Content uploaded by Dmitry Ivanov
Author content
All content in this area was uploaded by Dmitry Ivanov on Sep 14, 2022
Content may be subject to copyright.
Author version of the paper:
The Industry 5.0 Framework: Viability-based Integration of the Resilience, Sustainabil-
ity, and Human-centricity Perspectives
Dmitry Ivanov
Berlin School of Economics and Law
Supply Chain and Operations Management, 10825 Berlin, Germany
Phone: +49 30 308771155; E-Mail: divanov@hwr-berlin.de
Abstract
Industry 5.0 is a combination of organizational principles and technologies to design and man-
age operations and supply chains as resilient, sustainable, and human-centric systems. While
the general notion of Industry 5.0 has been elaborated, its implications for future operations and
supply chains remain underexplored. This paper contributes to the conceptualization of Industry
5.0 from the perspective of viability. We contextualize a framework of Industry 5.0 through the
lens of the viable supply chain model, the reconfigurable supply chain, and human-centric eco-
systems. Our study uncovers the major dimensions that characterize Industry 5.0 as a techno-
logical-organizational framework. First, the major technological principles of Industry 5.0 are
collaboration, coordination, communication, automation, data analytics processing, and identi-
fication. Second, Industry 5.0 covers four areas: organization, management, technology, and
performance assessment. Third, Industry 5.0 spans three levels: society level, network level,
and plant level. Last but not least, Industry 5.0 frames a new triple bottom line: resilient value
creation, human well-being, and sustainable society. We provide a definition of Industry 5.0
and discuss its implications by elaborating on the understanding of value in Industry 5.0, which
spans the dimensions of profit, people, and society. We also discuss open research areas.
Keywords: Industry 4.0; Supply chain resilience; Industry 5.0; Viable Supply Chain; Recon-
figurable Supply Chain; Human-Centric Ecosystem; Digital Supply Chain.
1. Introduction
Industry 5.0 is a term coined by the European Commission (EC 2021a). According to the EC
(2021b), ‘Industry 5.0 complements the existing Industry 4.0 paradigm by highlighting research
and innovation as drivers for a transition to a sustainable, human-centric and resilient European
industry. It moves focus from shareholder to stakeholder value, with benefits for all concerned.
Industry 5.0 attempts to capture the value of new technologies, providing prosperity beyond
jobs and growth, while respecting planetary boundaries, and placing the wellbeing of the indus-
try worker at the centre of the production process’.
While Industry 4.0 took on a technology-centred approach (Olsen and Tomlin 2020, Ivanov et
al. 2021, Zheng et al. 2021), Industry 5.0 triangulates and consolidates resilience, sustainability,
and human-centricity as key components of the value-creation systems supported by advanced
technology. Kusiak (2020, 2021) elaborates on the notions of resilient, open, and universal
manufacturing by combining resilience and Industry 4.0 perspectives. Xu et al. (2021) underline
the value-adding perspective that is taken by Industry 5.0. The European Commission (2021a)
states that Industry 5.0 comes ‘to make workplaces more inclusive, build more resilient supply
chains and adopt more sustainable ways of production’. These visions are echoed by Choi et al.
(2022), who point to human-machine interactions in the coming Industry 5.0 era and the con-
cept of ‘sustainable social welfare’.
The literature contains a large body of knowledge on resilience (Aldrighetti et al. 2021, Altay
et al. 2018, Blackhurst et al. 2011, Hosseini et al. 2019, Pettit et al. 2019, Ivanov 2021a, Qin et
al. 2021), sustainability (Brandenburg and Rebs 2015, Dubey et al. 2015, Sarkis 2021), and
human-centricity (Battaïa et al. 2020, Battini et al. 2016, Grosse et al. 2017, Katiraee et al.
2021). However, these studies either examine these topics individually or in pairwise combina-
tions, such as resilience and sustainability, sustainability and digital technology, and resilience
and efficiency (Chopra et al. 2021, Fahimnia et al. 2018, Pavlov et al. 2019, Dolgui et al. 2020,
Hosseini et al. 2020, Ivanov 2018, Tang et al. 2021, Lücker et al. 2021, Sawik 2020). As such,
Industry 5.0 suggests a distinctive context for triangulating resilience, sustainability, and hu-
man-centricity.
While the technological aspects of Industry 5.0 have started gaining research attention (Mad-
dikunta et al. 2021, Thakur and Sehgal 2021), its comprehensive understanding and conceptu-
alization across management, organization, and technology perspectives remains underex-
plored. We contribute to the literature with a conceptualization of Industry 5.0 from the per-
spectives of operations and supply chain management. Our framework of Industry 5.0 combines
society, network, and plant levels, and it is contextualized through the lens of the viable supply
chain model, reconfigurable supply chains, and human-centric ecosystems. We provide a defi-
nition of Industry 5.0 and discuss its technological, organizational, management, and perfor-
mance implications, spanning the perspectives of operations and supply chain management,
industrial engineering, computer science, and robotics and automation. The potential utility of
our framework can be seen in creating a structured view of the organizational principles and
technologies of Industry 5.0, along with an analysis of its implications on operations and supply
chain management transformation from the perspectives of resilience, sustainability, and hu-
man-centricity.
2. Dimensions of Industry 5.0
In order to understand the topics and associated literature related to each of the pillars of Indus-
try 5.0, we performed an automated search in scientific databases. Specifically, we used the
SCOPUS database and VOS software to identify the clusters that might be associated with the
three major pillars of Industry 5.0 – resilience, sustainability, and human-centricity, as declared
by the EC (2021b). We stress that we are not performing a classical structured literature review
following standard protocols; rather, we use the standard software (i.e., VOS) to deduce litera-
ture clusters and form a general picture of topics related to the different pillars of Industry 5.0.
Accordingly, we organized our search using the following keywords and logic: [Supply chain
OR Production OR Logistic* OR Sourcing OR Manufactur* OR Transport*] AND [Resilien*
OR Sustainab* OR Human]. We ran three independent searches for [Resilien*], [Sustainab*],
and [Human], respectively. We filtered the results to exclude disciplines not directly related to
management and engineering. Business, management and accounting, engineering, computer
science, mathematics, and decision sciences were selected as relevant categories for further
analysis. Moreover, we manually checked the keywords and excluded irrelevant ones. The
SCOPUS search results were exported into an Excel file that was used for cluster analysis in
the VOS software, which is a common tool for literature reviews (Ivanov et al. 2021). We detail
the results in the next sections.
2.1 Resilience and Industry 5.0
Figure 1 illustrates the cluster analysis results for the resilience area.
Figure 1. Cluster analysis results for the resilience area
Figure 1 Alt Text: resilience clusters structured by keywords
Analysis of Figure 1 allows for the identification of 7 major clusters, which are marked blue,
orange, red, purple, azure, green, and light green. The azure and purple clusters can be consid-
ered as context clusters: they accumulate keywords related to supply chain resilience, such as
uncertainty, risks, and disruptions (Azadegan and Dooley 2021, Gupta et al. 2021, ElBaz and
Ruel 2021, Ivanov 2021b, 2021c, Namdar et al. 2021). The red cluster is the technological one.
It is comprised of the keywords characterizing automation and information technology – e.g.,
robotics, Internet of things, and big data (Panetto et al. 2019, Pirola et al. 2020). The blue cluster
is related to communication and collates keywords in the areas of data transmission and secu-
rity. The orange cluster is devoted to intelligence – i.e., intelligent manufacturing and intelligent
transportation (Fragapane et al. 2020, Frazzon et al. 2021). The green cluster is comprised of
the keywords related to costs and investments in resilience, as well as associated analysis meth-
ods (Aldrighetti et al. 2021). Finally, the light green cluster accumulates the advanced methods
of machine learning and smart manufacturing, which can be generalized as the implementation
of reconfigurable supply chains and manufacturing systems (Brintrup et al. 2020, Cavalcante
et al. 2019, Dolgui et al. 2020, Dolgui and Ivanov 2022, Kosasih and Brintrup 2021, Rai et al.
2021, Yoon et al. 2020).
The cluster results provide us with some interesting observations. First, the composition of
clusters covers the major dimensions of supply chain resilience – i.e., organization, technology,
performance analysis, and management. Second, the keywords ‘reconfigurability’ and dynam-
ics’ play central roles in many inter-cluster relations. Third, the keyword ‘sustainability’ inter-
sects with ‘resilience’, which is in line with the findings presented by Ivanov et al. (2021) on
Industry 4.0 analysis and with the viable supply chain model (Ivanov 2020b). Fourth, the net-
work effects and associated systemic risks are characteristic for resilience, raising the chal-
lenges of managing the ripple effect (Ivanov et al. 2014, Dolgui et al. 2018, Pavlov et al. 2020,
Ghadge et al. 2021, Ivanov 2020a, Ivanov and Dolgui 2021b, Li et al. 2021, Park et al. 2021).
Finally, a clear dominance of digital supply chain technologies can be seen across the clusters.
2.2. Sustainability and Industry 5.0
Figure 2 illustrates the cluster analysis results for the sustainability area.
Figure 2. Cluster analysis results for the sustainability area
Figure 2 Alt Text: sustainability clusters structured by keywords
Analysis of Figure 2 allows for the identification of 6 major cluster, which are marked blue,
red, purple, azure, green, and light green. The red cluster can be considered a context one: it
accumulates keywords related to sustainable development, the triple bottom line, closed-loop
supply chains, and sustainable supply chains. The green and azure clusters are comprised of
energy efficiency aspects – e.g., energy-efficient manufacturing and alternative energy sources
(Alaouchiche et al. 2021). The blue cluster is related to transportation and collates keywords in
the area of sustainable logistics – i.e., CO2 emission reduction (Homayoonia et al. 2021). The
purple cluster is devoted to materials and recycling. Finally, the light green cluster accumulates
the keywords associated with environmental impact assessment – e.g., life cycle assessment.
The cluster results provide us with some interesting observations. First, the composition of
clusters covers the major dimensions of supply chain sustainability: organization, technology,
performance analysis, and management (Dubey et al. 2020). Second, we can observe three ma-
jor levels of analysis: the society level (e.g., sustainable resources and energy usage), the net-
work level (e.g., supply chain sustainability), and the plant level (e.g., energy-efficient manu-
facturing). Finally, a large number of keywords deal with performance and environmental im-
pact assessment (e.g., life cycle assessment) across different clusters.
2.3. Human-centricity and Industry 5.0
Figure 3 illustrates the cluster analysis results for the human-centricity area.
Figure 3. Cluster analysis results for the human-centricity area
Figure 3 Alt Text: human-centricity clusters structured by keywords
Analysis of Figure 3 allows for the identification of 5 major cluster, which are marked blue,
red, purple, green, and light green. The red cluster is comprised of automation technology (e.g.,
robotics) and human-machine and machine-to-machine collaboration – e.g., cyber-physical sys-
tems, Internet of things, human-robot interactions, and human factors (Panetto et al. 2019, Sgar-
bossa et al. 2020). It also contains the human resource management and sustainability key-
words. The green cluster is comprised of data analytics, artificial intelligence, computer simu-
lation, algorithms, and pattern recognition (Battini et al. 2020). The blue cluster is related to
transportation and collates keywords in the area of safety, which frequently intersect with the
statistical analysis. The purple cluster is devoted to organization and leadership (Calzavara et
al. 2020). Finally, the light green cluster accumulates the keywords associated with biological
models and bioengineering.
The cluster results provide us with some interesting observations. First, we can observe a high
importance of technology – i.e., artificial intelligence, human-machine collaboration, and
cyber-physical systems. Second, the technology aspects identified are multiple and consider
collaboration, communication, identification, coordination, automation, and data processing
technologies. Finally, we can observe intersections with sustainability keywords, especially in
the red cluster.
3. Industry 5.0: Connecting resilience, sustainability, and human-centricity through a
viability paradigm
The results of the literature analysis allow us to conceptualize Industry 5.0 from the perspectives
of operations and supply chain management. Based on a cluster analysis of the existing litera-
ture on supply chain and operations resilience, sustainability, and human-centricity, we derive
a framework of Industry 5.0 and contextualize it through the lens of the viable supply chain
model, the reconfigurable supply chain, and human-centric ecosystems.
3.1. The viable supply chain model, viability of intertwined supply networks, and business
ecosystems
Viability is a specific capability at the scale of survivability to avoid supply chain and market
collapses and to secure the provision of goods and services (Ruel et al. 2021). According to
Ivanov and Dolgui (2020), ‘viability is a behavior-driven property of a system with structural
dynamics. It considers system evolution through disruption-reaction balancing in the open sys-
tem context. The viability analysis is survival-oriented at a long-term scale’. Ivanov (2020b)
defines viability as an ‘ability of a supply chain to maintain itself and survive in a changing
environment through a redesign of structures and replanning of performance with long-term
impacts’.
In light of Industry 5.0, viability is comprised of the supply chain itself; the intertwined supply
network (ISN), which is an ‘entirety of interconnected supply chains which, in their integrity
secure the provision of society and markets with goods and services’ (Ivanov and Dolgui 2020);
a digital supply chain (Cavalcante et al. 2019, Ivanov and Dolgui 2021a, Frazzon et al. 2021);
and a human-centric ecosystem responsible for securing society’s needs in line with natural,
economic, and governance interests.
Angles of sustainability and resilience are integrated within the Viable Supply Chain Model
and extended toward survivability. Moreover, viability takes an ecosystem perspective. For ex-
ample, it is concerned with intertwined supply networks ‘that encapsulate entireties of inter-
connected supply chains, which, in their integrity, secure the provision of society and markets
with goods and services’ (Ivanov and Dolgui 2020). From the position of viability, the ISNs as
a whole provide services to society (e.g., food service, mobility service, communication ser-
vice) that are required to ensure society’s long-term survival. The example of the COVID-19
pandemic illustrates viability as a new and distinct construct.
Ruel et al. (2021) elaborated in detail on the commonalities and differences between the resili-
ence and the viability of supply chains. In particular, they noted that ‘supply chain viability can
be viewed from an overarching adaptation perspective that extends the supply chain resilience
notion of a closed-system, “bounce-back” view, with a viable, open supply chain system per-
spective incorporating “bounce-forward-and-adapt” options’. Moreover, Ivanov (2021a, Chap-
ter 5) provided a structured comparison of supply chain resilience and viability, concluding that
viability is an extended resilience perspective. A supply chain can be considered viable if it is
able to maintain an ecosystem balance (i.e., to achieve homeostasis) at different uncertainty
exposure levels.
The Viable Supply Chain Model is based on adaptable structural network designs for situational
supply-demand allocations and, most importantly, the establishment and control of adaptive
mechanisms for transitions between the structural designs (Ivanov 2021e). Moreover, supply
chain viability and the ecosystem view have been synthesized in the lens of the human-centred
ecosystem perspective by Ivanov and Dolgui (2021) and extended by Feizabadi et al. (2021)
and Wang and Yao (2021).
Finally, the reconfigurable supply chain framework can be considered a part of future Industry
5.0 developments (Dolgui et al. 2020, Ivanov 2021d). Supplementing the reconfigurable man-
ufacturing concept (Koren et al. 1999, Zennaro et al. 2019, Battaïa et al. 2020, Ivanov et al.
2021b), the reconfigurable supply chain adds three specific features: active behaviour of net-
work elements, networking effects across multiple structures and their dynamics (i.e., organi-
zational, information, financial, technological, energy), and network complexity (i.e., multi-
echelon supply chains). The reconfigurable supply chains are characterized by structural and
process variety, which is beneficial for supply chain resilience.
3.2. Industry 5.0 framework
Since viability integrates resilience, sustainability, and human-centricity, it can be considered
as a convenient category to approach the conceptualization of Industry 5.0 from an academic
perspective. The analysis of the literature and principles of the Viable Supply Chain Model, the
viability of intertwined supply networks and ecosystems, and the reconfigurable supply chain
allows us to identify some generalized notions associated with Industry 5.0. First, the major
technological principles of Industry 5.0 are collaboration, coordination, communication, auto-
mation, data analytics processing, and identification. Second, Industry 5.0 covers four areas:
organization, management, technology, and performance assessment. Third, Industry 5.0 spans
three levels: society level, network level, and plant level. Figure 4 illustrates the integration of
these notions as the framework of Industry 5.0.
Figure 4. Industry 5.0 framework
Figure 4 Alt Text: framework of Industry 5.0
At the society level, Industry 5.0 aims to build viable intertwined networks that are able to
secure the provision of society with products and services during periods of disruption and
crisis. This perspective is complemented by the human-centric contextualization of ecosystems
such as food and agriculture, communication, energy and water, education, mobility, textiles
and housing, healthcare, education, and leisure, sport, and culture (Ivanov and Dolgui 2021).
The design and operation of such intertwined networks and ecosystems presumes the sustaina-
ble usage of resources and energy available on the earth.
The network level is mostly comprised of designing and managing supply chain resilience and
sustainability capabilities. It is also involved with building reconfigurable, cyber-physical, and
digital supply chains. In order to ensure the development of resilience in a sustainable way, the
costs and investments in resilience need to be considered toward lean resilience (Aldrighetti et
al. 2021, Ivanov 2021e). Indeed, a traditional way of designing efficient supply chains and then
extending them by some redundancies (e.g., risk mitigation inventory, capacity buffers, backup
suppliers) frequently results in high costs and resource consumption. This way of building re-
silience is expensive since many of these redundancies will just be waiting for use in an emer-
gency case, without creating any value in business-as-usual times.
An alternative approach, the active usage of resilience assets (AURA; Ivanov 2021e), calls for
considering resilience from a value-creation perspective. Agile, flexible, and reconfigurable
supply chains can be efficient, and they are also resilient through structural and process variety
(Shekarian et al. 2020). In this setting, resilience is not built upon efficiency – resilience is
embedded into every business-as-usual operation and is a part of efficiency (i.e., lean resili-
ence). Examples include omnichannel distribution systems, multiple sourcing, diversified lo-
gistics networks, and flexible production lines, which are good both for efficiency and resili-
ence. One can also explain the AURA framework as follows. An expensive alarm system can
be installed to protect your home against burglary; this is a big investment that will not generate
any profit and can only pay off if there is a real danger. Alternatively, you can get a dog – you
will have a lot of fun every day, and it will protect you from burglars better than any alarm
system.
At the plant level, the human-centric perspective aims to create inclusive workplaces, foster the
collaboration of human and artificial intelligence, and create health protection protocols and
layouts as driven by the COVID-19 pandemic (Choi 2020, Shen et al. 2021, Sodhi et al. 2021,
Queiroz et al. 2020). Sustainable manufacturing and logistics, as well as increasing the resili-
ence of individual facilities in the networks (e.g., through a facility fortification), supplement
the plant level through resilience and sustainability perspectives.
We now arrive at the definition of Industry 5.0 that we derive from the framework in Figure 4,
as well as from the definition of Industry 4.0 by Ivanov et al. (2021).
Our definition of Industry 5.0:
Industry 5.0 is an integration of resilient, sustainable, and human-centric
technologies, organizational concepts, and management principles for de-
signing and managing cost-efficient, responsive, resilient, sustainable, and
human-centric value-adding systems at the levels of ecosystems, supply
chains, and manufacturing and logistics facilities, data-driven and dynam-
ically and structurally adaptable to changes in the demand and supply en-
vironment to secure the provision of society with products and services in
a sustainable and human-centric way through the rapid rearrangement
and reallocation of its components and capabilities.
4. Implications of Industry 5.0 for supply chain and operations management
The analysis of literature and the conceptualization of Industry 5.0 shows that Industry 5.0 is
characterized by strong networking effects. In contrast to Industry 4.0, Industry 5.0 goes beyond
the technological and enterprise level and expands to the level of supply chain networks, inter-
twined supply networks, human-centric ecosystems, and their viability.
4.1. Technology
Industry 5.0 technology covers network and physical process levels (Table 1).
Table 1: Technology of Industry 5.0
Infrastructure
technology
Communica-
tion technol-
ogy
Automation
technology
Identifica-
tion and alert
technology
Data pro-
cessing
technology
Collabora-
tion tech-
nology
Network
(planning)
level
Internet of
things
5G
Edge compu-
ting
Trace and
tracking sys-
tems
Blockchain
Early-warn-
ing system
Big data
analytics
Collabora-
tive sup-
plier por-
tals
Digital
supply
chain twin
Plant (reali-
zation) level
Cyber-physi-
cal systems
Real-time pro-
cess monitor-
ing
Sensors
Additive manu-
facturing
Collaborative
robots
Drones
Mobile robots
AGVs
RFID
ERP
Machine-
to-machine
(M2M)
Aug-
mented /
virtual re-
ality
Digital
twin
Digital platforms and supplier collaboration portals are used to ensure collaboration and com-
munication in Industry 5.0. M2M (machine-machine) tools make machines collaborate with
each other, while smart products facilitate machine-product collaborations. End-to-end visibil-
ity, which is so important for both proactive and reactive decision making, is supported across
the supply chain by ERP systems, blockchain, and T&T systems (Choi et al. 2022, Maccarthy
and Ivanov 2022). Visibility is enabled at the plant level by sensors and RFID through an in-
frastructure based on the Internet of things. Big data analytics and artificial intelligence are used
for planning and control decision making support. Collaborative robots and drones add flexi-
bility and efficiency to manufacturing and logistics processes. Additive manufacturing is used
to quickly deploy production with a very short supply chain. Quality and safety control can be
enhanced by monitoring and real-time detection systems.
The digital technologies in Industry 5.0 are also present in Industry 4.0. However, they provide
additional value when considered from the resilience, sustainability, and human-centric per-
spectives. For example, visibility and blockchain help improve resilience through supply chain
mapping (El Baz and Ruel 2021, Li et al. 2022). Additive manufacturing can help to improve
sustainability through the reduction of transportation in the supply chain and the working con-
ditions due to its technology (Peron et al. 2022).
4.2. Organizational implications
Technology determines organization. With Industry 5.0, cloud manufacturing and collaborative
human-machine networks become reality. Supply chain and operations planning is organized
as data-driven analysis, modelling, learning, and control processes. Digital twins of products,
manufacturing processes, and supply chain networks can be designed and help in decision mak-
ing through accurate data and complete representations of real systems and objects, thus im-
proving resilience (Burgos and Ivanov 2021, Psarommatis and May 2022). Customers become
part of the digital supply chain through the use of online digital tools, apps, live streaming, and
social media. Supply chains evolve into digital business ecosystems.
4.3. Operational implications
In the Plan area, supply chains and operations benefit from Industry 5.0 by using data analytics
(e.g., for demand prediction and inventory control prescription). The Source area is advanced
by collaborative supplier platforms, supply visibility, and real-time inventory control. In the
Make area, customized assembly and modular production systems are enabled. This is espe-
cially important for enhancing sustainability through resource efficiency–oriented production
and human-centric working environments (Sgarbossa et al. 2020). Delivery process manage-
ment benefits from the digital technology by online routing optimization and real-time shipment
control, among others.
4.4. Performance implications
Efficiency, productivity, resilience, sustainability, and viability are impacted in Industry 5.0.
Efficiency and productivity benefit from increased flexibility and responsiveness, along with
improved lead-time and capacity utilization. Resilience is enhanced by visibility, collaboration,
and adaptability. In the wake of the COVID-19 pandemic, firms with a digital supply chain and
visibility were able to map the available supply and re-allocate it to demand (Ardolino et al.
2022, Choi 2021, Paul and Chowdhury 2021, Rozhkov et al. 2022). Additive manufacturing
can be beneficial both for resilience and sustainability. In the context of viability, digital tech-
nology allows for the implementation of the viable supply chain model. Visibility, reconfigu-
rable manufacturing systems, and additive manufacturing, along with analytics and digital col-
laboration tools, are vital for viable manufacturing and supply chains. In light of the increasing
resource shortages in supply chains due to semiconductor shortage, workforce variability, en-
ergy blackouts, and inflation, the importance of viable supply chains and Industry 5.0 will con-
tinue to grow in the future.
5. Open research areas
Industry 5.0 brings new ideas, concepts, and technology to the debate about the future of man-
ufacturing and logistics. We further identify some open research areas.
5.1. Understanding Industry 5.0
The first open research area is the contextualization of Industry 5.0 as a novel and distinct par-
adigm. The current state of understanding for Industry 5.0 is not free of ambiguity and contra-
dictions. First, many companies just started implementing Industry 4.0, and the appearance of
Industry 5.0 just 10 years after the contextualization of Industry 4.0 may create some questions.
As such, the research should be clear about the relations between Industry 4.0 and Industry 5.0,
showing that Industry 5.0 does not replace Industry 4.0 but rather supplements and extends it.
In this sense, the term ‘Industry 4.1’ would perhaps be better to use in order to stress the further
development of Industry 4.0 and not the fact of a completely new industrial revolution. At the
same time, Industry 5.0 takes a much broader perspective than Industry 4.0, so the term ‘Indus-
try 5.0’ is also justified.
5.2. Understanding of sustainability, human-centricity, and resilience in Industry 5.0
One of the central perspectives of Industry 5.0 is the combination of sustainability, human-
centricity, and resilience. One could argue that human-centricity has been an inherent part of
the societal pillar of sustainability and that a differentiation of human-centricity from sustaina-
bility is not unambiguously allocatable. The contextualization of the human-oriented and soci-
ety-oriented aspects within Industry 5.0 is therefore a new and relevant research area.
Resilience in the framework of Industry 5.0 is spread over the levels of individual manufactur-
ing plants, supply chains, intertwined supply networks, and human-centric ecosystems. More-
over, resilience is supposed to be considered as an inherent property of efficient business-as-
usual operations following the AURA framework. In addition, resilience in Industry 5.0 can be
studied for different stressors ranging from an instantaneous natural disaster to long-term,
global crises such as the COVID-19 pandemic. While the resilience of individual plants and
supply chains is important at the level of disruptions of moderate severity, the consideration of
severe crises opens the lens of viability, viable supply chains, and viable human-centric eco-
systems. Resilience understanding at the viability level is a specific and new research area. Even
if an individual supply chain can lose its resilience during a severe crisis, the whole intertwined
supply network or the human-centric ecosystem should allow for some connectivity to secure
the existence and provision of society with goods and services across different ecosystems.
Viability, sustainability, human-centricity, and resilience become integrated in Industry 5.0.
This is a new and distinct context that comes with Industry 5.0 and needs further research to
increase our understanding of the interconnections between sustainability, human-centricity,
and resilience at the levels of organization, management, and technology.
5.3. Business models and value creation
The principles of Industry 5.0 call for adjusting the existing business model designs and devel-
oping new ones. Novel organizational principles, management perspectives, and digital tech-
nology represent a large variety of opportunities to modify the existing business models and
make them more resilient, sustainable, and human-centric. Digital technology can trigger new
business model designs.
Value in Industry 5.0 spans the dimensions of profit, people, and society. In addition, value
creation as a central pillar of Industry 5.0 should be aligned with the resilience, sustainability,
and human-centric perspectives. First, the stage of value usage needs to be explicitly considered
in the business model designs. Second, resilient and sustainable value co-creation in digital
business ecosystems is an important and relevant research area.
Furthermore, the new research direction to ensure survivability on the ecosystem level can be
related to the business model of supply chain as a service when large parts or even the whole
supply chain can be outsourced to a third party. This model – i.e., the cloud supply chain, see
Ivanov et al. (2022) – is closely linked to the digital supply chain – e.g., Amazon’s Fulfillment
by Amazon business. In this setting, a Viable Supply Chain Model (Ivanov 2020b) and recon-
figurable supply chains (Dolgui et al. 2020b) can be considered as interesting research avenues
to be applied under conditions of a shortage economy.
5.4. Multi-objective optimization paradigms
On the operational level, Industry 5.0 spans three major perspectives: resilience, efficiency, and
sustainability. In this setting, further development of multi-objective optimization paradigms
can be expected. For example, when designing an optimal supply chain network, a combination
of resilience, efficiency, and sustainability indicators should be considered in the objective
functions and constraints of the optimization models. The ultimate objective is to equip decision
makers with working tools that can be used to assess the resilience, efficiency, and sustainabil-
ity of systems and processes to select the best one with consideration of all three dimensions.
On the one hand, Pareto optimal approaches can be considered. On the other hand, even single-
criterion optimization (e.g., efficiency) with simultaneous computation of other indicators (e.g.,
resilience and sustainability) and informing managers about the resilience and sustainability of
different efficient alternatives are also of utmost importance.
6. Conclusion
Industry 5.0 is a combination of organizational principles and technologies to design and man-
age operations and supply chains as resilient, sustainable, and human-centric systems. This
combination of resilient, sustainable, and human-centric organization, along with technologies
for resilient, sustainable, and human-centric manufacturing and logistics, is unique and goes
beyond the mere technological lens of Industry 4.0. While the general notion of Industry 5.0
has been positioned as a value-driven approach, its implications for future operations and sup-
ply chains remain underexplored. This paper contributes to conceptualization of Industry 5.0
from the perspectives of operations and supply chain management.
Based on a cluster analysis of the existing literature on supply chain and operations resilience,
sustainability, and human-centricity, we derived a framework of Industry 5.0 and contextual-
ized it through the lens of the viable supply chain model, the reconfigurable supply chain, and
business ecosystems. The generalized notions associated with Industry 5.0 have four major di-
mensions. First, the major technological principles of Industry 5.0 are collaboration, coordina-
tion, communication, automation, data analytics processing, and identification. Second, Indus-
try 5.0 covers four areas: organization, management, technology, and performance assessment.
Third, Industry 5.0 spans three levels: society level, network level, and plant level. Last but not
least, Industry 5.0 frames a new triple bottom line of resilient value creation, human well-being,
and sustainable society.
The implications of Industry 5.0 are multiple and scattered across different dimensions and
disciplines. We considered technology, organization, process management, and performance
perspectives; some other areas, such as quality management and new product development, can
be considered in the future. Industry 5.0 navigates the development of manufacturing and sup-
ply chains into smart, flexible, and reconfigurable networks that are (re-)configured dynami-
cally and capture global markets. Industry 5.0 allows for data-driven and dynamically and struc-
turally adaptable manufacturing systems and supply chains to be able to react to changes in the
demand and supply environment through the rapid rearrangement and reallocation of their com-
ponents and capabilities. When utilized properly, Industry 5.0 can improve efficiency and
productivity while increasing the resilience, sustainability, and viability of manufacturing and
supply chains. Ultimately, Industry 5.0 enables the next generation of manufacturing and logis-
tics in cost-efficient, responsive, human-centric, resilient, and sustainable supply chain net-
works spanning perspectives of operations and supply chain management, industrial engineer-
ing, computer science, and robotics and automation, and thus calling for multi-disciplinary re-
search collaborations.
Acknowledgement: The author sincerely thanks organizers of IFIP APMS 2021 and IFIP
PRO-VE 2021 conferences – Prof. Alexandre Dolgui, Prof. Xavier Boucher and Prof. Xavier
Delorme for inviting him for keynotes. The discussions at these conferences have greatly con-
tributed to the development of this paper. I further thank two anonymous reviewers who pro-
vided invaluable comments and suggestions to improve the paper through two revision rounds.
Data Availability Statement
Data related with this paper is available with authors and will be available upon reasonable
request.
6. References
Aldrighetti R., Battini D., Ivanov D., Zennaro I. (2021). Costs of resilience and disruptions in supply
chain network design models: a review and future research directions. International Journal of
Production Economics, 235, 108103.
Alaouchiche, Y., Y Ouazene, F Yalaoui (2021). Energy-efficient buffer allocation problem in unreliable
production lines. The International Journal of Advanced Manufacturing Technology 114 (9), 2871-
2885.
Altay, N., Gunasekaran, A., Dubey, R., Childe, SJ (2018). Agility and Resilience as antecedents of Sup-
ply Chain Performance under moderating effects of Organizational Culture within Humanitarian Set-
ting: A Dynamic Capability View. Production Planning and Control, 29(14), 1158-1174.
Ardolino M., Bacchetti A., Ivanov D. (2022). Analysis of the COVID-19 pandemic’s impacts on man-
ufacturing: a systematic literature review and future research agenda. Operations Management Re-
search, DOI: 10.1007/s12063-021-00225-9
Azadegan, A., Dooley, K. (2021). A typology of supply network resilience strategies: complex collab-
orations in a complex world. Journal of Supply Chain Management,
https://doi.org/10.1111/jscm.12256
Battaïa, O., L Benyoucef, X Delorme, A Dolgui, S Thevenin (2020). Sustainable and energy efficient
reconfigurable manufacturing systems. Reconfigurable Manufacturing Systems: From Design to Im-
plementation, Springer, pp. 179-191
Battini, D., S. Finco, F. Sgarbossa (2020). Human-Oriented Assembly Line Balancing and Sequencing
Model in the Industry 4.0 Era. In: Sokolov B., Ivanov D., Dolgui A. (Eds.) Scheduling in Industry
4.0 and Cloud Manufacturing, Springer, Cham, 141-165.
Battini, D., X Delorme, A Dolgui, A Persona, F Sgarbossa (2016). Ergonomics in assembly line balanc-
ing based on energy expenditure: a multi-objective model. International Journal of Production Re-
search 54 (3), 824-845.
Berti N., Finco S., Battaïa, O., Delorme X. (2021). Ageing workforce effects in Dual-Resource Con-
strained job-shop scheduling. International Journal of Production Economics, 237, 108151
Blackhurst, J., Dunn, J., Craighead, C. (2011). An empirically derived framework of global supply re-
siliency. Journal of Business Logistics, 32(4), 347-391.
Brandenburg, M., and Rebs, T. (2015). Sustainable supply chain management: A modeling perspective.
Annals of Operation Research 229: 213–252.
Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., and McFarlane, D. (2020).
Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manu-
facturing. International Journal of Production Research, 58(11), 3330-3341.
Burgos, D., Ivanov D. (2021). Food Retail Supply Chain Resilience and the COVID-19 Pandemic: A
Digital Twin-Based Impact Analysis and Improvement Directions. Transportation Research – Part E:
Logistics and Transportation Review, 152, 102412.
Calzavara M, Battini D, Bogataj D, Sgarbossa F & Zennaro I (2020) Ageing workforce management in
manufacturing systems: state of the art and future research agenda, International Journal of Produc-
tion Research, 58:3, 729-747.
Cavalcante, I.M., Frazzon E.M., Forcellinia, F.A., Ivanov, D. (2019). A supervised machine learning
approach to data-driven simulation of resilient supplier selection in digital manufacturing. Interna-
tional Journal of Information Management, 49, 86-97.
Choi, T.-M. (2020). Risk analysis in logistics systems: A research agenda during and after the
COVID-19 pandemic. Transportation Research Part E: Logistics and Transportation, 140, 101961.
Choi, T.-M. (2021). Fighting Against COVID-19: What Operations Research Can Help and the Sense-
and-Respond Framework. Annals of Operations Research, https://doi.org/10.1007/s10479-021-
03973-w
Choi, T.M., S. Kumar, X. Yue, H.L. Chan. (2022). Disruptive technologies and operations manage-
ment in the Industry 4.0 era and beyond. Production and Operations Management, DOI:
10.1111/poms.13622
Chopra, S., M. Sodhi, F. Lücker (2021). Achieving supply chain efficiency and resilience by using
multi-level commons. Decision Sciences, 52(4), 8817-832.
Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent
literature. International Journal of Production Research. 56(1–2), 414–430.
Dolgui, A., Ivanov, D., Sokolov, B. (2020) Reconfigurable supply chain: The X-Network. International
Journal of Production Research, 58(13), 4138-4163.
Dolgui A., Ivanov D., (2022). 5G in Digital Supply Chain and Operations Management: Fostering Flex-
ibility, End-to-End Connectivity and Real-Time Visibility through Internet-of-Everything. Interna-
tional Journal of Production Research, 60(2), 442-451.
Dubey, R., A. Gunasekaran, S. J. Childe (2015). The design of a responsive sustainable supply chain
network under uncertainty. The International Journal of Advanced Manufacturing Technology, 80,
427–445.
Dubey, R., A. Gunasekaran, S. J. Childe, T. Papadopoulos, Z. Luo, D. Roubaud (2020). Upstream supply
chain visibility and complexity effect on focal company’s sustainable performance: Indian manufac-
turers’ perspective. Annals of Operations Research, vol. 290, no. 1, pp. 343-367.
EC (2021a). Industry 5.0: Towards more sustainable, resilient and human-centric industry.
https://ec.europa.eu/info/news/industry-50-towards-more-sustainable-resilient-and-human-centric-
industry-2021-jan-07_en, accessed on November 11, 2021.
EC (2021b). Industry 5.0. https://ec.europa.eu/info/publications/industry-50_de, accessed on Novem-
ber 14, 2021.
El Baz, J., & Ruel, S. (2021). Can supply chain risk management practices mitigate the disruption im-
pacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-
19 outbreak era. International Journal of Production Economics, 233, 107972.
Fahimnia, B., Jabarzadeh, A., Sarkis, J. (2018). Greening versus resilience: A supply chain design
perspective. Transportation Research- Part E, 119, 129-148.
Feizabadi, J., David M. Gligor & Thomas Y. Choi (2021). Examining the resiliency of intertwined
supply networks: a jury-rigging perspective. International Journal of Production Research, DOI:
10.1080/00207543.2021.1977865
Fragapane G., Ivanov D., Peron M, Sgarbossa F., Strandhagen J.O. (2020). Increasing flexibility and
productivity in Industry 4.0 production networks with autonomous mobile robots and smart
intralogistics. Annals of Operations Research, https://doi.org/10.1007/s10479-020-03526-7
Frazzon, E.M., Freitag, M., Ivanov, D. (2021). Intelligent Methods and Systems for Decision-Making
Support: Toward Digital Supply Chain Twins. International Journal of Information Management,
57, 102281.
Castañé, G., A. Dolgui, N. Kousi, B. Meyers, S. Thevenin, E. Vyhmeister & P-O. Östberg (2022) The
ASSISTANT project: AI for high level decisions in manufacturing, International Journal of
Production Research, DOI: 10.1080/00207543.2022.2069525
Ghadge, A., Er Kara, M., Ivanov, D., Chaudhuri, A. (2021). Visualisation of ripple effect in supply
chains under long-term, simultaneous disruptions: A System Dynamics approach. International
Journal of Production Research, forthcoming.
Grosse EH, M Calzavara, CH Glock, F Sgarbossa (2017). Incorporating human factors into decision
support models for production and logistics: current state of research. IFAC-PapersOnLine 50 (1),
6900-6905.
Gupta V., Ivanov, D., Choi, T.-M. (2021) Competitive pricing of substitute products under supply dis-
ruption. Omega, 101, 102279.
Homayoonia, Z., Pishvaee, M.S., Jahani, H., Ivanov, D. (2021). A robust-heuristic optimization ap-
proach to a green supply chain design with consideration of assorted vehicle types and carbon poli-
cies under uncertainty. Annals of Operations Research, https://doi.org/10.1007/s10479-021-03985-
6
Hosseini S., Ivanov D., & Blackhurst J. (2020). Conceptualization and measurement of supply chain
resilience in an open-system context. IEEE Transactions on Engineering Management,
10.1109/TEM.2020.3026465.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resili-
ence analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307.
Ivanov D. (2018) Revealing interfaces of supply chain resilience and sustainability: a simulation study.
International Journal of Production Research, 56(10), 3507-3523
Ivanov D. (2020b). Viable Supply Chain Model: Integrating agility, resilience and sustainability per-
spectives – lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Re-
search, DOI: 10.1007/s10479-020-03640-6
Ivanov D. (2021a) Introduction to Supply Chain Resilience. Springer Nature, Cham, ISBN 978-3-030-
70490-2.
Ivanov D. (2021b). Exiting the COVID-19 Pandemic: After-Shock Risks and Avoidance of Disruption
Tails in Supply Chains. Annals of Operations Research, DOI10.1007/s10479-021-04047-7.
Ivanov D. (2021c). Supply Chain Viability and the COVID-19 Pandemic: A Conceptual and Formal
Generalisation of Four Major Adaptation Strategies. International Journal of Production Research,
DOI: 10.1080/00207543.2021.1890852
Ivanov D., Tang C.S., Dolgui A., Battini D., Das A. (2021). Researchers’ Perspectives on Industry 4.0:
Multi-Disciplinary Analysis and Opportunities for Operations Management. International Journal
of Production Research, 59(7), 2055-2078.
Ivanov, D., Sokolov B., Chen W., Dolgui, A., Werner F., Potryasaev S. (2021). A control approach to
scheduling flexibly configurable jobs with dynamic structural-logical constraints. IISE Transac-
tions, 53(1), 21-38.
Ivanov D., Dolgui A. (2021). Stress testing supply chains and creating viable ecosystems. Operations
Management Research, DOI 10.1007/s12063-021-00194-z
Ivanov, D. (2020a). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-
based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case". Transportation
Research Part E: Logistics and Transportation Review, 136, 101922.
Ivanov, D. (2021d). Digital supply chain management and technology to enhance resilience by build-
ing and using end-to-end visibility during the COVID-19 pandemic. IEEE Transactions on Engi-
neering Management, DOI 10.1109/TEM.2021.3095193
Ivanov, D. (2021e). Lean Resilience: AURA (Active Usage of Resilience Assets) Framework for Post-
COVID-19 Supply Chain Management. International Journal of Logistics Management,
https://doi.org/10.1108/IJLM-11-2020-0448.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the sup-ply
chain resilience angles towards survivability: A position paper motivated by COVID-19 outbreak.
International Journal of Production Research, 58(10), 2904–2915.
Ivanov, D., & Dolgui, A. (2021a). A digital supply chain twin for managing the disruption risks and
resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788.
Ivanov, D., & Dolgui, A. (2021b). OR-methods for coping with the ripple effect in supply chains during
COVID-19 pandemic: Managerial insights and research implications. International Journal of Pro-
duction Economics, 232, 107921.
Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the
ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3),
829–846.
Ivanov, D., Sokolov, B., & Dolgui. A. ( 2014). The Ripple Effect in Supply Chains: Trade-off
‘Efficiency-Flexibility-Resilience’ in Disruption Management. International Journal of Production
Research, 52 (7), 2154–2172.
Ivanov D., Dolgui A., Sokolov B. (2022). Cloud Supply Chain: Integrating Industry 4.0 and Digital
Platforms in the “Supply Chain-as-a-Service”. Transportation Research – Part E: Logistics and
Transportation Review, 160, 102676.
Katiraee, N., M. Calzavara, S. Finco, D. Battini & O. Battaïa (2021) Consideration of workers’ differ-
ences in production systems modelling and design: State of the art and directions for future research,
International Journal of Production Research, 59:11, 3237-3268
Koren, Y., U. Heisel, F. Jovane, T. Moriwaki, G. Pritschow, G. Ulsoy, H. Van Brussel (1999). Recon-
figurable Manufacturing Systems. CIRP Annals, 48(2), 527-540.
Kosasih, E., Brintrup, A. (2021). A Machine Learning Approach for Predicting Hidden Links in Sup-
ply Chain with Graph Neural Networks. International Journal of Production Research,
https://doi.org/10.1080/00207543.2021.1956697
Kusiak, A. (2021). From digital to universal manufacturing. International Journal of Production Re-
search, forthcoming.
Kusiak, A. (2020). Open manufacturing: A design-for-resilience approach. International Journal of Pro-
duction Research, 58 (15), 4647-4658.
Li, Y., Chen, K., Collignon, S., & Ivanov, D. (2021). Ripple effect in the supply chain network: Forward
and backward disruption propagation, network health and firm vulnerability. European Journal of
Operational Research, 291 (3), 1117-1131.
Li G., Xue J., Li N., Ivanov D. (2022). Blockchain-supported business model design, supply chain re-
silience, and firm performance. Transportation Research – Part E: Logistics and Transportation Re-
view, 163, 102773.
Lücker F, Chopra S, Seifert RW (2021) Mitigating product shortage due to disruptions in multi-stage
supply chains. Production and Operations Management, doi: 10.1111/poms.13286
Maccarthy B., Ivanov D. (2022). Digital supply chain. Elsevier, Amsterdam.
Maddikunta, PKR, Pham, Q-V., Prabadevi B, N Deepa, K. Dev, et al. (2021). Industry 5.0: A survey
on enabling technologies and potential applications. Journal of Industrial Information Integration,
100257
Namdar J., S. A. Torabi, N. Sahebjamnia & N. N. Pradhan (2021). Business continuity-inspired resili-
ent supply chain network design. International Journal of Production Research, 59(5), 1331-1367.
Olsen T.L., Tomlin B. (2020). Industry 4.0: Opportunities and Challenges for Operations Management.
Manufacturing and Service Operations Management, 22(1), 113-122.
Panetto H., Iung B., Ivanov D., Weichhart G., Wang X. (2019). Challenges for the cyber-physical man-
ufacturing enterprises of the future. Annual Reviews in Control, 47, 200-213.
Park, Y.W., J. Blackhurst, C. Paul & K. P. Scheibe (2021). An analysis of the ripple effect for disrup-
tions occurring in circular flows of a supply chain network. International Journal of Production Re-
search, DOI: 10.1080/00207543.2021.1934745
Paul, S. K., & Chowdhury, P. (2021). A production recovery plan in manufacturing supply chains for a
high-demand item during COVID-19. International Journal of Physical Distribution & Logistics
Management, 51(2), 104-125.
Pavlov A., Ivanov D., Pavlov D., Slinko A. (2019). Optimization of network redundancy and contin-
gency planning in sustainable and resilient supply chain resource management under conditions of
structural dynamics, Annals of Operations Research, DOI: 10.1007/s10479-019-03182-6
Pavlov A., Ivanov D., Werner F., Dolgui A., Sokolov B. (2020). Integrated detection of disruption sce-
narios, the ripple effect dispersal and recovery paths in supply chains. Annals of Operations Re-
search, DOI:10.1007/s10479-019-03454-1
Pettit, T.J., Croxton, K.L., Fiksel, J. (2019). The Evolution of Resilience in Supply Chain Management:
A Retrospective on Ensuring Supply Chain Resilience. Journal of Business Logistics, 40(1):56–65.
Peron, Mirco; Basten, Rob; Knofius, Nils; Lolli, Francesco; Sgarbossa, Fabio (2022). Additive or Con-
ventional Manufacturing for Spare Parts: Effect of Failure Rate Uncertainty on the Sourcing Option
Decision. IFAC PapersOnLine, forthcoming.
Pirola, F., X. Boucher, S. Wiesner, G. Pezzotta (2020). Digital technologies in product-service systems:
a literature review and a research agenda. Computers in Industry, 123, 103301.
Psarommatis F., May G. (2022) A literature review and design methodology for digital twins in the era
of zero defect manufacturing, International Journal of Production Research, DOI:
10.1080/00207543.2022.2101960
Qin, T., R Du, A Kusiak, H Tao, Y Zhong (2021). Designing a resilient production system with recon-
figurable machines and movable buffers. International Journal of Production Research, forthcoming.
Queiroz, M. M., Ivanov, D., Dolgui, A., Fosso Wamba, S. (2020). Impacts of Epidemic Outbreaks on
Supply Chains: Mapping a Research Agenda Amid the COVID-19 Pandemic through a Structured
Literature Review. Annals of Operations Research, DOI: 10.1007/s10479-020-03685-7.
Rai, R., Tiwari, MK, Ivanov D., & A. Dolgui (2021). Machine learning in manufacturing and Industry
4.0 applications. International Journal of Production Research, 59(16), 4773-4778.
Rozhkov, M., Ivanov, D., Blackhurst, J., Nair, A. (2022). Adapting supply chain operations in anticipa-
tion of and during the COVID-19 pandemic. Omega, 110, 102635.
Ruel, S., El Baz J., Ivanov, D., Das, A. (2021). Supply Chain Viability: Conceptualization, Measure-
ment, and Nomological Validation. Annals of Operations Research, https://doi.org/10.1007/s10479-
021-03974-9
Sarkis J. (2021). Supply chain sustainability: learning from the COVID-19 pandemic. International
Journal of Operations & Production Management, forthcoming.
Sawik T. (2020) Supply chain disruption management. Springer, New York.
Sgarbossa, F., EH Grosse, WP Neumann, D Battini, CH Glock (2020). Human factors in production and
logistics systems of the future. Annual Reviews in Control 49, 295-305
Shekarian, M., Reza Nooraie, S.V., Parast, M.M., 2020. An examination of the impact of flexibility and
agility on mitigating supply chain disruptions. International Journal of Production Economics 220,
107438
Shen, B., M. Cheng, C. Dong & Y. Xiao (2021). Battling counterfeit masks during the COVID-19 out-
break: quality inspection vs. blockchain adoption. International Journal of Production Research,
DOI: 10.1080/00207543.2021.1961038
Sodhi, M., Tang, C., Willenson, E. (2021). Research opportunities in preparing supply chains of essen-
tial goods for future pandemics. International Journal of Production Research,
https://doi.org/10.1080/00207543.2021.1884310
Thakur P., Sehgal V.K. (2021). Emerging architecture for heterogeneous smart cyber-physical systems
for industry 5.0. Computers & Industrial Engineering, 1662, 107750.
Wang M., Yao J. (2021). Intertwined supply network design under facility and transportation disrup-
tion from the viability perspective. International Journal of Production Research, DOI:
10.1080/00207543.2021.1930237
Xu X., Lu, Y., Vogel-Heuser, B., Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, concep-
tion and perception. Journal of Manufacturing Systems. 61, 530-535.
Yoon J., S Talluri, H Yildiz, C Sheu (2020). The value of Blockchain technology implementation in
international trades under demand volatility risk. International Journal of Production Research,
58(7), 2163-2183.
Zennaro, I., S Finco, D Battini, A Persona (2019). Big size highly customised product manufacturing
systems: a literature review and future research agenda. International Journal of Production Re-
search 57 (15-16), 5362-5385
Zheng, T., Ardolino, M., Bacchetti A. & M. Perona (2021). The applications of Industry 4.0 technolo-
gies in manufacturing context: a systematic literature review, International Journal of Production
Research, 59:6, 1922-1954.
