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Int. J. Production Economics 233 (2021) 107992
Available online 28 November 2020
0925-5273/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Industry 4.0 and the human factor – A systems framework and analysis
methodology for successful development
W. Patrick Neumann
a
, Sven Winkelhaus
b
, Eric H. Grosse
b
,
c
,
*
, Christoph H. Glock
b
a
Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
b
Institute of Production and Supply Chain Management, Technische Universit¨
at Darmstadt, Hochschulstr. 1, Darmstadt, 64289, Germany
c
Juniorprofessorship of Digital Transformation in Operations Management, Saarland University, Campus C3 1, Saarbrücken, 66123, Germany
ARTICLE INFO
Keywords:
Human factors
Ergonomics
Industry 4.0
Digital transformation
Content analysis
System design
ABSTRACT
The fourth industrial revolution we currently witness changes the role of humans in operations systems.
Although automation and assistance technologies are becoming more prevalent in production and logistics, there
is consensus that humans will remain an essential part of operations systems. Nevertheless, human factors are
still underrepresented in this research stream resulting in an important research and application gap. This article
rst exposes this gap by presenting the results of a focused content analysis of earlier research on Industry 4.0. To
contribute to closing this gap, it then develops a conceptual framework that integrates several key concepts from
the human factors engineering discipline that are important in the context of Industry 4.0 and that should thus be
considered in future research in this area. The framework can be used in research and development to sys-
tematically consider human factors in Industry 4.0 designs and implementations. This enables the analysis of
changing demands for humans in Industry 4.0 environments and contributes towards a successful digital
transformation that avoid the pitfalls of innovation performed without attention to human factors. The paper
concludes with highlighting future research directions on human factors in Industry 4.0 as well as managerial
implications for successful applications in practice.
1. Introduction
The fourth industrial revolution, also termed Industry 4.0 (I4.0), has
recently gained considerable attention in the production research
domain (Lu, 2017; Xu et al., 2018; Liao et al., 2017). The aspiration
behind I4.0 was to propose an industrialisation model suited to Ger-
many’s position as both a producer and user of high-technology pro-
duction systems (Kagermann et al., 2013). Industrial revolutions are
often framed in a technological perspective: the 1st industrial revolution
relating to steam powered systems, the 2nd to the use of electrically
powered systems, and the 3rd to the adoption of information technology
and automation. Speaking broadly, I4.0 refers to the further digitaliza-
tion and integration of information technologies including applications
such as the internet of things (Lu, 2017), cloud-based systems (Lu,
2017), cobots (Bortolini et al., 2017), big data analytics (Wang et al.,
2016), additive manufacturing (Hofmann and Rüsch, 2017), and
cyber-physical systems (Xu et al., 2018). These systems enable a “smart
factory” (Frank et al., 2019; Osterrieder et al., 2020), in which humans,
machines and products communicate with each other via both physical
and virtual means (Kagermann et al., 2013), and can contribute to
increased sustainability (Bai et al., 2020). We point out the aspirational
nature of I4.0; while previous industrial revolutions were identied and
examined mainly after they had occurred, the conceptualisation of I4.0
and associated application of technologies is just beginning and is part of
a deliberate industrialisation strategy.
Beside technological push-factors (Frank et al., 2019), I4.0 is also
characterized by different pull-factors (Lasi et al., 2014) that contribute
to a shift of paradigms. For example, individualized customer demands
can be seen as a main driver of I4.0, since the fullment of individual-
ized demands without an increase in costs (this is often referred to as
‘mass customisation’ or ‘product individualisation’) is one of the su-
perordinate goals of using the different technologies (Winkelhaus and
Grosse, 2020). However, it is still not fully clear to many in both industry
and research what fully realised I4.0 applications might look like or how
they might operate. For most practitioners, the digital transformation
and its implications on operations processes remain a big black box. The
* Corresponding author. Juniorprofessorship of Digital Transformation in Operations Management, Saarland University, Campus C3 1, 66123 Saarbrücken,
Germany.
E-mail address: eric.grosse@uni-saarland.de (E.H. Grosse).
Contents lists available at ScienceDirect
International Journal of Production Economics
journal homepage: http://www.elsevier.com/locate/ijpe
https://doi.org/10.1016/j.ijpe.2020.107992
Received 26 May 2020; Received in revised form 30 September 2020; Accepted 10 November 2020
International Journal of Production Economics 233 (2021) 107992
2
transformation of production due to both technological and paradig-
matic drivers leads to fundamental changes of organisations and pro-
cesses (Matt et al., 2015) and nally also of human work (Neumann and
Village, 2012; Kadir et al., 2019). Within this context, attention to
human factors (HF) has been particularly sparse, despite the evident
centrality of HF in four of the eight I4.0 developmental priorities (i.e.
managing complex systems, safety and security, work organization and
design, and training and professional development) laid out in the
seminal I4.0-report by Kagermann et al. (2013). This centrality of
human aspects, we will show, is not reected in the I4.0 research to date.
I4.0 and technological change are rapidly transforming virtually all
areas of human life, work, and interaction. These changes are acutely
apparent in the way human work is organized and performed. Promi-
nent examples include the usage of mobile devices for the augmentation
of processes and support of workers, e.g. for maintenance or in order
picking. Collaborative robots support assembly workers, exoskeletons
empower production and logistics workers, and cloud-based software
solutions for enterprises are emerging at a high pace (see, e.g. Oster-
rieder et al., 2020). These examples are just a few new forms of inter-
action between humans and I4.0 technologies within business’
transformation that, however, highlight the various and novel in-
teractions humans are confronted with.
First attempts to structure these interactions are made, for example,
by Romero et al. (2016), Ruppert et al. (2018) and Fantini et al. (2020),
where the operator is interpreted in different roles, depending on the
technologies used. As described by Romero et al. (2016), augmented
reality used by an operator leads to the “augmented operator”, who is
presumably capable of making more informed decisions when main-
taining a machine, for instance. These works, however, still focus on
technological possibilities for the worker without analysing their in-
uences on HF demands and operator experience in depth. Moreover,
the “Operator 4.0” as proposed by these works is merely analysed in
isolation, without consideration of the organizational, processual, psy-
chosocial, and technological environment of the humans in the system.
This article discusses how failure to attend to HF in previous indus-
trial system generations has had negative consequences for individual
employees, production organisations, and for society as a whole. We
further show that there has also been a lack of attention to HF aspects in
research and development in I4.0, and present and discuss a framework
for the systematic consideration of HF in the design and evaluation of
I4.0 technologies and technology-assisted workplaces. Addressing these
aspects, this paper pursues two research objectives (ROs):
RO1: To identify which HF aspects have been considered to what
extent in the scientic literature on I4.0.
RO2: To provide a framework that includes foundational theories of
HF to support the incorporation of HF aspects into corporate I4.0-
system development efforts.
The remainder of this paper is structured as follows. A content
analysis of research dealing with I4.0 is performed in Section 2, which
highlights the denite lack of considering HF in this research area. In
Section 3, concepts of HF in engineering design are discussed that are
relevant for understanding the role of HF for system performance. In
Section 4, an analysis framework is derived based on the discussed
concepts to highlight how HF can be considered systematically in I4.0
research and development. In addition, an example application of the
framework to a typical I4.0 use case is presented. The framework’s
implications are discussed in light of the insights obtained from the
content analysis and theory section, and limitations as well as future
perspectives of HF in I4.0 for researchers and managers are outlined in
Section 5. Fig. 1 illustrates the outline of the paper and the research
steps.
2. Evidence of lack of HF in I4.0 research: a content analysis
We present a content analysis of the literature on I4.0 in the next
section to address RO1: examining which HF aspects have been included
in the I4.0 literature up to now. We rst briey summarize previous
related literature reviews. Subsequently, we outline the methodology
and results of a content analysis of the literature on I4.0.
2.1. Insights from literature reviews and related industry 4.0 works
Two reviews focusing on HF-related issues in I4.0 could be identied.
First, Badri et al. (2018) discussed occupational health and safety issues
in the emergence of I4.0. In their systematic review, they identied
eleven contributions as relevant, seven of these were conference articles.
They concluded that “most articles are focused on new technologies
driving this revolution and mentioned worker health and safety only
briey” (Badri et al., 2018). Second, Kadir et al. (2019) applied a
broader search for contributions that do not only consider health and
safety issues, but HF in general. Overall, 40 peer-reviewed articles were
identied that use I4.0- and HF-related terms in the title, abstracts or list
of keywords in Scopus, but again only 13 of these were journal articles.
In a qualitative assessment of the identied articles, the authors pointed
at mental, physical and organizational aspects considered, such as
human-machine interaction or necessary IT skills as well as the possi-
bility of automation of repetitive manual tasks. They concluded, how-
ever, that literature on this topic is still narrow and rare, and that more
I4.0 research with deeper attention to HF is needed. Since these reviews
generated their sample in 2018, we analyse these insights to explor-
atively update the outcomes. Based on this analysis, we noted that the
term “Operator 4.0” has emerged as a research area of note during the
last year. We provide a brief overview of this literature here.
Six recent journal articles could be identied dealing with the
“Operator 4.0” concept that have not been included in the reviews of
Kadir et al. (2019) and Badri et al. (2018) that are generally based on
technology-driven approaches. One of these works is the one of Ruppert
et al. (2018) that grounds a survey on technologies for the “Operator
4.0” based on the systematization of Romero et al. (2016). The focus is
on IoT-based infrastructure instead of software-based applications like
Fig. 1. Outline of the paper and interdependencies between sections.
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
3
big data. However, since the focus is on technologies, HF are only
considered briey although they are of great relevance for the
applications.
Kaasinen et al. (2020) performed user studies in three companies
focusing on “Operator 4.0” solutions. Each study used different outcome
measures, such as increasing job satisfaction, performance or control-
lability of production, which are achieved by empowering and engaging
the workers using I4.0 technologies, e.g. for knowledge sharing or
personalized learning. In the case studies, also challenges were
observed, including major doubts about technology usage raised by
workers. Zolotov´
a et al. (2020) performed laboratory case studies
implementing different technologies. The authors concluded that using
various technologies, for example for a “Smart Operator” or an
“Analytical Operator”, leads to better results compared to implementing
only a single technology. Segura et al. (2020) mainly focused on visual
computing technologies, especially augmented reality, but also virtual
reality, cobots and social networks. In use cases, the authors showed
how these technologies can facilitate decision-making processes of
workers.
The last two articles are of a more conceptual character. Dealing
specically with cognitive automation as part of the “Operator 4.0”
concept, Mattsson et al. (2020) provided a strategy answering the
question of how cognitive automation systems should be designed for an
optimal support of assembly workers. The authors developed a frame-
work that could be used to reduce stress and improve complexity
handling and transparency in cognitive automation. Lastly, Taylor et al.
(2020) provided a different perspective on the “Operator 4.0” asking for
chances of such a development for small, capital-constrained enter-
prises. Taking the economy of New Zealand as an example, they dis-
cussed whether there is a transition from an operator-role to a
maker-role, since employees are more involved in designing products
than in monitoring machines.
Overall, it can be seen that the explicit consideration of HF in I4.0-
related research is scarce. The above-mentioned reviews focus on arti-
cles that explicitly deal with HF in I4.0 and did not nd a large sample to
draw their conclusions on. Moreover, even articles that deal with the
“Operator 4.0” concept and its impacts on HF are only discussed in a few
cases in depth. In most of the studies and also the original contribution of
Romero et al. (2016), HF remain an afterthought and not a design
objective, nor a means to achieve good designs. This is consistent with
gaps in the industrialisation research identied by reviews in
manufacturing (Neumann and Dul, 2010) and in warehouse systems
research (Grosse et al., 2015; 2017). While I4.0 has been reviewed in
terms of its impact on sustainability (Bai et al., 2020), the focus was so
far on the external environment and not on the internal working envi-
ronment (Docherty et al., 2002). The work of Bai et al. (2020) deals with
the actual interaction of people and the system. These interactions will
be crucial to the success or failure of a system design effort to achieve the
functionalities proposed in that work. In contrast, the work of Pinzone
et al. (2020) addresses social sustainability in cyber-physical production
systems directly; it does so, however, from a high-level discussion of
functionalities and does not address the specic issue of human-system
interactions in the design and application of new technologies. Recog-
nizing that there may be relevant discussions inside the body of papers
that do not use HF terms in their title, keywords, or abstract, we con-
ducted a content analysis of the body of available I4.0 literature to
examine the extent of discussion of HF-related issues compared to purely
technical ones.
2.2. Methodology
A content analysis (CA) is an established method to analyse pub-
lished works systematically and to highlight the core of research as well
as to identify research gaps (Spens and Kov´
acs, 2006; Cullinane and Toy,
2000; Grosse et al., 2017). A CA is “a research technique for making
replicable and valid inferences from texts or other meaningful matters to
the context of their use” (Krippendorff, 2013). According to Neuendorf
(2002), a CA enables the recognition of patterns in large data sets. The
objective of the CA is to count specic keywords, called recording units
(RU), in the sample, assuming that a high number of hits is an indicator
for the importance of the keyword (Cullinane and Toy, 2000). We use
the CA here as a method to compare the occurrence of use of key terms
related to I4.0 and HF. The analysis at hand follows four steps: 1) ma-
terial collection, 2) descriptive analysis, 3) category selection, and 4)
material evaluation. We outline these steps in further detail in the
following.
The sample consists of all papers containing the keyword “Industry
4.0” in the title, which guarantees a strong focus on I4.0 and a broad
sample. The database Scopus was searched for articles, since it is among
the largest, transdisciplinary databases for peer-reviewed journal arti-
cles, leading to 2650 results in the rst step of the development of the
sample. We then limited our search to peer-reviewed journal articles
written in English, resulting in 646 hits. All sampled works were ob-
tained as or converted into readable PDF documents to allow for the use
of the text analysis software MAXQDA. To avoid biases, the reference
lists of all papers were removed before starting the count of RU.
As can be seen in Fig. 2, the sample shows a steady increase of
research interest from the rst paper on I4.0 published in 2014 until its
current climax in 2019. We observed an interdisciplinary character of
the sample with regard to journals, which stresses the complexity of
research in the emerging digital transformation of work.
In the next step, all articles were coded using the method of manifest
coding (Babbie, 2013). For the analysis, we chose both a deductive and
an inductive approach. In the deductive approach, three categories
(I-III) including various subcategories for I4.0 and HF aspects were
derived based on the theoretical insights presented in Sections 2.1 and 3.
I. Beginning with I4.0 concepts, there are three subcategories of
relevance: First, I4.0 systems are based on the implementation of
a wide range of different technologies, like IoT, CPS and Big Data.
Second, there is a paradigmatic change, which leads to new targets
of the actions performed. Third, a subcategory I4.0 characteristics
is added, considering terms like “smart” or “collaborative” which
could be seen as a mediator including terms that do not name a
certain technology, but instead a certain characteristic of it that is
necessary for target achievement.
II. With regard to HF, four subcategories were derived especially
based on the perception-cognition-motor action-cycle and the
demand-control-model (see Key Concepts 3 and 4 in Section 3.3).
The perception of a given situation leads to the cognitive processing
and, in accordance with memory interaction, to the decision to
perform a motor action. This loops back to the situation, which is
perceived again. Besides these, also psychosocial aspects inuence
the work environment for humans.
Fig. 2. Distribution of articles over time.
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
4
III. The third category “General HF terms” includes two sub-
categories. The rst generally refers to human capabilities or load
without referring to a certain system. These keywords are sum-
marized in the subcategory general terms. The second deals with
different and changing roles of humans in I4.0, e.g. from an
operator role to a machine supervisor role.
Table 1 summarizes the three categories, related subcategories, and
RU. Overall, we consider three categories (General HF terms, HF, and
I4.0) and nine subcategories. For every subcategory, RU were derived
deductively based on the theoretical background (Section 3). Different
spellings (AE/BE), common abbreviations and different word endings
(singular/plural) were considered in nalizing the list of RU. This
deductive approach was complemented by an inductive renement,
where an entire coding process of all abstracts of sampled papers was
performed, counting all words, abbreviations and symbols in the ab-
stracts. The resulting list was then evaluated carefully to inductively
rene the category system and RU. This approach ensured that all
important RU are contained in the category system.
To account for possible biases, a sensitivity analysis was carried out,
in which both the top ranked RU as well as the top contributing articles
were analysed. The results of this analysis show whether only a few RU
or a few articles contribute disproportionately to the result of a RU or
category. Hence, it is possible to qualitatively account for correction
factors (Abedinnia et al., 2017; Grosse et al., 2017).
2.3. Results
Comparing the hits for the two most prominent RU in I4.0 and HF,
the results of the CA reveal a strong disparity between both categories,
as illustrated in Fig. 3. In fact, we noticed 29,591 accumulated hits for
“Industry 4.0” and “Internet of Things” versus only 254 accumulated
hits for “Ergonomics” and “Human Factors”.
Table 2 summarizes the number of accumulated hits for the RU (#) in
each subcategory. % indicates the percentage share of each subcategory
(in terms of total number of hits of RU), and R shows the corresponding
rank. Moreover, considering the different amount of RU per category,
the mean # per RU for each category is calculated and the corresponding
rank is given to ease comparability.
As can be seen in Table 2, 69% of all RU hits are observed in the
category I4.0, with the subcategory technologies accounting for 50%
alone (although the number of RU presents only 19%), which points to a
high relevance this subcategory enjoyed in the sampled papers
compared to all HF subcategories together. Also the relative ranks (mean
hits per word) are led by all three I4.0-related subcategories. Within the
I4.0 category, the RU counting the most hits are general terms like ‘data’
or ‘Industry 4.0’ that are followed by primary technologies like ‘IoT’. On
the characteristics side, ‘smart’, ‘exible’ and ‘real-time’ are top ranked,
and targets focus on ‘performance’, ‘sustainability’ and ‘quality’, but
also ‘individualisation’ and ‘customisation’. Lastly, there are some terms
that were not found at all in the sample like “musculoskeletal disorder”,
“job demand”, “job control” or “order picker”, or only a few times as in
the case of “job satisfaction” (14 hits), “psychosocial” (3 hits) or “work
design” (11 hits).
The accumulated hits per subcategory are displayed in Fig. 4. As can
be seen, in the four HF subcategories, there are two bars displayed,
showing the results for all RU in the subcategory as shown in Table 2 in
dark grey, whereas the light grey ones belong to the subsequently dis-
cussed sensitivity analysis.
2.4. Sensitivity analysis
Generally, the results of a CA should be reected in light of possible
Table 1
Category system and recording units.
Category &
Subcategory
Recording Unit (RU)
General HF Terms
Roles of Humans customer, maintenance, user, human/s, employee/s,
operator/s, worker/s, manager/s, expert/s, partner/s/
partnership, researcher/s, engineer/s, consumer, leader,
stakeholder, workforce, staff, practitioner/s, personnel,
supervisor/s, technician, employer/s, entrepreneur/s,
instructor, programmer, shareholder, politician, co(−)
worker, assembler, order picker
General Terms work, social, risk, decision(−)making, labo(u)r, society,
health, human resource/HR, attention, age/ing, effort,
workload, human factor/s, assisted/assistive, socio-technical,
work organization, ethics, OHS
Human Factors
Mental learn, knowledge, training, capabilities, skill/s, experience/s,
education, behavio(u)r/al, teach/ing, cognitive/cognition,
talent, competencies, hmi, human-machine, mental,
qualication, creativity, psychology/psychological, human-
centered, confusion/ing/ed, human-robot, e-learning,
human-computer, forget/ting, human-technology, memory,
reasoning
Physical physical, safety, manual, ergonomic/s, fatigue/fatiguing,
posture, well-being, gesture, musculoskeletal disorder
Psychosocial involve*, culture/cultural, feedback, motivation, stress/ful/
ing, teamwork, fairness, work design, psychosocial, job
satisfaction, job demand, job control, support
Perceptual read/ing, perception/ual, information processing
Industry 4.0
Technologies data, Industry 4.0, technology/technologies, information,
machine, network/s, IoT/Internet of Things/Industrial
Internet/iiot, sensor, digital/ly, automation/automated/
automatic, CPS/cyber-physical, cloud, robot, virtual/VR, big
data, equipment, IT, simulation, digitiz(s)ation/digitaliz(s)
ation, mobile, augmented/AR, wireless, autonomous/
autonomy, articial/AI, rd, ICT, blockchain, additive, digital
twin, 3D printing, wearable, agv, cobot, gamication
Characteristics smart, environment/al, exible/exibility, real-time,
intelligent, integrated, predictive/prediction, complexity,
lean, embedded, collaborative, robust/ness, data-driven,
disruptive, ubiquitous, transparent, cooperative, visible, as a
service, self-learning
Paradigm and
Targets
performance, sustainability/sustainable, quality, energy,
individual, industrial revolution, optimiz(s)ation,
productivity, paradigm, customiz(s)ation/customiz(s)ed,
privacy, transparency, trust, virtual/virtualiz(s)ed/virtualiz
(s)ation, visibility, compliance, resilience, servitization,
personaliz(s)ation, cyber security, usability, predictability
Fig. 3. Number of recording units for I4.0 and HF.
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
5
biases or inuential points originating from some RU that can have
ambivalent interpretations (Grosse et al., 2017). In our case, a more
precise look at the hits in the HF categories is warranted. For example,
“learning”, which is a very important HF term (Glock et al., 2019), is
increasingly transferred to the I4.0 domain, for example in terms like
machine learning, learning algorithms and articial intelligence. Other
critical RU in this regard are “behaviour”, referred to as systems
behaviour, “read/ing”, which often refers to tag readings of RFID-based
systems, or “feedback”, which is often used in a technical “feedback
loop” context. Most of these RU are among the top three in the HF
categories; acknowledging their possible relation to I4.0 technologies,
the problem stated above becomes even more apparent. We identied
six RU as critical in terms of ambiguous interpretations both in I4.0 and
HF (shown in Table 3), and eliminated these from the analysis. The re-
sults change as illustrated in Fig. 4 (light grey bars in the HF sub-
categories). As can be seen, especially the subcategory mental HF as well
as perceptual HF decline in their importance. The lack of attention to HF
in I4.0 research becomes even more apparent.
Besides possible biases caused by RU, articles using a certain RU
quite frequently could have biased the results. Therefore, we identied
the top ten articles (out of the sample of 646 articles) that contribute
most hits for RU in the HF category without considering articles that
have been coded incorrectly in the HF category due to the above dis-
cussed ambiguous meanings of RU. Having a more precise look on them
helps to interpret additional possible biases due to an overestimation of
RU. The results are given in Table 4.
All articles focus on how to teach, learn and develop knowledge and
capabilities for a future I4.0 environment, but only a few investigate the
inherent changes induced by I4.0 for workers and especially for shop
oor workers. Following, within the most contributing articles, the focus
is not on how to design or interact with an I4.0 system. Based on this
analysis, we conclude that there is an even more tremendous neglect of
HF in I4.0 as suggested in the accumulated results, because these ten
articles are responsible for about 8% of the hits in the HF categories,
whereas they only account for about 1.5% of the sample. This is due to
an extreme distortion of the mental HF category where only a few ar-
ticles account for many hits and the different meanings of HF keywords
bias the results, too.
We can now conclude on the rst research objective, which focused
on identifying which HF aspects have been considered to what extent in
the I4.0 literature: In the sample of research articles containing the term
“Industry 4.0” in the title, we found a clear focus on technologies rele-
vant for paradigmatic changes. The discussion of general HF terms such
as roles, as revealed by the results of the CA, seems to indicate an
awareness among researchers that their technological developments will
inuence people. The absence of specic HF terms suggests, however,
that this technology focused research rather pays lip service to humans
but does not deal in any substantial way with human-system interaction,
which causes the concerns that researchers are not paying attention to
human aspects in their development work. When considered, then
mental HF followed by physical HF have tended to be more common
considerations, whereas psychosocial and perceptual aspects have been
widely neglected, which manifests a clear lack of HF in I4.0 research.
This suggests the I4.0 research is “blind” to the nature of the human
system interactions in the systems they are helping to design. This does
not bode well for the success of I4.0 approaches, or for the people forced
to endure them. To contribute to closing this gap, we develop a frame-
work for the systematic consideration and analysis of HF in the design
and evaluation of I4.0 systems in the following sections.
Table 2
Results of the CA category system.
Text only Words in Category
# % R Nr. of
RU
mean #
per RU
R Subcategory Category
30563 11 2 30 1019 4 Roles General HF
Terms 16457 6 6 18 914 5 General Terms
22372 8 5 27 829 6 Mental HF
7060 2 8 9 784 7 Physical
9693 3 7 13 746 8 Psychosocial
978 1 9 3 326 9 Perceptual
145767 50 1 34 4287 1 Technologies I4.0
29564 10 3 20 1478 2 Characteristics
27385 9 4 22 1245 3 Targets
Fig. 4. Accumulated hits of recording units per subcategory in full texts.
Table 3
Recording units with possible ambiguous interpretations.
HF Recording Units Subcategory Industry 4.0 context
learn/ing mental e.g. learning algorithms, machine learning
training mental e.g. training an algorithm/a machine
behavio(u)r/al mental e.g. system behaviour
feedback psychosocial e.g. feedback loops
read/ing perceptual e.g. RFID-/tag-reading
memory perceptual e.g. computer memory
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
6
3. HF in engineering design
3.1. HF and worker health
We adopt the denition of HF (synonymous with the term ergo-
nomics) from the International Ergonomics Association as being “con-
cerned with the understanding of interactions among humans and other
elements of a system […] design in order to optimize human well-being
and overall system performance” (IEA Council, 2019). The failure to
address HF adequately in the design of work can lead to substantial
problems. Current estimates from the International Labour Organisation
place the annual work-related mortality at 2.78 Million deaths per year
globally (ILO, 2019). This amounts to about one work-related death
every 11.3 s. Musculoskeletal disorders (MSDs), such as repetitive strain
injuries, are a global problem caused by the design of work – particularly
due to high forces, high duration and repetition of efforts, poor working
postures, and poor psychosocial work environments (NRC, 2001). Pop-
ulation studies indicate that 20% of the general population suffers from
a work-related MSD (Major and V´
ezina, 2015). In some manufacturing
sector studies, MSD rates among system operators approach 100% (NRC,
2001). These disorders are caused by the design of the work system -
when the demands on the system operators exceed their tolerance (NRC,
2001; Neumann and Village, 2012). Failures to attend to HF in design
have been identied throughout the design and operationalization
process (Neumann et al., 2002, 2006; Kihlberg et al., 2005; Kolus et al.,
2018). In short - these problems are caused by system designers. The
costs for workplace injuries are enormous, estimated in the USA to be on
par with the costs of all cancers combined (Bhattacharya and Leigh,
2011). While managers frequently look at direct compensation costs as
an indicator of the MSD problem, the indirect costs are often much larger
and can include hiring costs, training costs, reduced performance,
increased errors, increased scrap costs, and wasted managerial effort
among the many indirect costs aspects related to employees’ MSDs in
manufacturing (Rose et al., 2013). Efforts to model the costs associated
with increased MSD risk factor exposure suggest that 2–8% of product
costs may be caused by these risks (Sobhani et al., 2016). The problems
caused by poor HF in system design warrants Kagerman et al.’s 4th
priority regarding system safety. However, systematic reviews have
revealed very little attention to safety issues in the I4.0 context to date
(Badri et al., 2018).
3.2. HF and operations performance
While the negative consequences of poor HF in the design and
implementation of production innovations are a serious concern, we
note also that HF is an essential aspect of organisational protability and
can provide strategic advantages to companies (Dul and Neumann,
2009). Benets from the application of HF include improvements to
productivity, technology implementation, quality, and system reli-
ability. Studies examining both human outcomes and system benets
from HF application generally nd that the system gains are consider-
ably greater than the nancial cost avoidance from reduced compen-
sation costs alone (Rose et al., 2013). System modelling studies revealed
that substantial portions of production cost may be due to poor HF in the
work system design (Sobhani et al., 2016). While production managers
carry considerable tacit knowledge of the strategic advantages available
from HF (Village et al., 2016), the quantitative nancial benets are
often buried in nancial systems and very difcult to isolate – they are
‘hidden’ in the accounting system (Rose et al., 2013). This has inhibited
a broader understanding of the importance of HF amongst engineers and
managers in operations settings (Broberg, 2007).
Accordingly, in particular the joint objective of performance and
well-being are often seen as being in conict, even though empirical
research demonstrates the convergence of well-being and work system
performance (Goggins et al., 2008). Besides performance and
well-being, there is empirical evidence that considering HF in the design
of operations systems also improves quality and reduces errors (Zare
et al., 2016; Kolus et al., 2018). Indeed, people-forwards management
practices are linked to competitive advantages that are, in the
resource-based view of the rm, difcult to copy and that can be
leveraged for longer periods than technology-only strategies which are
easily replicated (Boudreau et al., 2003). However, despite the evidence
that HF contribute to sustained competitive advantage, attention to
humans is frequently separated from engineering design and manage-
ment processes (e.g. Neumann and Village, 2012) and is also under-
represented in I4.0 research, as shown in Section 2.
3.3. Key concepts of HF
We propose ve “Key Concepts” from the eld of HF that can provide
a basis for understanding the interrelation of I4.0 and HF. Key Concept 1
is the fundamental theoretical ground, namely the sociotechnical system
theory. Following, a theory of HF in design is given as Key Concept 2,
before the human-system-interaction cycle is discussed in Key Concept
Table 4
Most contributing articles in the HF category.
Paper Content Subcategory
(Main RU)
Number of
hits top RU/
all HF RU
Maisiri et al.
(2019)
Performs a systematic
literature review about
technical and non-technical
skill requirements for
engineers in I4.0 and how
to develop them
Mental (skill) 236/380
Shamim et al.
(2017)
Conceptualizes
management practices for
I4.0-analogue
developments in the
hospitality sector including
HF inuences
Mental
(knowledge)
131/351
Longo et al.
(2019)
Proposes a solution for
training of staff for
emergencies in industrial
plants using I4.0-
technologies
Mental
(training)
181/344
Chong et al.
(2018)
Investigates the impacts of
I4.0-technologies and 3D
printing for teaching
engineering programs
Mental (teach) 80/337
Hariharasudan
and Kot (2018)
Studies I4.0’s inuences on
employee qualication
focusing on the effects of
Education 4.0 and Digital
English
Mental (learn) 105/318
Chi (2019) Investigates an application
for English learning
especially for engineering
learners within Industry 4.0
Mental (learn) 90/303
Sackey et al.
(2017)
Surveys learning factories
for I4.0 education in
industrial engineering
programs
Mental (learn) 161/287
Stachov´
a et al.
(2019)
Analyses employee
education for I4.0 focusing
on external partnerships for
personal development
processes comparing
different countries
Mental
(knowledge)
89/279
Hang et al. (2018) Surveys inuences on
training quality and student
satisfaction in the context of
I4.0 education programs
Mental
(training)
148/276
Azmimurad and
Osman (2019)
Analyses vocabulary
learning strategies among
students for the expanded
vocabulary needs in
engineering within Industry
I4.0
Mental (learn) 185/273
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
7
3. Key Concept 4 focuses on psychosocial aspects and the demand-
control model; an extension of Key Concept 3, and lastly Key Concept
5 gives insights into the theory of organizational drift to unsafe states.
Key Concept 1: Industry 4.0 systems are sociotechnical systems. In the
sociotechnical system (STS) theoretic view, which is an outgrowth of
general systems theory (Skyttner, 2001), all work systems are
assumed to include social (human) and technical (machine) elements
(for a history of STS, see Eijnatten et al., 1993). If there is a mismatch
between worker capabilities and the demands placed on them by the
system, then dysfunctional results, including errors and injuries, can
be expected. This leads to a chain of negative consequences for both
the worker and ultimately for the system as a whole. There are, we
argue, no I4.0 systems that do not engage humans across the lifecycle
in designing, installing, maintaining, operating, and dismantling (at
end of life) these systems (Sgarbossa et al., 2020). Attention to the
demands on the people performing these tasks is, therefore, a design
requirement (Cherns, 1976; Clegg, 2000).
Key Concept 2: Attention to HF must occur throughout design. Fig. 5
illustrates the design process diagram showing key stages of the
design process in which decisions affecting HF are made that have,
rstly, (positive or negative) effects on humans in the system which,
ultimately, affect system performance. In this view, the design of the
product and the process as well as the management of the production
system itself will determine the working environment for the
employee. This, in turn, will have effects on the worker, which might
be good, like gains in experience and motivation, or bad in terms of
fatigue, pain and injuries. These worker effects then will have con-
sequences for human performance, which will determine the overall
system performance (Neumann and Village, 2012). If HF in system
design and management are not appropriately considered, then poor
system performance can be expected. This conceptual framework has
been validated in case research in a variety of manufacturing con-
texts (Neumann et al., 2002, 2006; Sobhani et al., 2015). A recent
review of HF-related quality problems in manufacturing identied
HF-related quality risk factors in product design, process design, and
workstation design stages (Kolus et al., 2018). From a design science
perspective, we note that making changes to a given design gets more
difcult and more expensive throughout the design process,
becoming maximal once the system is operating and only retrotting
solutions are possible (Neumann and Village, 2012). Unfortunately,
it is typical that HF are only deployed in these late, operational stages
(Neumann and Village, 2012; Wells et al., 2012). The lowest cost and
maximum opportunity - in essence the best cost-benet results -
come from considering HF from the earliest stages and then
throughout the design project.
Key Concept 3: Human-system interaction engages perceptual, cognitive,
and motor systems. In the context of human-system interaction, the
perception-cognition-motor action cycle is, we argue, always rele-
vant (e.g. Helander, 2006). This model posits a continuous stream of
interaction between the person and the system. In the rst step, in-
formation about the machine is gathered via the sensory system
including visual, tactile, olfactory, auditory, and vestibular systems –
each with their own capacities and limitations that vary by indi-
vidual. This sensory input is then processed cognitively – also with
individual capacities and limits to memory and processing – into an
understanding of the situation and planning for any desired action.
This plan is then put into action via the musculoskeletal system – also
with individual capacities and limitations. If this cycle is successful,
the system will respond to the action providing (if it is well designed)
new information to the sensory system allowing a new system state
to be understood. Humans are continually engaged in this cycle of
processing in an ongoing stream. From the design perspective then, it
is crucial that sensory, cognitive, and musculoskeletal system ca-
pacities of individuals are not overloaded (or in some cases under-
loaded), and that the user has a robust understanding of the system
allowing them to identify the correct actions required to bring the
system to its desired state. For this reason, I4.0 system designers must
ensure that the demands of their design are matched with human
sensory, cognitive and motor capabilities, or they risk negative
outcomes for the human or for the sociotechnical system as a whole.
Key Concept 4: People have psychosocial needs. Another critical aspect
for successful sociotechnical system functioning is the psychosocial
working environment – the perception of the social environment in
the workplace. Critical dimensions here include job demands, job
control, supervisory and co-worker support, and job satisfaction (for
a more detailed discussion of psychosocial factors and their effect on
physical and mental well-being, readers are referred to the review
papers of Bongers et al., 1993 and Netterstrøm et al., 2008). In the
foundational model of Karasek and Theorell (1990), employees
experience mental “strain” when under working conditions that
involve high work demands with a low sense of control. Under these
working conditions, employees will experience signicant and sub-
stantial increases in a broad range of mental illnesses and physical
disorders. The empirical evidence here is substantial (Taouk et al.,
2020; Letellier et al., 2018; Kerr et al., 2001; Moon and Sauter, 1996;
Nieuwenhuijsen et al., 2010; Amiri and Behnezhad, 2020), and well
developed survey tools, such as the Copenhagen psychosocial ques-
tionnaire (COPSOQ), exist to quantify these factors (Burr et al.,
2019). There are few engineering studies examining how system
design choices determine psychosocial conditions for employees, and
how these ultimately affect system performance (e.g., Neumann
et al., 2006). If, for example, I4.0 technologies are used to provide
automated performance monitoring and enforcement of employees’
working to a dened pace, then one might hypothesize that em-
ployee’s sense of control and job autonomy at work will decline and
the overall psychosocial prole will shift towards the ‘high strain’
states associated with negative outcomes.
Key Concept 5: Organisations tend to “drift to unsafe states”. Ras-
mussen (1997) has pointed out that complex organisations engaged
in process innovation and improvement will tend to “drift” to unsafe
Fig. 5. Design process diagram (adapted from Neumann and Village, 2012).
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
8
states. The rationale, supported by extensive analysis of organisa-
tional accidents, is that the efforts of many different actors working
to minimise costs and optimize within their own limited domains
will ultimately bring a complex system into an unstable state as they
push the boundaries within their various domains in the pursuit of
efciency gains – leading to catastrophic systems failures (Rasmus-
sen, 1997; Woo and Vicente, 2003; Burns and Vicente, 2000). If
Rasmussen’s assessment of dynamic organisations is correct – then
the pursuit of I4.0 innovations is likely to follow this pattern as well:
There will be unanticipated and unmanaged consequences emerging
from the combined efforts of personnel in different parts of the sys-
tem (Rasmussen, 2000). Emergent system characteristics can be
particularly difcult to manage in design processes (Steiner et al.,
1999; Burns and Vicente, 2000; Skyttner, 2001; Neumann et al.,
2009). In a case study of the implementation of automated guided
vehicles (AGVs), for example, unanticipated interaction between the
AGVs and the layout of the workstation resulted in poor working
postures and elevated pain levels in assembly operators (Neumann
et al., 2006). The “drift to unsafe states” effect of Rasmussen (1997)
helps explain why industrial revolutions, or fads like “Lean”
(N¨
aslund, 2008), have been seen as contributing to occupational
injuries, accidents, and deaths. If I4.0 innovations are to try and
break this pattern, then a systems approach to applying HF in design
is needed (Neumann, 2017; Neumann and Village, 2012) - and re-
searchers must develop better tools and approaches to support such
system integration efforts. Isolated developments create unantici-
pated system risks.
The key HF concepts listed here pose both a challenge and an op-
portunity for I4.0 innovation. If HF is ignored, or dealt with in isolation,
then underperforming systems and the ongoing problem of injured and
killed workers can be expected. Deliberate and systematic attention to
HF therefore poses an opportunity to break the pattern of previous in-
dustrial revolutions (see, e.g., Neumann et al. (2018)) examining the
effects of a shift from craft to line production) and create better, more
effective workplaces in the future. This, we will demonstrate next, is not
happening yet, and therefore the vision of Kagermann et al. (2013) will
not be achieved. The key concepts thus point to the need of multidis-
ciplinary research and development that integrates technological and
social foci in the design process – a classical problem in design (Kilker,
1999) and science (Snow, 1998). Hence, approaching I4.0 from a
technology-driven perspective falls short of the systematic consideration
of HF that is needed.
4. How to consider HF in I4.0 systematically
4.1. Framework and method development
Given the apparent inattention to humans in I4.0 research and
development work, we propose a systematic framework for considering
HF in the conceptualisation, design, and implementation of new tech-
nologies in operations systems. While we present this in the context of
the current I4.0 trend, it is not specic to certain technologies. The
framework is applied in ve steps: 1) dening the technology; 2) iden-
tifying affected humans; 3) identifying task scenarios; 4) task analysis
and impacts; and 5) outcome analysis. We describe each step briey and
then present an application example in the next section. A blank
worksheet is provided in the appendix (Fig. A2).
Step 1: Dening the technology. This step is important as the frame-
work operates, initially, as a thought experiment before more
detailed testing approaches are chosen. The analysis may require
knowledge of the physical form, assembly, use and maintenance
tasks as well as possible failure modes for the system in question.
Where these are unknown, the analyst would have to investigate
alternatives and rene these projections as their development work
proceeds. In addition, the characteristics of the technology should be
known.
Step 2: Identifying the humans in the system. Following on Key Concept
1 - that all engineered systems are sociotechnical systems -, it is
important to list the human roles that will interact with the design. A
life-cycle perspective is important here and should include attention
to stages of design, assembly, installation, operation, maintenance,
and disassembly. There may be multiple scenarios for any one stage.
For example, operations may include front-line workers in various
scenarios, and also programmers or engineers engaged in operating
the cyber-physical sociotechnical system. This list of stakeholders
and human roles should be inclusive and as expansive as possible
creating a set of “usage scenarios” (cf. Regnell et al., 1995) for
considering the design. Forgetting a stakeholder group, such as
maintenance personnel, means that their needs might be missed in
system design, with possible negative consequences both for
personnel and for long run system performance due, in this example,
to increased maintenance costs. The idea of the human in the system
can be very diverse. Each person entering the system will bring
knowledge and will require new knowledge in order to operate
effectively in the new sociotechnical system environment. As Key
Concept 2 implies, attention to these stakeholders must be part of
I4.0 development in the design stage, not an afterthought. While not
every stakeholder of a company will be inuenced by every I4.0
implementation, the inuences might be more diverse than antici-
pated when cleaning staff, maintenance and engineering teams etc.
are considered.
Step 3: Identifying task scenarios. For each usage scenario, the analyst
must consider what tasks are being added to (e.g. more computer
monitoring work) or removed from (e.g. paper work, or walking)
each human in the system over the technologies life-cycle. How, in
other words, will the persons’ jobs change when they use this new
technology, compared to the current scenario? Due to the design of
tasks within these sociotechnical systems, the performance of the
system as a whole will be inuenced.
Step 4: Assessing the human impacts of the task changes. For each
change in task identied in Step 3 - be it elimination of tasks or in-
clusion of new tasks - the analyst should assess the demands placed
on the human in terms of perceptual, cognitive, and motor system
demands (per Key Concept 3). In particular the implementation of
new technologies and the related task changes are suggested to
impact on the psychosocial stressors in the job (per Key Concept 4),
in particular the effect on psychological job demands, the possibility
of job control, role clarity, social support from co-workers and su-
pervisors, and job satisfaction. Where these impacts are not under-
stood, further investigation and evaluation may be required. By
assessing these impacts, across all stakeholders, it becomes possible
to identify more clearly the advantages and potential problems of
adopting the proposed technology. In addition, it is important to
consider the (new) knowledge needed to operate/use a certain
technology.
Step 5 - Outcome analysis. In this nal step the possible effects of the
human impacts, identied in Step 4, on system performance should
be considered. In particular, an analyst should consider the possible
implications on employee time, on training needs, the probability of
errors and hence quality, and on the health risks and wellbeing of the
worker. If desired, the nancial implications of these impacts could
also be estimated (e.g., investments costs). An extra consideration in
this step is the possibility of ‘side effects’ of the technology. For
example, if a “smart scanner” must be strapped to the user’s arm,
there might be side effects associated with the comfort of straps and
mass of the device when worn for 8 h. Another example for side ef-
fects of the use of I4.0 technology are headaches, which were re-
ported when using augmented reality glasses (e.g. Wille et al., 2013).
Therefore, the two sides of the framework can be considered as a
macro-perspective and a micro-perspective. On the one side,
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
9
especially the company’s economic situation could be considered in
the outcome analysis according to the work added and removed for
every stakeholder. On the other side of the framework, the outcome
analysis can especially be used for the micro-perspective, e.g. how to
tackle challenges, new demands, secondary effects like negative
outcomes of a used technology - although the context specicity of
these costs make detailing this beyond the scope of this article and
therefor a matter for further research.
As it is not possible to evaluate design and HF elements in isolation
and expect safe performance without risking the drift to unsafe states
(Key Concept 5), the proposed framework accounts for interaction of
Fig. 6. Example method application for a picking cobot.
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
10
system elements. We note, however, that this is highly subjective. If a
given user group is missed, if a task scenario is skipped, or if a task
analysis is poorly thought out, then the quality of the overall analysis
will be compromised. There are many different methods that could be
used to evaluate the task loads on users, ranging from qualitative to
quantitative, that are compatible with this framework. The exibility of
the framework may be a strength in terms of its ability to be used with a
broad range of technological or administrative innovation scenarios.
Hence, a comprehensive picture of the inuences of an I4.0 element can
serve as a basis for the specic design of a work system prior to imple-
mentation. Problems identied at this stage can be explored and
addressed within the system design to avoid future dysfunctional side
effects in the eventual I4.0 operations system. To better illustrate this,
we provide an example analysis next.
4.2. Example method application for a cobot
We consider the example of a collaborative picking robot (cobot)
introduced as a new I4.0 element in a warehouse (see, e.g., Coelho et al.,
2018). The cobot could be used to support operations processes in a
smart factory (e.g. in kit preparation or line feeding) or in a smart lo-
gistics system. While the framework can result in extensive analyses, an
abridged example is summarized in Fig. 6 as an illustration of an
approach to applying this analysis.
Step 1: Characteristics and objectives of the cobot use are listed. This
could be that the robot is intrinsically safe or that it is highly reliable,
so failures are reduced. The company’s objective in using the cobot is
to reduce the physical effort for the workers and to improve
efciency.
Step 2: Stakeholders and human roles are dened. Most affected by
the cobot are order pickers working in the same warehouse area, but
also workers in pre- and post-operations as the cobot integration
might require a different preparation of goods (e.g. special barcodes
or RFID tags). This also impacts supply chain partners. Considering
the life-cycle perpective, at rst, engineering staff is involved in
designing and integrating the system and afterwards, maintenance
personnel needs to maintain it. Furthermore, administration is also
affected and could even be broken down further. The tasks at the end
of life disposal of the cobot remains as a nal issue to address.
Step 3: In the next step, possible added and removed work is analysed
based on task scenarios. Applying the cobot for picking work
removes this task from the human worker. On the other side, work is
added for the human, such as troubleshooting in cases the cobot was
not able to identify goods or malfunctioned in some other way.
Maintenance and engineering roles change from a conventional
order picking facility to supporting a eet of high-tech electrome-
chanical systems. The cobot needs to be integrated and maintained
which adds new work for both of these groups. Role changes can be
derived, e.g. from manual work system designer to robot integrator.
Step 4: In the next step of the analysis, the impacts of technology use
on the humans in the system are described. The added and removed
work is analysed and the impacts of the technology usage are
described on the perceptual, cognitive, knowledge, physical and
psychosocial level. By using a cobot, the logistics workplace is
changed by transferring picking tasks from the worker to the robot,
which reduces the loads to the musculoskeletal system associated
with walking and material handling tasks, but increases times
working with computer systems using input devices and reading
screens (with physical, perceptual and cognitive task loads). The
worker then only receives goods with damaged barcodes or even
damaged goods that the cobot cannot process, leading to new
different loads for the perceptual system and maybe also the cogni-
tive system – e.g. when considering whether a good is still in
accordance with the standards or whether it should be rejected. The
psychosocial factors might be inuenced due to less autonomy and
control possibilities or higher pace at work. If for example an em-
ployee’s performance hinges on the cobot performance, frustration
and stress for the operator will ensue whenever the cobot malfunc-
tions. The new robot system can then require technical knowledge
and capabilities beyond the usual protocols, which could unin-
tendedly increase the workload not only for front-line workers, but
also induce new challenges for engineering and maintenance staff.
Step 5: In the last step of the analysis, the outcome analysis, the
objectively described changes are evaluated in terms of possible
performance impacts. Using the cobot can increase performance (e.g.
throughput by adding a night shift) and can contribute to better
service levels due to fewer pick errors. It requires, however, invest-
ment and installation costs. The company may be able to lay-off
front-line employees but will need more maintenance and engi-
neering staff to manage the cobot eet. If the cobot needs pre-
packaged goods, the working capital might increase as well.
Focusing on outcomes of the task and impact on the workers, higher
perceptual demands (in co-working with the cobot) could cause
headaches or require additional tools to avoid this. Increasing
cognitive and knowledge demands might inuence work organiza-
tion to allow for job rotation or additional training. Changes of the
musculoskeletal demands could lead to new ergonomics risks as back
injuries due to material handling decrease while shoulder and wrist
disorders may increase from the increase in computer workstation
tasks. If the robots are poorly designed for maintenance access, then
injuries to maintenance personnel may arise as they attempt to keep
the cobot eet operational.
Fig. 6 summarizes the cobot example. Here, we also highlight one
possible chain of effects: Implementing a cobot adds work to human
order pickers for the identication of goods with damaged barcodes.
Therefore, the worker has additional knowledge needs (1). As a result to
this human impact, training should be offered to provide the necessary
background for process handling (2). Training needs of course relate to
initial learning effects that lead to a lower throughput at the beginning
of the implementation and thus lower the system performance (3).
Lower system performance and additional training needs lastly also
have nancial impacts for ramp up and training provision resulting from
the added or removed piece of work (4). Hence, this chain of effects
directly relates to Key Concept 2 and veries the need to consider HF
early in design. To further illustrate the framework, a second application
example from manufacturing is presented in the appendix (Fig. A1).
These examples are, by necessity incomplete and are meant to illustrate
the analysis approach that the framework fosters. Further development
of this framework into a user-friendly method is still required.
5. Discussion and conclusion
5.1. Key concepts
The developed framework allows a systematic assessment of the
impacts of I4.0 technology implementation on human workers and
system performance. It is theoretically grounded on ve HF key con-
cepts: 1) I4.0 are sociotechnical systems that involve people; 2) the
needs of people must be considered via system design; 3) people have
perceptual, cognitive and motor capabilities and limitations; 4) people
have psychosocial needs; and 5) complex systems often drift to unsafe
states. While this list might be criticized as incomplete, it provides a
parsimonious basis to explain both the need and the opportunity for
considering HF in I4.0 development. The rst four concepts are
axiomatic in HF engineering. There are no engineered systems without
humans. Humans cannot be re-engineered, so designs must be made to
suit them. Humans have known characteristics that must be accommo-
dated. These four concepts would require a “black swan” case to refute
them - cases we doubt exist in practice.
The last concept, Rasmussen’s claim of the tendency to drift to unsafe
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
11
states, is perhaps more a cautionary observation than a “law”. This
principle, however, has been well illustrated in a number of disaster
scenarios (Rasmussen, 1997; Woo and Vicente 2003). Rasmussen went
on to describe a framework that could help analyse complex systems to
isolate the mechanisms that lead to system failures (Rasmussen, 2000).
In the case of industrial revolutions, we see similar evidence. The pattern
of specic injuries of workers in particular jobs was rst observed by
Ramazzini (1700) at the start of the rst industrial revolution as workers
began to spend a substantial amount of time performing a limited range
of tasks due to the increasingly ne division of labour noted by Adam
Smith (1776). This mechanism is still at play in “modern” production
systems today (Palmerud et al., 2012; Neumann et al., 2018). Similarly,
case studies of automation have shown that, while work tasks (and jobs)
were eliminated, some people, especially those working downstream
from the robot, had increases in repetitive movements implying
increased injury risk (Neumann et al., 2002). While the drift to unsafe
states effect is not an inevitable pattern, it seems to occur frequently. Key
Concept #5 warns us that I4.0 will also contribute to and extend the
global problem of work-related ill health if HF is not included in design
stages (Key Concept #2). HF can help I4.0 designers avoid the “inno-
vation pitfall” (Neumann et al., 2018) in which failure to attend to
secondary human effects compromises the benets that designers and
managers had counted on for their I4.0 innovation efforts.
The developed framework can assist researchers in nding new
topics and systematically addressing these gaps, for example in “human-
centered industrial engineering and management” (Sgarbossa et al.,
2020). To name only a few examples, the framework could be used to
contribute to the diversity agenda, as systematically managing and
customising the HF demands can open the door for increased diversity in
employee characteristics and hiring (e.g., older workforce or workers
with disabilities). There is also a need for more research that can help
design teams understand the psychosocial impacts of their design
choices (e.g. the implementation of a specic I4.0 technology) on em-
ployees and, ultimately, system performance. Psychosocial stressors and
their impact on work autonomy, motivation or job satisfaction in the
context of I4.0 are still not fully understood and require further research.
If new I4.0 systems increase demands on employees, and possibly apply
stringent monitoring of task performance (Kaasinen et al., 2020), then
the combination of demands and lack of control will increase psycho-
social strain in employees which is associated with a wide range of
health problems ranging from musculoskeletal disorders to fatal heart
diseases (see, e.g., Bongers et al., 1993). The CA suggests that relatively
little attention has been paid to these issues in I4.0 research.
5.2. Content analysis
The results of the CA highlighted the lack of attention to HF in cur-
rent I.40 research which appears to have a strong focus on technology
and only occasional attention of human-system interaction. This failure
to attend to HF in I4.0 research has been observed in previous industrial
system generations (e.g., Neumann and Dul, 2010; Grosse et al., 2017)
and has had negative consequences for individual employees, produc-
tion organisations, and for society as a whole. Although a CA is able to
identify such patterns providing quantitative attention to specic key-
words within a literature sample, it does not consider the actual content
of the paper as might be done in a conventional literature review
approach. We note that our analysis highlighted RU that have ambig-
uous meanings, for example a paper on “machine learning” yielded 260
hits for the HF “learning” category. The sensitivity analysis employed
aimed at reducing such biases and increase the reliability of the results.
This analysis only used papers with “Industry 4.0” in the title, which
might exclude relevant papers. One example for this are the works on
“Operator 4.0” (e.g., Romero et al., 2018), which are not included in the
sample as I4.0 is not mentioned in the title in these works. However, a
more precise look on current writings, as discussed in Section 3.1,
highlights that these approaches see the worker rather as the “problem”
that I4.0 attempts to “solve” by technological means. These articles,
however, do not necessarily address the needs of the people in the sys-
tem and the broader secondary impacts these technologies may have.
We argue that designing a system and considering HF as an afterthought
does not lead to an efcient and productive system. Instead of
re-designing the human (as proposed by the “Operator 4.0” concept), we
propose a “humane” sociotechnical system design approach prior to the
I4.0 implementation that will result in systems better suited to people
and less likely to be compromised by negative human effects of poor HF
in design. Substantial research is still needed to see how the proposed
framework and methodology can be applied in practice.
5.3. Application issues and managerial implications
The framework proposed here warrants discussion. We see the
framework as a starting point and a “thinking shell” that can be applied
as a tool, however without a xed application approach. While it is
comprehensive, the suggested approach (including the length and the
way of organising it) is subjective and hinges on the knowledge and
imagination of the user. To account for this limitation, a preliminary
version of the framework was presented to researchers and managers
working in the area of human factors, operations management, and In-
dustry 4.0 in an expert workshop where the face validity and utility of
the approach was afrmed.
While application studies are needed to identify practical, evidence-
based advice for using the methodology, we suggest a top down, staged
approach to the framework. Top level management can use the frame-
work on a higher level for a rst feasibility and protability estimation.
If the proposed innovation proceeds, the stakeholders involved in the
elaboration process, who are closer to the nal workplace, can be
engaged in cross-functional teams for the more detailed analyses. The
identication of specic stakeholders to consider in the analysis will be
highly dependent on the organizational context and the innovation
under consideration. One of the strengths of the proposed methodology
is the use of a life-cycle perspective to help identify relevant roles and
personnel to consider and engage. Participation of key stakeholders
implicated in the proposed change is a way to both draw on their
knowledge and secure their support for the innovation project (de Looze
et al., 2003). While this would reduce the chances of the analyst missing
a specic issue, it does not overcome problems of “unknown unknowns”
in the analysis. Although the framework does not guarantee a holistic
picture, it helps structuring the multiple directions and interactions of
complex I4.0 elements and channels thoughts on the actual inuences
on HF in a robust way. Fig. 6 and Fig. A1 provided, for example, do not
include specic action plan elements that would be required in a specic
application.
The current framework is intended to help managers avoid HF-
related pitfalls in their innovation processes. Further research work is
still needed to understand what kind of support, or tools, might be
needed to help managers and design teams do this in practice. To the
extent that appropriate design-level virtual HF assessment tools exist, it
may be possible to conduct quantitative and objective analyses of
identied scenarios (Perez and Neumann, 2015). Virtual reality and
digital human models (e.g. Case et al., 2016; Chafn, 2008) can assess
postural issues related to the physical layout of the workstation or to
access repair points in the system. For other issues, such as identifying
psychosocial strain issues, design tools are missing and making pro-
spective assessments can be difcult. More participative and engaging
development processes may be required to capture these issues. Further
research, including case studies of innovation projects, is needed to
develop this analysis approach to increase ease of use and to extend its
capabilities. Financial analysis, for example, could be included by
building on recent models that predict production costs based on
employee risk level exposures (cf. Sobhani et al., 2015, 2016).
There are a number of organisational dimensions that are implicit in
this framework that managers should consider. For example: who is
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
12
being held responsible for what in the design of the new system? Will
researchers take responsibility to attend to potentially harmful side ef-
fects in their research work? Are design teams being given the mandate
and resources they need to examine the unanticipated costs associated
with the new technologies? Differences in orientation of designers, as
being either technically focussed or socially focussed, has been sug-
gested to be a major source of conict in design teams (Kilker, 1999).
Engineering teams often lack knowledge and mandates to attend to
human aspects in their design work (Broberg, 1997). Similarly, social
issues have been suggested to have fallen off the agenda in management
research (Walsh et al., 2003). Case study research has shown that design
teams and managers need unambiguous targets and appreciate quanti-
tative indicators as they develop their capacity to include HF aspects in
production system design (Village et al., 2014; 2015). The dynamics
required to achieve buy-in for HF in engineering design of I4.0 systems
remains a research need. Given the range of technologies included in the
I4.0 concept, and the variety of organisations looking to exploit these
innovations, we doubt there will be a “single best way” to adopt and
deploy the current framework. Identifying and testing useful approaches
for a given context remains a research need.
Finally, the implications of this work are important for corporate
strategy (Dul and Neumann, 2009). Managers should be aware that each
technology will inevitably affect their people – and that people form a
difcult to copy strategic advantage that their origination can leverage
for competitive advantage (Barney et al., 2011). Attending to the needs
of people in I4.0 system design can support these strategic objectives.
Managers can ask themselves, for example: is this being used to make
work easier? or to control people tightly? These issues have implications
for both physical and psychosocial working conditions in the operations
system. Managers and researchers should consider HF as a means, not a
goal, to achieve performance and wellbeing. If new technologies are
developed or implemented without attention to HF, systems will
underperform yielding “phantom prots” (Rose et al., 2013; Neumann
and Dul, 2010) and tend to drift into unsafe states. This implies costs to
both society and the organisation as secondary effects compromise the
technology investment leading managers towards the “innovation
pitfall” (Neumann et al., 2018).
5.4. Key message
This article aimed at identifying which HF aspects have been
considered to what extent in the scientic literature on I4.0 and at
providing a systematic approach that supports corporate I4.0-system
development. We show that, to date, current research on I4.0 technol-
ogies and implementation have broadly ignored the humans in the I4.0
system. The systematic consideration and attention to HF in the digital
transformation of work can avoid negative consequences for individual
employees, production organisations, and for society as a whole. With
this contribution, researchers as well as practitioners have a systematic
approach to incorporate HF in the ongoing transformation, ensuring
their I4.0 investments do not fall into the “innovation pitfall”.
Concluding, we strongly call for the systematic integration of HF in
future I4.0 research and development, which can contribute to over-
coming the challenges of the digital transformation of work, supporting
a satised and motivated diverse workforce with expanding capabilities
suited to working in the I4.0 environment.
Acknowledgements
This research has received funding from the European Union’s Ho-
rizon 2020 research and innovation programme under the Marie
Sklodowska-Curie grant agreement No 873077 (MAIA-H2020- MSCA-
RISE 2019). One author (WPN) was supported by the Natural Sciences
and Engineering Research Council (NSERC) of Canada Discovery Grant
(RGPIN# 2018-05956).
Appendix
The appendix contains a second application of the framework exemplied at the implementation of augmented reality glasses for machine
maintenance (Fig. A1) and a blank worksheet (Fig. A2).
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
13
Fig. A1. Example method application for augmented reality glasses for machine maintenance.
W.P. Neumann et al.
International Journal of Production Economics 233 (2021) 107992
14
Fig. A2. Blank worksheet.
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