;Wu et al., 2020).
2.2.6 Education and training. Virtual technologies are used for education, training,
entertainment and better marketing. Modern technologies help solve various learning
problems with practical solutions (Jiang et al., 2017;Tao et al., 2018;Javaid and Haleem, 2018).
2.2.7 Security. New technology like cybersecurity is used to enhance security in
manufacturing industries. It shows the capability to defend against hackers constantly
(Zheng et al., 2020;Kamble et al., 2020).
2.2.8 Automatic controlled. Technologies like drones are remotely controlled through
software, and these drones are helpful for proper surveillance in industries. It provides real-
time video of the on-going working environment, and further improvements can be made
accordingly (Kusiak, 2018;Lu et al., 2020;Javaid and Haleem, 2020).
3. Research methodology
A three-phase methodology is adopted to fulfil the research objectives of this study. In the
first phase, the major barriers related to adopting smart technologies are identified through
the literature review and further supported by industrial experts. The expert panel consists
of 10 members, including industry professionals and academia. The industrial professional
is selected based on managerial experience in a reputable organisation. The industrial
professional selected has over 10 years of managerial experience in supply chain analytics,
technology adoption, technology transfer and industry 4.0. The participating academic
experts are working in supply chain analytics or industry 4.0. After formulating the expert
panel, a literature review is conducted to identify the barriers to adopting modern
technology to implement industry 4.0. The Scopus database was used to perform the
literature review since it contains the highest number of peer-reviewed science and
management journals. Furthermore, the relevant keyword such as “Industry 4.0
technologies”,“advance technologies”,“smart technologies”,“I 4.0 technology”,
“barriers”,“obstacles”,“challenges”,“roadblocks”,“enablers”,“solutions”and their
combination using Boolean operators are used for the literature identification. This
string is searched in the TITLE-ABS-KEY search field of Scopus by taking the period of
2005 to January 2021. Furthermore, the titles and abstracts of obtained articles are reviewed
to select the articles for a comprehensive review. In this manner, the finalised articles are
reviewed, and a total of 22 barriers are identified. These barriers are further categorised
into three main dimensions based on the expert’s panel input. Similarly, the solutions to
mitigate these barriers are also identified and discussed with thesameexpertgroup.Atotal
of 14 solutions are finalised for this study. In the study’s second phase, the identified
barriers are prioritised using a well-known MCDM method named the best-worst method
BIJ
(BWM). Several other methods, such as AHP, ANP, WASPAS and DANP are also used to
prioritise thebarriers(Khan et al., 2020;Hossain and Thakur, 2020;Singh et al., 2022). BWM
has some advantages over these methods, requiring fewer pairwise comparisons (Rezaei,
2015,2016;Guo and Zhao, 2017); as the BWM only compares the barriers concerning the
most significant barriers (best) and the least significant barriers (worst) with other barriers.
Therefore, the number of comparisons is less in BWM than in other MCDM methods such as
AHP (Khan et al., 2019). Additionally, the obtained reliability and consistency of the
acquired weights are competitively high. In order to obtain the response from the expert’s
team, a BWM-based questionnaire is developed and sent to each member. Furthermore, the
interaction with the expert panel is done through Google meet, and their response is
collected by one of the authors. In this manner, the expert’s response is collected to apply
the BWM method. In the third phase of the study, solutions to overcome barriers are
prioritised using the CoCoSo method. The CoCoSo method is one of the contemporary
MCDM techniques used to prioritise the alternatives/solutions (Yazdani et al., 2019;Peng
et al., 2019;Khan and Haleem, 2021;Ali et al., 2022). In order to collect the response for the
CoCoSo method, a structured CoCoSo questionnaire is developed and sent to the same
expert panel participating in the BWM analysis. Furthermore, two reminder mails are sent,
and a response is obtained. These responses rank the solutions to the modern technology
adoption barriers. Figure 2 shows the research framework for this study.
3.1 Best worst method (BWM)
BWM is a comparison-based MCDM which initially compares the best criterion (in this case,
barrier) to the other criteria (barriers) and afterwards compares all the other criteria (barriers) to the
worst criterion (barrier). This procedure aims to find the optimal weights of the criterion (barriers)
using a simple optimisation model. The stepwiseprocedureoftheBWMisprovidedasfollows:
Step 1: In this step are the criteria used to arrive at a decision. The barriers (B
1
,B
2
,...B
m
)
to advanced technology adoption are listed.
Step 2: The best and worst barriers are identified in the established barriers by the experts.
The best barriers signify that the most crucial barriers provide the foremost hurdle to
adopting smart technologies, while the worst is the opposite. The experts identify the best
barrier (C
B
) and the worst barrier (C
w
) in general. Kindly note that no comparison is made
in this step.
Step 3: Perform the comparisons of the best barriers (C
B
) over all the other identified barriers
using a nine-point scale (1–9) through expert input and represented by the A
B
vector as:
AB¼ðaB1;aB2 ; ::::::; aBn Þ(1)
where A
B
is the best-to-others (BO) vector, a
Bj
denotes the significant degree of the best one
over barriers jand a
BB
51.
Step 4: Perform the comparisons of the other barriers over the worst barrier (C
w
) using a
nine-point scale (1–9) through expert input and represented by A
W
vector as:
AW¼ða1W;a2W ; ::::::; anW ÞT(2)
where A
B
is the others-to-worst (OW) vector, a
jw
refers to a significant degree of barriers i
over the worst one W and a
ww
51.
Step 5: In this step, determine the optimal weights (w*
1;w*
2;...:w*
3) of all barriers. The
optimal weight for each barrier is the weight at which for each pair wB=wjand wj=wW,it
Modern
technologies
for industry 4.0
should have wB=WJ ¼aBj and wj=WW ¼ajW . Consequently, to meet these constraints for
each j, the set’sf
wB−aBjwj
;and
wj−ajW wW
g. Maximum absolute differences were
minimised. This problem can be represented as the following model:
Min max f
wB−aBjwj
;
wj−ajW wW
g.
determined the final weight of each solution
based on three relative weight
The optimum weight and associated rank
of each dimension and barrier are obtained
Finalised the barriers and categorised them
into three dimensions using expert input
Determine the Best and Worst dimensions
barriers by the expert team
Find out the preference of Best/Worst
dimensions and barriers w.r.t. others using
a linguistic scale.
Evaluation of optimum weight of each
dimension and barriers by solving the
Model 2
Formulate an Initial decision-making matrix
Calculate the total of the weighted
comparability sequences ( )foreach
solution
Obtain the normalised decision matrix
Calculate the whole of the power weighted of
comparability sequences ( ) for each
solution
Employ the three aggregation approaches to
generate the relative weights
Identification of barriers and solutions to
adopting modern technologies
Experts input Literature
review
Phase I
Rank the solutions according to the
decreasing value of weights
Discussion based on results, and provide implication and conclusion
Determine the global weight of each
barrier
Prioritise the barriers based on their global
weight
Phase II Phase III
Goal: Prioritisation of barriers and solutions to adopt modern technologies
Figure 2.
A proposed research
framework
BIJ
Subject to:
X
j
wj¼1 (3)
wj≥0;for all j
Model (3) can be transformed into the following linear problem.
min ξL
s:t:
│wB
wj
aBj│≤ξLfor all j
│wj
wW
ajW │≤ξLfor all j
X
j
wj¼1
wj≥0 for all j (4)
The optimal weights of each barrier (w*
1;w*
2;w*
3...:: w*
nÞand the optimal value of ξLare
obtained by solving the linear problem. Consistency of the comparison depends on the value
of ξL, a value closer to 0 that indicates a higher consistency and this value is > 0.1 desired for
consistency (Rezaei, 2016).
3.2 CoCoSo method
CoCoSo, proposed by Yazdani et al. (2019), is one of the new MCDM methods. This method
combines the simple additive weighting model with the exponentially weighted product
model. This approach involves ranking alternatives (in this case, solutions) that are assessed
against specific criteria (in this case, barriers). The stepwise procedure of the CoCoSo is
provided as follows:
Step 1: Initial decision-making matrix is formulated using the linguistic terms concerning
the evaluation criteria (in this case, barriers). The initial decision-making matrix is
depicted as follows:
Xij ¼2
6
6
6
4
x11 x12 x1n
x21 x22 x2n
.
.
..
.
.1.
.
.
xm1xm2 xmn
3
7
7
7
5
;i¼1;2; ::::n;j¼1;2; ::::m(5)
The matrix ½Xm3ndemonstrates the initial decision-making matrix that includes the
m-number of alternatives (solutions) and n-evaluation criteria (barriers). The matrix “xij”
element denotes the performance of i
t
s solution concerning the jth barrier. In this context, “xij”
shows the jth barriers’mitigation by adopting the ith solution. Table 1 depicts the linguistic
scale and associated crisp value.
Step 2: The initial decision matrix is normalised using equations (6) and (7) (please refer
Zeleny, 1973):
Modern
technologies
for industry 4.0
For benefit criteria
rij ¼
xij min
ixij
max
ixij min
ixij
;(6)
For non-benefit/cost criteria
rij ¼
max
ixij xij
max
ixij min
ixij
;(7)
Step 3: Weighted comparability sequence (Si) and power weight of comparability
sequences (Pi) of each solution is calculated using equations (8) and (9), respectively.
Si¼X
n
j¼1
ðwjrijÞ(8)
Pi¼Xn
j¼1ðrijÞwj(9)
Step 4: Relative weights of each solution are determined using the three aggregation
approaches, and the same are arranged as equations (10)–(12):
kia ¼SiþPi
P
m
i¼1
ðPiþSiÞ
;(10)
Equation (10) shows the arithmetic mean of sums of scores, weighted sum measure (Si) and
weight power measure (Pi)
kib ¼Si
min
iSi
þPi
min
iPi
(11)
Equation (11) shows a sum of relative scores of weighted comparability sequence (Si) and
power weighted comparability sequence (Pi) compared to the best.
kic ¼λðSiÞþð1λÞðPiÞ
λmax
iSiþð1λÞmax
iPi(12)
Equation (12) signifies the balanced compromise of Siand Piscores. Usually, the parameter λ
is 0.5, or experts could choose it per the situation.
Linguistic scale Crisp value
Very Low (VL) 1
Low (L) 2
Medium (M) 3
High (H) 4
Very High (VH) 5
Table 1.
Linguistic scale and
associated crisp value
BIJ
Step 5: The solution’s weight relies on the value of ki, and it is calculated using equation
(13).
ki¼ðkiakib kicÞ1
3þ1
3ðkiaþkib þkic Þ(13)
The final ranking of the solutions is provided as per the descending order of values, i.e. the
solution having the more excellent value of kiis more significant.
4. Results
This section provides the outcome of applying the proposed research framework for adopting
smart technologies in Indian manufacturing industries. In the first phase, the significant
barriers and solutions are identified through the literature review and checked through
experts’feedback. Furthermore, BWM is appropriately applied to prioritise the identified
barriers with the help of experts’input. In the last phase, the identified solutions are ranked
using the CoCoSo method. Each phase is provided in detail in the upcoming sections.
4.1 Phase I: Finalisation of the barriers and solution
The combined literature review approach and experts’input are utilised to identify the
significant barriers and solutions to adopting smart technologies. Initially, an expert group
was formulated, with experts being industrial professionals and academicians working
towards adopting smart technologies. In the beginning, we selected 15 experts from the
industries working in managerial positions with at least 10 years of experience. These experts
are contacted for participation in this study. Nine experts agreed to participate in this study
amongst the selected experts. Additionally, five experts from academia were contacted,
working in quality management and supply chain analytics. Three experts from academia
agreed to participate. In this manner, 12 experts agreed to participate in this study. Experts’
inputs were facilitated through Google meet, which two experts could not meet at that time. In
this manner, this study is conducted with the help of 10 experts.
Initially, 26 barriers are identified through a literature review and discussed with the
expert’s team through Google meet. The experts suggested that six barriers are unsuitable in
the Indian context and recommended dropping them. Furthermore, they also suggested
adding two new barriers relevant to adopting smart technologies in Indian organisations. In
this manner, a total of 22 barriers are identified and further categorised in three main
dimensions, as shown in Table 2.
Furthermore, some solutions help remove the barriers to adopting modern technologies.
These solutions are identified and discussed with the same expert group. A total of 18
solutions were identified with the help of a literature review, and after discussion, six
solutions seemed too redundant and were deleted. In this manner, 14 solutions are finalised
for this study and shown in Table 3.
4.2 Phase II: Prioritisation of barriers using BWM
After finalising the barriers, we have prioritised them using the BWM method. In order to do
so, a questionnaire is formulated to get the responses from the experts. The responses to the
BWM questionnaire are obtained from the experts on Google meet. Initially, one of the
authors provided an overview of the BWM method to the expert group, and then
we illustrated the questionnaire. The response to the questionnaire is collected based on the
consensus of the expert’s team. Initially, the best and worst barriers are asked by the experts,
they provide different opinions, and after discussion amongst the expert team, one barrier is
Modern
technologies
for industry 4.0
Main barrier Barrier Code Description of the situation References
Technological
barriers
The high initial cost of
technology
TG1 Generally, modern technologies have a high initial cost when employed in
industries
Most small-scale industries cannot afford to implement these technologies due to
cost barriers
Zeleny (2012),Raj et al. (2020),Moraes Silva et al.
(2020)
High level of technological
complexity
TG2 Complexity sometimes is part of the new developments
This may affect performing successful operations as the workforce and processes
are not able to adapt to the new complex environment
Sepasgozar and Davis (2018),Meijer et al. (2019),
Kouhizadeh et al. (2020)
Lack of simulation and software
support
TG3 Various modelling and simulation software packages are essentially required
It provides producers with methods to accelerate prove-outs of cutting and
assembly procedures, frequently with near-perfect precision
Bingimlas (2009),Thomas-Seale et al. (2018),
Abdelmegid et al. (2020)
Maintenance of technological
support, including IT
TG4 The requirement for good, sophisticated maintenance of these technologies is also
a major concern
Requires a highly skilled workforce and advanced tooling for maintenance,
including IT
Buntin et al. (2011),Zeleny (2012),Ifinedo et al. (2020)
Integration of technologies TG5 Improper integration of advanced technologies hampers the adoption of Win and Shen (2020),Sinsel et al. (2020)
Availability of raw material TG6 New and advanced raw material is required to meet the new manufacturing
solutions
Flexer et al. (2018),Capodaglio (2020)
Communication technology TG7 Good quality networks are required to facilitate proper communication in the
industry
Keller and Heiko (2014),Abugabah et al. (2020)
Systems not tested to handle
emergencies or disruption
TG8 Sometimes, technologies have not been tested to face emergencies as they have
less human interaction in their development phase or are not developed
comprehensively
Yates and Paquette (2011),Serrano et al. (2020)
(continued )
Table 2.
Barriers to the
implementation of
modern technologies
BIJ
Main barrier Barrier Code Description of the situation References
Management
barriers
Spare parts, logistics and
reverse logistics management
MG1 Sometimes, employees are not equipped with all the necessary equipment and
spare parts for workplace malfunctions. Secondly, spare parts are not readily
available
Xia et al. (2015),Ballardini et al. (2018)
Poor forecasting and prediction
and for decision making
MG2 Sometimes, these may not be suitable for forecasting market demand
Sometimes advanced information technologies are unsuccessful or wrong
predictions as they are not widely tested for decision-making
Fleiter et al. (2011),Kennedy and Basu (2013),Shah
et al. (2018),Elavarasan et al. (2020),Ellahham et al.
(2020)
Change management MG3 The emergence of modern technologies prompts fear and reluctance to change
Any change that affects the expected change results in fear, and fear causes
resistance
This is due to fear of errors, malfunctions and consequences; employees fear using
new technologies
Smits et al. (2010),Legrand et al. (2012),Wang et al.
(2020),Jung et al. (2020)
Policy, regulation and legal
issues
MG4 Many times, people do not adopt new technology without a proper regulatory
framework and legal recourses
Karatayev et al. (2016),Gailani et al. (2020)
Appropriate support
infrastructure
MG5 There is a requirement to strengthen the quality infrastructure, including
metrology, standardisation, accreditation and certification
In their absence, there is a failure in the adoption
Sen and Ganguly (2017),Barquet et al. (2020)
Skilled workforce MG6 In most cases, the operation of such technologies needs highly skilled human
resources
The new and experienced workforce is costly and is not readily available
Tortorella et al. (2020),Win and Shen (2020)
Training and capacity building MG7 When confronted with complex modern technology, many employees do not have
adequate background knowledge and training, and many are old
For this reason, they cannot be appropriately trained
Makundi et al. (2017),Tsai et al. (2020),Martinez et al.
(2020)
Industry–academia interaction MG8 The industry–academia interface is always required to strengthen the modern
manufacturing system
Parmentola et al. (2020),Manikandan et al. (2020)
Social barrier Fear of unemployment/Job
reduction
SC1 With the advent of these technologies in the existing system, workers are
apprehensive about layoffs and unemployment
Calitz et al. (2017),Bugdol and Pokrzywa (2020)
Import restrictions and
government policy
SC2 New technologies are not readily available everywhere; some countries face
various import restrictions
Abdulrahman et al. (2014),Lin et al. (2020)
Requirements for
environmental clearances
SC3 Sometimes, the adoption is unsuccessful because of various environmental
requirements or even the absence of such laws
Kothari et al. (2020),Gupta et al. (2020)
Ethical and privacy issues SC4 Digital technologies that depend on rapidly expanding digital sensing, storage and
transmission capacities to intervene in human processes may have a firm grasp on
the ethical considerations involved
Mir et al. (2020),Chege and Wang (2020)
Lack of awareness of new
technological developments
SC5 Most manufacturers are not adequately aware of the application’s modern
technologies
Schnackenberg (2009),Govindan et al. (2014)
Security concerns SC6 Issues related to privacy are always at the forefront and essential because of the
confidential nature of the stored data confidential nature
Fagnant and Kockelman (2015),Bag et al. (2020a,b)
Table 2.
Modern
technologies
for industry 4.0
S.
No Perceived solution Code Description References
1 Optimise production rate SO1 Industries can optimise the production
rate to combat this problem of the initial
high cost of advanced technologies and
algorithms
Subsidies can also be provided for the
production of natural and complex parts
The applications of these technologies
must be in a simplified and phased
manner
Sen et al. (2019),Kumar et al. (2020),
Li and Liang (2020)
2 Capacity building through
training
SO2 Capacity building and training should be
more comprehensive to take care of the
needs of even old and uneducated
employees
Better on-site training and capacity
building with a sound support system
are necessary before using the new
systems
The software packages and simulators
are used to train the workforce
Proper training for conducting the
maintenance
The hiring of an international workforce
which knows the technology and can
also impart knowledge
Cortiços (2019),Calzavara et al.
(2020),Dahooie et al. (2020),Bolster
and Kitada (2020),Tyulin and
Chursin (2020)
3 Development of proper
protocols
SO3 Proper protocols to be developed and
followed for both IT and non-IT
infrastructure
Create a higher level of automation in the
manufacturing processes
Implementing standard set rules to
improve machine and allied
communications
Wang and Xu (2013),Di Carlo Rasi
and Janssen (2019),Wang et al.
(2019)
4 The ready availability of raw
material
SO4 Make raw material availability an
integral part of the contract
Secure technology to produce raw
materials if it is commercially viable
Alternative input materials which have a
low cost have to be developed
Manninen and Knutsson (2014),
Boccella et al. (2020)
5 Management of modern and
supporting technologies
SO5 Proper management of newer
technologies being adopted, which is
helpful for a better understanding of the
modern system
Creating strong industry–academia
interaction
Retraining or capacity-building of the
existing workforce for new technological
applications
Help relocation and management of the
existing workforce instead of
retrenchment
Extensive support and exercises are
provided to facilitate and make
successful the adoption of business
process reengineering and redesign
Dalenogare et al. (2018),Iqbal et al.
(2020),Oztemel and Gusev (2020),
Kumar et al. (2020)
(continued )
Table 3.
Perceived solutions for
the successful adoption
of modern technologies
in manufacturing
BIJ
S.
No Perceived solution Code Description References
6 Better logistics and
warehousing management
SO6 Need long-term policy to support spare
part management during the acquisition
of such technologies
Better management of inventories for a
smooth production system
Improved support for logistics and
warehousing of advanced technological
systems
Ghaffar et al. (2018),Iqbal et al.
(2020),Leclerc and Badami (2020)
7 Acquire quality market data
and support study
SO7 With the help of the market’s
appropriate data, technologies can
analyse the market demand and make a
better predictions
Improved utilisation of machine learning
is possible with better data
Comprehensive review and testing are
required before adopting the technology;
again, data quality matters
Help the analysis of the validation of
results
Baldea et al. (2017),Shiboldenkov
and Nesterova (2020),Princes (2020)
8 Updated policy and practical
implementation
SO8 Policy, regulations and legal issues to be
continuously updated towards smooth
adoption and technology transfer of new
technologies
With the advent of modern technologies,
quality infrastructure is an essential
requirement and it should be a facility
with support facilities
Employees should be assured of no
punishment for making errors and
encouraged to take risks in adopting the
new technological developments
Ducas and Wilner (2017),Allen et al.
(2019)
9 The requisite amount of data SO9 Require appropriate data so that
emergencies can be handled
Need for better testing in varied
situations
Security is required in data handling,
storage and supportive systems
Bi et al. (2014),Neumann et al.
(2020),Lu et al. (2020)
10 No restrictions on imports of
advanced and supporting
technologies
SO10 Countries can avoid restrictions on
international imports through self-
reliant research and development
Easy and effective support to
organisations who want to adopt
advanced technologies in the industries
without any delay
Qu et al. (2020),Bokrantz et al. (2020)
11 Update environmental laws to
new needs
SO11 We need to continuously upgrade
environmental laws that can deal with
new technological developments
A proactive model is required that has a
vision and is facilitating
Zhang et al. (2019),Leng et al. (2020)
12 Adequate awareness and
freedom for industries to
select technologies
SO12 Proper awareness and freedom amongst
the industries for undertaking the proper
implementation of advanced
manufacturing solutions
A collaborative approach can be adopted
with developers in select technologies
Proactive action on providing freedom,
individual autonomy, transparency and
fairness to the developers and users
Oliveira et al. (2020),Rafiaani et al.
(2020),Rossi and Di Nicolantonio
(2020)
(continued )Table 3.
Modern
technologies
for industry 4.0
identified best and one is the worst. Furthermore, the reference comparison with the best and
worst barriers is made. In this manner, all entries in the questionnaire are filled. Table 4 gives
the final response of the expert team for the main dimension.
Based on the reference comparison, the following optimisation model is developed based
on the model 6.
min ξL
s:t:
│wMB
wTB
2│≤ξL
│wMB
wSC
2│≤ξL
│wSC
wTB
2│≤ξL
wTB þwMB þwSC ¼1
wTB;wMB ;wSC ≥0 (14)
The above optimisation problem is solved using the excel solver, and find the optimal weights
of each barrier and shown as wTB ¼0:3;wMB ¼0:5;wSC ¼0:2 and the consistency ratio is
S.
No Perceived solution Code Description References
13 Proper maintenance handling
of new technologies
SO13 More programs are designed for
employees to make them capable of
handling the maintenance of advanced
systems
For smooth operations with the new
technologies, proper education, capacity
building, training and expertise are
essentially required for the workers
Ansari et al. (2019),Ruiz-Sarmiento
et al. (2020)
14 Supportive research,
development and
commercialisation
environment
SO14 New research and development in
modern technologies be promoted to
take on technological challenges and
improve products
A major part of revenue be allocated for
undertaking basic and applied research
and developing applications
Development in organisation
infrastructure to facilitate
commercialisation
Qin and Chiang (2019),Szalavetz
(2019),Princes (2020)
Table 3.
BO (best of others) Technological barriers Management barriers Social barriers
Best criteria: Managerial Barriers 212
OW (other of worst) Worst Criteria: Social barriers
Technological Barriers 2
Management Barriers 2
Social Barriers 1
Table 4.
Main dimension
barriers comparison
BIJ
ξL50.1. Similar to the primary dimension, the responses for all sub-barriers are collected.
After obtaining the responses, the steps of the BWM method are provided in section 3.1.
These are further applied to calculate the relative weights of the dimensions and barriers.
Further, the global weight of the barriers is calculated by multiplying the relative weight and
their associated dimension weight. The barriers are ranked based on the global weights,
which are shown in Table 5.
4.3 Phase III: Prioritisation of the solutions to overcome barriers
In this phase, the solutions to overcome the significant barriers are prioritised using the
CoCoSo method. The same expert team is participating in this study phase and obtaining the
responses. The expert team provided the linguistic decision matrix, which is converted into
the initial decision-making matrix by substituting the linguistic terms with the crisp values as
per Table 1. The same is shown in Table 6 (full data table is provided in the Supplementary
material as Table S1).
Main dimension Weight CR Barriers Local weight CR Global weight Global rank
TG 0.3 0.1 TG1 0.278261 0.06087 0.083478 2
TG2 0.169565 0.05087 8
TG3 0.113043 0.033913 15
TG4 0.113043 0.033913 13
TG5 0.113043 0.033913 14
TG6 0.084783 0.025435 17
TG7 0.084783 0.025435 18
TG8 0.043478 0.013043 21
MG 0.5 MG1 0.082677 0.048055 0.041339 9
MG2 0.03937 0.019685 19
MG3 0.244094 0.122047 1
MG4 0.110236 0.055118 7
MG5 0.165354 0.082677 3
MG6 0.110236 0.055118 6
MG7 0.165354 0.082677 4
MG8 0.082677 0.041339 9
SC 0.2 SC1 0.326797 0.096386 0.065359 5
SC2 0.130719 0.026144 16
SC3 0.098039 0.019608 20
SC4 0.196078 0.039216 11
SC5 0.052288 0.010458 22
SC6 0.196078 0.039216 12
Solutions TG1 TG2 TG3 ... ... ... SC5 SC6
SO1 3 1 1 ... ... ... 11
SO2 2 4 1 ... ... ... 13
SO3 2 3 3 ... ... ... 14
... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ...
SO13 3 3 1 ... ... ... 31
SO14 4 4 5 ... ... ... 14
Table 5.
Weight and ranking of
the dimensions and
barriers
Table 6.
Initial decision-making
matrix
Modern
technologies
for industry 4.0
The normalised decision matrix is obtained from the initial decision-making matrix using
equations (2) and (3). The obtained normalised decision matrix is provided in Table 7 (refer to
Table S2).
After this, the comparability sequence matrix is formulated. In this process, the weights of
barriers obtained in phase II are utilised. The comparability sequence measures and S
i
are
calculated using equation (8) and shown in Table 8 (refer to Table S3).
The weighted comparability sequence and P
i
calculated using equation (9) are shown in
Table 9 (refer to Table S4).
Three aggregation methods are utilised to determine each solution’s relative weights
(kia;kib ;kic) using equations (10)–(13). Using equation (14), these relative weights are utilised
to get the final weights. Based on these relative and final weights, each solution is ranked and
the final rankings are shown in Table 10.
The relative and final weights of each solution are shown in Figure 3.
5. Discussion of the results
All the barriers are prioritised using the BWM method. Amongst the three main barriers, i.e.
technological, management and social barriers, the weight of the management barrier is 0.5
Solutions TG1 TG2 TG3 ... ... SC5 SC6
SO1 0.6667 0.0000 0.0000 ... ... 0.0000 0.0000
SO2 0.3333 1.0000 0.0000 ... ... 0.0000 0.5000
SO3 0.3333 0.6667 0.5000 ... ... 0.0000 0.7500
... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ...
SO13 0.6667 0.6667 0.0000 ... ... 0.6667 0.0000
SO14 1.0000 1.0000 1.0000 ... ... 0.0000 0.7500
Solutions TG1 TG2 TG3 ... ... SC5 SC6 Si
SO1 0.0557 0.0000 0.0000 ... ... 0.0000 0.0000 0.1245
SO2 0.0278 0.0509 0.0000 ... ... 0.0000 0.0196 0.5139
SO3 0.0278 0.0339 0.0170 ... ... 0.0000 0.0294 0.2616
... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ...
SO13 0.0557 0.0339 0.0000 ... ... 0.0070 0.0000 0.2232
SO14 0.0835 0.0509 0.0339 ... ... 0.0000 0.0294 0.5474
Solutions TG1 TG2 TG3 ... ... SC5 SC6 Pi
SO1 0.9667 0.0000 0.0000 ... ... 0.0000 0.0000 3.9436
SO2 0.9124 1.0000 0.0000 ... ... 0.0000 0.9732 11.7764
SO3 0.9124 0.9796 0.9768 ... ... 0.0000 0.9888 12.6636
... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ...
SO13 0.9667 0.9796 0.0000 ... ... 0.9958 0.0000 7.8001
SO14 1.0000 1.0000 1.0000 ... ... 0.0000 0.9888 18.4870
Table 7.
Normalised decision
matrix
Table 8.
Comparability
sequence measures
and S
i
Table 9.
Weighted
comparability
sequence and P
i
BIJ
and contains the first rank. Technological barriers have a weight of 0.3 and are placed in the
second rank, while social barriers, having a 0.2 weight, are ranked third.
5.1 Ranking of barriers and their sub barriers
5.1.1 Management barriers. For management barriers, we have identified eight sub-barriers.
Change management has a maximum weight of 0.122047 and is ranked one. Because change
management is the main barrier during the implementation of modern technologies, and it is
not easy to handle the technological change due to the requirement of a skilled workforce,
knowledge about the technologies and proper research and development.
Solutions kaRank kbRank kcRank K Final rank
SO1 0.0290 13 2.0255 13 0.2137 13 0.9884 13
SO2 0.0876 4 7.2170 3 0.6457 4 3.3918 3
SO3 0.0921 3 5.3697 7 0.6790 3 2.7420 7
SO4 0.0289 14 2.0000 14 0.2130 14 0.9781 14
SO5 0.0860 5 5.8936 5 0.6346 5 2.8900 5
SO6 0.0372 12 3.2086 11 0.2740 12 1.4929 11
SO7 0.0797 6 5.3506 8 0.5875 6 2.6363 8
SO8 0.1012 2 7.6745 2 0.7462 2 3.6744 2
SO9 0.0795 8 5.6824 6 0.5866 8 2.7586 6
SO10 0.0643 9 3.8389 9 0.4741 9 1.9482 9
SO11 0.0423 11 3.0883 12 0.3119 11 1.4916 12
SO12 0.0795 7 6.1447 4 0.5866 7 2.9297 4
SO13 0.0572 10 3.8174 10 0.4215 10 1.8834 10
SO14 0.1356 1 9.1991 1 1.0000 1 4.5214 1
0
1
2
3
4
5
6
7
8
9
10
SO1 SO2 SO3 SO4 SO5 SO6 SO7 SO8 SO9 SO10 SO11 SO12 SO13 SO14
Weight
SoluƟon
Ka Kb Kc K
Table 10.
Final aggregation and
ranking of the solution
Figure 3.
Relative and final
weights of each
solution
Modern
technologies
for industry 4.0
The second sub-barrier is “appropriate support infrastructure”because there is a
requirement for appropriate infrastructure while introducing these technologies. The plant
layout has been changed, so tackling this solution is not easily possible. Training and capacity-
building barriers are in the third position. The third main barrier is “training and capacity
building”, weighing 0.082677 because training must be required to operate these machines.
The fourth management barrier is the skilled workforce, weighing 0.055118 because the
skilled workforce cannot operate these machines. There is a lack of a skilled workforce in
rural areas, and this skilled workforce takes cost. Policy, regulation and legal issues are
another barrier to management, weighing 0.055118 and placed at the fifth number because,
during the implementation, there is a requirement for the best policy, regulation and other
issues. Industry–academia interaction and spare parts, logistics and reverse logistics
management have the same weight of 0.041339, at the sixth position. There is an essential
requirement for better industry–academia interaction for future development. Also, spare
parts for these technologies are not readily available in the market. The last management
barrier is “poor forecasting and prediction and for decision making”, having a low weight
amongst all management barriers of 0.019685. These technologies do not have human-like
intelligence, so there is a lack of forecasting and appropriate decision-making skills during
the complicated manufacturing processes and their interfaces.
5.1.2 Technological barriers. In technological barriers, we have identified eight sub-
barriers. The major sub-barrier amongst all sub-barriers is “high initial cost of technology”,
which weighs 0.083478. All modern technologies have a high initial cost, which is not
affordable for small-scale industries.
The second barrier is the “High level of technological complexity”, which weighs 0.05087.
This technology has complexity during its successful implementation and cannot be handled
by an unskilled workforce.
Maintenance of technological support, including IT barriers, weighs 0.033913. This
barrier has the third rank amongst all technological barriers. As modern technologies require
maintenance through the IT infrastructure, and in many places, sophisticated equipment is
controlled and maintained through IT support.
The barrier “integration of technologies”almost creates an effect on manufacturing because
there is an essential integration requirement. This barrier weighs 0.033913 and is placed at the
fourth rank. The lack of simulation and software support barrier weights 0.033913 and is placed
at the fifth rank. Sometimes, there is a lack of stimulation, and software support is not provided
due to its extra cost. The sixth barrier is “availability of raw material”, weighing 0.025435.
Technologieslike AM require the input of raw materials to manufacture products, which are not
readily available anywhere. Another technological barrier is “communication technology”,
which has a weight of 0.025435 and is placed in the seventh position. Communication technology
is an essential requirement, which helps during manufacturing and conveys timely information.
The last barrier is “systems not tested to handle emergencies/disruptions”, which weighs
0.013043. During any emergency, modern technologies are not tested to handle it.
5.1.3 Social barriers. Six sub-barriers of social dimention are identified and ranked using
the BWM method. The main barrier amongst all social barriers is the “fear of unemployment/
job reduction”which has the highest importance weight. With the implementation of modern
technologies, jobs have been reduced which creates a serious problem for human workers.
These technologies reduce human efforts or requirements, so there is a reduction in low skill
jobs, which causes fear amongst the people. The ethical and privacy issues barrier weighing
0.039216 is placed second amongst all social barriers. Some ethical and privacy issues are
involved in implementing modern technologies. Security concerns are another social barrier
in the third place with an importance weight of 0.039216. All digital technologies are digitally
controlled, with some security issues used during manufacturing and other required
operations. Another sub-barrier is “import restrictions and government policy”for modern
BIJ
technologies. This barrier weighs 0.026144 and is placed in the fourth position. During the
import of technologies, there is some restriction by which manufacturers/industries are
facing this problem—requirements of environmental clearances barrier having a weight of
0.019608 that is placed in the fifth place. There are issues regarding environmental laws
during the implementation of these technologies. The last social barrier amongst all social
barriers is “lack of awareness towards new technological developments”, weighing 0.010458.
Few industrialists have no awareness of future technological development.
5.2 Solutions
The CoCoSo method is used to identify the solutions for countering the effects of the above
barriers. Results indicate that a “supportive research, development and commercialisation
environment”is the most potent solution. These technological applications open up new
areas, as research and development are the backbones of technological development.
Continuous focus on new innovative techniques and creating a commercialisation
environment. The second effective solution is “updatedpolicyandeffective
implementation”which shows the importance and urgency of policy upgradation to
introduce these technologies into manufacturing. “Capacity building through training”is
placed in the third position, as it plays a vital role in improving the technical skills of the
workforce. There is an essential requirement for the timely training of the workforce. The
fourth rank of the solution is “adequate awareness and freedom to industries to select
technologies”, where proper awareness of technologies is required.
Different industries also give freedom to select various technologies, which is helpful for a
smooth manufacturing process. Management of modern and supporting technologies
solutions is at the fifth rank. This solution is also effective by which the proper management
of these technologies is required during manufacturing. The right human resources are
required at the right place during the manufacturing process. The requisite amount of data is
also a meaningful solution and is placed in the 6th position amongst all solutions. All modern
technologies can digitally operate, and data are digitally stored. The next solution is the
“development of proper protocols”which is placed in the seventh position. Proper
development of IT and non-IT is important and helpful for these technologies for proper
implementation. Acquire quality market data and support study solutions in the eighth
position. The quality market data are helpful, and another study is used for proper awareness
of technologies and related important products and manufacturing processes. There should
be no restrictions on importing advanced and supporting technologies because
manufacturers can buy any technologies to improve their efficiency and productivity. So
these solutions are also important for industries and placed at the ninth rank. Proper
maintenance handling of new technologies solutions is in the 10th position. The typical
workforce does not easily handle modern technologies, and proper maintenance of these
technologies is required to handle new technologies. Better logistics and warehousing
management are also the best solution for these technologies and is placed at rank 11. Proper
management of warehouses plays a vital role in handling manufacturing and marketing
systems. This helps deliver the product at the right quality, right time and at the right place.
Updating environmental laws to new needs is the best solution concerning the environmental
point of view, which is in the 12th position. From time to time, updating as per the
requirement of industries is essential to avoid the barriers. The 13th solution is optimising the
production rate amongst all the identified solutions. Optimising the production rate is vital in
decreasing the final product’s cost and helps to remove this barrier. The final solution is
“ready availability of raw material”. Raw materials can be readily available for this
technology for the smooth manufacturing process.
Modern
technologies
for industry 4.0
6. Implication of the study
This research has management and academic contribution. The significant implication of
this study is highlighted as follows.
6.1 Managerial implications
Industry 4.0 empowers organisations to adopt and utilise numerous advanced technologies to
increase the efficiency and effectiveness of operations across the supply chain. As these modern
technologies help the manufacturing organisation make better and quick decisions, the
performance of these organisations will improve. In this study, these modern technologies and
their capabilities are highlighted that motivate the top management to adopt them to gain
competitive advantages. Adopting these modern technologies improves customer relationships,
boosts efficiency, and decreases the risk and expense of product recall. This study identifies and
prioritises the barriers that hinder the adoption of modern technologies. This identification and
prioritisation help the manager understand the challenges before they face them to prepare
themselves better. Further, prioritising the barriers helps the managers focus on the high-
priority barrier at the initial stage as it is challenging to focus on each barrier simultaneously.
Furthermore, solutions are also suggested to overcome these barriers that are quite helpful for
managers. These solutions could be systematically adopted through optimal utilisation of their
resources. The organisation cannot adopt all the solutions at once, so their prioritisation
supports the managers in formulating a robust strategy for systematically implementing these
solutions. The solutions with the highest priority should be handled first, followed by those with
the lowest priority. The finding shows that a “supportive research, development and
commercialisation environment”,“updated policy and effective implementation”and “capacity
building through training”are the top three solutions to overcome the barriers. The top
management and policy planner must formulate strategies and action plansto implement these
solutions before adopting modern technologies. Furthermore, this study demonstrates the
benefits of modern technology adoptions, which encourage the supply chain stakeholders to
implement them in their respective supply chain.
6.2 Academic implications
This study contributes significantly to the literature on industry 4.0 by proposing a
comprehensive framework for evaluating modern technology adoption. The proposed
framework helps the academician evaluate the barriers to specific modern technology
adoption, such as big data, IoT and other technologies. Furthermore, identifying barriers to
modern technology adoption allows researchers to propose an action plan to mitigate the
same effects. Furthermore, the proposed solutions also guide the academician to develop their
model in order to implement these solutions. The finding also suggests that some barriers
make organisations reluctant to adopt modern technologies. Therefore, researchers could do
valuable research to mitigate these barriers so that the manufacturing organisation could
transform itself as per industry 4.0. Furthermore, the study recommends a research
requirement to accelerate the transformation of the convention system into industry 4.0.
7. Conclusion, limitations and future scope of the study
Modern technological advancements have entirely reshaped manufacturing by making their
processes highly integrated and more streamlined. Implementing modern technologies into
the manufacturing sector to automate processes provides various benefits such as increased
quality, productivity, efficiencies and profit. With modern technologies, errors are
significantly reduced and the collected data are already on a system that can be audited
and accessed far more quickly. Research and development are the most effective solution in
BIJ
modern technologies, which helps remove barriers to these technologies. The identified
solution can be effectively used to overcome these barriers, resulting in the development of an
industry 4.0-based manufacturing system. Similarly, as a result of the improvement in
quality, efficiency and downtime, resources can be allocated to other areas of the business to
facilitate its growth and development. With the help of these solutions, modern technology
will be successfully applied in manufacturing industries for an effective production system.
Apart from the various advantages of modern technologies, they have limitations and
pitfalls. This study is based on available literature and experts’opinions. Expert opinion is
perception dependent; in the COVID-19 era, many experts get biased towards implementing
modern technologies. The opinion is personal, and it has some errors. The emergence of
nanotechnology in manufacturing has led to pollution from trillions of minuscule plastic
particles in the oceans and waterways. The cost is also a significant limitation in adopting
modern manufacturing technologies. In terms of future scope, this study could be validated
with multiple case studies so that the findings could be generalised. Furthermore, the biases
present in the expected input could be overcome by integrating fuzzy and grey theory in
further studies. Moreover, the interrelationships amongst the barriers were also captured
through different modelling techniques such as ISM, TISM and DEMATEL. In future studies,
the identified barriers and solutions could be modelled through advanced statistical
techniques such as structural equation modelling.
References
Abdelmegid, M.A., Gonz
alez, V.A., Poshdar, M., O’Sullivan, M., Walker, C.G. and Ying, F. (2020),
“Barriers to adopting simulation modelling in the construction industry”,Automation in
Construction, Vol. 111, 103046.
Abdulrahman, M.D., Gunasekaran, A. and Subramanian, N. (2014), “Critical barriers in implementing
reverse logistics in the Chinese manufacturing sectors”,International Journal of Production
Economics, Vol. 147, pp. 460-471.
Abugabah, A., Nizamuddin, N. and Abuqabbeh, A. (2020), “A review of challenges and barriers
implementing RFID technology in the Healthcare sector”,Procedia Computer Science, Vol. 170,
pp. 1003-1010.
Akram, U., Nadarajah, M., Shah, R. and Milano, F. (2020), “A review on rapid responsive energy
storage technologies for frequency regulation in modern power systems”,Renewable and
Sustainable Energy Reviews, Vol. 120, 109626.
Ali, O., Ally, M. and Dwivedi, Y. (2020), “The state of play of blockchain technology in the financial
services sector: a systematic literature review”,International Journal of Information
Management, Vol. 54, 102199.
Ali, S., Kaur, R. and Khan, S. (2022), “Evaluating sustainability initiatives in warehouse for measuring
sustainability performance: an emerging economy perspective”,Annals of Operations Research.
doi: 10.1007/s10479-021-04454-w (In press).
Allen, D.W., Berg, C., Davidson, S., Novak, M. and Potts, J. (2019), “International policy coordination
for blockchain supply chains”,Asia and the Pacific Policy Studies, Vol. 6 No. 3, pp. 367-380.
Ansari, F., Glawar, R. and Nemeth, T. (2019), “PriMa: a prescriptive maintenance model for cyber-
physical production systems”,International Journal of Computer Integrated Manufacturing,
Vol. 32 Nos 4-5, pp. 482-503.
Arnold, C., Kiel, D. and Voigt, K.I. (2016), “How the industrial internet of things changes business
models in different manufacturing industries”,International Journal of Innovation Management,
Vol. 20 No. 08, 1640015.
Aumi, S., Corbett, B., Clarke-Pringle, T. and Mhaskar, P. (2013), “Data-driven model predictive quality
control of batch processes”,AIChE Journal, Vol. 59 No. 8, pp. 2852-2861.
Modern
technologies
for industry 4.0
Babatunde, O.K. (2020), “Mapping the implications and competencies for Industry 4.0 to hard and soft
total quality management”,The TQM Journal, Vol. 33 No. 4, doi: 10.1108/TQM-07-2020-0158.
Bag, S., Gupta, S. and Kumar, S. (2018), “Industry 4.0 adoption and 10R advance manufacturing
capabilities for sustainable development”,International Journal of Production Economics,
Vol. 231, 107844.
Bag, S. and Pretorius, J.H.C. (2020), “Relationships between industry 4.0, sustainable manufacturing
and circular economy: proposal of a research framework”,International Journal of
Organizational Analysis, Vol. 30 No. 4.
Bag, S., Dhamija, P., Gupta, S. and Sivarajah, U. (2020a), “Examining the role of procurement 4.0
towards remanufacturing operations and circular economy”,Production Planning and Control,
Vol. 32 No. 16, pp. 1-16.
Bag, S., Wood, L.C., Mangla, S.K. and Luthra, S. (2020b), “Procurement 4.0 and its implications on
business process performance in a circular economy”,Resources, Conservation and Recycling,
Vol. 152, 104502.
Baldea, M., Edgar, T.F., Stanley, B.L. and Kiss, A.A. (2017), “Modular manufacturing processes: status,
challenges, and opportunities”,AIChE Journal, Vol. 63 No. 10, pp. 4262-4272.
Ballardini, R.M., Ituarte, I.F. and Pei, E. (2018), “Printing spare parts through additive manufacturing:
legal and digital business challenges”,Journal of Manufacturing Technology Management,
Vol. 29 No. 6, pp. 958-982.
Barquet, K., J€
arnberg, L., Rosemarin, A. and Macura, B. (2020), “Identifying barriers and opportunities
for a circular phosphorus economy in the Baltic Sea region”,Water Research, Vol. 171, 115433.
Bi, Z., Da Xu, L. and Wang, C. (2014), “Internet of things for enterprise systems of modern
manufacturing”,IEEE Transactions on Industrial Informatics, Vol. 10 No. 2, pp. 1537-1546.
Bingimlas, K.A. (2009), “Barriers to the successful integration of ICT in teaching and learning
environments: a review of the literature”,Eurasia Journal of Mathematics, Science and
Technology Education, Vol. 5 No. 3, pp. 235-245.
Boccella, A.R., Piera, C., Cerchione, R. and Murino, T. (2020), “Evaluating centralized and heterarchical
control of smart manufacturing systems in the era of industry 4.0”,Applied Sciences, Vol. 10
No. 3, p. 755.
Bokrantz, J., Skoog, A., Berlin, C., Wuest, T. and Stahre, J. (2020), “Smart maintenance: an empirically
grounded conceptualization”,International Journal of Production Economics, Vol. 223, 107534.
Bolster, J. and Kitada, M. (2020), “Agile social learning–capacity-building for sustainable development
in higher education”,International Journal of Sustainability in Higher Education, Vol. 21 No. 7,
doi: 10.1108/IJSHE-07-2019-0212.
Bongomin, O., Gilibrays Ocen, G., Oyondi Nganyi, E., Musinguzi, A. and Omara, T. (2020),
“Exponential disruptive technologies and the required skills of industry 4.0”,Journal of
Engineering, Vol. 2020.
Bouranta, N. (2020), “Does transformational leadership influence TQM practices? A comparison
analysis between manufacturing and service firms”,The TQM Journal. doi: 10.1108/TQM-12-
2019-0296.
Boyce, J.M. (2016), “Modern technologies for improving cleaning and disinfection of environmental
surfaces in hospitals”,Antimicrobial Resistance and Infection Control, Vol. 5 No. 1, pp. 1-10.
Br€
uggemann, S. and Gr€
uning, F. (2008), “Using domain knowledge provided by ontologies for
improving data quality management”,Proceedings of I-Know, pp. 251-258.
Bugdol, M. and Pokrzywa, M. (2020), “The feeling of fear among local government administration
employees as a result of the introduction of E-administration”,Administrative Sciences, Vol. 10
No. 3, p. 67.
BIJ
Buntin, M.B., Burke, M.F., Hoaglin, M.C. and Blumenthal, D. (2011), “The benefits of health
information technology: a review of the recent literature shows predominantly positive results”,
Health Affairs, Vol. 30 No. 3, pp. 464-471.
Calabrese, A., Levialdi Ghiron, N. and Tiburzi, L. (2020), “‘Evolutions’and ‘revolutions’in manufacturers’
implementation of industry 4.0: a literature review, a multiple case study, and a conceptual
framework”,Production Planning and Control, Vol. 32 No. 3, pp. 213-227, doi: 10.1080/09537287.
2020.1719715.
Calitz, A.P., Poisat, P. and Cullen, M. (2017), “The future African workplace: the use of collaborative
robots in manufacturing”,SA Journal of Human Resource Management,Vol.15
No. 1, pp. 1-11.
Calzavara, M., Battini, D., Bogataj, D., Sgarbossa, F. and Zennaro, I. (2020), “Ageing workforce
management in manufacturing systems: state of the art and future research agenda”,
International Journal of Production Research, Vol. 58 No. 3, pp. 729-747.
Capodaglio, A.G. (2020), “Fit-for-purpose urban wastewater reuse: analysis of issues and available
technologies for sustainable multiple-barrier approaches”,Critical Reviews in Environmental
Science and Technology, Vol. 51 No. 15, pp. 1-48.
Chege, S.M. and Wang, D. (2020), “The influence of technology innovation on SME performance
through environmental sustainability practices in Kenya”,Technology in Society, Vol. 60,
101210.
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M. and Yin, B. (2017), “Smart factory of industry 4.0: key
technologies, application case, and challenges”,IEEE Access, Vol. 6, pp. 6505-6519.
Cheng, J., Chen, W., Tao, F. and Lin, C.L. (2018), “Industrial IoT in 5G environment towards smart
manufacturing”,Journal of Industrial Information Integration, Vol. 10, pp. 10-19.
Chiarini, A. (2020), “Industry 4.0, quality management and TQM world. A systematic literature review
and a proposed agenda for further research”,The TQM Journal, Vol. 32 No. 4, pp. 603-616.
Corbett, B. and Mhaskar, P. (2016), “Subspace identification for data-driven modeling and quality
control of batch processes”,AIChE Journal, Vol. 62 No. 5, pp. 1581-1601.
Cortiços, N.D. (2019), “Renovation tool to improve building stock performance—higher education
context”,Sustainable Cities and Society, Vol. 47, 101368.
Cui, Y., Kara, S. and Chan, K.C. (2020), “Manufacturing big data ecosystem: a systematic literature
review”,Robotics and Computer-Integrated Manufacturing, Vol. 62, 101861.
Dahooie, J.H., Dehshiri, S.J.H., Banaitis, A. and Binkyt_
e-V_
elien_
e, A. (2020), “Identifying and prioritizing
cost reduction solutions in the supply chain by integrating value engineering and grey multi-
criteria decision-making”,Technological and Economic Development of Economy, Vol. 26 No. 6,
pp. 1-28.
Dalenogare, L.S., Benitez, G.B., Ayala, N.F. and Frank, A.G. (2018), “The expected contribution of
Industry 4.0 technologies for industrial performance”,International Journal of Production
Economics, Vol. 204, pp. 383-394.
Dallasega, P., Revolti, A., Sauer, P.C., Schulze, F. and Rauch, E. (2020), “BIM, augmented and virtual
reality empowering lean construction management: a project simulation game”,Procedia
Manufacturing, Vol. 45, pp. 49-54.
Dangelmaier, W., Fischer, M., Gausemeier, J., Grafe, M., Matysczok, C. and Mueck, B. (2005), “Virtual
and augmented reality support for discrete manufacturing system simulation”,Computers in
Industry, Vol. 56 No. 4, pp. 371-383.
De Corato, U. (2020), “Improving the shelf-life and quality of fresh and minimally-processed fruits and
vegetables for a modern food industry: a comprehensive critical review from the traditional
technologies into the most promising advancements”,Critical Reviews in Food Science and
Nutrition, Vol. 60 No. 6, pp. 940-975.
Di Carlo Rasi, D. and Janssen, R.A. (2019), “Advances in solution-processed multijunction organic
solar cells”,Advanced Materials, Vol. 31 No. 10, 1806499.
Modern
technologies
for industry 4.0
Dubey, R., Bryde, D., Graham, G., Foropon, C., Kumari, S. and Gupta, O. (2021), “The role of alliance
management, big data analytics and information visibility on new-product development
capability”,Annals of Operations Research. doi: 10.1007/s10479-021-04390-9 (In press).
Dubey, R., Gunasekaran, A. and Childe, S. (2019a), “Big data analytics capability in supply chain
agility”,Management Decision, Vol. 57 No. 8, pp. 2092-2112, doi: 10.1108/md-01-2018-0119.
Dubey, R., Gunasekaran, A. and Foropon, C. (2022), “Improving information alignment and
coordination in humanitarian supply chain through blockchain technology”,Journal of
Enterprise Information Management, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/jeim-
07-2022-0251.
Dubey, R., Gunasekaran, A., Childe, S., Blome, C. and Papadopoulos, T. (2019b), “Big data and predictive
analytics and manufacturing performance: integrating institutional theory, resource-based view
and big data culture”,British Journal of Management, Vol. 30 No. 2, pp. 341-361, doi: 10.1111/1467-
8551.12355.
Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S., Shibin, K. and Wamba, S. (2017),
“Sustainable supply chain management: framework and further research directions”,Journal of
Cleaner Production, Vol. 142, pp. 1119-1130, doi: 10.1016/j.jclepro.2016.03.117.
Ducas, E. and Wilner, A. (2017), “The security and financial implications of blockchain technologies:
regulating emerging technologies in Canada”,International Journal, Vol. 72 No. 4, pp. 538-562.
Elavarasan, R.M., Afridhis, S., Vijayaraghavan, R.R., Subramaniam, U. and Nurunnabi, M. (2020),
“SWOT analysis: a framework for comprehensive evaluation of drivers and barriers for
renewable energy development in significant countries”,Energy Reports, Vol. 6, pp. 1838-1864.
Ellahham, S., Ellahham, N. and Simsekler, M.C.E. (2020), “Application of artificial intelligence in the
health care safety context: opportunities and challenges”,American Journal of Medical Quality,
Vol. 35 No. 4, pp. 341-348.
Ershadi, M.J., Najafi, N. and Soleimani, P. (2019), “Measuring the impact of soft and hard total quality
management factors on customer behavior based on the role of innovation and continuous
improvement”,The TQM Journal, Vol. 31 No. 6, pp. 1093-1115.
Esposito, C., Castiglione, A., Martini, B. and Choo, K.K.R. (2016), “Cloud manufacturing: security,
privacy, and forensic concerns”,IEEE Cloud Computing, Vol. 3 No. 4, pp. 16-22.
Fagnant, D.J. and Kockelman, K. (2015), “Preparing a nation for autonomous vehicles: opportunities,
barriers and policy recommendations”,Transportation Research Part A: Policy and Practice,
Vol. 77, pp. 167-181.
Fleiter, T., Worrell, E. and Eichhammer, W. (2011), “Barriers to energy efficiency in industrial bottom-
up energy demand models—a review”,Renewable and Sustainable Energy Reviews, Vol. 15
No. 6, pp. 3099-3111.
Flexer, V., Baspineiro, C.F. and Galli, C.I. (2018), “Lithium recovery from brines: a vital raw material
for green energies with a potential environmental impact in its mining and processing”,Science
of the Total Environment, Vol. 639, pp. 1188-1204.
Frank, A.G., Dalenogare, L.S. and Ayala, N.F. (2019), “Industry 4.0 technologies: implementation
patterns in manufacturing companies”,International Journal of Production Economics, Vol. 210,
pp. 15-26.
Gailani, A., Crosbie, T., Al-Greer, M., Short, M. and Dawood, N. (2020), “On the role of regulatory policy
on the business case for energy storage in both EU and UK energy systems: barriers and
enablers”,Energies, Vol. 13 No. 5, p. 1080.
Ghaffar, S.H., Corker, J. and Fan, M. (2018), “Additive manufacturing technology and its
implementation in construction as an eco-innovative solution”,Automation in Construction,
Vol. 93, pp. 1-11.
Ghobakhloo, M. and Iranmanesh, M. (2021), “Digital transformation success under Industry 4.0:
a strategic guideline for manufacturing SMEs”,Journal Of Manufacturing Technology
Management, Vol. 32 No. 8, pp. 1533-1556, doi: 10.1108/jmtm-11-2020-0455.
BIJ
Ghobakhloo, M., Iranmanesh, M., Vilkas, M., Grybauskas, A. and Amran, A. (2022), “Drivers and
barriers of Industry 4.0 technology adoption among manufacturing SMEs: a systematic review
and transformation roadmap”,Journal Of Manufacturing Technology Management, Vol. 33
No. 6, pp. 1029-1058, doi: 10.1108/jmtm-12-2021-0505.
Govindan, K., Kaliyan, M., Kannan, D. and Haq, A.N. (2014), “Barriers analysis for green supply chain
management implementation in Indian industries using analytic hierarchy process”,
International Journal of Production Economics, Vol. 147, pp. 555-568.
Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M. (2013), “Internet of Things (IoT): a vision,
architectural elements, and future directions”,Future Generation Computer Systems, Vol. 29
No. 7, pp. 1645-1660.
Guo, S. and Zhao, H. (2017), “Fuzzy best-worst multi-criteria decision-making method and its
applications”,Knowledge-Based Systems, Vol. 121, pp. 23-31.
Gupta, H., Kusi-Sarpong, S. and Rezaei, J. (2020), “Barriers and overcoming strategies to supply chain
sustainability innovation”,Resources, Conservation and Recycling, Vol. 161, 104819.
Haleem, A. and Javaid, M. (2019), “Additive manufacturing applications in industry 4.0: a review”,
Journal of Industrial Integration and Management, Vol. 4 No. 04, 1930001.
Haleem, A., Javaid, M. and Rab, S. (2020a), “Impact of additive manufacturing in different areas of
Industry 4.0”,International Journal of Logistics Systems and Management, Vol. 37 No. 2, pp. 239-251.
Haleem, A., Javaid, M., Khan, S. and Khan, M. (2020b), “Retrospective investigation of flexibility and
their factors in additive manufacturing systems”,International Journal of Industrial and
Systems Engineering, Vol. 36 No. 3, p. 400, doi: 10.1504/ijise.2020.110932.
Haleem, A., Javaid, M., Singh, R. and Khan, S. (2022), “Guest editorial: industry 4.0 special issue”,
Industrial Robot: The International Journal of Robotics Research and Application, Vol. 49 No. 3,
p. 385, doi: 10.1108/ir-05-2022-459.
Hossain, M.S. and Muhammad, G. (2016), “Cloud-assisted industrial internet of things (iiot)–enabled
framework for health monitoring”,Computer Networks, Vol. 101, pp. 192-202.
Hossain, M. and Thakur, V. (2020), “Benchmarking health-care supply chain by implementing
Industry 4.0: a fuzzy-AHP-DEMATEL approach”,Benchmarking: An International Journal,
Vol. 28 No. 2, doi: 10.1108/bij-05-2020-0268.
H€
uner, K.M., Ofner, M. and Otto, B. (2009), “Towards a maturity model for corporate data quality
management”,Proceedings of the 2009 ACM symposium on Applied Computing, pp. 231-238.
Hultcrantz, M., Yellapantula, V. and Rustad, E.H. (2020), “Genomic profiling of multiple myeloma: new insights
and modern technologies”,Best Practice and Research Clinical Haematology, Vol. 33 No. 1, 101153.
Ifinedo, E., Rikala, J. and H€
am€
al€
ainen, T. (2020), “Factors affecting Nigerian teacher educators’
technology integration: considering characteristics, knowledge constructs, ICT practices and
beliefs”,Computers and Education, Vol. 146, 103760.
Ingaldi, M. and Ulewicz, R. (2019), “Problems with the implementation of industry 4.0 in enterprises
from the SME sector”,Sustainability, Vol. 12 No. 1, p. 217, doi: 10.3390/su12010217.
Iqbal, R., Doctor, F., More, B., Mahmud, S. and Yousuf, U. (2020), “Big data analytics: computational
intelligence techniques and application areas”,Technological Forecasting and Social Change,
Vol. 153, 119253.
Javaid, M. and Haleem, A. (2018), “Additive manufacturing applications in medical cases: a literature
based review”,Alexandria Journal of Medicine, Vol. 54 No. 4, pp. 411-422.
Javaid, M. and Haleem, A. (2020), “Critical components of Industry 5.0 towards a successful adoption
in the field of manufacturing”,Journal of Industrial Integration and Management, Vol. 5 No. 03,
pp. 327-348.
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R. and Vaish, A. (2020), “Industry 4.0 technologies
and their applications in fighting COVID-19 pandemic”,Diabetes and Metabolic Syndrome:
Clinical Research and Reviews, Vol. 14 No. 4.
Modern
technologies
for industry 4.0
Javaid, M., Haleem, A., Singh, R., Rab, S., Suman, R. and Khan, S. (2021), “Exploring relationships
between Lean 4.0 and manufacturing industry”,Industrial Robot: The International Journal of
Robotics Research and Application, Vol. 49 No. 3, pp. 402-414, doi: 10.1108/ir-08-2021-0184.
Javaid, M., Haleem, A., Pratap Singh, R., Khan, S. and Suman, R. (2022), “Sustainability 4.0 and its
applications in the field of manufacturing”,Internet of Things and Cyber-Physical Systems,
Vol. 2, pp. 82-90, doi: 10.1016/j.iotcps.2022.06.001.
Jiang, J., Sun, S., Sekar, V. and Zhang, H. (2017), “Pytheas: enabling data-driven quality of experience
optimization using group-based exploration-exploitation”,14th {USENIX} Symposium on
Networked Systems Design and Implementation ({NSDI} 17), pp. 393-406.
Jung, J., Bozeman, B. and Gaughan, M. (2020), “Fear in bureaucracy: comparing public and private sector
workers’expectations of punishment”,Administration and Society, Vol. 52 No. 2, pp. 233-264.
Kamble, S., Gunasekaran, A. and Arha, H. (2018), “Understanding the Blockchain technology adoption
in supply chains-Indian context”,International Journal of Production Research, Vol. 57 No. 7,
pp. 2009-2033, doi: 10.1080/00207543.2018.1518610.
Kamble, S.S., Gunasekaran, A. and Gawankar, S.A. (2020), “Achieving sustainable performance in a
data-driven agriculture supply chain: a review for research and applications”,International
Journal of Production Economics, Vol. 219, pp. 179-194.
Karatayev, M., Hall, S., Kalyuzhnova, Y. and Clarke, M.L. (2016), “Renewable energy technology
uptake in Kazakhstan: policy drivers and barriers in a transitional economy”,Renewable and
Sustainable Energy Reviews, Vol. 66, pp. 120-136.
Keller, J. and Heiko, A. (2014), “The influence of information and communication technology (ICT) on
future foresight processes—results from a Delphi survey”,Technological Forecasting and Social
Change, Vol. 85, pp. 81-92.
Kennedy, M. and Basu, B. (2013), “Overcoming barriers to low carbon technology transfer and
deployment: an exploration of the impact of projects in developing and emerging economies”,
Renewable and Sustainable Energy Reviews, Vol. 26, pp. 685-693.
Khalil, R.A. and Saeed, N. (2020), “Network optimization for industrial internet of things (IIoT)”,IEEE
Sensors Letters, Vol. 4 No. 7, pp. 1-4.
Khan, S. and Haleem, A. (2021), “Investigation of circular economy practices in the context of
emerging economies: a Cocoso approach”,International Journal of Sustainable Engineering,
Vol. 14 No. 3, pp. 357-367, doi: 10.1080/19397038.2020.1871442.
Khan, S., Haleem, A. and Khan, M.I. (2020), “Assessment of risk in the management of Halal supply
chain using fuzzy BWM method”,Supply Chain Forum: An International Journal, Vol. 22 No. 1,
pp. 1-17.
Khan, S. (2021), “Barriers of big data analytics for smart cities development: a context of emerging
economies”,International Journal of Management Science and Engineering Management,
Vol. 17 No. 2, pp. 123-131, doi: 10.1080/17509653.2021.1997662.
Khan, S., Kaushik, M., Kumar, R. and Khan, W. (2022a), “Investigating the barriers of blockchain
technology integrated food supply chain: a BWM approach”,Benchmarking: An International
Journal, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/bij-08-2021-0489.
Khan, M.I., Khan, S. and Haleem, A. (2019), “Analysing barriers towards management of Halal Supply
Chain: a BWM approach”,Journal of Islamic Marketing, Vol. 13 No. 1, pp. 66-80.
Khan, S., Singh, R. and Kirti (2021), “Critical factors for blockchain technology implementation:
a supply chain perspective”,Journal of Industrial Integration and Management, 2150011, doi:
10.1142/s2424862221500111 (In press).
Khattab, A.R., Guirguis, H.A., Tawfik, S.M. and Farag, M.A. (2019), “Cheese ripening: a review on
modern technologies towards flavor enhancement, process acceleration and improved quality
assessment”,Trends in Food Science and Technology, Vol. 88, pp. 343-360.
Kim, S., Laschi, C. and Trimmer, B. (2013), “Soft robotics: a bioinspired evolution in robotics”,Trends
in Biotechnology, Vol. 31 No. 5, pp. 287-294.
BIJ
Kim, S., Del Castillo, R.P., Caballero, I., Lee, J., Lee, C., Lee, D., Lee, S. and Mate, A. (2019), “Extending
data quality management for smart connected product operations”,IEEE Access, Vol. 7, pp.
144663-144678.
Ko, T., Lee, J. and Ryu, D. (2018), “Blockchain technology and manufacturing industry: real-time
transparency and cost savings”,Sustainability, Vol. 10 No. 11, p. 4274.
Kolberg, D. and Z€
uhlke, D. (2015), “Lean automation enabled by industry 4.0 technologies”,IFAC-
PapersOnLine, Vol. 48 No. 3, pp. 1870-1875.
Kothari, R., Vathistha, A., Singh, H.M., Pathak, V.V., Tyagi, V.V., Yadav, B.C., Ashokkumar, V. and
Singh, D.P. (2020), “Assessment of Indian bioenergy policy for sustainable environment and its
impact for rural India: strategic implementation and challenges”,Environmental Technology
and Innovation, Vol. 20, 101078.
Kouhizadeh, M., Saberi, S. and Sarkis, J. (2020), “Blockchain technology and the sustainable supply
chain: theoretically exploring adoption barriers”,International Journal of Production Economics,
Vol. 231, 107831.
Kumar, H., Singh, M.K., Gupta, M.P. and Madaan, J. (2020), “Moving towards smart cities: solutions
that lead to the smart city transformation framework”,Technological Forecasting and Social
Change, Vol. 153, 119281.
Kusiak,A.(2018),“Smart manufacturing”,International Journal of Production Research, Vol. 56 Nos 1-2,
pp. 508-517.
Leclerc, S.H. and Badami, M.G. (2020), “Extended producer responsibility for E-waste management:
policy drivers and challenges”,Journal of Cleaner Production, Vol. 251, 119657.
Lee, J., Davari, H., Singh, J. and Pandhare, V. (2018), “Industrial Artificial Intelligence for industry 4.0-
based manufacturing systems”,Manufacturing Letters, Vol. 18, pp. 20-23.
Lee, J., Azamfar, M. and Singh, J. (2019), “A blockchain enabled Cyber-Physical System architecture
for Industry 4.0 manufacturing systems”,Manufacturing Letters, Vol. 20, pp. 34-39.
Legrand, W., Kirsche, C., Sloan, P. and Simons-Kaufmann, C. (2012), “Making 20 2020 happen: is the
hospitality industry mitigating its environmental impacts? The barriers and motivators that
German hoteliers have to invest in sustainable management strategies and technologies and
their perceptions of online self help”,WIT Transactions on Ecology and the Environment,
Vol. 161, pp. 115-126.
Leng, J., Ruan, G., Jiang, P., Xu, K., Liu, Q., Zhou, X. and Liu, C. (2020), “Blockchain-empowered
sustainable manufacturing and product lifecycle management in industry 4.0: a survey”,
Renewable and Sustainable Energy Reviews, Vol. 132, 110112.
Li, H. and Liang, J. (2020), “Recent development of printed micro-supercapacitors: printable materials,
printing technologies, and perspectives”,Advanced Materials, Vol. 32 No. 3, 1805864.
Liaw, S.T., Pearce, C., Liyanage, H., Cheah-Liaw, G.S. and De Lusignan, S. (2014), “An integrated
organisation-wide data quality management and information governance framework: theoretical
underpinnings”,Journal of Innovation in Health Informatics, Vol. 21 No. 4, pp. 199-206.
Lin, C., Braund, W.E., Auerbach, J., Chou, J.H., Teng, J.H., Tu, P. and Mullen, J. (2020), “Policy decisions
and use of information technology to fight coronavirus disease, taiwan”,Emerging Infectious
Diseases, Vol. 26 No. 7, p. 1506.
Lu, Y. (2017), “Industry 4.0: a survey on technologies, applications and open research issues”,Journal
of Industrial Information Integration, Vol. 6, pp. 1-10.
Lu, Y., Liu, C., Kevin, I., Wang, K., Huang, H. and Xu, X. (2020), “Digital Twin-driven smart
manufacturing: connotation, reference model, applications and research issues”,Robotics and
Computer-Integrated Manufacturing, Vol. 61, 101837.
Luthra, S., Kumar, A., Zavadskas, E.K., Mangla, S.K. and Garza-Reyes, J.A. (2020), “Industry 4.0 as an
enabler of sustainability diffusion in supply chain: an analysis of influential strength of drivers
in an emerging economy”,International Journal of Production Research, Vol. 58 No. 5,
pp. 1505-1521.
Modern
technologies
for industry 4.0
Majeed, A., Zhang, Y., Ren, S., Lv, J., Peng, T., Waqar, S. and Yin, E. (2020), “A big data-driven
framework for sustainable and smart additive manufacturing”,Robotics and Computer-
Integrated Manufacturing, Vol. 67, 102026.
Makundi, H., Huyse, H., Develtere, P., Mongolia, B. and Rutashobya, L. (2017), “Training abroad and
technological capacity building: analysing the role of Chinese training and scholarship
programmes for Tanzanians”,International Journal of Educational Development, Vol. 57, pp. 11-20.
Manikandan, S., Sundarakani, B. and Pereira, V. (2020), “Skill development: role of industry-academia
dyadic collaboration for sustaining the construction supply chain in rural India”,Sustainable
Business Practices for Rural Development, Palgrave Macmillan, Singapore, pp. 27-40.
Manninen, M.A. and Knutsson, K. (2014), “Lithic raw material diversification as an adaptive
strategy—technology, mobility, and site structure in Late Mesolithic northernmost Europe”,
Journal of Anthropological Archaeology, Vol. 33, pp. 84-98.
Martinez, B., Reaser, J.K., Dehgan, A., Zamft, B., Baisch, D., McCormick, C., Giordano, A.J., Aicher, R.
and Selbe, S. (2020), “Technology innovation: advancing capacities for the early detection of
and rapid response to invasive species”,Biological Invasions, Vol. 22 No. 1, pp. 75-100.
Mathivathanan, D., Mathiyazhagan, K., Rana, N., Khorana, S. and Dwivedi, Y. (2021), “Barriers to the
adoption of blockchain technology in business supply chains: a total interpretive structural
modelling (TISM) approach”,International Journal Of Production Research, Vol. 59 No. 11,
pp. 3338-3359, doi: 10.1080/00207543.2020.1868597.
Meijer, L.L.J., Huijben, J.C.C.M., Van Boxtel, A. and Romme, A.G.L. (2019), “Barriers and drivers for
technology commercialization by SMEs in the Dutch sustainable energy sector”,Renewable and
Sustainable Energy Reviews, Vol. 112, pp. 114-126.
Mir, R.H., Pottoo, F.H., Sawhney, G., Masoodi, M.H. and Bhat, Z.A. (2020), “Nanophytomedicine ethical
issues, regulatory aspects, and challenges”,Nanophytomedicine, Springer, Singapore,
pp. 173-192.
Mittal, S., Khan, M.A., Romero, D. and Wuest, T. (2019), “Smart manufacturing: characteristics,
technologies and enabling factors”,Proceedings of the Institution of Mechanical Engineers, Part
B: Journal of Engineering Manufacture, Vol. 233 No. 5, pp. 1342-1361.
Mondragon, A.E.C., Mondragon, C.E.C. and Coronado, E.S. (2018), “Exploring the applicability of
blockchain technology to enhance manufacturing supply chains in the composite materials
industry”,2018 IEEE International conference on applied system invention (ICASI), IEEE,
pp. 1300-1303.
Moraes Silva, D.R.D., Lucas, L.O. and Vonortas, N.S. (2020), “Internal barriers to innovation and
university-industry cooperation among technology-based SMEs in Brazil”,Industry and
Innovation, Vol. 27 No. 3, pp. 235-263.
Neethirajan, S. (2020), “The role of sensors, big data and machine learning in modern animal farming”,
Sensing and Bio-Sensing Research, Vol. 29, 100367.
Neumann, A., Strenge, B., Uhlich, J.C., Schlicher, K.D., Maier, G.W., Schalkwijk, L., Waßmuth, J., Essig,
K. and Schack, T. (2020), “AVIKOM: towards a mobile audiovisual cognitive assistance system
for modern manufacturing and logistics”,Proceedings of the 13th ACM International
Conference on PErvasive Technologies Related to Assistive Environments, June, pp. 1-8.
Ni
zeti
c, S.,
Soli
c, P., Gonz
alez-de, D.L.D.I. and Patrono, L. (2020), “Internet of Things (IoT):
opportunities, issues and challenges towards a smart and sustainable future”,Journal of
Cleaner Production, Vol. 274, 122877.
Novak-Marcincin, J., Barna, J., Janak, M. and Novakova-Marcincinova, L. (2013), “Augmented reality
aided manufacturing”,Procedia Computer Science, Vol. 25, pp. 23-31.
O’donovan, P., Leahy, K., Bruton, K. and O’Sullivan, D.T. (2015), “Big data in manufacturing:
a systematic mapping study”,Journal of Big Data, Vol. 2 No. 1, p. 20.
Oliveira, J.P., LaLonde, A.D. and Ma, J. (2020), “Processing parameters in laser powder bed fusion
metal additive manufacturing”,Materials and Design, Vol. 193, 108762.
BIJ
Ooi, K.B., Lee, V.H., Tan, G.W.H., Hew, T.S. and Hew, J.J. (2018), “Cloud computing in manufacturing:
the next industrial revolution in Malaysia?”,Expert Systems with Applications, Vol. 93,
pp. 376-394.
Oztemel, E. and Gusev, S. (2020), “Literature review of Industry 4.0 and related technologies”,Journal
of Intelligent Manufacturing, Vol. 31 No. 1, pp. 127-182.
Papadopoulos, T., Singh, S., Spanaki, K., Gunasekaran, A. and Dubey, R. (2021), “Towards the next
generation of manufacturing: implications of big data and digitalization in the context of
industry 4.0”,Production Planning and Control, Vol. 33 Nos 2-3, pp. 101-104, doi: 10.1080/
09537287.2020.1810767.
Parmentola, A., Ferretti, M. and Panetti, E. (2020), “Exploring the university-industry cooperation in a
low innovative region. What differences between low tech and high tech industries?”,
International Entrepreneurship and Management Journal, Vol. 17, pp. 1-28.
Peng, X., Zhang, X. and Luo, Z. (2019), “Pythagorean fuzzy MCDM method based on CoCoSo and
CRITIC with score function for 5G industry evaluation”,Artificial Intelligence Review, Vol. 53,
pp. 1-35.
Princes, E. (2020), “Integrating ambidexterity into the modern manufacturing era of industry 4.0”,
International Journal of Supply Chain Management, Vol. 9 No. 4, pp. 58-64.
Qi, Q. and Tao, F. (2018), “Digital twin and big data towards smart manufacturing and industry 4.0:
360 degree comparison”,IEEE Access, Vol. 6, pp. 3585-3593.
Qian, C., Zhang, Y., Jiang, C., Pan, S. and Rong, Y. (2020), “A real-time data-driven collaborative
mechanism in fixed-position assembly systems for smart manufacturing”,Robotics and
Computer-Integrated Manufacturing, Vol. 61, 101841.
Qin, S.J. and Chiang, L.H. (2019), “Advances and opportunities in machine learning for process data
analytics”,Computers and Chemical Engineering, Vol. 126, pp. 465-473.
Qu, C., Shao, J. and Cheng, Z. (2020), “Can embedding in the global value chain drive green growth in
China’s manufacturing industry?”,Journal of Cleaner Production, Vol. 268, 121962.
Rafiaani, P., Dikopoulou, Z., Van Dael, M., Kuppens, T., Azadi, H., Lebailly, P. and Van Passel, S.
(2020), “Identifying social indicators for sustainability assessment of CCU technologies: a
modified multi-criteria decision making”,Social Indicators Research, Vol. 147 No. 1, pp. 15-44.
Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A.B.L. and Rajak, S. (2020), “Barriers to the
adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative
perspective”,International Journal of Production Economics, Vol. 224, 107546.
Rajput, S. and Singh, S. (2019), “Industry 4.0 challenges to implement circular economy”,
Benchmarking: An International Journal,Vol.28No.5,doi:10.1108/bij-12-2018-0430.
Rezaei, J. (2015), “Best-worst multi-criteria decision-making method”,Omega, Vol. 53, pp. 49-57.
Rezaei, J. (2016), “Best-worst multi-criteria decision-making method: some properties and a linear
model”,Omega, Vol. 64, pp. 126-130.
Rossi, E. and Di Nicolantonio, M. (2020), “Integrating human-centred design approach into
sustainable-oriented 3D printing systems”,Human-Intelligent Systems Integration, Vol. 2,
pp. 1-17.
Ruiz-Sarmiento, J.R., Monroy, J., Moreno, F.A., Galindo, C., Bonello, J.M. and Gonzalez-Jimenez, J.
(2020), “A predictive model for the maintenance of industrial machinery in the context of
industry 4.0”,Engineering Applications of Artificial Intelligence, Vol. 87, 103289.
Saad, W., Bennis, M. and Chen, M. (2019), “A vision of 6G wireless systems: applications, trends,
technologies, and open research problems”,IEEE Network, Vol. 34 No. 3, pp. 134-142.
Salam, M. (2019), “Analyzing manufacturing strategies and Industry 4.0 supplier performance
relationships from a resource-based perspective”,Benchmarking: An International Journal,
Vol. 28 No. 5, doi: 10.1108/bij-12-2018-0428.
Modern
technologies
for industry 4.0
Salk, J.J. and Kennedy, S.R. (2020), “Next-generation genotoxicology: using modern sequencing
technologies to assess somatic mutagenesis and Cancer risk”,Environmental and Molecular
Mutagenesis, Vol. 61 No. 1, pp. 135-151.
Schnackenberg, D. (2009), “Understanding the real barriers to technology-enhanced innovation in
higher education”,Educational Research, Vol. 51 No. 4, pp. 411-424.
Sen, S. and Ganguly, S. (2017), “Opportunities, barriers and issues with renewable energy
development–A discussion”,Renewable and Sustainable Energy Reviews, Vol. 69, pp. 1170-1181.
Sen, B., Mia, M., Krolczyk, G.M., Mandal, U.K. and Mondal, S.P. (2019), “Eco-friendly cutting fluids in
minimum quantity lubrication assisted machining: a review on the perception of sustainable
manufacturing”,International Journal of Precision Engineering and Manufacturing-Green
Technology, Vol. 8, pp. 1-32.
Senna, P., Ferreira, L., Barros, A., Bonn
ın Roca, J. and Magalh~
aes, V. (2022), “Prioritizing barriers for
the adoption of Industry 4.0 technologies”,Computers and Industrial Engineering, Vol. 171,
108428, doi: 10.1016/j.cie.2022.108428.
Sepasgozar, S.M. and Davis, S. (2018), “Construction technology adoption cube: an investigation on
process, factors, barriers, drivers and decision-makers using NVivo and AHP analysis”,
Buildings, Vol. 8 No. 6, p. 74.
Serrano, A., Garcia-Guzman, J., Xydopoulos, G. and Tarhini, A. (2020), “Analysis of barriers to the
deployment of health information systems: a stakeholder perspective”,Information Systems
Frontiers, Vol. 22 No. 2, pp. 455-474.
Shah, N.D., Steyerberg, E.W. and Kent, D.M. (2018), “Big data and predictive analytics: recalibrating
expectations”,Jama, Vol. 320 No. 1, pp. 27-28.
Shiboldenkov, V. and Nesterova, K. (2020), “The smart technologies application for the product life-
cycle management in modern manufacturing systems”,MATEC Web of Conferences, Vol. 311,
EDP Sciences.
Sidhu, M.K., Singh, K. and Singh, D. (2019), “Strategic impact of SCM and SCQM practices on
competitive dimensions of Indian manufacturing industries”,The TQM Journal, Vol. 31 No. 5,
pp. 696-721.
Sidwall, K. and Forsyth, P. (2020), “Advancements in real-time simulation for the validation of grid
modernization technologies”,Energies, Vol. 13 No. 16, p. 4036.
Singh, R. and Bhanot, N. (2019), “An integrated DEMATEL-MMDE-ISM based approach for
analysing the barriers of IoT implementation in the manufacturing industry”,International
Journal of Production Research, Vol. 58 No. 8, pp. 2454-2476, doi: 10.1080/00207543.2019.
1675915.
Singh, R., Khan, S. and Dsilva, J. (2022), “A framework for assessment of critical factor for circular
economy practice implementation”,Journal of Modelling in Management, Vol. ahead-of-print
No. ahead-of-print, doi: 10.1108/jm2-06-2021-0145.
Sinsel, S.R., Riemke, R.L. and Hoffmann, V.H. (2020), “Challenges and solution technologies for the
integration of variable renewable energy sources—a review”,renewable Energy, Vol. 145,
pp. 2271-2285.
Smits, J.A., Tart, C.D., Presnell, K., Rosenfield, D. and Otto, M.W. (2010), “Identifying potential barriers
to physical activity adherence: anxiety sensitivity and body mass as predictors of fear during
exercise”,Cognitive Behaviour Therapy, Vol. 39 No. 1, pp. 28-36.
Stepinac, M. and Ga
sparovi
c, M. (2020), “A review of emerging technologies for an assessment of
safety and seismic vulnerability and damage detection of existing masonry structures”,Applied
Sciences, Vol. 10 No. 15, p. 5060.
Stojanovic, L., Dinic, M., Stojanovic, N. and Stojadinovic, A. (2016), “Big-data-driven anomaly detection
in industry (4.0): an approach and a case study”,2016 IEEE International Conference on Big
Data (Big Data), IEEE, pp. 1647-1652.
BIJ
Strong, D., Kay, M., Conner, B., Wakefield, T. and Manogharan, G. (2018), “Hybrid manufacturing–
integrating traditional manufacturers with additive manufacturing (AM) supply chain”,
Additive Manufacturing, Vol. 21, pp. 159-173.
Szalavetz, A. (2019), “Industry 4.0 and capability development in manufacturing subsidiaries”,
Technological Forecasting and Social Change, Vol. 145, pp. 384-395.
Tahir, M.F., Chen, H., Khan, A., Javed, M.S., Cheema, K.M. and Laraik, N.A. (2020), “Significance of
demand response in light of current pilot projects in China and devising a problem solution for
future advancements”,Technology in Society, Vol. 63, 101374.
Tao, F., Cheng, Y., Da Xu, L., Zhang, L. and Li, B.H. (2014), “CCIoT-CMfg: cloud computing and
internet of things-based cloud manufacturing service system”,IEEE Transactions on Industrial
Informatics, Vol. 10 No. 2, pp. 1435-1442.
Tao, F., Qi, Q., Liu, A. and Kusiak, A. (2018), “Data-driven smart manufacturing”,Journal of
Manufacturing Systems, Vol. 48, pp. 157-169.
Taylor, R.H., Kazanzides, P., Fischer, G.S. and Simaan, N. (2020), “Medical robotics and computer-
integrated interventional medicine”,Biomedical Information Technology, Academic Press,
pp. 617-672.
Thomas-Seale, L.E., Kirkman-Brown, J.C., Attallah, M.M., Espino, D.M. and Shepherd, D.E. (2018),
“The barriers to the progression of additive manufacture: perspectives from the UK industry”,
International Journal of Production Economics, Vol. 198, pp. 104-118.
Tiwari, S. (2020), “Supply chain integration and Industry 4.0: a systematic literature review”,
Benchmarking: An International Journal, Vol. 28 No. 30, doi: 10.1108/bij-08-2020-0428.
Tortorella, G.L., Fogliatto, F.S., Esp^
osto, K.F., Vergara, A.M.C., Vassallo, R., Mendoza, D.T. and
Narayanamurthy, G. (2020), “Effects of contingencies on healthcare 4.0 technologies adoption
and barriers in emerging economies”,Technological Forecasting and Social Change, Vol. 156,
120048.
Tsai, Y.S., Rates, D., Moreno-Marcos, P.M., Mu~
noz-Merino, P.J., Jivet, I., Scheffel, M., Drachsler, H.,
Kloos, C.D. and Ga
sevi
c, D. (2020), “Learning analytics in European higher education–trends
and barriers”,Computers and Education, Vol. 155, 103933.
Tyulin, A. and Chursin, A. (2020), “Modern manufacturing process management methods”,The New
Economy of the Product Life Cycle, Springer, Cham, pp. 275-319.
Vostrovsk
y, V., Tyrychtr, J. and Kvasni
cka, R. (2020), “Open data quality management based on ISO/
IEC SQuaRE series standards in intelligent systems”,Computer Science On-line Conference,
Cham, Springer, pp. 625-631.
Wang, X.V. and Xu, X.W. (2013), “An interoperable solution for cloud manufacturing”,Robotics and
Computer-Integrated Manufacturing, Vol. 29 No. 4, pp. 232-247.
Wang, J., He, T. and Lee, C. (2019), “Development of neural interfaces and energy harvesters towards
self-powered implantable systems for healthcare monitoring and rehabilitation purposes”,Nano
Energy, Vol. 65, 104039.
Wang, W., Zhao, X., Cao, J., Li, H. and Zhang, Q. (2020), “Barriers and requirements to climate change
adaptation of mountainous rural communities in developing countries: the case of the eastern
Qinghai-Tibetan Plateau of China”,Land Use Policy, Vol. 95, 104354.
Win, I.Y. and Shen, G.Q. (2020), “Barriers to the adoption of modular integrated construction:
systematic review and meta-analysis, integrated conceptual framework, and strategies”,Journal
of Cleaner Production, Vol. 249, 119347.
Wu, D., Wang, H. and Seidu, R. (2020), “Smart data driven quality prediction for urban water source
management”,Future Generation Computer Systems, Vol. 107, pp. 418-432.
Wuest, T., Weimer, D., Irgens, C. and Thoben, K.D. (2016), “Machine learning in manufacturing:
advantages, challenges, and applications”,Production and Manufacturing Research, Vol. 4
No. 1, pp. 23-45.
Modern
technologies
for industry 4.0
Xia, X., Govindan, K. and Zhu, Q. (2015), “Analyzing internal barriers for automotive parts
remanufacturers in China using grey-DEMATEL approach”,Journal of Cleaner Production,
Vol. 87, pp. 811-825.
Xu, L.D., Xu, E.L. and Li, L. (2018), “Industry 4.0: state of the art and future trends”,International
Journal of Production Research, Vol. 56 No. 8, pp. 2941-2962.
Yates, D. and Paquette, S. (2011), “Emergency knowledge management and social media technologies:
a case study of the 2010 Haitian earthquake”,International Journal of Information
Management, Vol. 31 No. 1, pp. 6-13.
Yazdani, M., Zarate, P., Kazimieras Zavadskas, E. and Turskis, Z. (2019), “A combined compromise
solution (CoCoSo) method for multi-criteria decision-making problems”,Management Decision,
Vol. 57 No. 9, pp. 2501-2519.
Zeleny, M. (1973), “Compromise programming”, in Cocchrane, J.L. and Zeleny, M. (Eds), Multiple
Criteria Decision Making, University of South Carolina Press, Columbia, SC, pp. 262-301.
Zeleny, M. (2012), “High technology and barriers to innovation: from globalization to relocalization”,
International Journal of Information Technology and Decision Making, Vol. 11 No. 02,
pp. 441-456.
Zhang, G., Zhang, P., Zhang, Z.G. and Li, J. (2019), “Impact of environmental regulations on industrial
structure upgrading: an empirical study on the Beijing-Tianjin-Hebei region in China”,Journal
of Cleaner Production, Vol. 238, 117848.
Zheng, P., Sang, Z., Zhong, R.Y., Liu, Y., Liu, C., Mubarok, K., ... and Xu, X. (2018), “Smart
manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future
perspectives”,Frontiers of Mechanical Engineering, Vol. 13 No. 2, pp. 137-150.
Zheng, P., Xu, X. and Chen, C.H. (2020), “A data-driven cyber-physical approach for personalised
smart, connected product co-development in a cloud-based environment”,Journal of Intelligent
Manufacturing, Vol. 31 No. 1, pp. 3-18.
Further reading
Khan, S., Singh, R., Haleem, A., Dsilva, J. and Ali, S.S. (2022b), “Exploration of critical success factors
of Logistics 4.0: a DEMATEL approach”,Logistics, Vol. 6 No. 1, p. 13.
Sharma, H.P. and Chaturvedi, A. (2021), “Adoption of smart technologies: an Indian perspective”,
2021 5th International Conference on Information Systems and Computer Networks (ISCON)
[Preprint].
Corresponding author
Shahbaz Khan can be contacted at: shahbaz.me12@gmail.com
BIJ
Supplementary material
Solutions TG1 TG2 TG3 TG4 TG5 TG6 TG7 TG8 MG1 MG2 MG3 MG4 MG5 MG6 MG7 MG8 SC1 SC2 SC3 SC4 SC5 SC6
SO1 3111131144111111111111
SO2 2413311111511553412313
SO3 2333313311121113123414
SO4 3111151131111111131111
SO5 4311313343311311243111
SO6 4111141143114111111111
SO7 4341311434131113111441
SO8 2413114311433343114415
SO9 3331111535143114111512
SO10 4211213312111113151131
SO11 1111111131243112115111
SO12 3311111311433413311143
SO13 3314113131112113111131
SO14 4453412433221325353314
Table S1.
Initial decision-making
matrix
Modern
technologies
for industry 4.0
Solutions TG1 TG2 TG3 TG4 TG5 TG6 TG7 TG8 MG1 MG2 MG3 MG4 MG5 MG6 MG7 MG8 SC1 SC2 SC3 SC4 SC5 SC6
SO1 0.6667 0.0000 0.0000 0.0000 0.0000 0.5000 0.0000 0.0000 1.0000 0.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
SO2 0.3333 1.0000 0.0000 0.6667 0.6667 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 1.0000 1.0000 0.5000 1.0000 0.0000 0.2500 0.5000 0.0000 0.5000
SO3 0.3333 0.6667 0.5000 0.6667 0.6667 0.0000 0.6667 0.5000 0.0000 0.0000 0.0000 0.3333 0.0000 0.0000 0.0000 0.5000 0.0000 0.2500 0.5000 0.7500 0.0000 0.7500
SO4 0.6667 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.6667 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5000 0.0000 0.0000 0.0000 0.0000
SO5 1.0000 0.6667 0.0000 0.0000 0.6667 0.0000 0.6667 0.5000 1.0000 0.5000 0.5000 0.0000 0.0000 0.5000 0.0000 0.0000 0.3333 0.7500 0.5000 0.0000 0.0000 0.0000
SO6 1.0000 0.0000 0.0000 0.0000 0.0000 0.7500 0.0000 0.0000 1.0000 0.5000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
SO7 1.0000 0.6667 0.7500 0.0000 0.6667 0.0000 0.0000 0.7500 0.6667 0.7500 0.0000 0.6667 0.0000 0.0000 0.0000 0.5000 0.0000 0.0000 0.0000 0.7500 1.0000 0.0000
SO8 0.3333 1.0000 0.0000 0.6667 0.0000 0.0000 1.0000 0.5000 0.0000 0.0000 0.7500 0.6667 0.6667 0.5000 0.7500 0.5000 0.0000 0.0000 0.7500 0.7500 0.0000 1.0000
SO9 0.6667 0.6667 0.5000 0.0000 0.0000 0.0000 0.0000 1.0000 0.6667 1.0000 0.0000 1.0000 0.6667 0.0000 0.0000 0.7500 0.0000 0.0000 0.0000 1.0000 0.0000 0.2500
SO10 1.0000 0.3333 0.0000 0.0000 0.3333 0.0000 0.6667 0.5000 0.0000 0.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.5000 0.0000 1.0000 0.0000 0.0000 0.6667 0.0000
SO11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.6667 0.0000 0.2500 1.0000 0.6667 0.0000 0.0000 0.2500 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
SO12 0.6667 0.6667 0.0000 0.0000 0.0000 0.0000 0.0000 0.5000 0.0000 0.0000 0.7500 0.6667 0.6667 0.7500 0.0000 0.5000 0.6667 0.0000 0.0000 0.0000 1.0000 0.5000
SO13 0.6667 0.6667 0.0000 1.0000 0.0000 0.0000 0.6667 0.0000 0.6667 0.0000 0.0000 0.0000 0.3333 0.0000 0.0000 0.5000 0.0000 0.0000 0.0000 0.0000 0.6667 0.0000
SO14 1.0000 1.0000 1.0000 0.6667 1.0000 0.0000 0.3333 0.7500 0.6667 0.5000 0.2500 0.3333 0.0000 0.5000 0.2500 1.0000 0.6667 1.0000 0.5000 0.5000 0.0000 0.7500
Table S2.
Normalised decision
matrix
BIJ
Solutions TG1 TG2 TG3 TG4 TG5 TG6 TG7 TG8 MG1 MG2 MG3 MG4 MG5 MG6 MG7 MG8 SC1 SC2 SC3 SC4 SC5 SC6 Si
SO1 0.0557 0.0000 0.0000 0.0000 0.0000 0.0127 0.0000 0.0000 0.0413 0.0148 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1245
SO2 0.0278 0.0509 0.0000 0.0226 0.0226 0.0000 0.0000 0.0000 0.0000 0.0000 0.1220 0.0000 0.0000 0.0551 0.0827 0.0207 0.0654 0.0000 0.0049 0.0196 0.0000 0.0196 0.5139
SO3 0.0278 0.0339 0.0170 0.0226 0.0226 0.0000 0.0170 0.0065 0.0000 0.0000 0.0000 0.0184 0.0000 0.0000 0.0000 0.0207 0.0000 0.0065 0.0098 0.0294 0.0000 0.0294 0.2616
SO4 0.0557 0.0000 0.0000 0.0000 0.0000 0.0254 0.0000 0.0000 0.0276 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0131 0.0000 0.0000 0.0000 0.0000 0.1217
SO5 0.0835 0.0339 0.0000 0.0000 0.0226 0.0000 0.0170 0.0065 0.0413 0.0098 0.0610 0.0000 0.0000 0.0276 0.0000 0.0000 0.0218 0.0196 0.0098 0.0000 0.0000 0.0000 0.3544
SO6 0.0835 0.0000 0.0000 0.0000 0.0000 0.0191 0.0000 0.0000 0.0413 0.0098 0.0000 0.0000 0.0827 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2364
SO7 0.0835 0.0339 0.0254 0.0000 0.0226 0.0000 0.0000 0.0098 0.0276 0.0148 0.0000 0.0367 0.0000 0.0000 0.0000 0.0207 0.0000 0.0000 0.0000 0.0294 0.0105 0.0000 0.3148
SO8 0.0278 0.0509 0.0000 0.0226 0.0000 0.0000 0.0254 0.0065 0.0000 0.0000 0.0915 0.0367 0.0551 0.0276 0.0620 0.0207 0.0000 0.0000 0.0147 0.0294 0.0000 0.0392 0.5102
SO9 0.0557 0.0339 0.0170 0.0000 0.0000 0.0000 0.0000 0.0130 0.0276 0.0197 0.0000 0.0551 0.0551 0.0000 0.0000 0.0310 0.0000 0.0000 0.0000 0.0392 0.0000 0.0098 0.3571
SO10 0.0835 0.0170 0.0000 0.0000 0.0113 0.0000 0.0170 0.0065 0.0000 0.0049 0.0000 0.0000 0.0000 0.0000 0.0000 0.0207 0.0000 0.0261 0.0000 0.0000 0.0070 0.0000 0.1939
SO11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0276 0.0000 0.0305 0.0551 0.0551 0.0000 0.0000 0.0103 0.0000 0.0000 0.0196 0.0000 0.0000 0.0000 0.1982
SO12 0.0557 0.0339 0.0000 0.0000 0.0000 0.0000 0.0000 0.0065 0.0000 0.0000 0.0915 0.0367 0.0551 0.0413 0.0000 0.0207 0.0436 0.0000 0.0000 0.0000 0.0105 0.0196 0.4151
SO13 0.0557 0.0339 0.0000 0.0339 0.0000 0.0000 0.0170 0.0000 0.0276 0.0000 0.0000 0.0000 0.0276 0.0000 0.0000 0.0207 0.0000 0.0000 0.0000 0.0000 0.0070 0.0000 0.2232
SO14 0.0835 0.0509 0.0339 0.0226 0.0339 0.0000 0.0085 0.0098 0.0276 0.0098 0.0305 0.0184 0.0000 0.0276 0.0207 0.0413 0.0436 0.0261 0.0098 0.0196 0.0000 0.0294 0.5474
Table S3.
Comparability
sequence measures
and S
i
Modern
technologies
for industry 4.0
Solutions TG1 TG2 TG3 TG4 TG5 TG6 TG7 TG8 MG1 MG2 MG3 MG4 MG5 MG6 MG7 MG8 SC1 SC2 SC3 SC4 SC5 SC6 Pi
SO1 0.9667 0.0000 0.0000 0.0000 0.0000 0.9825 0.0000 0.0000 1.0000 0.9944 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 3.9436
SO2 0.9124 1.0000 0.0000 0.9863 0.9863 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 1.0000 1.0000 0.9718 1.0000 0.0000 0.9732 0.9732 0.0000 0.9732 11.7764
SO3 0.9124 0.9796 0.9768 0.9863 0.9863 0.0000 0.9897 0.9910 0.0000 0.0000 0.0000 0.9412 0.0000 0.0000 0.0000 0.9718 0.0000 0.9644 0.9865 0.9888 0.0000 0.9888 12.6636
SO4 0.9667 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.9834 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.9820 0.0000 0.0000 0.0000 0.0000 3.9321
SO5 1.0000 0.9796 0.0000 0.0000 0.9863 0.0000 0.9897 0.9910 1.0000 0.9864 0.9189 0.0000 0.0000 0.9625 0.0000 0.0000 0.9307 0.9925 0.9865 0.0000 0.0000 0.0000 11.7242
SO6 1.0000 0.0000 0.0000 0.0000 0.0000 0.9927 0.0000 0.0000 1.0000 0.9864 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 4.9792
SO7 1.0000 0.9796 0.9903 0.0000 0.9863 0.0000 0.0000 0.9963 0.9834 0.9944 0.0000 0.9779 0.0000 0.0000 0.0000 0.9718 0.0000 0.0000 0.0000 0.9888 1.0000 0.0000 10.8686
SO8 0.9124 1.0000 0.0000 0.9863 0.0000 0.0000 1.0000 0.9910 0.0000 0.0000 0.9655 0.9779 0.9670 0.9625 0.9765 0.9718 0.0000 0.0000 0.9944 0.9888 0.0000 1.0000 13.6941
SO9 0.9667 0.9796 0.9768 0.0000 0.0000 0.0000 0.0000 1.0000 0.9834 1.0000 0.0000 1.0000 0.9670 0.0000 0.0000 0.9882 0.0000 0.0000 0.0000 1.0000 0.0000 0.9471 10.8087
SO10 1.0000 0.9456 0.0000 0.0000 0.9634 0.0000 0.9897 0.9910 0.0000 0.9731 0.0000 0.0000 0.0000 0.0000 0.0000 0.9718 0.0000 1.0000 0.0000 0.0000 0.9958 0.0000 8.8304
SO11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.9834 0.0000 0.8443 1.0000 0.9670 0.0000 0.0000 0.9443 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 5.7391
SO12 0.9667 0.9796 0.0000 0.0000 0.0000 0.0000 0.0000 0.9910 0.0000 0.0000 0.9655 0.9779 0.9670 0.9843 0.0000 0.9718 0.9738 0.0000 0.0000 0.0000 1.0000 0.9732 10.7508
SO13 0.9667 0.9796 0.0000 1.0000 0.0000 0.0000 0.9897 0.0000 0.9834 0.0000 0.0000 0.0000 0.9132 0.0000 0.0000 0.9718 0.0000 0.0000 0.0000 0.0000 0.9958 0.0000 7.8001
SO14 1.0000 1.0000 1.0000 0.9863 1.0000 0.0000 0.9724 0.9963 0.9834 0.9864 0.8443 0.9412 0.0000 0.9625 0.8917 1.0000 0.9738 1.0000 0.9865 0.9732 0.0000 0.9888 18.4870
Table S4.
Weighted
comparability
sequence and P
i
BIJ
