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The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation Towards Human-Automation Symbiosis Work Systems

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A vision for the Operator 4.0 is presented in this paper in the context of human cyber-physical systems and adaptive automation towards human-automation symbiosis work systems for a socially sustainable manufacturing workforce. Discussions include base concepts and enabling technologies for the development of human-automation symbiosis work systems in Industry 4.0.
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The Operator 4.0: Human Cyber-Physical Systems
& Adaptive Automation towards
Human-Automation Symbiosis Work Systems
David Romero1-2, Peter Bernus2, Ovidiu Noran2, Johan Stahre3, Åsa Fast-Berglund3
1 Tecnológico de Monterrey, Mexico
david.romero.diaz@gmail.com
2 Griffith University, Australia
O.Noran@griffith.edu.au, P.Bernus@griffith.edu.au
3 Chalmers University of Technology, Sweden
johan.stahre@chalmers.se, asa.fasth@chalmers.se
Abstract. A vision for the Operator 4.0 is presented in this paper in the context
of human cyber-physical systems and adaptive automation towards human-
automation symbiosis work systems for a socially sustainable manufacturing
workforce. Discussions include base concepts and enabling technologies for
the development of human-automation symbiosis work systems in Industry 4.0.
Keywords: Operator 4.0, Human Cyber-Physical Systems, Adaptive Automation,
Human-Automation Symbiosis, Socially Sustainable Manufacturing.
1 Introduction
Industry 4.0 enables new types of interactions between operators and machines [1],
interactions that will transform the industrial workforce and will have significant
implications for the nature of work, in order to accommodate the ever-increasing
variability of production. An important part of this transformation is the emphasis on
human-centricity of the Factories of the Future [2], allowing for a paradigm shift from
independent automated and human activities towards a human-automation symbiosis
(or ‘human cyber-physical systems’) characterised by the cooperation of machines with
humans in work systems and designed not to replace the skills and abilities of humans,
but rather to co-exist with and assist humans in being more efficient and effective [3].
In this sense, the history of the interaction of operators with various industrial
and digital production technologies can be summarised as a generational evolution.
Thus, Operator 1.0 generation is defined as humans conducting ‘manual and dextrous
work’ with some support from mechanical tools and manually operated machine tools.
Operator 2.0 generation represents a human entity who performs ‘assisted work’ with
the support of computer tools, ranging from CAx tools to NC operating systems (e.g.
CNC machine tools), as well as enterprise information systems. The Operator 3.0
generation embodies a human entity involved in ‘cooperative work’ with robots and
other machines and computer tools, also known as - human-robot collaboration. The
Operator 4.0 generation represents the ‘operator of the future’, a smart and skilled
operator who performs ‘work aided’ by machines if and as needed. It represents a new
design and engineering philosophy for adaptive production systems where the focus is
on treating automation as a further enhancement of the human’s physical, sensorial and
cognitive capabilities by means of human cyber-physical system integration (see Fig 1).
Fig. 1. Operator Generations (R)Evolution
This paper explores a vision for the Operator 4.0 in the context of human cyber-
physical systems and adaptive automation towards human-automation symbiosis work
systems for a socially sustainable manufacturing workforce. The discussions within the
following
sections
include
base
concepts
and
enabling
technologies
for
the
development
of the proposed human-automation symbiosis work systems in Industry 4.0.
2 Base Concepts
The
concept
of
Balanced
Automation
Systems
(BAS)
[4]
was
introduced
in
the
early
90’s
as
an
attempt
to
achieve
the
right
combination
of
automation
and
manual
operations
(cf.
Operator
2.0
&
3.0)
in
production
systems,
taking
into
account
economic
and
socio-
organisational aspects for the (re-)engineering of competitive and socially sustainable
production systems. BAS implementations have mainly been based on the principles
of ‘anthropocentric production systems’ [5] and the advantages offered by flexible
automation
as
an
extension
of
programmable
automation
in
manufacturing
systems.
In [6], it has been previously defined a Next Generation BAS concept with the aim of
stepping
beyond
the
‘right
balance’
between
automated
and
manual
tasks
in
production
systems, so as to the achieve ‘human-automation symbiosis’ for enhancing workforce
capabilities (cf. Operator 4.0) and increasing manufacturing flexibility (cf. Factory 4.0)
of production systems. The vision of Next Generation BASs is that while they will
still rely on the guidelines of ‘anthropocentric production systems’ [5], they will
moreover feature ‘adaptive automation’ [7-9] for the dynamic allocation of control
over manufacturing and assembly tasks to a human operator and/or a machine for
the purpose of optimising overall production system performance. This will be
done considering [10-11]: (a) sustainable technical and
economic
benefits
for
the
manufacturing
enterprise
(e.g.
improved
quality,
increased responsiveness, shorter
throughput times, easier planning and control of production processes,
increased
capacity for innovation and continual improvement) and (b) social-human benefits for
the workforce (e.g. increasing quality of working life, higher job satisfaction through
meaningful tasks, greater personal flexibility and adaptation, improved ability and skills
of shop-floor personnel).
Based on the previous context, we define Human Cyber-Physical Systems (H-CPS)
as systems engineered to: (a) improve human abilities to dynamically interact with
machines in the cyber- and physical- worlds by means of ‘intelligent’ human-machine
interfaces, using human-computer interaction techniques designed to fit the operators’
cognitive and physical needs, and (b) improve human physical-, sensing- and cognitive-
capabilities, by means of various enriched and enhanced technologies (e.g. using
wearable devices). Both H-CPS aims are to be achieved through computational and
communication techniques, akin to adaptive control systems with the human-in-the-loop.
The Adaptive Automation (AA) movement [7-8] [12-13] aims at optimising human-
machine cooperation to efficiently allocate labour (cognitive & physical) and distribute
tasks between the automated part and the humans in the workstations of an adaptive
production system [13]. AA allows the human and/or the machine to modify the level
of automation by shifting the control of specific functions whenever predefined
conditions (e.g. critical-event, measure-based and/or modelling-based) are met [14].
The ultimate AA goal is the achievement of human-automation symbiosis by means of
adaptation of automation & control across all workstations of a human-centred and
adaptive production system in order to allow a dynamic and seamless transition of
functions (tasks) allocation between humans and machines that optimally leverages
human skills to provide inclusiveness and job satisfaction while also achieving
production objectives.
Human-in-the-loop (HITL) feedback control systems are defined as systems that
require human interaction [15]. HITL control models offer interesting opportunities to
a broad range of H-CPS applications, such as the ‘Operator 4.0’. HITL control models
can help to supervise an operator’s performance in a human-machine interaction,
and (a) let the operator directly control the operation under supervisory control,
(b) let automation monitor the operator and take appropriate actions, or (c) an hybrid of
‘a’ and ‘b’, where automation monitors the operator, takes human inputs for the control,
and takes appropriate actions [15]. HITL control models, although being challenging
due to the complex physiological, sensorial and cognitive nature of human beings,
are an important enabler for ‘human-automation symbiosis’ achievement.
3 Human-Automation Symbiosis: Intelligent Hybrid Agents
In this section, the strategy to attain human-automation symbiosis in manufacturing
work systems is explored through a discourse of ‘adaptive automation’ and ‘intelligent
multi-agent systems’ as the bases for a sharing and trading of control strategy [14].
An intelligent agent is an entity (human, artificial or hybrid) with the following
characteristics [16]: (a) purposeful - displays goal-seeking behaviour, (b) perceptive
- can observe information about the surrounding world and filter it according to
relevance for orientation, (c) aware - can develop situational awareness that is relevant
for the agent’s purpose, (d) autonomous - can decide a course of action (plan) to achieve
the goal, (e) able to act - can mobilise its resources to act on its plan; these resources
may include parts of the self or tools at the autonomous disposal of the agent, and
resources for physical action or information gathering/processing, (f) reflective - can
represent and reason about the abilities and goals of self and those of other agents,
(g) adaptable and learning - can recognise inadequacy of its plan and modify it, or
change its goal, and (h) conversational and cooperative - can negotiate with other
agents to enhance perception, develop common orientation, decide on joint goals,
plans, and action; essentially participate in maintaining the ‘emergent agent’ created
through joint actions of agents. Note that this classification of agent functions may
be interpreted as the ability to perform the Observe, Orient, Decide and Act (OODA)
Loop of Boyd [17] [18], developed as a theory to explain the conditions and functions
of successful operation, and therefore this classification may be used to direct the
engineering and development of intelligent agents [16], which, as we shall see below,
are expected to be ‘adaptive’ and ‘hybrid’ in nature.
Human agents, under certain circumstances, and in defined domains of activity,
are able to act as intelligent agents (e.g. able to perform complex assembly sequences
and operations in a flexible production line). However, once the assumptions are
no longer true (e.g. due to a heavy physical, sensorial and/or cognitive workload),
the quality of agenthood deteriorates; thus the human does no longer have the ability to
perform one or several functions that are normally attributed to an intelligent agent.
Consequently, the question is: how to restitute human agenthood by extending human
capabilities (physical, sensorial and/or cognitive) through automation-aided means?
Similarly, artificial (machine) agents, under certain circumstances and in defined
domains of activity can act as intelligent agents (e.g. they are able to perform repetitive
and routine tasks in a high volume production line, make decisions based on learnt
patterns, etc.). Nevertheless, once the assumptions are no longer true (e.g. the need
(ability) to improvise and use flexible processes to reduce production downtime due
to an error), the quality of agenthood deteriorates; thus the machine does no longer
have the ability to perform one or several functions that are normally attributed
to an intelligent agent. Therefore, the question is: how to restore machine agenthood
by extending the machine’s capabilities through human-aided means?
Hybrid agents are intelligent agents established as a symbiotic relationship (human-
automation symbiosis) between the human and the machine, so that in situations where
neither would display agenthood in isolation, the symbiotic hybrid agent does. In this
research, the vision is that at any time a human (the ‘Operator 4.0’) lacks some of these
agenthood abilities, such as due to heavy physical, sensorial and/or cognitive workload,
automation will extend the human’s abilities as much as necessary to help the operator
to perform the tasks at hand, according to the expected quality of performance criteria.
Thus, it is proposed to implement hybrid agents, as a form of ‘adaptive automation’,
in order to sustain agenthood by determining whenever and wherever the operator
requires augmentation (e.g. using advanced trained classifiers to recognise this need
[19]), and prompting the appropriate type and level of automation to facilitate optimal
operator performance. An important objective is that the level of this extension need
not be a ‘design time’ decision, but should be able to be dynamically configured
as needed. Furthermore, the ‘hybrid agents’ view of the Operator 4.0 is a component
of the solution to preserve the operator’s situation awareness [20], as the status,
experience and information processing capability of the operator can cause loss of
agenthood and consequent decision-making errors, thus the need for ‘symbiotic
technical support’. Work on affective computing [21] showed that the task allocation
and adaptation between humans and machines/computers supporting them is not a
trivial task and should involve sensory assessments of humans’ physical and cognitive
states in order to be efficient.
For the purpose of comparison, in the case of an Operator 3.0 (cf. human-robot
collaboration), the design time decision would be determined by the required capability
of the manufacturing or assembly operation (e.g. speed, accuracy, capacity, reliability,
etc.), which then would decide (based on technical, economic, social and human
benefits) the level of automation of the process, as well as the accompanying skills and
abilities required by the human role. In contrast, in the case of an Operator 4.0,
automation level would be determined in less detail at design time, allowing an initial
detailed procedure and much automated support (e.g. in case of a novice or new-to-
the-task operator), while providing ‘on the fly’ solutions that develop together with
the individual operator’s skills. Apart from achieving job satisfaction and a variety
of desired process ‘ilities’ [22], such dynamic allocation of different levels/extent
of automation fosters the use of human skills and abilities. This includes the creation
of favourable conditions for workforce development and learning, the improvement of
human-robot collaboration and tacit knowledge development, as it is well known that
in many (although not all) tasks acting based on tacit knowledge are much more
efficient and effective than following predetermined procedures.
Emergent agents are virtual entities, who exist as a cooperative and negotiated
arrangement between multiple agents of either kind above (sometimes on multiple
levels of static or dynamic aggregation), whereupon two human agents, or a human and
a machine/robot, or two machine/robot agents, or more than two agents of any of these
types, form a ‘join entity agent’ that from the external observer’s viewpoint acts as a
single intelligent agent. It is expected that an Operator 4.0 will have the ability to be
part of an intelligent group of agents with appropriate functionality for the formation,
operation, transformation and dissolution of these groups. Note that it is not necessary
for every agent to have the same level of contribution to such self-organising ability;
agents may specialise in certain tasks and assume different roles in the lifecycle of
the emergent agent.
4 The Operator 4.0: Aiding for Enhanced Workers Capabilities
A capability is the “measure of the ability of an entity (e.g. department, organisation,
person, system) to achieve its objectives, especially in relation to its overall mission”
[23]. In the case of human beings, this involves having the resources and the ability
to deploy their capabilities for a purpose.
4.1 Automation Aiding for Enhanced Physical Capabilities
A physical activity is any bodily movement produced by skeletal muscles that requires
energy expenditure. We define physical capability as the operator’s capacity and ability
to undertake physical activities needed for daily work, and can be characterised by
multiple attributes, including the description of the physical function (e.g. ability to lift,
walk, manipulate and assemble) together with its non-functional properties (e.g. speed,
strength, precision and dexterity), as well as the description of the ability in terms of
maturity- and expertise- level. The agent’s activity supported is that of (physically)
acting, i.e. the ‘A’ in the OODA loop.
For example, the operator may be: (a) ‘procedure following - novice’ with no
autonomy over the details of the operation and under supervision along the whole
procedure, (b) ‘procedure following - advanced’ with limited operational autonomy
and less supervision across the procedure, or (c) ‘expert’ - featuring internalised tacit
knowledge (know-how) and autonomy towards improving the operation, where only
the operation’s outcome is supervised. The vision of Operator 4.0 acknowledges that
capabilities are not static, but they evolve over time, as well as change depending on
context (e.g. the operator may be tired or rested, new- or accustomed- to-the-task),
therefore physically aiding an Operator 4.0 assumes that one can assess the physical
capabilities in a dynamic and timely fashion, preferably in real-time. Some assessment
tools for testing an operator’s physical capabilities may include: (a) Physical Abilities
Tests (PATs) [24] [25] capable of matching the physical abilities of an operator with
the physical demands of a job (or operation) up-front to its allocation (e.g. such methods
are getting increased attention in the defence community); and (b) Advanced Trained
Classifiers (ATCs) [26], based on a variety of machine learning techniques, to measure
(test) in real-time the operator’s physical performance and dynamically identify when
an assisted/enhanced operation is necessary in an unobtrusive manner, relying on
physiological measures (cf. ergonomics [27]). This is done in order to actively determine
when an operator actually requires assistance and subsequently prompt the appropriate
type and level of physical (aided) capability to facilitate optimal physical performance
by the operator. Moreover, PATs may be useful for job role allocation and/or for
determining training needs (e.g. how to handle lifting, posture correction, etc.), while
ATCs may be advantageous for reducing the chances of accidents due to tiredness or
of injuries due to repetitive strain, or to improve product quality by reducing errors and
re-work.
4.2 Automation Aiding for Enhanced Sensing Capabilities
A sensorial capability is the operator’s capacity and ability to acquire data from the
environment, as a first step towards creating information necessary for orientation and
decision-making in the operator’s daily work [28]. There are two components to sensing:
(a) the physical ability to collect data from the environment (by vision, smell, sound,
touch, vibration), and (b) the ability to selectively perceive it (as we know that a very
low percentage of the data generated by the physical sense of an operator enters the
short-term memory and is made available for processing). It is known that an operator
is selectively filtering out what he/she does not consider important: “of the entire
amount of new information generated by our environment, our senses filter out >99%
of signals before they reach our consciousness” [29]. It is also known that this filtering
is not a conscious process. Therefore, OODA is not a simple loop; there is information
that flows to make an operator perceive selectively what his/her brain considers
important (i.e. what data are useful for analysis and decision-making). This selectivity
is acquired by the operator through learning. As a consequence, there are two points
where the operator’s sensing abilities are subject to assessment and where these abilities
may need improvement, as further described.
The first potential sensory improvement is the creation of new- or augmentation-
of existing senses (e.g. by way of using sensor devices to collect, convert, aggregate
signals that would not be accessible for the operator, either due to physical accessibility
of the data source, general human limitations, or due to individual personal limitations).
Also, due to the different levels of sensitivity of humans across senses, transforming
one signal to another form may increase the ability of the human to identify information
within the data (e.g. transforming temperature to visible colour, vibration to audible
spectrum sound, or using data aggregation, can enable the human to make use of
otherwise inaccessible data). The second type of sensory limitation is more difficult to
overcome if it is to be done exclusively at sensor level. This is because information
feedback produced by analysis (orientation) and decision-making must be used to filter
out unwanted data (i.e. containing irrelevant information) and to sensitise selective
perception to smaller signals, which may carry relevant information.
Some assessment tools for testing an operator’s sensorial capabilities may include:
(a) Sensorial Abilities Tests (SATs) [30] capable of matching the sensorial abilities of
an operator with the sensorial demands of a job (or operation) up-front to its allocation.
This is not a trivial tasks, because even though the sensorial abilities of an operator
can be tested (such as by using simple vision and hearing tests), sensing successfully in
the situation (i.e. registering/perceiving signals necessary for analysis and orientation)
is also dependent on the nature and level of prior experience of the operator as previously
explained.
It is therefore expected that the solution to selective perception deficits is not simply
providing operators with ‘bionic ears and eyes’ (even though in some situations that
may be sufficient), but in using the ‘emergent agent’ model, where the machine agent
has its own intelligence in terms of analysis and orientation, and the ability to reason
about the human agent’s needs and decision what data to present for the human’s needs
and when.
The traditional limitation for decision-making has been scarcity of information,
requiring human (and machine) agents to make decisions in light of insufficient data
about the operations. With the proliferation of sensor devices (the so-called ‘Internet of
Things’) this situation could change, but only if sensor agents are made intelligent
in terms of what data to register and transmit to other agents.
New algorithms are needed for cooperative and collaborative learning of situations
for collective sense-making and decision-making by sensor agents (including agent
networks). This is so that the situational knowledge base of participating agents can
be utilised to adaptively filter unwanted data and to ‘zoom-in’ to enhance faint but
relevant signals, as well as negotiate signal bandwidth for priority communication.
Part of this situation recognition may be implemented by machine learning techniques,
such as (b) Advanced Trained Classifiers (ATCs) [26], where part of an intelligent
sensor agent may use machine learning to support human-automation symbiosis and
to learn about the individual operator and that operator’s behaviour in action, to actively
determine when an operator actually requires assistance, and to subsequently prompt
the appropriate type and level of sensing (aided) capabilities to facilitate optimal
sensing performance by the operator.
4.3 Automation Aiding for Enhanced Cognitive Capabilities
A cognitive capability is the operator’s capacity and ability to undertake the mental
tasks (e.g. perception, memory, reasoning, decision, motor response, etc.) needed for
the job and under certain operational settings [31]. In the OODA model, these cognitive
tasks are to ‘Orient’ and to ‘Decide’, together amounting to a mental workload, decision-
making, skilled performance, human-computer interaction, maintaining reliability in
performance, dealing with work stress whether in training or in the job.
As the Factories of the Future become increasingly dynamic working environments
(cf. Industry 4.0) due to the upsurge in the need for flexibility and adaptability of
production systems, the upgraded shop-floors (cf. Factory 4.0) call for cognitive aids
that help the operator perform these mental tasks, such as those provided by augmented
reality (AR) technologies or ‘intelligent’ Human-Machine Interfaces (HMI) to support
the new/increased cognitive workload (e.g. diagnosis, situational awareness, decision-
making, planning, etc.) of the Operator 4.0. It can be expected that this aid would
increase human reliability in the job, considering both the operator’s well-being and
the production system’s performance.
Some assessment tools for testing an operator’s cognitive capabilities may include:
(a) Cognitive Abilities Tests (CATs) [32] capable of matching the cognitive abilities of
an operator with the mental demands and cognitive skills needed for performing a job
(or operation) up-front to its allocation; and (b) Advanced Trained Classifiers (ATCs)
[26] based on various machine learning techniques, to measure (test) in real-time
the operator’s cognitive performance and dynamically identify when an assisted/
enhanced action is necessary, and do so in an unobtrusive manner, relying on cognitive
load measurements (cf. cognitive ergonomics [33]).
5 Conclusions and Further Work
Industry 4.0 would be inconceivable without human beings. Hence, human-automation
symbiosis by means of H-CPS and AA aims to take into account established principles
of the design of operator-friendly working conditions [34] for aiding the workforce [35],
such as: (a) practicability, considering compliance with ‘anthropometric’ and physical,
sensorial and cognitive norms in the design of a work system; (b) safety, bearing in
mind in the design of work systems embedded security and safety measures to avoid
accidents; (c) freedom from impairment, by providing automation-aided means to
compensate various individual (human) limitations and thus keep with the physical,
sensorial and cognitive quality performance of the job; and (d) individualisation and
personalisation of the working environment thanks to adaptive systems (cf. AA) that
support the operator as an individual and promote learning (e.g. by means of sharing
and trading of control strategy [14]).
The development of ‘human-automation symbiosis’ in work systems [6] [36] offers
advantages for the social sustainability of the manufacturing workforce in Industry
4.0, in terms of improving operational excellence, safety and health, satisfaction and
motivation, inclusiveness, and continuous learning. Hence, the purpose of H-CPS
and AA in this research is to support the Operator 4.0 to excel in the job by means of
automation-aided systems that aim to provide a sustainable relief of physical and
mental stress and contribute to the development of workforce creativity, innovation and
improvisational skills, without compromising production objectives.
Further work aims to explore ‘intelligent’ human-machine interfaces and interaction
technologies, and adaptive and human-in-the-loop (HITL) control systems to support
the development of ‘human-automation symbiosis’ work systems for the Operator 4.0
in the Factory of the Future.
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... For instance, the implementation of these technologies encourages the change from lowskilled jobs to medium or high-skilled ones with high cognitive load [6]. Likewise, low-skilled and high-physical load jobs can be improved with smart tools to improve the quality of life [7]. Then, to involve adequately the human within the advances of the fourth industrial revolution, it is needed to explore mechanisms and tools that take into consideration the role, conditions, and well-being of the human operator within the manufacturing system. ...
... The last cluster, Human 4.0 addresses the period from 2015 to 2020, where the implementations of enabling technologies for Industry 4.0 are more numerous [10,[21][22][23]. Authors introduced terms like artificial intelligence, digital twin, robotics and simulation [7,12,14,[23][24][25][26]. In this technological framework, human-robot interactions are observed in a workspace with activities developed collaboratively [27][28][29][30][31]. ...
... Another relevant feature is the inclusion of cognitive variables and attributes (stress, fatigue, etc.) in the representation of the human operator [12,25,35,36]. This reinforces the transformation of the human as a resource included as complex actor within the system [7,33,[37][38][39][40]. In addition, the importance of realtime communication between the human and the virtual environment is recognized, which allows for timely and dynamic feedback [8,34]. ...
Conference Paper
This paper focuses on the analyses and evolution of the representation of the human operator within manufacturing production systems. This exploratory study reviews the scientific literature for examining the human representation in a phys- ical and virtual level, the interaction of human with components of the manufac- turing system, the human’s involvement in the manufacturing process, and the human capabilities featured in such environment. The findings of the present paper should make an important contribution to the definition of the virtual human counterpart, its corresponding attributes, methods and functionalities.
... Technologies used to build Industry 4.0 cannot impose their choices on people but offer them to them. The first mentions of the role of operators in Industry 4.0 appeared in the publications of Romero et al. (2016). It was emphasized that symbiosis between humans and new technologies is needed. ...
... Based on a literature review and a forecast analysis, the essence of Industry 5.0 focuses on three determinants of development: human-centric, sustainable and resilient development (Romero et al., 2016;Felsberger & Reiner, 2020). The adjective "resilient" has been used by EC. ...
... In the research field: "Human-centric" several subsegments can be distinguished, and so: "Society Industry" was formed by 5 publications, "Human in Industry 4.0" was built by 2 publications and "Operator 4.0" had as many as 35 publications with the sum of the times cited: 367 and average citations 10.49 by h-index: 11). The highest citation index was obtained by Romero et al. (2016) (presented in the first row in Table 3). ...
Article
The widespread digitization and dynamic development of the technologies of the fourth industrial revolution, leading to the dehumanization of industry, have increased the interest of the scientific community in aspects of industrial humanization, sustainability and resilience. Hence, the aim of the article is to identify areas related to humanization and sustainability of the concept of Industry 4.0. A bibliometric analysis of Web of Science using Vosviewer tools, Excell and content analysis of selected papers were applied. The most important results include the determination of the dynamics of the increase in the number of publications in the segment of Industry 4.0 and Industry 5.0. The article indicates the weaknesses of the concept of Industry 4.0, especially in the area of the role of man in smart factories and sustainable development. Thus, the framework of the concept of Industry 5.0 was identified. In addition, the bibliometric analysis carried out allowed the identification of an important stream of employee skill development.
... In human-centric smart factories of the future [23], humans continue to play a crucial role in production processes [24]. Hence, human-cobot interaction aims to synergize the complementary skills and abilities of both the human operators and their robotic counterparts [25]. ...
Article
Full-text available
Advanced human–robot interaction becomes an essential resource in Industry 4.0. Specifically, the deployment of collaborative robots (cobots) has changed the game in modern smart factories. These robotic agents assist human operators, working with them side-by-side on joint task execution. Because cobots are designed to be more co-workers than tools, fluent interaction between the operators and their robotic counterparts is critical for employees’ task accomplishment and, thus, high performance. The current study investigates the relationships between four perspectives of human–robot interaction fluency (i.e., the human emotions-oriented, the human contribution-oriented, the robot-oriented, and the team-oriented fluency) and operators’ subjective job performance. It also examines the mediating role of work engagement in these relationships. The analysis carried out on 190 male and female cobot operators working on the shop floor showed positive associations between human–robot interaction (HRI) fluency and job performance. The study confirmed the mediating role of work engagement in the relationships of human contribution-oriented fluency and team-oriented fluency with job performance. The obtained results suggest that HRI fluency relates to employee job performance because of the positive affective–cognitive state experienced by the operator when cooperating with a cobot in a coordinated and well-synchronized manner. The findings of the study are discussed within the theoretical framework of cognitive ergonomics, the Job Demand-Control-Support model, the job demands-resources model, and the job design perspective. The article finishes with a conclusion of the results and implications for organizational practice.
... Due to the ongoing implementation of industry 4.0, new technological possibilities are becoming available for industrial work, e.g., intelligent automation. They lead to changed work processes and thus affect the human work, e.g., regarding a new work allocation, changed roles, new tasks, or new assistance functions [18,30]. These results lead to more complex work. ...
Article
Full-text available
In consequence of an ongoing change in manufacturing towards cyber-physical production systems (Industry 4.0), there is pressure for industry innovation to maintain and improve competitiveness. One aspect of this process is the digitalisation of work instructions, for example, in assembly or maintenance to use optimization potentials and counteract the increasing complexity and variety of products. A 1:1 transformation of paper-based instructions into a digitalised version often can not assess the full potential of digitalisation and does not fit the underlying hardware. Therefore, the present work deals with a presentation and analysis of existing procedures and possibilities for the digitalisation of work instructions and relates them methodically to the industry’s environmental, work and process-related conditions and requirements. Here, we distinguish between (1) different digitalisation options (text-based, image-based, video/animation-based and Augmented Reality (AR)-based) and (2) different industrial requirements (task and process requirements, user requirements, environmental conditions). A special emphasis is put on AR-based approaches. As a result, we provide design guidance for practitioners’ when chosing digitalisation options for work instructions with respect to present industrial requirements and create a basis for further research work.
... For instance, it has been reported that metaverse technologies will further advance smart manufacturing by reducing labor and resource costs and project implementation time [11]. In this context, the human-cyber-physical systems (HCPS), as an emerging paradigm to understand and establish the complex human-centric smart systems, have drawn attention from both industry and academia [10][11][12][13][14][15], especially towards human-centric smart manufacturing. ...
Article
Full-text available
Advances in human-centric smart manufacturing (HSM) reflect a trend towards the integration of human-in-the-loop with technologies, to address challenges of human-machine relationships. In this context, the human-cyber-physical systems (HCPS), as an emerging human-centric system paradigm, can bring insights to the development and implementation of HSM. This study presents a systematic review of HCPS theories and technologies on HSM with a focus on the human-aspect is conducted. First, the concepts, key components, and taxonomy of HCPS are discussed. HCPS system framework and subsystems are analyzed. Enabling technologies (e.g., domain technologies , unit-level technologies, and system-level technologies) and core features (e.g., connectivity, integration, intelligence , adaptation, and socialization) of HCPS are presented. Applications of HCPS in smart manufacturing are illustrated with the human in the design, production, and service perspectives. This research offers key knowledge and a reference model for the human-centric design, evaluation, and implementation of HCPS-based HSM.
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Since almost the onset of computer-supported cooperative work (CSCW), the community has been concerned with how expertise sharing can be supported in different settings. Here, the complex handling of machines based on experience and knowledge is increasingly becoming a challenge. In our study, we investigated expertise sharing in a medium-sized manufacturing company in an effort to support the fostering of hardware-based expertise sharing by using augmented reality (AR) to ‘retrofit’ machines. We, therefore, conducted a preliminary empirical study to understand how expertise is shared in practice and what current support is available. Based on the findings, we derived design challenges and implications for the design of AR systems in manufacturing settings. The main challenges, we found, had to do with existing socio-technical infrastructure and the contextual nature of expertise. We implemented a HoloLens application called RetrofittAR that supports learning on the production machine during actual use. We evaluated the system during the company’s actual production process. The results show which data types are necessary to support expertise sharing and how our design supports the retrofitting of old machines. We contribute to the current state of research in two ways. First, we present the knowledge-intensive practice of operating older production machines through novel AR interfaces. Second, we outline how retrofitting measures with new visualisation technologies can support knowledge-intensive production processes.
Chapter
In this chapter, the authors focus on the design and evaluation of industrial cyber‐physical systems that will be interacting or even cooperating with a human operator. Since they are dealing with systems dedicated to Industry 4.0, these human operators are called Operators 4.0. The designer must thus identify the tasks and sub‐tasks of the human operators and the Human–Industrial Cyber‐Physical System (HICPS) at each level of activity, also known as the decision‐making level, such as the operational, tactical and strategic levels regularly mentioned in industrial systems organizations. The definition and organization of the functions of the human operators and the HICPS are important steps in the design process, but the design of the interface, external representation of the Common Workspace, and thus support of the cooperative activity are also a crucial step to the success of the new HICPS.
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Manufacturing machines need to be re-tooled approximately 15 times per week and in the future even more often because of decreasing batch sizes and increasing short-cyclic demands. Collaborative robots promise to offer a versatile automation approach for priorly manual tasks in small and medium-sized enterprises. However, their configuration needs to change at least as often as the re-tooling rate because different parts are produced by the machines or might require different handling in general. Therefore, it would be great if robots and autonomous factory systems, in general, would automatically adjust to these changes in an intelligent way. In our approach, we propose a context-aware and location-based approach for agile manufacturing, in which the manufacturing plant parts, especially the collaborative robots, store i) their constellation, ii) their configuration, and iii) their adaptation strategy, and can react to re-tooling changes and even re-location changes adaptively. For example, moving one collaborative robot to a different location next to the production machine will automatically load its new configuration and consult with the operator on the adaptation strategy (i.e., the safety requirements). Such an approach requires precise location information. To realize the localization and the network capabilities, we propose to use localization based on heterogeniclocalization technologies like ultrasound and wireless communications. We suggest a wireless small-cell-based approach around a nomadic 5G core network, which integrates multiple wireless and wired communication technologies as well as localization support combined with an intelligent asset management strategy. Such nomadic cells can operate on an island without a heavy operator backend and optimize end-to-end communication. Furthermore, these small cells can federate with each other and thus extend their coverage when getting into each other's range.
Chapter
The Industry 4.0 paradigm is a strategic initiative that uses advancements in intelligent instrumentation and Internet to promote digital transformation in the industry. It integrates physical and computational environments and envisages a smart ecosystem that creates business growth in the supply chain. Connectivity is an essential prerequisite for the highly anticipated Industry 4.0 and provides seamless communication between all instances involved in the connected economy. In this context, wireless communication and cellular networks are key technologies that support the vision of the industrial Internet. Different wireless technologies and standards are proposed to handle communications in production and process areas. In this chapter we will review the connectivity requirements in the Industry 4.0 era and elaborate on multiple wireless communication technologies, cellular networks, and machine type communications (MTCs) for industrial systems.
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In this chapter, we discuss the smart warehouse concept and the challenges it entails with the increasing digitalization of the supply chain. The principal enabling technologies that play a major role in the progression towards smart warehouses are identified and discussed in the context of the changes occurring across business, industry, and the retail economy. The warehouse processes affected and potential influences of the technologies on warehouse management and operations are described. The chapter focuses on one of the most important process steps in the smart warehouse, order-picking, which is currently subject to major developments and transformations. Using a technological grid, four types of order-picking system are derived, which systematize how technologies can support human operators in warehouses to reduce physical workload and/or improve cognitive ergonomics. The four system types are classified based on supportive (digital) and substitutive (automation) technologies. The impacts of increased digitalization in warehouses on the physical, cognitive, perceptual and psychosocial human factors are examined from a sociotechnical perspective. These manifold influences are exemplified for the case of a collaborative order-picking system and broken down using an analysis framework that can be used for the systematic development of sociotechnical systems in the digitalisation of the supply chain.
Chapter
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This paper analises the problems and trends of the introduction of anthropocentric production systems (APS), specifically in small less industrialised member states of the European Union. The aim of this paper is to characterize APS and to present some special considerations related to the socioeconomic factors affecting the prospects and conditions for APS that is defined as a system based on the utilization of skilled human resources and flexible technology adapted to the needs of flexible and participative organisation. Among socioeconomic factors, some critical aspects for the development of APS will be focused, namely technological infrastructure, management strategies, perceived impact of introduction of automated systems on the division of labour and organisational structure, educational and vocational training and social actors strategies towards industrial automation. This analysis is based on a sample of industrial firms, built up for qualitative analysis, and on case studies analysis that can be reference examples for further development of APS, and not just for economic policy purposes alone. We have also analysed the type of existing industrial relations, the union and employer strategies and some aspects of public policies towards the introduction of new technologies in the order to understand the extente to which there exists obstacles to and favorable conditions for the diffusion of anthropocentric systems. Finally some recomendations are presented to stress the trends for the implementation and development of anthropocentric production systems.
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Human-centricity in manufacturing is becoming an essential enabler to achieve social sustainable manufacturing. In particular, human-centric automation can offer new means to increase competitiveness in the face of new social challenges for the factories of the future. This paper proposes a Human-Centred Reference Architecture that can structure and guide efforts to engineer Next Generation Balanced Automation Systems featuring adaptive automation that take into account various criteria in the operating environment such as time-lapse, performance degradation, age-, disability-and inexperience-related limitations of operators to increase their working capabilities.
Conference Paper
Full-text available
Human-centricity in manufacturing is becoming an essential enabler to achieve social sustainable manufacturing. In particular, human-centric automation can offer new means to increase competitiveness in the face of new social challenges for the factories of the future. This paper proposes a Human-Centred Reference Architecture that can structure and guide efforts to engineer Next Generation Balanced Automation Systems featuring adaptive automation that take into account various criteria in the operating environment such as time-lapse, performance degradation, age-, disability- and inexperience-related limitations of operators to increase their working capabilities.
Chapter
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Book
Understanding "what" consumers want and "why" are two of the most significant hurdles faced by any business creating products for consumers. Properly conducted sensory research experiments can provide answers to these questions and more. Sensory evaluation provides strategic information at various stages in the product lifecycle including the front end of innovation, new product development, product optimization, marketplace audits, and quality control among others. Sensory research can help identify issues that contribute to a product's success (or failure). This fourth edition draws on the author's practical experience in partnering with business associates in marketing and development teams to bring creativity and innovation to consumer driven product development in today's global business environment. The field of sensory science continues to grow and is now recognized as a strategic source of information for many Fortune 500 companies. Many scientists working in this field depend on the core textbooks such as this one to enhance their working knowledge base with practical business applications. * Appeals to sensory professionals in both in academia and business * Methods to integrate sensory descriptive information and consumer assessment * Coordinate marketing messages and imagery with the product's sensory experience.
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The development of Industry 4.0 will be accompanied by changing tasks and demands for the human in the factory. As the most flexible entity in cyber-physical production systems, workers will be faced with a large variety of jobs ranging from specification and monitoring to verification of production strategies. Through technological support it is guaranteed that workers can realize their full potential and adopt the role of strategic decision-makers and flexible problem-solvers. The use of established interaction technologies and metaphors from the consumer goods market seems to be promising. This paper demonstrates solutions for the technological assistance of workers, which implement the representation of a cyber-physical world and the therein occurring interactions in the form of intelligent user interfaces. Besides technological means, the paper points out the requirement for adequate qualification strategies, which will create the required, inter-disciplinary understanding for Industry 4.0.
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Understanding "what" consumers want and "why" are two of the most significant hurdles faced by any business creating products for consumers. Properly conducted sensory research experiments can provide answers to these questions and more. Sensory evaluation provides strategic information at various stages in the product lifecycle including the front end of innovation, new product development, product optimization, marketplace audits, and quality control among others. Sensory research can help identify issues that contribute to a product's success (or failure). This fourth edition draws on the author's practical experience in partnering with business associates in marketing and development teams to bring creativity and innovation to consumer driven product development in today's global business environment. The field of sensory science continues to grow and is now recognized as a strategic source of information for many Fortune 500 companies. Many scientists working in this field depend on the core textbooks such as this one to enhance their working knowledge base with practical business applications. * Appeals to sensory professionals in both in academia and business * Methods to integrate sensory descriptive information and consumer assessment * Coordinate marketing messages and imagery with the product's sensory experience.