The New Safety Trends: The Challenges through Industry 4.0
DI NARDO MARIO1, BOROWSKI PIOTR2, MARYAM GALLAB3, MURINO TERESA4,
YU HAOXUAN5
1Department of Materials Engineering and Operations Management,
University of Naples "Federico II", Naples, ITALY
mario.dinardo@unina.it
2Institute of Mechanical Engineering, Warsaw University of Life Sciences, Warsaw, POLAND
3Mines-Rabat School, MOROCCO
gallab@enim.ac.ma
4Department of Materials Engineering and Operations Management,
University of Naples "Federico II", Naples, ITALY
murino@unina.it
5School of Resources and Safety Engineering, Central South University, Changsha, CHINA
yuhaoxuan@csu.edu.cn
Key-Words: - Safety Culture, Safety 4.0, Industry 4.0, Risk Management
Received: June 19, 2021. Revised: December 14, 2021. Accepted: December 29, 2021. Published: January 17, 2022.
1 Introduction
Industrial safety necessitates a consistent way of life
and a positive mindset. Safety refers to the absence
of danger or injury. Additionally, the phrase "safety"
refers to the steps taken by individuals to avoid
accidents, harm, and risk. Additionally, advances in
workplace safety have been made to benefit
employee health. Management is accountable for
employee safety while on the job.
industrial security in cities. Also, Nakhal A, A. J. et
al. (2021) came up with the concept of an intelligent
industrial information system to ensure the efficiency
and safety of industrial production.
All undesirable events in the workplace that can
result in death, ill health, injury, damage, or other
loss must be thoroughly evaluated; personnel must
be trained to protect against and eradicate them.
Similarly, any critical hazards, i.e., the
source/situation that poses a risk of injury or illness,
must be identified and action plans developed to
mitigate them.
For example, A. A. Artamonov et al. (2021) used
mathematical models to assess the impact of
chemical production on industrial safety; G.
Vasilescu et al. (2021) did the same, and they used
computer modeling for integration to assess the risks
associated with different industrial hazard scenarios
(shock wave propagation, debris propagation) and
the impact on location; S. Meramo-Hurtado et al.
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Abstract: Industrial engineering achieved rapid growth in providing safety measurements in all industries,
following different safety policies to prevent faults in sectors. Industrial safety is an essential feature to give an
accident-free environment. Implementation of safety policies and measurements encourage industrial people to
work in various perilous conditions. Industries prepare their safety policy and safety manual to identify various
faults and risks. It is necessary to create awareness to industry workers, and industries maintain special
departments for safety. Safety guidelines prevent occupational injuries and accidents. Safety rules and
regulations reduce the waste of human and other resources in industries. The study evaluates safety models used
in industry to identify issues involved in the selection, implementation, and evaluation. This research provides
insight into the overall process for industrial safety and, most essential, overviews on the methodology.
Predicting industrial faults and risks emphasized the industrial engineering process and used machine learning
algorithms for classifications. Many issues and challenges discussed industrial safety and provided novel
innovation ideas for researchers.
Therefore, research and discussion of industrial
safety are becoming increasingly important and the
focus of all societies in the world. For example,
according to the MENA Report (2021), the
popularization and use of safety helmets have gradually
become the focus of development in central Africa, the
Middle East, and other backward industrial regions. For
example, H. Ebrahimi and S. M. Kharghani Moghadam
(2021) recently proposed an information management
system for industrial cities, which is used to strengthen
the construction of
(2021) developed an inherent safety analysis of
chitosan microbeads modified with TiO2
nanoparticles for mass production using the inherent
safety index (ISI) method; Li, S. et al. (2021)
assessed the impact of some mining industry
activities on environmental safety.
It is not only sufficient to care for safety but
other two inter-related aspects, viz., health
(wellbeing of employees) and environment, are also
given equal importance and considerations. For
example, Yu, H. et al. (2021) thought about using
"safe" new industrial products to benefit mankind,
and he developed an intelligent scraper; Li, S et al.
(2021) and Yu, H et al. (2021) used communication-
based train control system (CBTC system) to
improve the industrial transport efficiency to benefit
mankind. All these three elements, i.e., health, safety,
and environment (also known as HSE), are
interrelated and affect each other. For instance, if
employee health is not given due regard, it may lead
to accidents (Henmi et al., 2016).
If the industry pollutes the environment around
its facilities, it will hurt employees' health, eventually
harming productivity. Safety can be guaranteed only
when health and the environment are under control.
As a result, each industry bears specific
environmental stewardship and public health
responsibilities: Guo, Q. et al. (2021) and Nardo M.D,
et al (2021) have considered the impact of industrial
pollution on the environment and try to solve the
problem.
We examined several industrial safety techniques
that have aided in improving industrial engineering
plant safety in this article. The remainder of the study
is divided into many sections that discuss various
industrial safety mechanisms and their significance
for industrial safety procedures. It concludes with a
discussion of the findings and potential directions for
research.
2 Literature Review
2.1 Industrial Safety Models
This section discussed the literature review on
different safety approaches to improve industrial
safety in engineering plants.
RHR is an approach suggested by Yoon et al.
(2008) for discovering high-R connections that is
entirely automated. The use of high resistance
connections in industrial facilities' electrical
distribution systems causes overheating, reduces
efficiency, and threatens safety. The RHR approach
regulates the circumstances upstream and
downstream of the industrial facility, increasing the
dependability of the electrical distribution system
and the facility's overall safety.
However, the system fails in the maintenance of
monitor alarms.
Henmi et al. (2016) proposed a machine learning
approach to the early detection of plant faults. The
abnormal plant state signal is not available before
failures occur. The previous mechanisms can
measure the normal state of the plants only. This
research work introduced a machine learning
approach called Support Vector Machine (SVM) to
overcome this problem. This approach better
classifies the state of the plant, which is normal or
abnormal. For advanced classification, the Kernel
Gaussian technology was also used. Nevertheless,
this algorithm is flexible for only water plants. There
is no scope for other industrial plants.
De Souza (2014) used the Support Vector
Machines (SVM) approach to identify and diagnose
faults in his system. This paper discusses two
approaches: support vector machines for
classification (Support Vector Classification – SVC)
and support vector machines for regression (Support
Vector Regression – SVR). SVM approach shows
promise for process monitoring in instances where an
automatic monitoring system is required to improve
process efficiency while also ensuring worker and
public safety in the workplace. Using both
approaches in a process monitoring system at the
same time allows researchers to make use of the
rapid detection time provided by the SVR approach
and the classification capabilities provided by the
SVC-based methodology. According to the
comparison of the SVM techniques and the PCA
methods, the SVM methodology, which requires less
information than the PCA methodology, performs
better than the conventional approach for fault
identification in the non-isothermal reactor for the
faulty situation under consideration.
Pfeffer et al. (2015) proposed the HAZOP
approach for industrial safety. The modular process
plants should be integrated with safety engineering.
However, safety solutions and relevant measures are
identified by the HAZOP efficiently. But the
proposed approach does not give automatic solutions
for plant safety. The present industry needs dynamic
methods for plant maintenance and safety.
Choi et al. (2011) proposed an industrial pipe
rack safety monitoring system based on Wireless
Sensor Networks (WSN). The system is designed
and implemented for large-scale networks. The
system consists of many safety devices such as
sensor nodes, gateways, and servers. Based on the
safety sensors, it can be monitored and provide
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safety guidelines for industrial plant management.
However, this research work concludes that the
proposed system maximizes safety maintenance and
minimizes risks. However, the system does not
provide security maintenance. The WSN based
system cannot complete reliable data transmission
due to large-scale networks.
Laurenty et al. (2010) offer an approach for
configuring instrumentation systems to meet the
legal requirement of maintaining high reliability in
regular and fail-safe operation circumstances. A
series of models operating as Expert systems is
proposed to attain the required state: each model is
responsible for watching and diagnosing pipeline
leaks in real-time. In addition, the suggested system
validates the activities under the business rules that
have been applied to it. It is necessary to apply
several systems approaches to perform its purpose,
including fuzzy logic, neural networks, genetic
algorithms, and statistical analysis. This solution is
one option to be used as an aid in various industry
fault detection problems.
G. H. Choi and B. G. Loh (2017) focused on the
dynamic characteristics of industrial safety systems
in the Republic of Korea and their impact on safety
performance. This dynamic property is essential for
the reconstruction of industrial safety systems. They
also studied the impact of damping and elastic
properties of industrial safety system models on
safety performance and explained the feedback
control performance in terms of cost and benefit.
They also explored the implications of restructuring
industrial safety systems for safety policy. They
argued that the strong correlation between safety
budget and industrial accident rate makes it possible
to model industrial safety systems with these
variables as inputs and outputs, respectively, and that
weaker elastic characteristics and stronger damping
characteristics can achieve more effective and
efficient industrial safety systems.
J. Le Coze (2013) described what is defined as
the sensitization model. They proposed that the
model was developed to support empirical and
methodological research in different high-risk
industries, to assist current and future industrial
safety assessment practices by considering the input
of the social sciences, in particular reflecting their
insights into significant incidents. At the same time,
they described the design principles of this
exploration in order to generate a sensitive model for
security assessment. The model was developed by
combining two general management and sociological
(safety) literature models. In this model, security was
viewed as a dynamic interaction between multiple
dimensions, including technological Design and
tasks, organizational structural and functional
characteristics, and cognitive, cultural, and power
issues, at various levels of analysis.
From the perspective of system safety and
control theory, W. Li, L. Zhang, and W. Liang (2017)
put forward a model of accident cause analysis and
classification, taking human, machine, management,
environment, information and resources as system
factors, and then put forward a new accident
mechanism. At the same time, they analyzed and
classified the causes of the Texas City refinery
explosion according to their proposed safety incident
analysis mechanism.
2.2 New Arising Risks
The increasing complexity of manufacturing
facilities forces companies to move towards
decentralized decision-making. At the moment,
decentralized instances may make decisions using
artificial intelligence incorporated into people or
equipment. During the Industrial Revolution, the
advent of Industry 4.0 impacted OH&S, particularly
in terms of job diversity, management, and other
organizational variables.
The risk assessment must continue to change to
handle current and future concerns. While
digitalization creates opportunity, it also adds
complexity to cyber-physical systems. Zio Enrico
(2018). Climate warming and major natural
occurrences are putting our infrastructures in
growing jeopardy. These sources of danger are
unknown, making them challenging to explain and
model statistically. Many emerging research and
development directions are discussed, including the
use of simulation to identify and explore accident
scenarios, the extension of risk assessment into the
context of resilience and business continuity, the
reliance on data for dynamic and condition
monitoring-based risk assessment, and the safety and
security assessment of cyber-physical systems.
According to Brocal et al. (2019), risk
governance, risk management, occupational health
and safety management systems, and emerging risk
management comprise the four fundamental risk
management system categories in Industry 4.0. The
next section discusses the many models listed,
including historical and contemporary models.
2.2.1 Risk Management Models
From an international level, we propose a framework
for risk governance. This framework provides tactics
for designing and implementing comprehensive
assessments and strategies to manage these issues.
Risk governance includes the application of
governance principles to risk identification,
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assessment, management, and communication.
Therefore, the risk management process may be
regarded as integrated with the risk governance
process.
The International Standard for Managing Risk
(ISO 31000:2018 provides guidelines and a common
approach for all risk types. Risk management is
defined as an organization's coordinated activities to
direct and control risks across the organization. SRM
(also known as risk management) involves
prevention, mitigation, adaptation, and sharing, to
reduce risk. Although the new industry was arising
and reinforcing, no guide was provided by the new
standardization.
A management system is a set of interrelated or
interacting elements of an organization to establish
policies and objectives, and processes to achieve
those objectives.
L. M. Steege and B. Pinekenstein (2016) thought
that to solve the professional nursing system fatigue
and reduce the risks associated with the nurse's need
of strategic management and the high-level decision-
making and daily management through operational
and tactical operations. Therefore, they applied risk
management models in the professional nurse
assessment of fatigue risks; the model nurse includes
support for safety and welfare in the culture of
monitoring and decision support tools.
M. Jafari et al. (2011) presented a multi-stage
research approach to develop knowledge risk
management models in project-based organizations
in Iran. This analytical model can be used to prepare
a broad assessment of the risk of knowledge loss in a
project-based organization and to provide
recommendations on preservation plans to mitigate
its impact. D. Kern et al. (2012) developed an
upstream supply chain risk management model that
links risk identification, risk assessment and risk
mitigation with risk performance and validated the
model empirically. The model also includes the
impact of the continuous improvement process on
identification, assessment, and mitigation. M. M.
Silverman (2014) even applied the risk management
model to psychiatry.
3 Industrial Safety Issues
The safety problems in industrial engineering
through the plants in endanger. The different
emerging issues are impacting industrial safety. Such
issues are tendering, customer system and practices,
time pressure, managing safety, dangerous work
tasks, communication, hazard identification. Figure
1 represents the manage safety problems in industrial
engineering plants.
Industrial
Safety Problems
Communication
Hazard
Identification Custom Work and
Practices
Safety Maintenance
Resources
Responsibilities
Time Pressure
Tendering
High Risk Tasks
Fig. 1: They manage safety problems in industrial engineering plants.
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3.1 Tendering
The tendering system indirectly represents the
importance of safety risks. The industrial people
select only low price service providers. The
competition between the service can divert them into
low bids and less spent on industrial safety even
though the budget problems reduce the safety
performance and increase the accident rates.
Neglecting the safety in tendering is the main
disadvantage of industrial safety.
3.2 Custom Work and Practices
The customers worked in different companies and
different work locations. Each industrial company
has custom policies in the work environment. The
unfamiliar work tasks and work locations and low
awareness about the hazards may lead to high risk in
industrial plants. The complexity of management
safety is increased due to different management
policies between the service providers and customers.
To ensure safety, customers should know the work
policies and fully gain awareness of risk hazards.
3.3 Time Pressure
The customers worked in different companies and
different work locations. Each industrial company
has custom policies in the work environment. The
unfamiliar work tasks and work locations and low
awareness about the hazards may lead to high risk in
industrial plants. The complexity of management
safety is increased due to different management
policies between the service providers and customers.
To ensure safety, customers should know the work
policies and fully gain awareness of risk hazards.
3.4 Safety Maintenance Resources
The industries are suffering from maintenance safety
resources and implementing safety policies. Small-
scale industries have insufficient resources to provide
safety in industrial plants. Especially these industries
do not have time, money, and human resources to
manage safety efficiently. In the case of large-scale
industries failed in the implementation of safety
maintenance. Industrial engineering safety has
emerging problems due to inadequate resources that
are emphasized.
3.5 Responsibilities
Mostly the management of safety is unaware of
service providers. Many industries miss the
decryption of safety responsibilities, and providers
do not know their requirements. Both the companies
and service providers often forget to ensure safety
procedures. Due to the limited abilities, the service
will not take responsibility for improving industrial
engineering plants' safety.
3.6 High-Risk Tasks
Maintenance and installation work involved the most
significant risks. Besides, an employee of industrial
companies works under different conditions. The
temporary employees are not aware of the worksite
and the work's risk due to the high risk of tasks that
the insufficient knowledge employees perform. The
other problems also lead to high-risk accidents, such
as soft budget works, tight schedules, fewer
experience workers, unfamiliar workplaces.
3.7 Communication
The different work was that operating parties did not
transfer the work importance and safety models
between them. The lack of communication among
the various work professionals leads to safety
problems in industrial plants. How service is
provided to the customer affects the quality of
service. Poor communication between the provider
and the client may, at worst, put the safety of both
the provider and client's workers at risk. Most
providers believe that customers are not as cavalier
about their opinions as staff members. Furthermore,
some managers consider that providing critical
customer feedback and identifying unsafe practices
are part of their responsibility.
3.8 Hazard Identification
Many industries do not have the facility to
systematically identify hazards and cause poor safety
procedures. Many industries are negligent in the
implementation of the hazard identification system.
4 Industrial Engineering Safety
Challenges
In industrial engineering manage safety is essential
to improve industrial performance. Even the fourth
generation industry has challenges in safety
management, such as lack of supervision, poor safety
policies, equipment quality, technology impact, weak
precautions.
4.1 Lack of Supervision
Supervision is essential for safety management in
industrial engineering. Safety management has terms
and policies. Every department in industrial
engineering has awareness about safety management
policies. The safety management department
observes the safety policies implementation in
industries. The management department cannot
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implement safety management policies, which leads
to a rise in safety problems. If the supervision
department does not inspect the issues, it will affect
safety management. Due to lack of supervision,
security problems arise in industries.
Fig. 2: The safety risks occurred in industrial departments due to lack of supervision in safety management.
Figure 2 shows the safety risks that occurred in
industrial departments in an Energy company in
2019 due to a lack of supervision in safety
management. The more risks occurred in the
facilities department, the lower, the fewer risks in the
administration department. In bar graphs on X-axis
refer to department name, and Y-axis refers to no. of
issues that occurred.
4.2 Poor Safety Polices
The industrial policies implement very standard
policies for improved performance in different
departments. Nevertheless, in safety
management, the policies for safety are very
poor and negligible. The industries should
follow high-level safety management as per the
company level.
Fig. 3: The Impact of Poor Safety Policies.
Nevertheless, some industries give very little
importance to safety management policies. There
are no professional managers in the plants in
industries that have not set up relevant management
departments, increasing safety accidents. Some
industries do not implement safety management up
to the required level, its influence on the guarantee
safety management. Figure 3 shows the injuries that
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occurred with the implementation of low policies in
different industrial fields in the year 2019 ((Brocal F.
et al. (2019))
4.3 Equipment Quality
In industries, the equipment also affects safety
management. The weak equipment leads to many
safety accidents in industrial plants. The equipment
which is a long term used, improper maintenance
also influences the safety management. In some
cases, the migrant workers also reason for safety
risks. They have weak knowledge about the wearing
of safety equipment.
Fig. 4: The number of persons injured each year due to poor quality equipment.
The problem is that firefighting equipment
installation also leads to safety management risks.
Finally, the poor quality of equipment is the cause of
severe safety accidents in industrial plants. Figure 4
describes the number of persons injured each year
due to poor-quality equipment in the Energy Sector.
4.4 Impact of Technology
In the safety management process, the novel
techniques have some hidden hazards. So the
industrial management should be aware of the
technology that has been newly implemented. The
industries can provide investment funds to different
departments, but they invest tiny amounts in
installing safety technology. It significantly impacts
safety management in industrial plants. Finally, the
professional knowledge level and ability level are
relatively low, and they cannot utilize the technology,
increasing the safety risk.
Fig. 5: Technology impact on safety management.
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Figure 5 can observe the work accidents in
different industries due to lack of knowledge on
technology and failure to implement novel
technologies in safety management. The rate of
accidents is high in the construction industry (Brocal
F. et al. (2019))
4.5 Weak Precautions
In this challenge, discuss the handle of safety
accidents that occurs. The safety protection setups
and facilities are very poor. The present safety
precautions are unable to reach current industry
requirements. The safety accident will not be timely
through effective measures to reduce the loss caused
by the safety accident.
5 Solutions to Safety Management
Challenges and Issues
5.1 Industry 4.0
The best definition for Industry 4.0 paradigm defines
the re-imagining of the entire organization and
management of the entire value chain in the life
cycle of products, which is constantly focused on
individual customer needs from the product
development and production order to the entire chain,
including a product end-user distribution and
recycling. In the fourth industrial revolution, devices
communicate and synchronize through networks,
obtaining only the necessary production information
through data mining in the cloud system, enabling
intelligent consumers and plants at the application
level. With the advent of Industry 4.0, it will also be
possible to openly innovate products and processes,
instant customer particular product and service
development, and customer involvement in these
processes. With Industry 4.0, production processes
will have the capability to make instant decisions and
integrate with other production areas.
According to Muhuri et al. (2019), with the
advent of Industry 4.0, the whole transformation
through digital integration and intelligent
engineering has made a huge leap toward futuristic
technology. Today, all gadgets include machine
learning capabilities, automation has become a
priority, and a new industrial revolution is underway.
Additionally, according to Stock t. et al. (2016),
the emergence of industry 4.0 presents enormous
prospects for achieving sustainable production.
The fourth industrial revolution is referred to as
Industry 4.0 or Smart Manufacturing. In order to
realize Smart Manufacturing, several techniques such
as cutting-edge ranging from CPS, cloud
manufacturing, big data analytics, the Internet of
Things, and intelligent sensors to additive production,
energy efficiency, and holograms are being
developed and implemented on manufacturing sites.
According to Kang et al. (2016), the most critical
issues for achieving Smart Manufacturing are
technically interoperability, as well as the
development of technologies themselves and the
requirement for integrated technology, and
strategically a system that supports technology
development and application according to the
purposes, levels, and steps of application for
developing and introducing practical technologies.
Emerging information technologies such as the
Internet of Things, big data, and cloud computing, in
conjunction with artificial intelligence technologies,
assist in implementing Industry 4.0's smart factory. S.
Wang (2016). Intelligent machines, conveyors, and
products interact and negotiate with one another to
rearrange themselves for flexible product
manufacturing. The industrial network gathers large
amounts of data from intelligent things and sends it
to the cloud. This offers system-wide feedback and
cooperation to maximize system performance via big
data analytics. The self-organizing reconfiguration
described above and the feedback and coordination
enabled by big data provide the structure and
operating mechanism of the smart factory.
Alcácer et al. (2019). Industry 4.0 ushers in a
new era of digitalization. Everything is digital;
business models, settings, production systems,
equipment, operators, goods, and services all have a
digital component. It's all interwoven within the
digital scene's virtual depiction. Physical fluxes will
be continuously tracked on digital platforms. At a
higher level of automation, numerous systems and
software enable factory communications with the
latest trends in information and communication
technologies, resulting in a state-of-the-art factory,
not just inside but also outside the factory, engaging
all value chain elements in real-time.
Some sectors have implemented innovations,
digitization, and high-tech solutions much earlier.
Advanced Digital Production (ADP) technologies
used in production have enormous potential to
accelerate economic growth and human well-being
and protect the environment, contributing to the
implementation of the 2030 sustainable development
plans (Borowski 2021a).
5.1.1 Cyber-Physical Systems
The physical system is a system that people can
understand with their five sensory organs. The cyber
system should be understood that like cybernetics, a
scientific discipline that researches the
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communication and control of living things and
machines from the cyber system, the control process
is based on information technologies, computers, and
the Internet Pfeffer. Cyber-physical systems connect
the physical world with the cyber world via the
Internet.
Recent advancements in manufacturing have
prepared the way for a systematic deployment of
Cyber-Physical Systems (CPS), in which information
is closely monitored and synced from all relevant
viewpoints between the physical factory floor and
the cyber computational realm, Lee J. et al. (2015).
Additionally, by leveraging modern data analytics,
networked machines will be able to operate more
effectively, cooperatively, and resiliently. This
movement is changing manufacturing into the fourth
industrial revolution, dubbed Industry 4.0.
5.1.2 Horizontal and Vertical Integration
System Integration allows multiple systems to work
together as a single system. In terms of production
with Industry 4.0, continuous flow provided by
interconnected structures has a critical prescription.
To preserve this constant flow and to enable rapid
response to changes in production processes and
problems, to provide customized products to
customers, to increase resource efficiency, to
optimize the global supply chain, and to have a more
flexible production structure in industry 4.0,
horizontal and vertical integration at every point is
required. System integration is critical for Industry
4.0 functionality.
Horizontal integration entails a continuous flow
of stages between one business's production and
planning processes and other enterprises' production
and planning processes. This integration
encompasses all aspects of the business, from raw
material sourcing through Design, manufacturing,
marketing, and referral. Horizontal integration
between businesses enables the development of new
business models as well. Horizontal integration, in a
nutshell, generates integrated and end-to-end systems.
5.1.3 Big Data and Data Analysis
When we consider Industry 4.0, we see that
businesses will begin to gain valuable information
due to the massive amounts of data being stored on
secure systems and extensively evaluated and
transformed into meaningful information about
production systems, enterprise systems, and
customer-based management systems. When
potential failures can be predicted and protections
implemented, opportunities may be identified in
advance and acted upon immediately. Industry 4.0
solutions allow for communication between man and
machine, giving wide opportunities to reduce
production costs (Borowski 2021b). Costs associated
with manufacturing can be lowered while operations
associated with service maintenance are facilitated.
Industry 4.0 allows manufacturers to take their
production lines to a higher technological level
through complete system integration and networking.
Industry 4.0 has decentralized analytics, critical
decision-making, and increased response time during
productions (Borowski 2021b).
To summarize, analysis and forecasting are
facilitated across the board, from customer
expectations to market movements, hence enhancing
decision-making processes and value chains.
Additionally, decision-making can occur in real-time.
Di Nardo (2020) emphasizes industry 4.0
concepts, such as control via Cyber-Physical
Systems, in a push automation environment where
the human operator's function is not marginal, but
critical, in addition to supervision and issue solutions.
The real-time control of the machines enables the
optimal setup of production parameters, ensuring that
output remains robust and economic harm caused by
equipment failure or maintenance is minimized.
The area of reliability engineering must
constantly improve in order to keep pace with
industrial and societal changes. This necessitates
constant improvement in technological knowledge
and capability. In today's technological landscape,
with widespread digitization and connectivity at all
levels of cyber-physical systems and across all
industrial sectors, reliability engineering faces new
difficulties and new chances for improvement. Zio
(2016) focuses on component and system reliability
modeling and discusses some of the issues and
possibilities associated with degradation modeling
and PHM. These research and application fields have
significant potential for enhancing industrial
components and systems' safety, productivity, and
service capacity. The increasing number of KIDs
enable novel modeling and analytic techniques with
huge potential benefits. The practical deployment of
these new methods must be accompanied by due
diligence to ensure that such benefits are collected
efficiently.
As Industry 4.0 progresses, and the digital,
physical, and human worlds become increasingly
integrated, reliability engineering must change to
handle current and future concerns. Farsi et al. (2019)
outline the concept of Industry 4.0 and address some
of the reliability engineering difficulties and
possibilities associated with it. The paper proposes
new paths for study in system modeling, large data
analysis, health management, cyber-physical systems,
human-machine interaction, uncertainty,
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collaborative optimization, communication, and
interfaces.
The industry 4.0 principles are outlined and the
key technologies that enable their creation and
diffusion.
5.1.4 Internet of Things
Today, the Internet, which we generally know,
provides an environment that connects people
worldwide and each other, while the Internet of
Things connects devices by providing them to
communicate with other devices via wired or
wireless means. This can be summarized as "Things,
(i.e., devices) physically connected to each other and
functionally connected to the internet". For example,
smart house technologies are based on the Internet of
Things solution. Also, a refrigerator can identify the
food or vegetables and send messages to the mobile
phone through the Internet of Things about storage
conditions.
The Internet of Things (IoT) enables seamless
connection and interoperability across devices,
systems, services, different networks, and in
particular, control systems, according to Condry et al.
(2016). End consumers anticipate a seamless
connection via any endpoint device. Smart Internet
of Things devices can act as diverse control system
interfaces, enabling quick response and possibly
ubiquitous access. Remotely controlled control
systems provide dependable, scalable, and long-term
solutions for enhanced usability, management, and
reaction time.
5.1.5 Smart Robots
A robot, which is thinkable first for automation,
becomes increasingly autonomous, flexible, and
cooperative and costs less. Thus, by analyzing events
and situations objectively, robots, which are expected
to reduce human-based errors the least, result in
increasingly widespread use in the production
process. So that, robotics will also be used in
factories where Industry 4.0 is used effectively. For
example, in smart factories, robots will manage the
production process by communicating and
recognizing each other, doing work-sharing
automatically, analyzing things, and adapting more
quickly to changes.
5.1.6 Virtual Reality
Virtual reality is described as the "technical
reproduction of a real-world process or system
through time24." Simultaneously, three-dimensional
simulations of goods, materials, and manufacturing
processes are used during the design phase, but as we
progress toward Industry 4.0, virtual reality will
become even more ubiquitous in factory operations.
As a result, Virtual Reality is regarded one of
Industry 4.0's primary characteristics. For instance,
there is no requirement to wait until the factory is
physically completed to determine the efficiency of a
fabrication. Industry 4.0 is a virtual environment in
which the factory is designed, run, and assessed. Not
only the factory as a whole may be reviewed and
improved, but also particular manufacturing
processes or machines. For instance, workers
responsible for repairing and maintaining machinery
may receive practical training using virtual reality;
even inaccessible components can be viewed, and
simulation and virtual reality tools can anticipate
failure probability. Additionally, operators will be
able to save machine setup time and enhance product
quality by identifying possibilities for virtual reality
testing prior to actually setting machine parameters
for the product on the production line.
5.1.7 Cloud Computing
Cloud computing, or online information distribution
in its functional sense, is a generic term for services
that enable common data sharing among computer
devices. When a complete description is produced,
the apps, programs, and data are stored on a virtual
server (in the cloud) and are easily accessible via the
devices while connected to the Internet. In terms of
industry 4.0, data mining enables machines to
discover the information required for cloud
computing manufacturing. The information included
in a machine is not limited to a single field; it may
potentially span many regions. By facilitating
connectivity between smart devices, Big Data, the
Internet of Things, and Cloud Computing, we can lay
the groundwork for Industry 4.0.
5.1.8 Smart Factories
The automation process in Smart factories is
happening via an interconnection between machines
and devices. For example, if a resource shortage
problem happens during the manufacturing process,
the resource is ordered automatically without human
need, and the malfunctions can be spotted
instantaneously only to repair by themselves. Those
features help the Smart factory to operate at its
maximum capacity without any serious problems.
5.1.9 Cyber Security
Security is the very first question when it comes to
Data Volume and Intensity. This situation has more
importance on systems, aiming to connect
unconnected systems before, such as Industry 4.0.
Industry 4.0 will increase the number of
interconnections and smart devices in factories.
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Cloud connection and the connection between other
machines create the intranet of the factory. Besides
the intranet, there is an external link to communicate
between customers and the factory to smooth out the
order and delivery process. Cybersecurity is essential
for all those procedures to operate without any
problem. However, it is essential to notice that
cybersecurity should not interrupt or slow down the
manufacturing process while defending the factory to
outer-treats. When we point out Cyber Security facts,
such as; increasing security and the parts of advanced
security developments, we clearly see that a factory
that has the advanced features should only be
accessible to authorized and educated personnel.
This is important for data verification and intensity
of data. For instance, critical data should only be
viable to authorized personnel in a manufacturing
facility. To verify inputs to the system, sources
should be secure and verifiable to strengthen their up
cyber market; industrial hardware companies buy out
or merge with smaller cyber-security companies.
6 Conclusion
Process safety management is crucial to industrial
production, and safety performance needs to be
improved. Many people are concerned about the
sustainable growth of many sectors. It has been
acknowledged as critical by both industry and
academia. These years have seen a renewed
emphasis on sustainable development in the
engineering curriculum worldwide. Numerous
advancements have been achieved in sustainable
development, engineering tools, and technology.
Nonetheless, industrial events continue to occur.
It is critical to employ engineering concepts to
prevent future catastrophes rather than increase
sustainable growth. However, the complexity and
changing nature of processing plants (which are
frequently composed of numerous subsystems that
add complexity to the core system) make
sustainability a challenge for engineers and an
opportunity for them. We may improve process
safety performance by embracing novel techniques
such as systems analysis of processes and accidents,
the use of complex systems methodologies, and
multi-scale modeling.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Mario Di Nardo, idea, Design, writing
Piotr Borowsky, writing, data analysis
Maryan Gallab corrections, writing
Yu Haouxuan, study and writing preliminary draft
Teresa Murino, supervisor
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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Maryam Gallab, Murino Teresa, Yu Haoxuan
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