111
Send Orders for Reprints to reprints@benthamscience.net
Recent Advances in Computer Science and Communications, 2022, 15, 111-130
RESEARCH ARTICLE
Assessment of Risks for Successful Implementation of Industry 4.0
Rimalini Gadekar1,*, Bijan Sarkar2 and Ashish Gadekar3
Recent Advances in Computer Scienceand Communications
1
Mechanical Engineering Department, Government Polytechnic, Gondia, Maharashtra, India; 2Production Engineering
Department, Jadavpur University Kolkata, West Bengal, India; 3Faculty of Management, Amity Institute of Higher
Education, Ebene, Mauritius
Abstract: Purpose: The transformation happening globally, though referred to by different names
and nomenclatures, the overall objective to inspire digitalization and smart practices by reducing
human intervention and enhancing machine intelligence to take on the global manufacturing and
production to another level of excellence is a proven fact now. However, earlier research has been
found lacking in the strategic approach to evaluate and analyze the I4.0 adoption-related risks for its
implementation. This ultimately deprived organizations of a multitude of the benefits of I4.0 adoption. This research proposes a systematic methodology for understanding and evaluating the most
evident risks in the context of I4.0 implementation.
ARTICLE HISTORY
Received: November 03, 2019
Revised: April 29, 2020
Accepted: July 14, 2020
DOI:
10.2174/2666255813999200928215915
Design/Methodology/Approach: The research is mainly based on the inputs from experts/consultants
along with robust literature review and researcher’s experience in the area of risk handling. The
MCDM methods used for investigation and assessment are Fuzzy AHP and Fuzzy TOPSIS. The
outcomes of the study are further validated through sensitivity analysis and real-world scenario.
Results: Technical and Information Technology (IT) risks are found to be on the top of the priority
list, which needs urgent attention while embarking on I4.0 adoption in the industry, and the most
important criteria, which needed urgent attention was Information Security. The paper has also developed the ‘Industry 4.0 Risks Iceberg model’ and systematically categorized the challenges into 5
dimensions for easy assessment and analysis.
Practical Implications: This systematic and holistic study of the I4.0 associated risks can be used to
find the most critical and crucial risks based on which the strategies and policies may be modified to
harness the best of I4.0. This will not only ensure the returns on investment but also will build trust
in the system. The research would be very beneficial to managers, academicians, researchers, and
technocrats who would be involved in I4.0 implementation.
Keywords: Industry 4.0, risks management, sustainability, sensitivity analysis, fuzzy AHP, fuzzy TOPSIS, multi-criteria decision-making method (MCDM).
1. INTRODUCTION
I4.0 is being discussed and deliberated since the year
2011, on every global platform directly or indirectly because
of its inevitable nature, after been introduced the first time
during the Hannover fair in Germany. Hence, it is imperative
to understand how prepared we are to harness the best from
the adoption of I4.0. The vision of I4.0 is to create an ecosystem, where machines will do the maximum of the tasks
with a minimum of intervention from a human. This means
the amalgamation of technologies like Big Data Analytics
(BDA), Internet of Things (IoT), Industrial Internet of
Things (IIoT), Artificial Intelligence (AI), Cyber-Physical
System (CPS), and Augmented Reality/Virtual Reality
*Address correspondence to this author at the Mechanical Engineering
Department, Government Polytechnic, Gondia, Maharashtra, India;
E-mail: raggpn@gmail.com
2666-2566/22 $65.00+.00
(AR/VR) is expected to lead the overall transformation. The
smart industrial environment created out of the I4.0 vision is
not only transforming the manufacturing and service world
through continuous disruption and innovation but also challenging the traditional industrial practices which do not have
much significance and relevance in the fast, flexible, dynamic and volatile industrial world. This can be referred to as
just the beginning of I4.0 the 4th Industrial Revolution as it is
referred to sometimes, which has the potential to address and
meet all the present and future expectations [1-4].
In the last few decades, technological disruptions systematically and strategically have revolutionized the manufacturing practices to extreme levels, breaking all the age-old
limits. The glimpses of these technological advancements
can be experienced through a shorter product life cycle,
higher industrial performance standards than ever, efficient
and effective use of the resources, and the sophisticated ap© 2022 Bentham Science Publishers
112
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Gadekar et al.
proach to customer care giving rise to the development of
new business models which are more customer and environment-friendly [5] than ever before.
tives, criteria, and understanding of critical nature and application of Multi-Criteria Decision Making Method (MCDM)
relevant to the research problem considered for the study.
This revolution on one side seems to have very promising
and aspiring possibilities for a bright and happening future,
while on the other hand, the challenges are numerous. Only
continuous research and innovation will foster rapid development and nurture the ability to withstand these challenges
[6].
In this paper, the researcher has discussed and analyzed
the risks identified as crucial and critical for I4.0 implement
ation. Firstly the researcher created an all-inclusive list of the
micro and macro risks based on the literature review. The list
further was optimized by the intervention of the experts in
the field of I4.0 to make it precise and specific, thereby leaving the five most critical and significant risks, which were
adopted for the study. These risks were meticulously analyzed and assessed conceptually by studying the specific
nature and, characteristics before considering them for the
next level assessment. A similar process was followed to
identify the ten criteria to assess the five critical and significant risks identified above. These criteria were selected to
reflect the capacity and capability of the organizations in
terms of resource availability and intrinsic ability to avert the
risks and adopt I4.0. The detailed information on the risks
and criteria is elaborated in section 3.5 and 3.6.
The experience of the large companies until the date
shows that I4.0 has all the possibilities to create a high-value
manufacturing system, by the right use of their huge capacity
and capabilities [7]. Hence, the impact on this section of value creators and contributors to Gross Domestic Product
(GDP) needs to be studied and attended. This research is a
holistic attempt to develop a robust framework, which will
streamline the efforts to achieve the I4.0 objectives by assessing and evaluating various risks, impacting the adoption
of vision I4.0, [2, 7, 8].
This paper provides an overview of the key risks, companies may encounter while adopting I4.0 vision. The risk
identification, assessment, and evaluation for any transformation and transition are most important [7]. The thorough
assessment of the risks not only reduces the chance of project failures but also equips the strategists with riskmitigating plans. In order to have a sustainable implementation of I4.0, it is very much necessary to pay attention to
risks like Economic, Social, Legal and Political, Ecological,
Technical, and Information Technology (IT). This will help
to sustain the inevitable changes caused by the transformation of the new industrial age [2, 7]. Hence, assessing and
evaluating these risks in the same framework and determining the most critical risk is also essential and considered as
one of the prerequisites while adopting the vision of the I4.0
transformations.
The robustness and the critical nature of the literature are
reflected through the quality and quantity of the references
cited and referred. The researcher referred to the electronic
databases like EBSCO, Web of Science, SCOPUS, and Science Direct over the 2010-2020 timeframe period, by the
publishers Emerald, Springer, Taylor and Francis, and IEEE,
etc., to reach out to 203 research papers, by searching the
key terms like “Industry 4.0 implications”, “Industry 4.0”
and “sustainability”, “Industry 4.0” and “risk management”,
“Industry 4.0” and “social dimensions”, “Industry 4.0” and
“readiness assessment”, “Industry 4.0” and “framework”,
“Industry 4.0” and “challenges”, “Industry 4.0” and “technologies”, “Smart Manufacturing” and “Smart Factory” as a
primary source of knowledge and information. The documents from non-English language magazines, editorials, and
non-peer-reviewed journals were excluded.
After the preliminary screening, 153 research papers
were found relevant to the study. The screening criteria were
based on the sources of publication, degree of relevance, the
period (2010–2020), type of the study, and methods used.
The majority of these papers (102) were from refereed journals and the rest (51) were from academic peer-reviewed
journals, conference proceedings, and book chapters. The
review of 153 papers led to fine-tuning the choice of alterna-
A Hybrid MCDM approach was used to identify the most
crucial risk out of five risks, by using the Fuzzy Analytical
Hierarchy Process (FAHP) and Fuzzy Technique for Order
of Preference by Similarity to Ideal Solution (FTOPSIS)
methods as it fully addressed the critical needs of the problem.
This paper has nine sections. The first section elaborates
on the importance of the study on risk management in I4.0
implementation. The rest of the paper has been organized in
sections 2 to 9. Section 2 explains the ‘Industry 4.0 Risks
Iceberg Model’, while section 3 is about the comprehensive
literature review highlighting the different dimensions of
I4.0 knowledge and applications. In section 4, the methodology is elaborated in the right contexts. Section 5 deals with
testing and validating the risks assessment model by applying it to the company which is aspiring to adopt I4.0 through
a systematic process of transition and transformation. Section 6 presents results and findings, section 7 presents sensitivity analysis, section 8 presents discussion, and section 9
presents conclusion.
2. INDUSTRY 4.0 RISKS ICEBERG MODEL
The iceberg model is one of the simplest and most
preferred research tools to graphically present the
disproportionate inputs and outputs, as is evident from Fig.
(1). This model also helps to logically demonstrate what is
seen on the surface is an outcome, and what is generally
unseen in terms of resources and efforts are the impacting
factors [9]. The ‘Industry 4.0 Risks Iceberg Model’ is the
outcome of a detailed survey, explorations, and exhaustive
experience of the researchers in the field of digitalization and
smart technologies. The set of challenges and risks are
numerous. The concerns responsible for the prominent risks
are placed in the downside of the iceberg and not always
directly visible. Whereas the prominent risks placed on the
tip of the iceberg directly affects the business decisions. The
risks are visible but the concerns are not always visible [10].
Mostly the stakeholders and governments are in a confused
state of mind because of the absence of global standards and
lack of clarity. Even though the risks seem enormous and
Risk Assessment and Implementation of Industry 4.0
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
dreadful at the first sight, with the continuous evolution of
knowledge for handling I4.0 risks, the day is not far, when
companies will be aggressively thinking of adopting I4.0
[11].
Economic Risks
Ecological Risks
Social Risks
Legal and Political Risks
Technical and IT Risks
High level of uncertainty
Dynamic demand
Huge capital investment
Lack of the data handling capability
Human skills
Cyber security
Poor quality of the data available
Poor research and development
113
because of its very specific area applications like Tradebased Money Laundering behavior detection which is building trust in using the I4.0 technology-based solutions [15].
Mostly the researchers have explored five research heads,
i.e., I4.0 Concept, Technologies used in I4.0, I4.0 Sustainability, Human-Machine communication, and MachineMachine communication [16, 17], leaving huge scope for the
current study, focused on, ‘Industry 4.0 Risks Framework’. It
is interesting to note that the most adopted technologies,
those changing the industrial ways of doing business, are IoT
and Information and Communication Technology (ICT),
because of their awareness and ease to use [18]. The paper
will serve the purpose of simplifying, streamlining, and formulating the ways and means not only to create awareness
and understanding but also to mitigate the I4.0 risks by applying the findings so that I4.0 adoption becomes a reality
soon.
Political will
Unclear economic conditions
3.2. Industry 4.0 and MCDM in Indian Scenario
Technological adoption
Low awareness of vision of I4.0 implications
Poor digital awareness
Lack of global standards
Lack of management support and vision
Reluctance to change
Technological compatibility
Lack of supporting infrastructure
Fig. (1). Industry 4.0 risks iceberg model. (A higher resolution /
colour version of this figure is available in the electronic copy of
the article).
3. LITERATURE REVIEW
This literature review is an attempt to understand and
critically synthesize, assess, analyze, evaluate, and apply the
existing knowledge in the present study. The literature review is arranged in four sections. The first section highlights
the perspective of I4.0 as a revolution in the manufacturing
and production sector. While the second section is focused
on the current status of I4.0 adoption in India. The third section is dedicated to exploring the risks framework for I4.0
implementation in the existing industrial setup and the fourth
section is referring to the extraction of the research gap.
3.1. Industry 4.0
The majority of the systematic and scientific research
happening in Engineering and Technology in recent years is
paving the path for future trends and developments [12]. In
that case, the use of emerging technologies applications like
big data, cloud computing, and IoT in the manufacturing
process are the real game-changer [13]. As a result, global
businesses today are evolving at the fastest pace than ever,
willingly or forcefully. The companies are moving from traditional practices to smart practices, to meet the numerous
challenges like volatile demand, frequently changing business model, fierce competition and economic ups and downs,
etc. [14]. The vision of I4.0 states that the technology-driven
transformation should benefit people's lives than just establishing policies, strategies, frameworks, structures, and layouts. Machine learning technology is also becoming popular,
The majority of the Indian industries perceive that the
adoption of I4.0 vision may not be of any worth in the absence of clear, standards, and frameworks. Even though the
missing link is evident, few large-scale visionary industries
have made up their mind to embark on smart manufacturing
practices, fully or partially as they perceive the impact otherwise. Few first movers can be seen in various sectors like
Automobile, Pharmaceuticals, Information Technology, etc.
who are leading the change [9] giving a big boost to specific
sectors in India. In this regards, a study conducted by Kamble et al. [16] which is based on two methods namely Interpretive Structural Modeling (ISM) and Fuzzy MICMAC
(Matriced’ Impacts Croise´s Multiplication Applique´e a´ un
Classement) reflects true situation prevailing in India regarding the implementation of I4.0 [17]. It seems the I4.0 vision
of India, ‘Make in India’ is picking up.
The extensive survey conducted by Luthra et al. in the
Indian manufacturing supply chain sector, during 2018, to
study the main challenges for the adoption of I4.0 revealed
that there are 18 main challenges. In addition to this, the researcher has explored the crucial challenges as it showed the
relationship with the risks being analyzed. The developed,
framework is presented in Fig. (8) section 8.
Another exhaustive study conducted by Vaidya and others in 2018 [20] reported that I4.0 implementation is based
on the degree of adoption of nine technologies like Business
Data Analytics, System Integration, Simulation, Cybersecurity, IIoT, CPS, Cloud computing, Additive manufacturing,
and AR. Indian industries must adopt Industry 4.0 technologies [21] specifically cloud computing practices as their top
priority to further harness the growth of Industry 4.0 vision
[22]. This substantiated significantly that the Indian manufacturing industries are seriously aspiring to surmount challenges on the way to conquer the I4.0 vision at the earliest by
thoughtfully devising robust methods and methodologies
[17]. Gawankar [23] has also emphasized the large scale
investment in BDA to make the Indian retail sector supply
chain more agile and resilient.
114
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Gadekar et al.
Table 1. Issues addressed and not addressed by earlier researchers.
Sr
Title
Refs.
Issues Addressed
Intervention used
Issues not Addressed
• Assessed following Risks
•
agement
A fuzzy based risk evaluation
1
model for industry 4.0 transition process.
Manufacturing process man-
[6]
•
Operations methods and tools,
•
Machines and manufacturing
Technologies,
•
Human sources
•
Machine environments
• Interval Type-2
Fuzzy AHP
• Hesitant Fuzzy
TOPSIS, MCDM
sidered
izational adoption of industry
4.0 based on multi-criteria
decision techniques
• Sensitivity analysis not considered.
• Risk as an alternative not con-
An assessment model for organ2
• Sustainable Risk assessment
• Criteria selected are limited
• Assessed preparedness/ maturity to
[24]
adopt I4.0 through the self-assessment
analytical model.
• AHP
• TOPSIS
• FAHP and FTOPSIS method to
deal with uncertainty and ambiguity in the decision-making
process, not considered.
• Eco-friendly business practices
and awareness assessment is not
Identifying key success factors
3
of sustainability in supply chain
management for industry 4.0
using DEMATEL method
[30]
4
• Developed a sustainable risk framework
[2]
• Provided guidelines about the risk that
----------------
may occur during the transition stage
towards I4.0.
• Surveyed current researches on financial
Machine learning methods for
systemic risk analysis in financial sectors
[25]
systemic risk assessment and measurement with machine learning methods.
----------------
6
review identifying the current
trends and future perspectives
[16]
economic sustainability and environ-
dependence power of barriers to
adopting industry 4.0 in the
Indian manufacturing industry
----------------
mental protection as an important research aspect of I4.0
• Scope to apply MCDM tech[17]
• Barriers to I4.0 Implementation identified.
• ISM
• Fuzzy MICMAC
operations management perspective with fuzzy DEMATEL
(Table 1) Contd….
niques not considered.
• Comparative study of different
manufacturing organizations not
considered.
• This study is limited to the hightech sector. It could be extended
fourth industrial revolution and
proposing a road map from an
considered.
• Validation of the framework to
achieve a sustainable outcome is
missing.
Analyzing workforce 4.0 in the
8
Big data analysis, Policies related to data science.
the proposed framework is not
Analysis of the driving and
7
ed to a Data-driven approach,
• An empirical study to validate
work.
• Identified process safety and automaton,
the risks and their evaluation.
• Investigating interdependencies
of risks using MCDM methods.
• Further research can be extend-
• Developed a sustainable I4.0 frameSustainable industry 4.0 framework: A systematic literature
considered.
• FARE, CRITIC method, the
• Quantitative information about
in the context of I4.0.
established manufacturers
5
contributors to the success of I4.0
• DEMATEL
Best worst method also could be
used.
Development of a risk framework for industry 4.0 in the
context of sustainability for
• IoT environment are identified as key
[11]
• Developed a structural model to identify • Fuzzy
the criteria for workforce selection in the
DEMATEL
I4.0 environment.
to the other sectors.
• CRITIC method, the Best worst
method could be tried.
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Risk Assessment and Implementation of Industry 4.0
Sr
Title
Refs.
Issues Addressed
Intervention used
115
Issues not Addressed
• Advance digital technologies in manufacturing companies are investigated and
Evaluation of advanced digital
9
technologies in manufacturing
companies: Hybrid fuzzy
MCDM approach
[26]
their contribution in context to I4.0 is
• FAHP
evaluated.
• PROMETHEE
• Other MCDM like EDAS,
MABAC could be tried.
• The use of digital technologies
• According to this study digitalization in
in customer follow-up and inter-
supply chain management contribute the
action as an alternative could be
considered.
most to the production principles.
• Key drivers of I4.0 i.e., Cybersecurity, risk management and
Industry 4.0 through organiza10
11
tional interoperability perspective: a multicriteria decision
analysis
Aspects of risk management
implementation for industry 4.0
• The case discussed the automotive sup[27]
ply chain.
• DEMATEL
• PROMETHEE
sidered.
• FARE, CRITIC method, the
Best worst method also could be
tried.
[28]
• Proposed design of the framework for
risk management in I4.0 implementation
----------------
• Six clustering algorithms applied to
Evaluation of clustering algo-
• Three MCDM methods rank the cluster[29]
using MCDM methods
• TOPSIS
ing algorithms based on eleven perfor-
• DEA
mance criteria
• VIKOR
• Rankings obtained by three methods are
• No risk evaluation
• No assessment of the framework
• How to deal with conflicting
three financial risk data sets.
12 rithms for financial risk analysis
network connectivity not con-
rankings obtained by MCDM
methods to find a compromise
solution need to explore.
• The large size of datasets can be
considered for precise findings.
not the same.
Hence, a strong framework is a must to support the sustainable implementation of I4.0 [30]. Most of the researchers
have been found researching the basic needs of the I4.0
adoption in general. Very rarely the research has addressed
the specific and precise needs of the industries aspiring to
adopt I4.0. Table 1 has summarized broadly the issues addressed and not addressed to date by the researchers in the
present context. This table has been a key reference to lay
down the foundation of this specific research.
3.3. Risks for Industry 4.0 Implementation
Risks identification, assessment, and evaluation are key
to any successful technology transfer and change management. If the companies are exposed to this knowledge, the
adoption of I4.0 will be faster and smoother. This will also
help companies to reorganize, restructure, and reorient policies to achieve the new benchmarks without any turbulence
[6]. The risks in the transition phase could be Economic,
Ecological, Social, Technical and IT, and Political and Legal
risks [2].
3.4. Research Gap and Highlights
After conducting the detailed literature review, researchers concluded that there are very limited publications highlighting the risks related issues being faced by companies
while embarking on the I4.0 vision, thereby leaving the
scope for new research that will come up with an analytical
model/framework to help decision-makers, who in the absence of academic research were afraid of stepping forward
to form a part of the digital revolution.
This research is unique as, the researcher has mitigated
the earlier research limitations by involving the experts, researchers, and policymakers, in order to provide the most
appropriate, feasible, optimal, and viable solution. Expert
profile details may be referred to in section 5, Table 3. The
criteria chosen by Colak and others [6] were further extended
by adding two more crucial criteria to make the study robust
and insightful, i.e., a) Flexibility and b) cost, details explained in section 3.6 in context with the Indian manufacturing sector. Birkel and others in 2019 [2] provided a model of
the risks for the implementation of I4.0 but did not evaluate
the priority of risks in order to help the policymakers. The
present research focuses on the crucial aspects of ranking the
risks to help the academician, industry practitioners, I4.0
consultants, policymakers, researchers, etc. for supporting
their decision-making capability to handle I4.0 related risks
successfully in their business. This research is novel as it
precisely focuses on the internal and external risks management approach for I4.0 implementation.
In this paper, the researcher has used the PEEST model
for selecting the critical and pertinent risks as an alternative,
details explained in Section 4.1. These alternatives further
were ranked on the basis of selected criteria, explained in
section 3.6, using the Hybrid Fuzzy method (FAHP and
FTOPSIS). This approach of measuring and ranking the risks
according to its critical nature and importance will help to
optimize the resources to avert the negative impact of risks,
effectively and efficiently. The higher the impact, the better
will be the ranking in the list, indicating that the best risk
should be urgently attended. Sensitivity analysis is carried
116
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
out further to check the robustness of the findings. Details
are explained in section 7.
3.5. Risks Categories Considered for the Study
3.5.1. Economic Risks
Economic risks comprise the fear of losing the investment if the idea does not yield the desired outcomes in terms
of the set objectives [8]. Industries always are not very keen
on investing in marketing research to know the market well.
This has been one of the drawbacks of the traditional business models, which made our businesses susceptible to failures [2]. Assessment of economic risks will largely immunize the system of debacles and also ensure the effective usage of company resources like data management, IT infrastructure building, Artificial Intelligence (AI), Networking,
CPS, Robotics, and Cloud computing [31]. This risk could
be one of the most decisive and deceptive if not methodologically accounted. Investment-related to expansion, upgradation of the existing resources always attracts a huge
cost [32] which may lead to an irreparable dent on the balance sheet of the company if not addressed within limits [7].
Gadekar et al.
As a result, the old machines which could not be upgraded to
the level of I4.0 standards may contribute to the waste management problems [33]. Radiation emitted by the mobile
towers, wireless networks contribute to the adverse effects
on humans, animals and bird’s health. Can we afford further
harm to our treasured environment, while we are already
experiencing the worst effects of the massive industrialization in the past (mainly 1st, 2nd, and 3rd industrial revolution)?
We need a much-balanced approach [2].
3.5.5. Technical and Information Technology (IT) Risks
This is one of the biggest challenges most of the SME
and even large-scale industries are afraid of [32]. Companies
will have to change from the traditional success model to a
more complex and challenging business model, which will
be all-time exposed to threats like, Cyber Attack, Cyber Security, Data theft, Data loss, Data Privacy, Technology disruption, etc. [32]. Hence, this risk is also one of the most
important to be evaluated [2, 13]. Fig. (2) is the logical
presentation of the risks framework for a sustainable change
of I4.0. The sub risks shown in the diagram give the microlevel understanding of the possible impact on the last entity
in the chain.
3.5.2. Social Risks
Most of the governments globally are struggling when it
comes to the rate of unemployment and social balancing
among the nationals [32]. The change is always perceived to
harm many and benefit a few unless people are made aware
of the consequences in an organized manner [8]. As the I4.0
phenomenon is spreading all over the world, the fear of losing the job is rising. This is not just limited to the low skilled
jobs, as they may be easily automated, but the repetitive jobs
like planning, decision making also may be handed over to
AR/VR, Cloud computing, IoT, CPS, and Machine Learning
(ML) systems, to make them more productive, effective and
compliant to I4.0 vision [11, 31]. This has already built-up
the stress in the companies as well as the societies, leading to
the uncertainty on the adoption of the I4.0 vision [2, 7].
3.6. Criteria Considered for the Study
1)
Information Security (C1): It consists of privacy and
limited access to information, which is an important
concern. It is expected that the confidential information
access should be given to only authorized and concerned employees based on the need, use, and value of
the information.
2)
Integrity (C2): It means maintaining significant reliability and validity of the resources and transaction practices when it comes to the exchange of data and other valuable resources that may impact the functioning of the
business.
3)
Availability (C3): It means the required information,
resources should be available for the authorized person
as and when needed in ready to use manner by putting
in minimum efforts.
4)
Quality (C4): It can be defined as the ability to meet
expectations of the I4.0 compliant system. The efficient
quality of information system, connectivity, collaboration, and integration among the peripherals of I4.0 is
key to the successful implementation of the I4.0 system.
5)
Performance (C5): It is an important criterion that will
help to measure and define the gaps between present
and desired systems when the I4.0 is implemented. It is
the function of the productivity of people and other resources in an organization. It is also the ability of the
existing infrastructure to become self-organized and
I4.0 compliant.
6)
Design (C6): The design of the product, workplace layout, policies, strategies, and organizational hierarchy is
also a crucial factor for handling risks related to I4.0
implementation in companies [34].
3.5.3. Legal and Political Risks
Political support to the overall vision and mission of I4.0
will positively influence the propagation of the idea faster
and wider. This will also help to develop favorable infrastructure to make the local companies more competitive in
local as well as the global environment [31].
Before we take a plunge into the smart world of manufacturing, we must deliberate on the creation of guidelines
for data protection, cybersecurity, work environment, and
creation of a legal framework to resolve any issues arising as
a part of smart business. Otherwise, this will give rise to major concerns and risks issues in the coming times [2].
3.5.4. Ecological Risks
It is very evident from the existing knowledge, that the
data centers will need an enormous amount of energy to remain alive and functional all the time [8]. This means the
rate of energy production must be increased. At the same
time, companies will have to sideline the old technology and
machines, which might be inefficient and unproductive [7].
Risk Assessment and Implementation of Industry 4.0
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Economic
Risks
A1
117
• Massive Capital Investment
• Volume and time of investment
• Shift in ways of doing business
• Extreme competition from all the
players in the market
• External Support
• Fear of losing the job
• Drastic changes in organizational
structure and leadership reliability.
• Internal resistance to change
• Up skilling of existing employees as
a part of retention policy.
• Business related stress and
uncertainty
• Lack of skilled manpower
Social
Risks
A2
Legal and
Political
Risks
A3
Ecological
Risks
A4
• Policies related to infrastructural
support in terms of energy, cyber
security,networking etc.
• Laws related to enabling
technologies of I4.0 and other
resources.
• Further deterioration in the quality of
environment
• Disposal of electronics waste
• Increase in pollution
• Technology Transfer
• Setting the standards
• Susceptible to cyber attack
• Data Protection
• Data Privacy
• Third party data handling
• Third party collaboration
Technical
and IT
Risks
A5
Fig. (2). Mind map for risks framework of sustainable I4.0. (A higher resolution / colour version of this figure is available in the electronic
copy of the article).
7)
8)
Infrastructure (C7): It signifies the electronic hardware
and software which give rise to the seamless integration
between machine-human and machine-machine networks all over the company. The strength of the infrastructure could be defined by speed, reliability, realtime data collection, decision making, and capability to
provide efficient and effective solutions to the endusers.
Interoperability (C8): It is the measure of the technical
compliance and feasibility of the devices when they are
connected to other devices. The devices which perform
at the same level of efficiency, when connected with
different devices are said to be interoperable. Such de-
vices make a positive impact on overall system performance.
9)
Flexibility (C9): I4.0 offers more attention to discovering the methods for improving the flexibility of production processes and trying to convert a fixed production
line system into a self-configurated assembly line [35,
36, 37].
10) Cost (C10): It comprises the capital investment associated with hardware like sensors, networking, servers interface, and the software needed to set up the communication system. The software could be application
software, operating system, networking software and
118
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
•
•
•
•
Degree of IT security
Data security
Data privacy
Verification, processing and
validation of data collected from
various resources
Gadekar et al.
• Accuracy and completeness of
information.
• Standerdised processing
methods
• Data integrity
• Trustworthiness of data or
resources
• Resources for I4.0 complient
system
• Information to the concerned
person as and when required.
• Network connectivity
•
•
•
•
Information
Security
Integrity
Availability
Quality
• Integration and connectivity
• Machine and IoT devices
• CPS,BDA,AI,ML etc.
Functioning.
• Product
• Process
• Business model complient to
I4.0
•
•
•
•
• Communication mechanism
• Interconnectivity between the
system
• Ability of systems to connect
and work in a coordinated and
self-sufficient manner.
• Integration of the classical
systems with the modern
models.
Performance
Design
Infrastructure
• Response of process ,machine
etc. to the envirnment change of
the system
• Response to the disturbances
and failures
• Capabity to accommodate last
minut changes,
• Responsiveness to changing
customer demand
•
•
•
•
•
•
Flexibility
Cost
Equipment, Machines
Connectivity and networking
Self organising
IoT and CPS devices.
Data acquisition
Data processing
Infrastructure
Networking and connectivity
Interoperability
Operation
Transition
Maintinance
Technical Support
Training
Development of Infrastructure
Fig. (3). Criteria and sub criteria.
other special software to run the required application
[20, 36-39].
The company must invest to design and develop a sound
capacity to make the machine to machine and machine to
human interaction possible [40]. Fig. (3) shows the ten criteria and sub-criteria used in the research to evaluate the five
risks identified as crucial to Industry 4.0 adoption.
4. METHODOLOGY
This research is carried out in a company, that is very
keen to imbibe I4.0 practices and trends from the Automobile sector in India. The details regarding the company and
the focus group profile are explained for more clarity in section 5. The researcher’s objective is to define, synthesize,
assess, evaluate, and analyze the risks the company may be
prone to, on the journey to become I4.0 compliant. The research also assessed the risks handling capacity and capability of the company. Fig. (4) is the systematic representation
of the steps followed in the research.
4.1. PEEST Model for Industry 4.0 Risks Identification
Every industrial success is prone to external and internal
environmental conditions. PEEST model framework has
been frequently used to analyze macro-environment elements, i.e., Political, Economic, Ecological, Social, and
Technical. Past research and experience have demonstrated
that success and failure depend on the ability of a company
to sense the opportunity or threats before they make an impact [41]. The PEEST model is a powerful tool being used
extensively by companies to keep track of macro-environment forces [42].
Fig. (5) shows the elements of the PEEST model in the
current context. This model has a dual advantage, firstly it
helps to understand the external environment, and secondly,
allows analyzing the ability of the organization to handle the
risks at its best. This model application is crucial in reaching
decisions like business development, diversification, warehouse selection, and implementation of long-term strategies
in business [10]. The accuracy of the PEEST model is found
to increase by involving experts and working professionals
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Risk Assessment and Implementation of Industry 4.0
119
Preparatory Phase
Knowledge
Hub of Industry 4.0
D1
D2
D3
Literature Review for Risks Assessment for Industry 4.0 Implementation
Finalization of Alternatives
Finalization of Criteria
Framework Preparation for Decision Hirarchy
N
Approve Decision Hierarchy
Y
Fuzzy AHP Phase
Construction of Pairwise Comparison Matrix
Y using FAHP (Step 1)
Computation of Criteria Weights Using Geometric Mean Method (Step 2 & 3)
Check Consistency Ratio (step 4 & 5)
Consistency Ration < 0.1 (Step 5)
N
Y
Assigning Ratio to the Alternatives w.r.t. criteria (Step 6)
Fuzzy TOPSIS Phase
Construct Aggregate Fuzzy Decision Matrix (Step 7)
Construct Normalized Fuzzy Decision Matrix (Step 8)
Construct Weighted Normalized Fuzzy Decision Matrix (Step 9)
Calculate the distance of each alternative from FPIS and FNIS (Step 10 & 11)
Compute the closeness coefficient ratio (Step 12)
Analysis
Phase
Assigning ranking to the alternatives (Step 13)
Sensitivity Analysis and its Interpretation
Discussion and Conclusion
Fig. (4). Research methodology framework.
in the field, which has been well adapted in the current
study.
4.2. Fuzzy Multi-Criteria Decision-Making Approach
(MCDM)
The real-world decision-making problems are very complex in nature. Hence, MCDM methods are best suited to
address these problems where situations are subjected to
some conflicting, ambiguous and confusing objectives which
are not precisely known to the decision-maker. As a result,
the MCDM methods are the first choice of researchers and
scientists to solve such complex and complicated problems.
The MCDM became smarter by the fusion with Fuzzy set
theory to form a Fuzzy Multi-Criterion Decision-Making
Method (FMCDM). The specialty of the method is the ability to deal with the situation where insufficient and uncertain
knowledge and information is inevitable.
4.3. Fuzzy MCDM Problem Formulation
Decision matrix for the MCDM problem with m alternatives {A1, A2 ,… ,Am} which is assessed by applying n criteria {C1, C2… Cn} to those alternatives can be represented as
follows:
120
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Gadekar et al.
Legal and
Political Risks
Technical and IT
Risks
Economical
Risks
I4.0 Implementation
Risks
Ecological
Risks
Social Risks
Fig. (5). PEEST Model for Industry 4. 0 Implementation Risks. (A higher resolution / colour version of this figure is available in the electronic copy of the article).
Table 2. Linguistic scales and corresponding triangular fuzzy number.
The Fuzzy Scale of Relative Importance for Pairwise Comparison Matrix for Criteria
Linguistic Scale for Ratings to Alternative and Criteria
Term
Crisp Number
Fuzzy number
Term
Equivalent Fuzzy Number
Equal (E)
1
1,1,1
Very Low (VL)
1,1,3
Moderate(M)
3
2,3,4
Low (L)
1,3,5
Strong(S)
5
4,5,6
Average(A)
3,5,7
Very Strong (VS)
7
6,7,8
High (H)
5,7,9
Extremely Strong (ES)
9
9,9,9
Very High (VH)
7,9,9
Intermediate values (IV)
2,4,6,8
(1,2,3),(3,4,5),(5,6,7), (7,8,9)
th
(1)
Where the value of the i alternative with respect to j
criterion is denoted by xij .
4.4.1. The Steps Followed for FAHP by Geometric Mean
[48-50]
Step 1: Construct a fuzzy pairwise comparison matrix.
th
A fuzzy scale of relative importance for the pairwise
comparison matrix along with the equivalent fuzzy numbers
to the crisp values is expressed in Table 2.
4.4. Fuzzy AHP
Satty [43] introduced the first AHP approach which is
mostly used to determine criteria weights [44, 45]. In reallife situations, decision-makers express their judgment in
linguistic form. AHP uses a crisp number, which is insufficient and imperfect due to uncertainty and vagueness of the
judgment of decision-makers. To overcome this drawback of
AHP, fuzzy logic is introduced into a pairwise comparison of
the AHP process proposed by Buckley [46] and Nădăban
[47].
Using Table 2, the linguistic term assigned by each decision-maker to the pairwise comparison between criteria is
converted into equivalent triangular fuzzy numbers
Step 2: The geometric mean of fuzzy comparison values
of each criterion is calculated [48-50].
Step 3: The fuzzy weight of each criterion is calculated.
Step 4: Obtained fuzzy weights are fuzzy numbers that
are to be defuzzified into crisp numbers to check consistency
ratio through various defuzzification methods i.e. Linguistic
method, Centroid Method (COA Method), Graded Mean
Integration Representation (GMIR) method, Median or
Signed-distance or Area of compensation method. Out of all
of them, the Center of Area method is mostly the preferred
method.
Step 5: Consistency check should be done for the preference of weights of the criteria allotted by the decisionmakers to prove the validation of weight allotment. The con-
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Risk Assessment and Implementation of Industry 4.0
sistency ratio (CR) should be less than 0.1 [2, 19]. Once the
consistency check is performed on the weights obtained by
FAHP, and it is less than 0.1, then the weights obtained are
considered valid.
4.5. Fuzzy TOPSIS
Weights calculated by the FAHP method are used as input for further calculations by FTOPSIS Method [52]. TOPSIS was first developed by C.L. Hwang [53]. It is based on
the principle to select the alternative which has the shortest
distance from Positive Ideal Solution (PIS) and farthest distance from Negative Ideal Solution. (NIS), i.e., maximizes
the benefit criteria and minimizes cost criteria [55].
121
Chen [11] proposed a vertex method to calculate the distance between two triangular FNs.
If are two triangular
fuzzy numbers, then the distance between them is calculated
by Eqs. 8 and 9.
Let,
,
(8)
(9)
and
be the distance from each alternative Ai
Where
to the FPIS, and to the FNIS, respectively.
Step 12: For each alternative Ai,, CCi computes the
Closeness Coefficient as follows:
FTOPSIS technique uses linguistic variables to evaluate
the ratings of criteria and alternatives which is very convenient for decision-makers to express their judgments. Using
Fuzzy TOPSIS, Fuzzy Positive Ideal Solution (FPIS) and
Fuzzy Negative Ideal Solution (FNIS) are calculated as per
the procedure described below.
Step 13: Ranking of alternatives done with the highest
closeness coefficient as the best alternative.
4.5.1. The Procedure of FTOPSIS
5. CASE STUDY
Step 6: Ratings to the alternatives and criteria and corresponding fuzzy scale are assigned as presented in Table 2.
The company chosen for the study to test the developed
analytical hybrid fuzzy framework (Fig. 4) is one of the
highly successful, large scale companies in Maharashtra,
India. The company was established in the pre-independence
era and has gone through many ups and downs. It is one of
the largest automobile manufacturing companies in India.
The company decided to embrace the I4.0 vision to sustain
itself in a globally competitive market. The volatile demand
in the dynamic market conditions has made the business
highly unpredictable and sensitive to global dynamics.
Hence, the company decided to adopt vision I4.0 to meet the
challenges of the new era by embarking on digitalization and
smart practices in manufacturing. Automobile assembly,
Engine shop, Transmission shop, Hydraulic shop, and Machine shop are the important modules of the company. All
these modules are simultaneously working on making the
company I4.0 compliant by implementing the emerging new
technologies of I4.0 i.e. IIoT, CPS, Cloud computing, AI,
ML, AR/VR, 3D printing, Robotics, Digitalization of various
manufacturing activities, data acquisition, and business analytics.
Step 7: Calculate the aggregated fuzzy ratings for alternatives and the aggregated fuzzy weights for criteria.
The aggregated fuzzy rating of ith alternative w.r.t. the jth criterion is calculated by following
formulae.
(2)
Step 8: Normalized fuzzy decision matrix calculated by
following formulae. The normalized fuzzy decision matrix is
.
Where,
a
(3)
(4)
Step 9: The weighted normalized fuzzy decision matrix
is calculated by the following formulae
, where
(5)
Step 10: The Fuzzy Positive Ideal Solution (FPIS) and
Fuzzy Negative Ideal Solution (FNIS) is calculated as follows:
FPIS = where
(6)
FNIS= where
(7)
Step 11: The distance from each alternative to the FPIS
and to the FNIS is computed using the formulae.
(10)
The selection of the criteria for the evaluation of specific
alternatives was an outcome of the rigorous and laborious
process which included expert interviews (Delphi Method)
and discussions along with industry visits and other means of
data collection. The researcher prepared a list of 33 experts,
who had the required skillset and qualification to be on the
expert’s panel. Based on the level of expertise, the projects
and consultancies delivered by these experts, the three most
suitable were selected on the panel of experts for the discussion and other valuable inputs. The expert’s contribution and
involvement in fostering the vision of I4.0, as a changemaker in respective areas of expertise have been key to guide
the research. These experts have studied I4.0 in-depth and
accumulated a wealth of Industrial experiences and Managerial expertise and are very keen on implementing I4.0 in their
respective departments of the company. The expert’s competency mapping is detailed in Table 3.
122
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Gadekar et al.
Table 3. Competency mapping of experts.
Traits
D1
D2
D3
Gender
Male
Female
Male
Age
43
41
39
Education
Doctorate
Doctorate
Master’s Degree
Experience
22
17
16
Industry
Manufacturing
Production
Manufacturing
Yes
Yes
Yes
Researcher
-----
Yes
Yes
Consultant
Yes
Yes
Yes
Global Exposure
Yes
Yes
Yes
Knowledge
IIoT, BDA, CPS, AI, ML, Cloud Computing, Cybersecurity and AR/VR etc.
Even though the organization is keen to go for the implementation of I4.0, the uncertainties and lack of risk evaluation are found to be a big hurdle. As a result, the company’s
leadership is scared of the risks issues related to the sustainable implementation of I4.0. This has attracted the attention
of researchers; hence the prime focus of the research is defined as synthesize, assess, evaluate, and analyze the risks
related to I4.0 implementation.
Many researchers have used different MCDM techniques
to analyze different facets of I4.0. However, very limited
research has addressed the fuzzy aspect of decision-making
for risk ranking of I4.0. Hence, very thoughtfully, the choice
is made to select FMCDM, i.e., a hybrid approach, FAHP,
and FTOPSIS method for further analysis, to address the
fuzzy nature of the real-life situation.
This method is already discussed and explained in detail
in section 4. The fuzzy aspect of investigation and analysis
in the current situation makes the research different. Here,
the focus is on risk assessment and evaluation while embracing the vision of I4.0. Fig. (6) shows the hierarchy of decision making with criteria and alternatives [43].
To begin with, 35 criteria and 14 alternatives were listed
as prospective parameters to cover the I4.0 risks ambit well.
The final structure evolved after the series of discussions and
deliberations between experts and extensive literature review, where five alternatives and ten criteria (Design, Infrastructure and Cost referred to as non-beneficial criteria and
rest are referred to as beneficial criteria, details in section
3.6) were accepted for the study. Criteria weights are calcu-
lated by FAHP with the Geometric mean approach, which is
explained in section 4.4.1., is used for assigning the weights
to the selected criteria. Further pairwise comparison matrix
between the criteria is constructed by another round of exhaustive discussion with the experts. They evaluated the criteria based on the scale provided in Table 2. After this, the
fuzzified pairwise comparison matrix for criteria is formulated, by decision-makers. The outcome of the Fuzzy AHP process through Geometric Mean is described in steps 2 and 3
of section 4.4.1. Weights of each criterion are defuzzified
using step 4 in a crisp number and then consistency check is
carried out as mentioned in step 5, shown in Table 4. If any
inconsistent evaluation was observed, then the data was reverted to the experts until the consistency ratio obtained was
less than 0.1.
The consistency ratio obtained was 0.02 which was much
lesser than 0.1. It gave the researcher confidence that there
was no inconsistency in the judgment of the experts in a
pairwise comparison of criteria. After obtaining the criteria
weights, the FTOPSIS method was initiated. Experts were
consulted again to score the alternatives according to the
criteria. Linguistic expressions for alternative and criteria
ratings were converted into triangular fuzzy numbers according to the scale presented in Table 2. This was followed by
three decision-makers evaluations, aggregated as described
in steps 6 and 7 shown in Table 5. Followed by this normalized and weighted normalized fuzzy decision matrix was
constructed using steps 8 and 9 as shown in Table 6.
The Fuzzy Positive Ideal Solution (FPIS), i.e., select the
alternative having the shortest distance from Fuzzy Positive
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Risk Assessment and Implementation of Industry 4.0
123
Information Security
Risks Evaluation of Industry 4.0 Implementation While Transition to Industry 4.0
Integrity
Availability
Economic Risks
Quality
Social Risks
Performance
Design
Legal and Political
Risks
Infrastructure
Ecological Risks
Interoperability
Technical and IT
Risks
Flexibility
Cost
Criteria
Alternatives
Fig. (6). Hierarchy of decision making with criteria and alternative. (A higher resolution / colour version of this figure is available in the electronic copy of the article).
Table 4. Weights of criteria in triangular fuzzy numbers obtained by FAHP by geometric mean method.
List of Criteria
Fuzzy Weights
List of Criteria
Fuzzy Weights
Information Security C1
(0.309,0.298,0.270)
Design C6
(0.051,0.047,0.045)
Integrity C2
(0.120,0.132,0.135)
Infrastructure C7
(0.0372,0.037,0.038)
Availability C3
(0.106,0.115,0.124)
Interoperability C8
(0.049,0.047,0.048
Quality C4
(0.113,0.113,0.121)
Flexibility C9
(0.021,0.019,0.025)
Performance C5
(0.099,0.099,0.105)
Cost C10
(0.093,0.093,0.088)
Table 5. Decision matrix by experts in linguistic term for rating criteria and alternatives.
Criteria
Alternatives
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
D1
H
H
L
VH
L
H
A
L
A
VH
D2
H
A
L
A
A
VH
L
VH
H
H
D3
A
H
A
L
A
H
A
L
A
H
D1
H
A
VL
VH
A
A
L
H
H
H
Experts
A1
A2
(Table 5) Contd….
124
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Gadekar et al.
Criteria
Alternatives
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
D2
A
L
H
H
L
A
VL
VH
H
A
D3
A
A
L
H
H
A
L
H
L
L
D1
L
L
H
L
A
A
A
A
H
H
D2
A
A
A
H
A
L
H
L
L
VH
D3
H
A
A
A
H
A
A
A
H
A
D1
L
A
H
A
A
VH
VL
L
L
H
D2
VH
VL
L
L
VH
A
H
A
A
A
D3
L
VH
VL
L
L
VH
VL
L
L
H
D1
A
VH
H
A
H
A
VH
VH
A
H
D2
A
VH
VH
A
A
VH
H
A
A
VH
D3
L
A
VH
VH
A
A
VH
VH
A
VH
Experts
A3
A4
A5
Table 6.
Normalized fuzzy decision matrix.
Fuzzy wts. Wj 0.31
0.3
0.27 0.12 0.13 0.14 0.11 0.12 0.13 0.1
0.1 0.11 ----- 0.04 0.04 0.038 0.05 0.05 0.05 0.02 0.02 0.03 0.09 0.09 0.08
Alternatives
C1
C2
C3
C4
C7
/Criteria
C8
C9
C10
-----
A1
0.52 0.72
A2
0.44 0.68 0.92 0.28 0.52 0.76 0.26 0.41 0.63 0.74
A3
0.36
0.6
0.84 0.28 0.52 0.76 0.41 0.63 0.85 0.39 0.65 0.91 ----- 0.27 0.18 0.13 0.28 0.52 0.76 0.41 0.63 0.85 0.6 0.43 0.36
A4
0.36
0.6
0.76 0.36 0.52 0.76 0.26 0.41 0.63 0.22 0.48 0.74 ----- 0.43 0.33 0.2
A5
0.28 0.52 0.76 0.68 0.92
Table 7.
1
0.6 0.84
1
1
0.19 0.41 0.63 0.48 0.74 0.91 ----- 0.43 0.23 0.158 0.28 0.52 0.76 0.41 0.63 0.85 0.53 0.39 0.33
0.7 0.93
1
1
0.57 0.83
1.17 -----
1
1
0.33 0.2
0.6 0.84 1.08 0.56 0.78
1
1
0.6 0.42
0.2 0.44 0.68 0.19 0.41 0.63 0.69 0.47 0.36
----- 0.16 0.12 0.111 0.68 0.92
1
0.33 0.56 0.78 0.47 0.36 0.33
Ranking of alternatives.
Alternatives
Rank
Economic Risks A1,
0.591010643
2
Social Risks A2,
0.42116946
3
Legal and Political Risks A3
0.395479765
4
Ecological Risks A4,
0.26392737
5
Technical and IT Risks A5
0.694600356
1
Ideal Solution and Fuzzy Negative Ideal Solution (FNIS),
i.e., select the alternative having farthest distance from Fuzzy
Negative Ideal Solution were found using step10 and 11. The
closeness Coefficient ratio (CCi) for each alternative was
calculated using step 12 and shown in Table 7.
6. RESULTS AND FINDINGS
The systematic calculations carried out as above clearly
state that the five risks (alternatives) identified as the
potential threat to the way towards the adoption of I4.0 by
SMEs and Large Scale industries have complete relevance
and context to the ground reality.
Risk Assessment and Implementation of Industry 4.0
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
The research assessed the five risks namely Economic
Risks, Social Risks, Legal and Political Risks, Ecological
Risks, and Technical and IT Risks on ten crucial criteria
such as Information Security, Integrity, Availability, Quality,
Performance, Design, Infrastructure, Interoperability, Flexibility and Cost.
The expert's engagement was crucial as the primary data
was collected from all of them which later was fed to the
MCDM techniques to test the model. The extensive literature
review was a big help to customize and orient the problem to
accommodate the key aspects of risk management in the
study. The Technical and IT-related risks made it to the top
of the list, seeking the highest attention of the researcher,
experts and company personnel, followed by Economic
risks. IT infrastructure and technology are considered to be
the driver of the I4.0 transition phase. At the same time,
Economic risk should not be neglected as it impacts overall
business budgeting and resource management. The company
must be calculative in achieving the right trade-offs and
investment policies to maintain high employee productivity
and business operations efficiency. The sensitivity analysis is
carried out as the last step to check the robustness of the
result obtained. The findings of the sensitivity analysis are
elaborated in Fig. (7).
7. SENSITIVITY ANALYSIS
Sensitivity analysis is an instrument to check the
robustness and sustainable nature of the derived solution in a
dynamic environment. The quality and acceptability of the
decisions can be established by carrying out the sensitivity
analysis. This assures the reliability and stability of the
obtained solution. This also helps to calculate the probable
impact of minor or major changes in weights of criteria on
the overall decision-making system under consideration [55,
56].
In this study, the sensitivity analysis is applied to the
final results of FAHP and FTOPSIS. The main aim of this
analysis was to confirm the consistency of the ranking obtained by the FTOPSIS method. In total, 45 combinations
were analyzed by interchanging the weights between two
criteria out of the total ten criteria (2C10) in every iteration
[57, 58]. Details are given in Table 8.
The weights of the criteria were interchanged in order to
assess the impact on the final decision. All 45 combinations
of outcomes were critically assessed and analyzed by calculating the Closeness Coefficient ratio (CCi ) using steps 9 to
12 repetitively as mentioned in section 4.5.1 and shown in
Table 8.
It is evident from the Table 8 and the sensitivity radar diagram Fig. 7 that the base ranking is unchanged for the top
rank and bottom rank alternatives through all the iterations.
The alternatives A5 and A1 have held the first and second
positions, respectively, consistently on the higher side of the
ranking, and A4 has been stable on rank 5. Whereas the alternatives A2 and A3 have been exchanging positions 3 and 4
regularly, thereby highlighting their sensitive nature to the
change of weights.
125
Key Observations of Sensitivity Analysis:
1) Base ranking of the alternatives using FAHP
and FTOPSIS method is A5>A1>A2>A3> A4.
The topmost alternative is A5, i.e., Technical
and IT Risks.
2) From the Table 8, it is evident that the A5 obtained the topmost rank (1st position), 45 times
on all the 45 iterations, i.e., 100% throughout
the sensitivity analysis. It reveals the ideas that
the experts are persistent, consistent, and insistent in their cognitive process.
3) A1 obtained the 2nd rank, 42 times out of 45 observations, i.e., 93% throughout the sensitivity
analysis. The alternative A1 pushed to the 3rd position only 3 times.
4) The alternative A2 remained at the same 3rd position 31 times out of 45 observations, i.e., 68%
and was pushed to 2nd positions 2 times and 4th
position 12 times.
5) Alternative A3 held the 4th position 32 times out
of 45 observations, i.e., 71%. A3 was pushed to
3rd position 11 times, 2nd and 5th position once
each.
6) Alternative A4 held the 5th position 43 times out
of 45 observations, i.e., 95% and pushed to 3rd
and 4th position once each.
The radar diagram shown in Fig. (7) is the graphical
presentation of Table 8 for quick assimilation of the sensitivity of the alternative to the change of weights of the criteria.
The paths for A5, A1, and A4 are consistently maintaining the
circular shape without interfering much with the paths of A2
and A3, whose paths are entangled, thereby showing the sensitive nature of the alternatives.
It can be concluded from Table 8 and Fig. (7) that the
best rank alternative A5 (Technical and IT Risks) is superior
and the ranking suggested is confirmed and found to be credible.
8. DISCUSSION
The researchers found during the literature review that
the risks being studied and analyzed have a close relationship with the challenges of I4.0. Hence, it is imperative to
study the challenges and threats simultaneously with risks
assessment and analysis. The researchers identified different
challenges either in isolation or in association with the
different situations that the I4.0 strategists must be aware of
while making key decisions. This research paper further
classified these challenges into five categories for systematic
assessment and analysis. This research paper has distributed
the challenges depending upon nature and relevance in five
different categories. This categorization is based on Technology, Culture, Legal, Capital, and Leadership.
Fig. 8 presents the classified view of the pertinent challenges summarized and divided into five sectors mainly.
C3
C4
C5
Closeness Coefficient Ratio
C6
C7
C8
C9
C10
CC1 CC2 CC3 CC4 CC5
Alternative ranking
Case1 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.591 0.421 0.395 0.264 0.695 A5>A1>A2>A3>A4
Case2 0.122 0.133 0.137 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.584 0.323 0.335 0.240 0.822 A5>A1>A3>A2>A4
Case4 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.315 0.301 0.272 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.527 0.549 0.394 0.203 0.748 A5>A2>A1>A3>A4
Case5 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.315 0.301 0.272 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.486 0.409 0.471 0.328 0.803 A5>A1>A3>A2>A4
Case6 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.315 0.301 0.272 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.668 0.350 0.294 0.411 0.724 A5>A1>A4>A2>A3
Case7 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.315 0.301 0.272 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.590 0.265 0.550 0.370 0.851 A5>A1>A3>A4>A2
Case8 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.315 0.301 0.272 0.022 0.019 0.026 0.095 0.094 0.089 0.446 0.524 0.340 0.179 0.820 A5>A1>A3>A4>A2
Case9 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.315 0.301 0.272 0.095 0.094 0.089 0.580 0.540 0.468 0.191 0.672 A5>A1>A2>A3>A4
Case10 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.315 0.301 0.272 0.638 0.339 0.497 0.304 0.793 A5>A1>A3>A4>A2
Case11 0.315 0.301 0.272 0.107 0.116 0.125 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.576 0.421 0.403 0.263 0.696 A5>A1>A2>A3>A4
Case12 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.122 0.133 0.137 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.592 0.453 0.399 0.260 0.684 A5>A1>A2>A3>A4
Case13 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.122 0.133 0.137 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.588 0.432 0.410 0.278 0.690 A5>A1>A2>A3>A4
Case14 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.122 0.133 0.137 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.642 0.413 0.356 0.302 0.669 A5>A1>A2>A3>A4
Case15 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.122 0.133 0.137 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.615 0.380 0.444 0.290 0.716 A5>A1>A3>A2>A4
Case16 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.122 0.133 0.137 0.022 0.019 0.026 0.095 0.094 0.089 0.565 0.470 0.376 0.228 0.707 A5>A1>A2>A3>A4
Case17 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.122 0.133 0.137 0.095 0.094 0.089 0.624 0.474 0.429 0.234 0.643 A5>A1>A2>A3>A4
Case18 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.122 0.133 0.137 0.633 0.439 0.432 0.253 0.664 A5>A1>A2>A3>A4
Case19 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.598 0.435 0.394 0.263 0.690 A5>A1>A2>A3>A4
Case20 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.107 0.116 0.125 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.596 0.424 0.399 0.271 0.693 A5>A1>A2>A3>A4
Case21 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.107 0.116 0.125 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.639 0.410 0.356 0.290 0.676 A5>A1>A2>A3>A4
Case22 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.107 0.116 0.125 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.617 0.383 0.429 0.280 0.715 A5>A1>A3>A2>A4
Case23 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.107 0.116 0.125 0.022 0.019 0.026 0.095 0.094 0.089 0.576 0.456 0.374 0.231 0.707 A5>A1>A2>A3>A4
Case24 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.107 0.116 0.125 0.095 0.094 0.089 0.627 0.459 0.420 0.236 0.652 A5>A1>A2>A3>A4
Case25 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.107 0.116 0.125 0.632 0.439 0.420 0.246 0.664 A5>A1>A2>A3>A4
Details of sensitivity analysis.
Case3 0.122 0.133 0.137 0.107 0.116 0.125 0.315 0.301 0.272 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.410 0.326 0.420 0.226 0.832 A5>A3>A1>A2>A4
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
C2
126
Criteria Weights
C1
Table 8.
Cases
Case26 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.591 0.419 0.397 0.266 0.696 A5>A1>A2>A3>A4
Case27 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.100 0.104 0.114 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.628 0.406 0.360 0.282 0.681 A5>A1>A2>A3>A4
Case28 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.100 0.104 0.114 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.608 0.382 0.425 0.274 0.716 A5>A1>A3>A2>A4
Case29 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.100 0.104 0.114 0.022 0.019 0.026 0.095 0.094 0.089 0.574 0.445 0.378 0.231 0.709 A5>A1>A2>A3>A4
Case30 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.100 0.104 0.114 0.095 0.094 0.089 0.619 0.447 0.419 0.236 0.660 A5>A1>A2>A3>A4
Case31 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.100 0.104 0.114 0.621 0.437 0.418 0.241 0.666 A5>A1>A2>A3>A4
Case32 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.100 0.101 0.106 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.627 0.409 0.360 0.281 0.681 A5>A1>A2>A3>A4
Case33 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.100 0.101 0.106 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.607 0.385 0.425 0.273 0.715 A5>A1>A3>A2>A4
Case34 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.100 0.101 0.106 0.022 0.019 0.026 0.095 0.094 0.089 0.574 0.445 0.378 0.231 0.708 A5>A1>A2>A3>A4
Case35 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.100 0.101 0.106 0.095 0.094 0.089 0.618 0.448 0.418 0.236 0.661 A5>A1>A2>A3>A4
Case36 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.100 0.101 0.106 0.620 0.440 0.418 0.240 0.665 A5>A1>A2>A3>A4
Case37 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.038 0.037 0.038 0.052 0.047 0.045 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.587 0.416 0.410 0.263 0.702 A5>A1>A2>A3>A4
Case38 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.038 0.037 0.038 0.051 0.047 0.048 0.052 0.047 0.045 0.022 0.019 0.026 0.095 0.094 0.089 0.587 0.416 0.410 0.262 0.702 A5>A1>A2>A3>A4
Case39 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.052 0.047 0.045 0.095 0.094 0.089 0.602 0.417 0.424 0.264 0.685 A5>A1>A3>A2>A4
Case40 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.052 0.047 0.045 0.589 0.463 0.418 0.239 0.658 A5>A1>A2>A3>A4
Case41 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.051 0.047 0.048 0.022 0.019 0.026 0.038 0.037 0.038 0.095 0.094 0.089 0.608 0.407 0.416 0.276 0.687 A5>A1>A3>A2>A4
Case43 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.022 0.019 0.026 0.051 0.047 0.048 0.095 0.094 0.089 0.606 0.422 0.409 0.266 0.678 A5>A1>A2>A3>A4
Case44 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.022 0.019 0.026 0.095 0.094 0.089 0.051 0.047 0.048 0.594 0.468 0.403 0.240 0.650 A5>A1>A2>A3>A4
Case45 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.095 0.094 0.089 0.022 0.019 0.026 0.570 0.495 0.385 0.223 0.650 A5>A1>A2>A3>A4
Gadekar et al.
Case42 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.038 0.037 0.038 0.591 0.465 0.408 0.244 0.652 A5>A1>A2>A3>A4
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
Risk Assessment and Implementation of Industry 4.0
Sensitivity Analysis
CCi A1
CCi A2
CCiA3
Case1
Case45
Case44
0.9
Case43
0.8
Case42
Case41
CCi A4
Case2
Case3
CCi A5
Case4
Case5
Case6
0.7
Case40
Case7
0.6
Case39
Case8
0.5
Case38
Case9
0.4
Case37
Case10
0.3
0.2
Case36
Case11
0.1
Case35
Case12
0
Case13
Case34
Case14
Case33
Case15
Case32
Case16
Case31
Case17
Case30
Case29
Case18
Case28
Case27
Case26
Case25Case24
Case19
Case20
Case21
Case23Case22
Fig. (7). Result of sensitivity analysis. (A higher resolution / colour version of this figure is available in the electronic copy of the article).
Fig. (8). Crucial challenges of I4.0. (A higher resolution / colour version of this figure is available in the electronic copy of the article).
127
128
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
The structured approach taken at every step during the
research has led the researchers to firmly put forward the
finding to the company for discussion and implementation.
The critical literature review and the appropriate choice of
the methods backed by the expert's inputs have been the base
of the overall research. The study considered 5 alternatives
and 10 criteria to lay down the structure of the model
development process. The researcher carried out a number of
iterations before reaching the conclusion, thereby leaving no
chance of ambiguity. The findings were further validated and
confirmed by experts, and company management. The results for potential implementation are very commendable
and optimistic as they have developed through a solid conceptual framework and based on real-world experiences. The
concept is in the company strategy framework by now.
Gadekar et al.
LIST OF ABBREVIATIONS
AHP
=
Analytical Hierarchy Process
AI
=
Artificial Intelligence
AR
=
Augmented Reality
BDA
=
Big Data Analytics
CPS
=
Cyber-Physical System
DEA
=
Data envelopment analysis
DEMATEL
=
Decision making trial and evaluation laboratory
FAHP
=
Fuzzy Analytical Hierarchy Process
FTOPSIS
=
Fuzzy Technique for Order of Preference by Similarity to Ideal Solution
The research paper is based on a critical literature review
supported by 153 research papers and 3 expert consultations.
The key findings like I4.0 risks iceberg model and the systematic categorization of the challenges have added extra
value to the paper. The I4.0 enormous potential is highlighted continuously to demonstrate the immense opportunities
the companies may harness by adopting I4.0 technologies at
the earliest. The research also strongly advocates the balance
between the conceptual framework and application framework to finetune the approach while implementing I4.0 technologies.
GDP
=
Gross Domestic Product
I4.0
=
Industry 4.0
IIoT
=
Industrial Internet of Things (),
IoT
=
Internet of Things
ISM
=
Interpretive Structural Modeling
IT
=
Information Technology
MCDM
=
Multi-Criteria Decision-Making
Method
The critical finding that IT and Technology risks should
be attended as the top priority, is the vital scientific contribution of this paper along with the model development, to the
new knowledge.
MICMAC
=
Matriced’ Impacts Croise´s Multiplication Applique´e a´ un Classement
ML
=
Machine Learning
Sensitivity analysis through the radar diagram assures the
reliability and robustness of the derived solution. This study
reveals that the I4.0 phenomenon has a tremendous potential
to impact industry operations. However, to harness the best
of I4.0 a systematic and holistic study of the associated risks
is a must. This will not only ensure the returns on investment
but also will build trust in the system.
PROMETHEE
=
Preference Ranking Organization
METHod for Enrichment of Evaluations
SMEs
=
Small and Medium Enterprise
VIKOR
=
VIseKriterijumska Optimizacija I
Kompromisno Resenje
VR
=
Virtual Reality
CONCLUSION
CURRENT & FUTURE DEVELOPMENT
The results obtained are validated and tested methodologically before putting it forward to the company for implementation. The research would be very beneficial to managers, academicians, researchers, and technocrats who would
be involved in I4.0 implementation.
CONSENT FOR PUBLICATION
The I4.0 has unlimited opportunities and so are the challenges (Fig. 8). Even though the study is one of its kind and
most relevant in the current context, it has a wide scope for
future research. The results can be further validated by solving the same problem through different MCDM methods.
The current study, though well supported by wide and critical literature review, still is based on the expert's opinion,
which is subjected to variation in cognitive, experiential, and
local demand and supply dynamics. The size of the group of
experts may also affect the outcome. Lastly, the study is focused on only pertinent and most evident risks. Further sub
risks/ micro risks and sub-criteria could be included in the
analysis to get the micro-level exposure to the study.
All data generated or analyzed during this study are included within the article.
Not applicable.
AVAILABILITY OF DATA AND MATERIALS
FUNDING
None.
CONFLICT OF INTEREST
The authors declare no conflict of interest, financial or
otherwise.
Risk Assessment and Implementation of Industry 4.0
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
ACKNOWLEDGEMENTS
The authors are grateful to the anonymous reviewers for
their useful remarks and suggestions, and industrial experts,
who want to remain anonymous. The authors would also like
to thank the editorial assistance.
[16]
[17]
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
M. Ben-Daya, E. Hassini and Z. Bahroun, "Internet of things and
supply chain management: A literature review", Int. J. Prod. Res.,
Vol. 57, No. (15-16), pp. 4719-4742, 2019.
http://dx.doi.org/10.1080/00207543.2017.1402140
H. S. Birkel, J. W. Veile, J. M. Müller, E. Hartmann and K. I.
Voigt, "Development of a risk framework for industry 4.0 in the
context of sustainability for established manufacturers", Sustainability, Vol. 11, No. 2, pp. 384, 2019.
http://dx.doi.org/10.3390/su11020384
H. Kagermann, J. Helbig, A. Hellinger and W. Wahlster, Recommendations for implementing the strategic initiative INDUSTRIE
4.0 April 2013 Securing the future of German manufacturing industry Final report of the Industrie 4.0 Working Group, 2013.
https://www.din.de/blob/76902/e8cac883f42bf28536e7e8165993f1
fd/recommendations-for-implementing-industry-4-0-data.pdf
H. Lasi, P. Fettke, H. G. Kemper, T. Feld and M. Hoffmann, "Industry 4.0", Bus. Inf. Syst. Eng., Vol. 6, No. 4, pp. 239-242, 2014.
http://dx.doi.org/10.1007/s12599-014-0334-4
A. Khan and K. Turowski, “A Survey of Current Challenges in
Manufacturing Industry and Preparation for Industry 4.0,” In Advances in Intelligent Systems and Computing, Springer Verlag,
2016, pp. 15-26.
M. Colak, I. Kaya and M. Erdogan, "A Fuzzy Based Risk Evaluation Model for Industry 4.0 Transition Process", In Industrial Engineering in the Big Data Era, Springer, Cham, 2019, pp. 201-215.
http://dx.doi.org/10.1007/978-3-030-03317-0_17
T. Stock, M. Obenaus, S. Kunz and H. Kohl, "Industry 4.0 as
enabler for a sustainable development: A qualitative assessment of
its ecological and social potential", Process Saf. Environ. Prot.,
Vol. 118, pp. 254-267, 2018.
http://dx.doi.org/10.1016/j.psep.2018.06.026
D. Kiel, J. M. Müller, C. Arnold and K. Voigt, "Sustainable industrial value creation: Benefits and challenges of industry 4.0", International Journal of Innovation Management, 2017.
http://dx.doi.org/10.1142/S1363919617400151
L. Georghiou, "What lies beneath: Avoiding the risk of underevaluation", Sci. Public Policy, Vol. 34, No. 10, pp. 743-752, 2007.
http://dx.doi.org/10.3152/030234207X259003
S. Nath and B. Sarkar, "An integrated cloud manufacturing model
for warehouse selection in a smart supply chain network: A comparative study", J. Inst. Eng. (India) Ser. C, Vol. 101, No. 1, pp. 2541, 2020.
http://dx.doi.org/10.1007/s40032-019-00544-8
Y. Kazancoglu and Y. D. Ozkan-Ozen, "Analyzing workforce 4.0
in the fourth industrial revolution and proposing a road map from
operations management perspective with fuzzy DEMATEL", J. Enterp. Inf. Manag., Vol. 31, No. 6, pp. 891-907, 2018.
http://dx.doi.org/10.1108/JEIM-01-2017-0015
A. Ahmi, H. Elbardan and R. H. Ali, "Bibliometric analysis of
published literature on industry 4.0", In 2019 International Conference on Electronics, Information and Communication (ICEIC),
2019, pp. 1-6.
B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee and B. Yin, "Smart
factory of industry 4.0: Key technologies, application case, and
challenges", IEEE Access, Vol. 6, pp. 6505-6519, 2017.
http://dx.doi.org/10.1109/ACCESS.2017.2783682
M. Khan, X. Wu, X. Xu and W. Dou, "Big data challenges and
opportunities in the hype of Industry 4.0", In 2017 IEEE International Conference on Communications (ICC), 2017, pp. 1-6.
X. Chao, G. Kou, Y. Peng and F. E. Alsaadi, "Behavior monitoring
methods for trade-based money laundering integrating macro and
micro prudential regulation: A case from China", Technol. Econ.
Dev. Econ., Vol. 25, No. 6, pp. 1081-1096, 2019.
http://dx.doi.org/10.3846/tede.2019.9383
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
129
S. S. Kamble, A. Gunasekaran and S. A. Gawankar, "Sustainable
Industry 4.0 framework: A systematic literature review identifying
the current trends and future perspectives", Process Saf. Environ.
Prot., Vol. 117, pp. 408-425, 2018.
http://dx.doi.org/10.1016/j.psep.2018.05.009
S. S. Kamble, A. Gunasekaran and R. Sharma, "Analysis of the
driving and dependence power of barriers to adopt industry 4.0 in
Indian manufacturing industry", Comput. Ind., Vol. 101, pp. 107119, 2018.
http://dx.doi.org/10.1016/j.compind.2018.06.004
D. K. Maurya, A. Kumar, S. Kaunoujiya, D. Prasad and V. Nath,
“Study and design of smart industry: A review”, Nanoelectronics,
Circuits and Communication Systems, Springer, Singapore, 2019,
pp. 591-598.
B. Sivathanu, "Adoption of industrial IoT in auto-component manufacturing SMEs in India", Inf. Res. Manag. J., Vol. 32, No. 2, pp.
52-75, 2019.
http://dx.doi.org/10.4018/IRMJ.2019040103
S. Vaidya, P. Ambad and S. Bhosle, “Industry 4.0 - A glimpse,”
Procedia Manuf., 2018, pp. 233-238.
P. D. Jadhav, "A study on impact of industry 4.0 in India", Sagar.
Int. Adv. Res. J. Sci. Eng. Technol, Vol. 2, No. 1, pp. 1-5, 2015.
P. Priyadarshinee, R. D. Raut, M. K. Jha and S. S. Kamble, "A
cloud computing adoption in Indian SMEs: Scale development and
validation approach", J. High Technol. Manage. Res., Vol. 28, No.
2, pp. 221-245, 2017.
http://dx.doi.org/10.1016/j.hitech.2017.10.010
S. A. Gawankar, A. Gunasekaran and S. Kamble, "A study on
investments in the big data-driven supply chain, performance
measures and organisational performance in Indian retail 4.0 context", Int. J. Prod. Res., Vol. 58, No. 5, pp. 1574-1593, 2020.
http://dx.doi.org/10.1080/00207543.2019.1668070
F. D. Keskin, İ. Kabasakal, Y. Kaymaz and H. Soyuer, "An assessment model for organizational adoption of industry 4.0 based
on multi-criteria decision techniques", In Proceedings of the International Symposium for Production Research, 2018, pp. 85-100.
G. Kou, X. Chao, Y. Peng, F. E. Alsaadi and E. Herrera-Viedma,
"Machine learning methods for systemic risk analysis in financial
sectors", Technol. Econ. Dev. Econ., Vol. 25, No. 5. pp. 716-742,
2019.
N. Medić, J. Prester and I. Palcic, “Evaluation of advanced digital
technologies in manufacturing companies: Hybrid fuzzy MCDM
approach building competitiveness of croatian manufacturing view
project”, In 25th International European Manufacturing Conference, 2018, pp. 1-10.
https://www.researchgate.net/publication/326040450
M. Peruzzini, M. Pellicciari and C. Bil. Transdisciplinary engineering methods for social innovation of industry 4.0: In Proceedings
of the 25th ISPE Inc. International Conference on Transdisciplinary Engineering, Vol. 7, pp. 72, 2018.
J. Tupa, J. Simota and F. Steiner, "Aspects of risk management
implementation for industry 4.0", Procedia Manuf., Vol. 11, pp.
1223-1230, 2017.
http://dx.doi.org/10.1016/j.promfg.2017.07.248
G. Kou, Y. Peng and G. Wang, "Evaluation of clustering algorithms for financial risk analysis using MCDM methods", Inf. Sci.,
Vol. 275, pp. 1-2, 2014.
http://dx.doi.org/10.1016/j.ins.2014.02.137
M. T. Bhagawati, E. Manavalan, K. Jayakrishna and P. Venkumar,
"Identifying key success factors of sustainability in supply chain
management for industry 4.0 using DEMATEL method", In Proceedings of International Conference on Intelligent Manufacturing
and Automation, 2019, pp. 583-591.
http://dx.doi.org/10.1007/978-981-13-2490-1_54
J. M. Müller, O. Buliga and K. I. Voigt, "Fortune favors the prepared: How SMEs approach business model innovations in Industry
4.0", Technol. Forecast. Soc. Change, Vol. 132, pp. 2-17, 2018.
http://dx.doi.org/10.1016/j.techfore.2017.12.019
D. O. Sanchez, “Sustainable development challenges and risks of
industry 4.0: A literature review”. In 2019 IEEE Global IoT Summit (GIoTS), 2019, pp. 1-6.
T. Bányai, P. Tamás, B. Illés, Ž. Stankevičiūtė and Á. Bányai,
"Optimization of municipal waste collection routing: Impact of industry 4.0 technologies on environmental awareness and sustaina-
130
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1
bility", Int. J. Environ. Res. Public Health, Vol. 16, No. 4, p. 634,
2019.
http://dx.doi.org/10.3390/ijerph16040634 PMID: 30795548
A. Benešová and J. Tupa, "Requirements for education and qualification of people in industry 4.0", Procedia Manuf., Vol. 11, pp.
2195-2202, 2017.
http://dx.doi.org/10.1016/j.promfg.2017.07.366
M. Gill and S. VanBoskirk, "The digital maturity model 4.0. benchmarks: Digital transformation playbook", 2016, pp. 17.
S. Mittal, D. Romero and T. Wuest, "Towards a smart manufacturing maturity model for SMEs", In IFIP International Conference
on Advances in Production Management Systems, 2018, pp. 155163.
U. Niesen and M. A. Maddah-Ali, "Coded caching with nonuniform demands, " IEEE Trans. Inf. Theory, Vol. 63, No. 2, pp. 11461158, 2017.
http://dx.doi.org/10.1109/TIT.2016.2639522
I. Castelo-Branco, F. Cruz-Jesus and T. Oliveira, “Assessing industry 4.0 readiness in manufacturing: Evidence for the European
union”, Comput. Ind., Vol. 107, pp. 22-32, 2019.
https://www2.deloitte.com/content/dam/Deloitte/cy/Documents/inn
ovation-andentrepreneurship%20centre/Industry%204.0%20readiness%20report%202019.p
df
K. Lichtblau, V. Stich, R. Bertenrath, M. Blum, M. Bleider, A.
Millack, K. Schmitt, E. Schmitz and M. Schröter, IMPULSIndustrie 4.0-Readiness, Impuls-Stiftung des VDMA: Aachen,
Köln, 2015.
S. Wibowo and S. Grandhi, "Fuzzy multicriteria analysis for performance evaluation of internet-of-things-based supply chains",
Symmetry, Vol. 10, No. 11, pp. 603, 2018.
http://dx.doi.org/10.3390/sym10110603
P. Potyrańska, Opportunities and Threats for the Introduction of
Nuclear Power in Poland, Energy Policy Studies, 2019.
D. Jobber and F. Ellis-Chadwick, Principles and practice of marketing (No. 7th), McGraw-Hill Higher Education, 2012.
T. L. Satty, The Analytic Hierarchy Process, McGraw-Hill: New
York, 1980.
E. K. Zavadskas and V. Podvezko, "Integrated determination of
objective criteria weights in MCDM", Int. J. Inf. Technol. Decis.
Mak., Vol. 15, No. 2, pp. 267-283, 2016.
http://dx.doi.org/10.1142/S0219622016500036
E. K. Zavadskas, Z. Turskis and V. Bagočius, "Multi-criteria selection of a deep-water port in the Eastern Baltic Sea", Appl. Soft
Comput. J., Vol. 26, pp. 180-192, 2015.
http://dx.doi.org/10.1016/j.asoc.2014.09.019
J. J. Buckley, "Ranking alternatives using fuzzy numbers", Fuzzy
Sets Syst., Vol. 15, No. 1, pp. 21-31, 1985.
http://dx.doi.org/10.1016/0165-0114(85)90013-2
[47]
[48]
[49]
[50]
[51]
[52]
[53]
[54]
[55]
[56]
[57]
[58]
Gadekar et al.
S. NǎdǍban, S. Dzitac and I. Dzitac, "Fuzzy TOPSIS: A general
view", Procedia Comput. Sci., Vol. 91, pp. 823-831, 2016.
P. Priyadarshinee, R. D. Raut, M. K. Jha and S. S. Kamble, "A
cloud computing adoption in Indian SMEs: Scale development and
validation approach", J. High Technol. Manage. Res., Vol. 28, No.
2, pp. 221-245, 2017.
http://dx.doi.org/10.1016/j.hitech.2017.10.010
B. M. Elomda, H. A. Hefny and H. A. Hassan, "An extension of
fuzzy decision maps for multi-criteria decision-making", Egypt. Inform. J., Vol. 14, No. 2, pp. 147-155, 2013.
http://dx.doi.org/10.1016/j.eij.2013.05.001
H. Y. Wu, G. H. Tzeng and Y. H. Chen, "A fuzzy MCDM approach for evaluating banking performance based on balanced scorecard", Expert Syst. Appl., Vol. 36, No. 6, pp. 10135-10147, 2009.
http://dx.doi.org/10.1016/j.eswa.2009.01.005
A. N. Haq and G. Kannan, "Fuzzy analytical hierarchy process for
evaluating and selecting a vendor in a supply chain model", Int. J.
Adv. Manuf. Technol., Vol. 29, No. 7-8, pp. 826-835, 2006.
http://dx.doi.org/10.1007/s00170-005-2562-8
V. K. Mittal and K. S. Sangwan, "Fuzzy TOPSIS method for ranking barriers to environmentally conscious manufacturing implementation: Government, industry and expert perspectives", Int. J.
Environ. Technol. Manag., Vol. 17, No. 1, pp. 57-82, 2014.
http://dx.doi.org/10.1504/IJETM.2014.059466
C. L. Hwang and K. Yoon, Multiple Attribute Decision Making:
Methods and Applications A State-Of-The-Art Survey, Springer
Science & Business Media,Springer, 2012.
C. T. Chen, "Extensions of the TOPSIS for group decision-making
under fuzzy environment", Fuzzy Sets Syst., Vol. 114, No. 1, pp. 19, 2000.
http://dx.doi.org/10.1016/S0165-0114(97)00377-1
K. Govindan, A. Diabat and K. M. Shankar, "Analyzing the drivers
of green manufacturing with fuzzy approach", J. Clean. Prod., Vol.
96, pp. 182-193, 2015.
http://dx.doi.org/10.1016/j.jclepro.2014.02.054
H. C. Lee and C. T. Chang, "Comparative analysis of MCDM
methods for ranking renewable energy sources in Taiwan", Renewable and Sustainable Energy Reviews, Vol. 92, pp. 883-896,
2018.
http://dx.doi.org/10.1016/j.rser.2018.05.007
F. Ma, Y. Zhao, Y. Pu and J. Li, "A comprehensive multi-criteria
decision making model for sustainable material selection considering life cycle assessment method", IEEE Access, Vol. 6, pp.
58338-58354, 2018.
http://dx.doi.org/10.1109/ACCESS.2018.2875038
D. Pamučar and G. Ćirović, "The selection of transport and handling resources in logistics centers using Multi-Attributive Border
Approximation area Comparison (MABAC)", Expert Syst. Appl.,
Vol. 42, No. 6, pp. 3016-3028, 2015.
http://dx.doi.org/10.1016/j.eswa.2014.11.057