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The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability).
Author version of the paper published in IJPR
Metaverse Supply Chain and Operations Management
Alexandre Dolgui1 and Dmitry Ivanov2*
1 IMT Atlantique, LS2N - CNRS, La Chantrerie, 4 rue Alfred Kastler, 44307 Nantes, France
2 Berlin School of Economics and Law
Supply Chain and Operations Management, 10825 Berlin, Germany
Phone: +49 30 308771155; E-Mail:
* Corresponding author
The metaverse and Web 3.0 have created a new digital world with specific properties and be-
haviours replicating and influencing the behaviours and processes of physical entities. This
study aims to advance our understanding of how the metaverse will impact supply chain and
operations management (SCOM). Using elements of a structured literature search and building
on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and
Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multi-
ple socio-technological dimensions. We conclude that further metaverse developments could
result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of
the physical and metaverse worlds. We offer a structured future research agenda pointing to
new research questions and topics stemming from metaverse-driven visibility, computational
power for data analytics, digital collaboration, and connectivity. New research areas can emerge
for the novel metaverse SCOM processes and decision-making areas (e.g., joint demand fore-
casting for metaverse and physical products, digital inventory allocation in the metaverse, inte-
grated production planning for the metaverse and physical worlds, and pricing and contracting
for digital products), as well as new performance measures (e.g., virtual customer experience
level, availability of digital products, and digital resilience and sustainability).
Keywords: manufacturing; supply chain management; metaverse; digital twin; blockchain; dig-
ital supply chain.
1. Introduction
The metaverse creates a new world where every physical entity (e.g., people, products, and
enterprises) has a digital twin. The entities in the metaverse are not just digital replicas of some
physical entities; rather, they are equipped with artificial intelligence possessing and changing
their own properties and behaviours and even influencing the behaviours and processes of phys-
ical entities. The metaverse is expected to completely change our lives in the very near future,
much faster than we can think of now.
What is the metaverse? The term ‘metaverse’ was coined in 1992 in Neal Stephenson’s literary
work Snow Crash (Stephenson, 1992), where it was visualized as a black spherical planet ac-
cessible to users through terminals with integrated virtual reality capabilities through which
users could appear as avatars (The Economist, 2020). According to Maersk (2022), [t]he
metaverse is the next evolution of the Internet. It’s a fusing of the digital and physical worlds
powered by technologies, including virtual and augmented reality, blockchain, artificial intelli-
gence, and the Internet of things that connects smart devices”. Moreover, Lovich (2022) defines
the metaverse as “a combination of the virtual reality and mixed reality worlds accessed through
a browser or headset, which allows people to have real time interactions and experiences across
The metaverse develops fast. Digital technology leaders like Nvidia with Omniverse and Face-
book with Meta have invested deeply in metaverse solutions (Huynh-The et al., 2023). The
SupplyOn supplier collaboration platform (Holzwarth et al., 2022) and the Catena-X data eco-
system have also been developed in the automotive industry, allowing for the creation of digital
product passports and improving the sustainability and resilience of supply chains from the
perspective of the ecosystem (Catena, 2022). For instance, Siemens and BMW have developed
smart manufacturing platforms using cloud technology (Siemens, 2022; Open Manufacturing,
2022). Furthermore, manufacturing companies like Puma, Nike, Gucci, and Adidas have started
using the idea of the metaverse in marketing and e-commerce to interact with customers, allow-
ing them to view or even buy digital versions of their products (Barrera and Shah, 2023). How-
ever, the role of the metaverse in supply chain and operations management (SCOM) remains
underexplored (Kathiala, 2022).
Mourtzis et al. (2022) claim that the metaverse represents a new era in Internet connectivity,
characterized by interactivity, simulation, a decentralized environment, and persistent reality
facilitated by the next evolution of the Internet (also known as Web 3.0) to combine the digital
and physical worlds. Lee and Kundu (2022) point to conceptual similarities between the
metaverse and cyber-physical systems, the application of which to manufacturing has been dis-
cussed by Panetto et al. (2019). A survey by Accenture (2022a) found that 64% of supply chain
management executives believe that the metaverse will have an impact on their organizations.
Analysis of the existing literature shows that the metaverse’s impacts on SCOM go beyond the
technological dimension: the metaverse is not only a technology but a complex socio-techno-
logical phenomenon. In this setting, a scientific approach is needed to reflect on the chances,
barriers, and challenges that the development of the metaverse will impose on SCOM.
Thus, this study aims to advance our understanding as to how the metaverse will influence
SCOM. In particular we are interested in exploring the following research questions (RQ):
RQ1: How will the metaverse impact SCOM in the physical world?
RQ2: What will be the potential SCOM processes and decision-making areas in the metaverse?
RQ3: What will be the mutual impacts of the co-existence of physical and metaverse SCOM?
We contribute to the literature by proposing a framework describing metaverse SCOM from
multiple socio-technological-economic perspectives, i.e., people, organizations, technology,
management, scope, tasks, and models. We propose that the further metaverse developments
could result in three major decision-making domains in SCOM, i.e., physical supply chains and
operations, metaverse supply chains and operations, and the coordination of physical and
metaverse supply chains and operations. These domains are mutually interconnected.
To provide some guidelines and structure for further research on metaverse SCOM and the
interrelations between physical and metaverse SCOM, we discuss a future research agenda,
namely that future research can explore new opportunities for SCOM that stem from the
metaverse, i.e., visibility, computational power for data analytics, digital collaboration, and
connectivity. At the same time, we show that new SCOM activities can appear specifically
dedicated to the metaverse and lead to novel SCOM processes and decision-making areas (e.g.,
joint demand forecasting for metaverse and physical products, digital inventory allocation in
the metaverse, integrated production planning for the metaverse and physical worlds, and pric-
ing and contracting for digital products), as well as new performance measures (e.g., virtual
customer experience level, availability of digital products, and digital resilience and sustaina-
The remainder of this paper is organized as follows. In Section 2, the results of a SCOPUS
search for metaverse literature related to SCOM are presented. Drawing upon keyword analysis,
we propose the metaverse SCOM framework in Section 3. In Section 4, future research ques-
tions and new topics that focus on the metaverse are discussed. We conclude in Section 5 by
summarizing the major insights of this study and pointing to some future extensions of them.
2. Analysis of the main topics in the research on the metaverse and SCOM
To understand the state of the art in research on the metaverse and SCOM, we first ran a SCO-
PUS search organised as follows:
TITLE-ABS-KEY ( metaverse ) AND ( LIMIT-TO ( DOCTYPE , ar ) ) AND ( LIMIT-
We searched for “metaverse” in the titles, abstracts, and keywords of the journal articles in the
business and management, decision sciences, and engineering areas. The search yielded 217
papers and 119 keywords with a minimum threshold of using a particular keyword in at least 3
articles. The result of the VOS Viewer co-occurrence analysis is presented in Fig. 1.
Fig. 1. Metaverse and SCOM research map
Figure 1. Alt Text: Keywords related to the metaverse and SCOM research map
We carefully analysed the keywords identified by SCOPUS and structured them based on the
7-element digital twin framework (Ivanov, 2023b). Accordingly, we propose the following
seven elements to be included in the metaverse SCOM framework: technology, people, man-
agement, organisation, scope, task, and modelling (Table 1).
Table 1. Metaverse SCOM elements
Virtual Reality
Augmented Reality
Artificial Intelli-
Mixed Reality
3D Computer
Industry 4.0
Cloud Computing
Internet of Things
Interactive Com-
puter Graphics
Network Security
Interactive Com-
puter Systems
Remote Control
3D Printers
Cyber Physical
Time Sys-
Social Net-
X Reality
Aided In-
Multi Criteria
Web 3.0
User ex-
Big Data
3D Mod-
It can be observed in Table 1 that the existing research on the metaverse and SCOM covers a
broad socio-technological-economic spectrum. On the one hand, our analysis allows us to iden-
tify the key digital technologies enabling the metaverse. On the other hand, the key role of
people and the human-machine interface becomes evident through the keywords represented in
Table 1, which was built based on Fig. 1 and supplemented by some additional items based on
our expert review. The metaverse enables new organizational forms and management capabil-
ities (e.g., visibility and interaction). A large variety of artificial intelligence-based modelling
methods supports decision-making tasks in forecasting, manufacturing, and contracting in dif-
ferent system scopes.
3. Metaverse SCOM framework
Based on the keyword analysis, we can now propose a metaverse SCOM framework (Fig. 2).
Fig. 2. The metaverse SCOM framework
Figure 2. Alt Text: Elements of the metaverse and SCOM framework
The metaverse SCOM framework is based on the 7-element digital twin framework proposed
by Ivanov (2023b). The seven major dimensions are people, organization, management, tech-
nology, modelling, scope, and task. In Fig. 2, we combine keywords identified by the SCOPUS
search with our integrative analysis of the relevant frameworks such as digital twins (Negri et
al., 2017; Badakhshan et al., 2022; Berti and Finco, 2022; Burgos and Ivanov, 2021; Frazzon
et al., 2021; Huang et al., 2022), cloud and digital supply chain (Ivanov et al., 2022; MacCarthy
and Ivanov, 2022; Zhang et al., 2022), collaborative networks (Camarinha-Matos and Af-
sarmanesh, 2005), reconfigurable supply chain (Dolgui et al., 2020), cloud manufacturing
(Moghaddam and Nof, 2018), open manufacturing (Kusiak, 2020), Physical Internet (Pan et al.,
2017), and Industry 4.0/Industry 5.0 (Yin and Stecke, 2018; Tang and Veelenturf, 2019; Zen-
naro et al., 2019; Winkelhaus and Grosse, 2020; Choi et al., 2022; Ivanov, 2022a).
Further, in Fig. 3 we illustrate the extension of traditional SCOM understanding as a cross-
department and cross-enterprise integration and coordination of material, information, and fi-
nancial flows to transform and use the supply chain resources in the most rational way along
the entire value chain, from raw material suppliers to customers” (Ivanov et al., 2021b, p. 9)
towards a triple-SCOM view wherein physical, metaverse, and physical-metaverse SCOMs co-
Fig. 3. SCOM in the metaverse era
Figure 3. Alt Text: Metaverse, physical, and digital supply chains
Fig. 3 depicts that physical and metaverse worlds are connected through digital technology such
as augmented/virtual reality, blockchain, artificial intelligence, the Internet of things, 5G/Edge
computing, ERP (enterprise resource planning), big data analytics, and simulation (Brintrup et
al., 2020; Cai et al., 2021; Chabanet et al., 2022; Cui et al., 2022; Dolgui and Ivanov, 2022;
Dubey et al., 2021; Elmachtoub and Grigas, 2022). Smart devices and sensors in physical prod-
ucts along with 3D printers represent other data sources for the metaverse. In the metaverse,
digital customers (i.e., avatars) act in the digital markets where digital products are offered and
sold using digital money (probably, a mix of physical and digital products can be considered
too). Managers use digital collaboration spaces for sourcing, production, and logistics coordi-
nation. Also, digital stores, factories, and warehouses represent the supply chain in the
metaverse, which can be digital replicas of physical stores, factories, and warehouses, or repre-
sent new, additional entities which do not exist in the physical supply chain.
4. Open research questions
In this section, we outline open research questions related to the metaverse SCOM.
4.1 Area 1: Scope and task
The scope of the metaverse SCOM will cover digital products, digital factories and warehouses,
the digital supply chain, and digital ecosystems. The metaverse supply chain is not just a digital
replica of a physical supply chain: the digital and physical supply chains evolve autonomously
but co-jointly. When we assume that a digital twin is a digital replica of a physical supply chain,
then the metaverse is more than a digital twin. On the one hand, the metaverse enhances deci-
sion-making support and analytics applications for physical SCOM. On the other hand, the dig-
ital and physical supply chains mutually influence each other (Liu et al., 2020; Lv et al., 2022).
For example, the increased popularity of a product in the metaverse can lead to an increased
demand for this product in the physical supply chain. A timely recognition of these trends
through data analytics can help supply chain managers to prepare for the peak load. The
metaverse data analysis can also be used for the introduction of new products into the market
and decisions on initial order quantity for example, a product can be first introduced in the
metaverse, and the sales/inventory data from the digital supply chain can be used to set up the
physical supply chain processes. In another example, a product shortage in the physical supply
chain can be substituted by an increased supply of this product in the metaverse so that custom-
ers (or their avatars) who cannot buy the physical product could obtain it in its digital form.
This is a novel context for supply chain resilience management.
Assuming that people will have more and more activities to partake in the metaverse, we can
expect new SCOM activities specifically dedicated to the metaverse and leading to the appear-
ance of novel SCOM processes and decision-making areas. For example, joint demand fore-
casting for metaverse and physical products belongs to a new research area. Since digital prod-
ucts will also require some storage place in the metaverse, digital inventory allocation in the
metaverse can arise as a novel optimization context. Pricing and contracting for digital products
as well as new performance measures (e.g., virtual customer experience level and availability
of digital products, as well as digital resilience and sustainability) can motivate new research.
Through digital analytics, testing and forecasting customer and supplier behaviours can be used
for demand, inventory, and capacity planning. Circular SCOM can receive a new perspective
combining digital and physical reverse flows (Meier et al. 2023).
Inventory management research can also be innovated through the metaverse. For example, one
group of customers might like to have both physical and digital products, another group only
physical, and another only digital for example, a luxury car, which can be too expensive in
real life can be purchased in the metaverse. Competition between digital and physical products
can lead to interesting new problem settings in pricing and inventory management. In some
cases, digital products can even be wanted more than physical ones a new setting for revenue
management. Furthermore, sourcing and production planning in the metaverse SCOM can be
adjusted through digital collaboration spaces with improved delivery visibility and coordina-
tion. In addition, physical products might be increasingly supplemented by some digital ser-
vices, and digital products can include some physical add-ons. In this setting, sourcing and
production planning can encounter novel and counter-intuitive decision-making problems.
4.2 Area 2: Management
As indicated in Fig. 2, three SCOMs could exist when the metaverse becomes an important part
of everyday life SCOM for physical world, SCOM for digital world, and SCOM for coordi-
nating physical and digital worlds. The metaverse can be used for decision-making support in
physical SCOM through enhanced management capabilities such as visibility, computational
power for data analytics, digital collaboration, and connectivity (Dolgui and Ivanov, 2022).
Through supply chain mapping, it becomes possible to obtain more accurate, real-time data on
lead-times, delays, deliveries, shortages, physical locations of containers and trucks, and costs
(MacCarthy et al., 2022). Forecasted known-unknown becomes knowable. For example, in a
metaverse “collaboration room”, supply chain managers could “review expected sales fore-
casts, projected production plans and possible supplier limitations that could affect manufac-
turing volume. They could also see, on an immersive supply chain network map, exactly where
inventory is, virtually walk through key ports to identify possible shipping delays because of
congestion, and model possible alternatives to keep products moving to the right stores and
shelves” (Accenture, 2022b).
Resilience management can be enhanced by disruption recognition, impact prediction, and re-
covery actions (Ralston and Blackhurst, 2020; Ivanov, 2021; Ivanov and Dolgui, 2021; Ivanov,
2022b). Using digital twin-based simulation environments, managers can analyse different sce-
narios in the virtual world using the digital supply chain before implementing decisions in the
physical supply chain. Recognizing bottlenecks and enforcing supply chains for peak loads
(e.g., demand increase) and supply disruptions becomes easier, and stress-testing supply chains
can be performed with higher knowledge awareness (Aldrighetti et al., 2021; Aldrighetti et al.,
2023; Ivanov and Dolgui, 2022a).
Sustainability management can also be improved through transparency about carbon emissions,
visibility about the entire product life cycle, and associated environmental footprints. The dig-
ital supply chain can help in tracing the upstream suppliers to ensure that suppliers do not use
child labour (e.g., by using blockchain or some product-tracking technologies) and produce
products according to sustainability standards and laws.
4.3 Area 3: Technology
Huynh-The et al. (2023) point to six major technological elements of the metaverse, i.e., a dig-
ital twin (cyber-physical interface), neural interface (brain-computer interface), machine vision
(virtual/augmented reality), networking (e.g., multi-access edge computing), blockchain (data
collection, storage, sharing, and management), and natural language processing (e.g., text-to-
speech processing). Bhandal et al. (2022) point to the Internet of things, blockchain, artificial
intelligence and data analytics, augmented and virtual reality, and Industry 4.0 as digital twin
enablers. In addition, through 3D printing, production can be triggered by customers them-
selves. Customers can also design products and have them produced on demand (Boute et al.,
2022; Peron et al., 2022). This will have implications on supply chain complexity and environ-
mental footprints, along with increased customer satisfaction. Finally, digital platforms and
supplier collaboration portals will be used to ensure collaboration and communication in Indus-
try 5.0 (Reim et al., 2022; Holzwarth et al., 2022; Sawik, 2022). End-to-end visibility, 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 (Roeck et al., 2020; Choi et al., 2022; Li
et al., 2022; Maccarthy and Ivanov, 2022).
Digital twins can be enabled by technologies of different scopes (Boyes and Watson, 2022;
Nguyen et al., 2022, Jahani et al. 2023). CAD/CAM (computer aided design/computer aided
manufacturing) systems are applied at the product level, while MES and ERP systems enable
the building of the digital twins of processes and organisations. At the supply chain level, spe-
cial software such as anyLogistix in combination with external data sources (e.g., data from
logistics service providers, weather data, financial market data) are used to build supply chain
digital twins (Ivanov and Dolgui, 2021; Burgos and Ivanov, 2021). Future research areas high-
light both a technical understanding of system integration and interoperability and management
conceptualisation of the needs and limits of data-driven decision-making support. Technologies
allow for the integration of models with external data sources and ensure interactions with other
digital twins.
A specific, and very important part of future research on digital twins will be related to human-
artificial intelligence collaboration. According to Ivanov (2023c), three levels of digital twins
can be classified: digital twin, cognitive digital twin, and intelligent digital twin. The latter type
of digital twin is based on human-artificial intelligence collaboration and will therefore be rel-
evant to the metaverse SCOM (Ivanov 2023b).
One particular area of human-artificial intelligence collaboration in the metaverse will be re-
lated to generative AI artificial intelligence (e.g., ChatGPT). Generative AI is expected to have
a profound impact on the physical and digital supply chains taking over (or supporting) a large
variety of SCOM tasks such as demand forecasting, routing optimization, process monitoring,
and risk control. All the activities related to prediction, optimization, and anomaly/failure de-
tection in SCOM will use generative AI.
Finally, cybersecurity issues are of utmost importance for metaverse SCOM. Multiple technol-
ogies and users of the metaverse may lead to increased SCOM cyber threats, resulting in various
new cybersecurity challenges. For example, real-time metaverse SCOM applications may re-
quire new countermeasures against the new cyber threats.
4.4. Area 4: People
The metaverse will change the work and role of people in SCOM. Automatic responses with
minimal human intervention, new standards for working places and remote work, collaboration
of people (virtual meeting platforms), and human-robot collaboration are just some examples
of this change (Rozanek et al., 2022; Sheu and Choi, 2022; Saghafian et al., 2022; Sun et al.,
2022). The metaverse is being developed and used by people, and at the same time it changes
human behaviours and SCOM decision-making.
Decisions in SCOM depend on the expertise of the manager, the knowledge and skills they
exhibit, and their access to real-time data and information (Sgarbossa et al., 2020). The
metaverse can help managers providing decision-making support by acquiring real-time data
and simulating the potential outcomes of certain decisions (e.g., alternative recovery policies
after a disruption or changes in an environmental footprint due to a supply chain redesign).
Digital twins can also consider the level of competence in making decisions (e.g., placing orders
in an inventory control system). Most centrally, the metaverse offers real-time, data-driven de-
cision-making support.
Further research is needed to examine the impacts of continuous access to real-time data on
managerial decision-making. In addition, behavioural aspects of data-driven decision-making
and cognitive biases in human-artificial intelligence interactions belong to the novel topics that
will emerge when digital twins can be explored in SCOM research (Fahimnia et al., 2019; Fu
et al., 2022; Sun et al., 2022). At the manufacturing system level, human-robot collaboration is
one of the central digital twin-related future research topics (Sheu and Choi, 2022). Mourtzis et
al. (2022) stress the human-centric perspective of the metaverse, echoing the integration of hu-
man-centricity, resilience, and sustainability into the Industry 5.0 framework (Ivanov, 2022a).
4.5 Area 5: Organisation
Technology determines organisation. The metaverse will not only mirror physical SCOM or-
ganisations but also create and develop new business and operational models. Through digital
twins, novel organisational constructs such as digital manufacturing, cloud supply chains, and
collaborative platforms will arise (Sharma et al., 2022; Ivanov et al., 2022). Examination of the
metaverse-driven transformations in the organisation of SCOM can be conducted in future re-
search areas where impactful and substantial contributions can be made. In addition, digital
twins can lead to new organisational structures and a redistribution of decision-making compe-
tencies across departments. Metaverse solutions can also be applied to factory design and plan-
ning through simulation of their digital twins. In the created virtual simulation environments,
processes and flows can be represented, analysed, and improved. Furthermore, new organiza-
tional forms (e.g., cloud supply chains, intertwined supply networks, ecosystems) and new cat-
egories in SCOM such as creator economy, discovery, and digital experience could appear too
(Ivanov and Dolgui, 2020).
In the context of viability, digital technology allows for the implementation of the viable supply
chain model (Ruel et al., 2021; Ivanov, 2022a; Ivanov and Keskin, 2023). Visibility, reconfig-
urable manufacturing systems, and additive manufacturing, along with analytics and digital
collaboration tools, are vital for viable manufacturing and supply chains. In light of the increas-
ing resource shortages in physical supply chains due to component (e.g., semiconductors) short-
ages, workforce variability, energy blackouts, and inflation (Ivanov and Dolgui, 2022b; Hägele
et al., 2023), the importance of viable supply chains and the metaverse will continue to grow in
the future.
Following Ivanov (2022a), “the Viable Supply Chain Model is based on adaptable structural
network designs for situational supply-demand allocations and, most importantly, the establish-
ment and control of adaptive mechanisms for transitions between the structural designs
(Ivanov, 2021e). Moreover, supply chain viability and the ecosystem view have been synthe-
sized through the lens of the human-centred ecosystem perspective by Ivanov and Dolgui
(2022a). In addition, the reconfigurable supply chain framework can be considered a part of
future Industry 5.0 developments (Dolgui et al., 2020; Ivanov, 2023a). Dolgui et al. (2020) note
that by “supplementing the reconfigurable manufacturing concept (Zennaro et al., 2019; Battaïa
et al., 2020), the reconfigurable supply chain adds three specific features: active behaviour of
network elements, networking effects across multiple structures and their dynamics (i.e., or-
ganizational, 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.”
4.6. Area 6: Modelling
Analytics capabilities offered through the metaverse can hardly be imagined now to a full ex-
tent. Modelling in the metaverse will be based on shifting historical data-based forecasting for
supply chains and operational planning methods towards real time, data-driven decisions (Fig.
Fig. 4. Modelling in the SCOM metaverse framework
Figure 4. Alt Text: Modelling in the metaverse and SCOM framework
Optimisation, discrete-event simulation, neural networks, machine and reinforcement learning,
agent-based modelling, and system dynamics allow for the implementation of the full variety
of descriptive, predictive, and prescriptive algorithms in SCOM (Cavalcante et al., 2019; Rai et
al., 2021; Fu et al., 2022; Kusiak, 2022; Rolf et al., 2022). While real-time data-driven models
constitute a narrow view of digital twins (i.e., a digital twin as a standalone software package),
in a broader sense, digital twins can be considered as a combination of different information
systems and models. Seamless digital and physical integration can become the centric element
of the SCOM metaverse. For example, imagine a product that knows its location, inventory
status, price, and costs. Using edge computing, an algorithm would trigger automatic replen-
ishment, routing, pricing, and demand prediction decisions, thus enhancing margins, product
availability, on-time delivery, and overall profitability. New computational capacities for sup-
ply chain and operations analytics and the use of synthetic data along with the industrial Internet
of things can be used to predict customer and supplier behaviours in terms of demand recogni-
tion and delivery accuracy. Future research can shed more light on the transition from offline
to real-time data-driven modelling, revealing its value and barriers through improved end-to-
end visibility in the supply chain.
5. Conclusion
The metaverse and Web 3.0 represent new and strong triggers for the further evolution of
SCOM. They not only create a new, digital world with specific properties and behaviours rep-
licating the behaviours and processes of physical entities, but also influence physical SCOM.
Despite some fragmented literature that focuses on the metaverse and SCOM, there is a lack of
understanding as to how the metaverse will impact SCOM in the physical world and what the
potential SCOM processes and decision-making areas in the metaverse and the mutual impacts
of the co-existence of physical and metaverse SCOM will be.
Driven by these questions, our study aimed to advance our understanding of how the metaverse
will impact SCOM by drawing on cyber-physical systems, digital twins, cloud and digital sup-
ply chains, and Industry 4.0/Industry 5.0 concepts. With regard to the first research question,
we proposed a framework for metaverse SCOM encompassing seven socio-technological di-
mensions, i.e., organization, management, people, technology, scope, task, and modelling.
Concerning the second research question, our study indicates that new research areas can appear
which are specifically dedicated to the metaverse and novel SCOM processes and decision-
making areas (e.g., joint demand forecasting for metaverse and physical products, digital in-
ventory allocation in the metaverse, integrated production planning for the metaverse and phys-
ical worlds, and pricing and contracting for digital products), as well as new performance
measures (e.g., virtual customer experience level, availability of digital products, and digital
resilience and sustainability).
In answering the third question, our analysis shows that in the future we can expect a co-exist-
ence of physical SCOM, metaverse SCOM, and SCOM for the coordination of the physical and
metaverse worlds. We offered a structured future research agenda pointing to new research
questions and topics stemming from metaverse-driven visibility, computational power for data
analytics, digital collaboration, and connectivity. Further research on digital technology in
SCOM will contribute to the coordination of physical and metaverse supply chains and opera-
The authors thank two anonymous reviewers whose comments helped us enormously in im-
proving the paper.
Data Availability Statement
Data related with this paper is available with authors and will be available upon reasonable
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... For example, in industrial settings, gamification could be utilized in order to engage employees in participating in training activities and provide rewards for completing milestones or achieving certain goals [48]. Similarly, a logistics company could create a virtual reality game in which players must optimize supply chain operations to transport goods as efficiently as possible [49]. By incorporating gamification into Industry 5.0, companies can improve workforce engagement, increase productivity, and foster a culture of continuous learning and improvement. ...
... However, the existence of outdated production and logistic systems can lead to heightened emissions and pollution, significantly impacting the environment. As a result, the adoption of Industry 4.0-driven digital transformation might necessitate disruptive changes in supply chains, including environmentally conscious investments in products, processes, and supply chain networks, which could potentially compromise the efficiency of processes and workflows [49]. ...
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In the context of Industry 5.0, the concept of the Metaverse aligns with the vision of Web 4.0, representing a digital ecosystem where individuals and organizations collaborate in a human-centric approach to create personalized value. This virtual universe connects multiple interconnected worlds, enabling real-time interactions between users and computer-generated environments. By integrating technologies like artificial intelligence (AI), virtual reality (VR), and the Internet of Things (IoT), the Metaverse within Industry 5.0 aims to foster innovation and enhance productivity, efficiency, and overall well-being through tailored and value-driven solutions. Therefore, this entry explores the concept of the Metaverse in the context of Industry 5.0, highlighting its definition, evolution, advantages, and disadvantages. It also discusses the pillars of technological advancement, challenges, and opportunities, including its integration into manufacturing. The entry concludes with a proposal for a conceptual framework for integrating the human-centric Metaverse into manufacturing.
... These technologies are also currently assessed independently, their performance and compatibility, when integrated as part of a DT, still needs to be explored. For example, Dolgui and Ivanov (2023) have conceptualised the supply chain metaverse as supply chains enabled by DTs with additional properties and behaviours. There needs to be clarity as to how different technologies and data sources should be integrated to create a reliable and efficient supply chain. ...
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Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012-2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research, including the need for investigations into technology integration, development of DT performance evaluation metrics, and exploration of inter-level supply chain applications. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry.
... The rapid advancement and integration of emerging digital technologies, such as IoT (Rai, Tiwari, Ivanov and Dolgui, 2021), 5G , Metaverse (Dolgui and Ivanov, 2023), Blockchain (Li, Xue, Li and Ivanov, 2022), and Cloud technologies (Ivanov, Dolgui and Sokolov, 2022), also present a promising direction for future research. The potential these technologies hold in improving demand forecasting and understanding consumer behavior warrants extensive exploration. ...
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This research focuses on the profound impact of the shocks caused by the COVID-19 pandemic on the accuracy of AI-based demand forecasting in the beauty care industry. It aims to understand the key factors that led to decreased forecasting accuracy during the pandemic and employs causal mediation analysis to systematically investigate this complex issue. The empirical analysis is conducted using extensive order data from a major beauty care product manufacturer and distributor, covering the pre-pandemic, pandemic, and post-pandemic periods. The findings reveal that it is primarily the increase in demand volatility, and not the surge in sales volume, that has led to an increase in forecasting errors. This research provides crucial insights into the nuanced effects of macroeconomic shocks and consumer behavior changes on AI-based forecasting within the beauty care industry. Furthermore, it highlights the importance of understanding the underlying mechanisms that drive forecasting errors, paving the way for more resilient and robust demand forecasting systems in the future.
... Our recent studies showed that this type of data can be used to predict supplier delays or even possible relations between suppliers , Zheng et al 2023. Data that is externally available may consist of social media, company annual reports, and news outlets and even phone, shipment and postal records, as well as the emerging metaverse (Dolgui and Ivanov 2023), that may then be used to infer disruptions, supplierbuyer relations, financial health, and production capabilities. ...
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Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply chain network, without needing the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated detection of actors and dependencies involved in a supply chain, which in turn, can help firms to detect risky, unethical and environmentally unsustainable practices. In this paper we define DSCS, after which we review priority areas using a survey conducted in theUnited Kingdom. Our results show that visibility, sustainability, resilience, financial health detection are all significant areas thatDSCS can support, through a number of machine learning approaches such as natural language processing and predictivealgorithms. Despite anecdotal narrative on the importance of explainability of algorithmic results, practitioners often prefer accuracy over explainability, however there are significant differences between industrial sectors and application areas. We highlight a number of concerns on the unchecked use of AI in DSCS, such as bias in data or misinterpretation resulting in erroneous conclusions, which may lead to suboptimal decisions or relationship damage. Building on this observation, we develop and discuss a number of illustrative cases to highlight risks that practitioners should be aware of, highlighting key areas of further research.
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Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models.
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Data science and big data analytics (DS &BDA) methodologies and tools are used extensively in supply chains and logistics (SC &L). However, the existing insights are scattered over different literature sources and there is a lack of a structured and unbiased review methodology to systematise DS &BDA application areas in the SC &L comprehensively covering efficiency, resilience and sustainability paradigms. In this study, we first propose an unique systematic review methodology for the field of DS &BDA in SC &L. Second, we use the methodology proposed for a systematic literature review on DS &BDA techniques in the SC &L fields aiming at classifying the existing DS &BDA models/techniques employed, structuring their practical application areas, identifying the research gaps and potential future research directions. We analyse 364 publications which use a variety of DS &BDA-driven modelling methods for SC &L processes across different decision-making levels. Our analysis is triangulated across efficiency, resilience, and sustainability perspectives. The developed review methodology and proposed novel classifications and categorisations can be used by researchers and practitioners alike for a structured analysis and applications of DS &BDA in SC &L.
Achieving a circular economy (CE) is considered one of the most significant challenges of our time, as environmental challenges and social discrepancies keep increasing. While companies need to transform their supply chains towards sustainability and circularity, implementing CE concepts into practice is not straightforward and requires technical and non-technical capabilities , referring to dynamic capabilities. One of the most promising digital technologies for improving sustainability and circularity is blockchain. However, the literature on relationships between blockchain technology (BCT), sustainability, and the CE is still in its infancy. This study illustrates how BCT can help implement circular supply chain management (CSCM) from a dynamic capabilities perspective. A multiple case study approach examines how BCT's potential impacts CSCM. Building on the case evidence, necessary dynamic capabilities are deduced for successful CE implementations with BCT. The analysis demonstrates that supply chain traceability and related sensing capabilities are major benefits of BCT-driven CSCM. Thus, we contribute to theory by examining which dynamic capabilities need to be developed when realizing CSCM through BCT. The results indicate that the BCT potentials depend on the business model pointing to certain contingencies.
Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to several factors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced in the context of Industry 4.0 has generated high interest and has been studied in a variety of domains, including production planning and control. In this paper, we investigate the benefits digital twins would bring to the sawmill industry via a literature review on the wider subject of sawmill production planning and control. Opportunities facilitating their implementation, as well as ongoing challenges from both academic and industrial perspectives, are also studied.
This tertiary study systematically analyzes 65 literature reviews on supply chain resilience (SCR) published in academic journals or conference proceedings. Our focus is on the vulnerabilities and capabilities of a supply chain that need to be balanced to achieve resilience. We explore the interdependencies of these two categories of SCR by developing an innovative framework to realize capabilities after identifying the SCR vulnerabilities. First, we propose a framework that systematizes the vulnerabilities and capabilities identified in the literature. Then, we discuss the identified SCR characteristics based on the framework and quantitatively evaluate the literature reviews’ focus on the two SCR categories. A synthesis of the research results shows the SCR characteristics addressed in the literature and reveals deficits for specific vulnerabilities. Finally, we outline future research opportunities based on these findings by mapping SCR capabilities and vulnerabilities in light of Industry 4.0 and digital supply chain developments. Then, we derive research gaps and recommended actions for practitioners in the context of SCR and Industry 4.0. Appendices/Supplementary materials are available on request by emailing the corresponding author or can be obtained under
A hyper-connected digital universe referred to as the ‘metaverse’ bears the promise of fundamentally changing how consumers, brands, and firms will transact and interact in a seamlessly interconnected space of virtual realities. The potential of the metaverse is being accelerated by the increasing tendency of (i) consumers engaging and transacting in virtual spaces and (ii) firms investing millions of dollars in developing metaverse-related technologies. However, given the rapid evolution, there is a lack of clear understanding of the current scope of the metaverse and the consequent implications for marketing practice and research. This study integrates the findings from an extensive literature review of multiple disciplines and expert viewpoints of industry leaders to propose a definition and an organizing framework for the emergent metaverse. Subsequently, the authors discuss how metaverse-induced changes contribute to novel implications for marketing practice and propose a research agenda to guide future academic studies and marketing initiatives.
Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse has been introduced as a shared virtual world that is fueled by many emerging technologies. Among such technologies, artificial intelligence (AI) has shown the great importance of enhancing immersive experience and enabling human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI, including machine learning algorithms and deep learning architectures, in the foundation and development of the metaverse. As the main contributions, we convey a comprehensive investigation of AI-based methods concerning several technical aspects (e.g., natural language processing, machine vision, blockchain, networking, digital twin, and neural interface) that have potentials to build virtual worlds in the metaverse. Furthermore, several primary AI-aided applications, including healthcare, manufacturing, smart cities, and gaming, are studied to be promisingly deployed in the virtual worlds. Finally, we conclude the key contribution and open some future research directions of AI for the metaverse. Serving as a foundational survey, this work will help researchers, including experts and non-experts in related fields, in applying, developing, and optimizing AI techniques to polish the appearance of virtual worlds and improve the quality of applications built in the metaverse.
The design and management of an efficient, resilient, and viable supply chain (SC) capable of operations and demand fulfillment continuity despite severe disruptions is imperative for the survivability of firms and for providing society with essential goods and services in long-term crises. This Special Issue focuses on SC adaptation and viability as novel decision-making settings for operations research and management science (OR/MS) emerged in the wake of the COVID-19 pandemic, which goes beyond short-term, singular event-driven disruptions. Papers in the Special Issue present new and original OR/MS research to support decision-making related to long-term SC crises with inherent uncertainty about the present and future. Since SC viability theory is relatively new, this Special Issue contributes to advancing our knowledge and application fields for designing and managing SCs as viable systems. We present fundamentals of SC viability theory, review and summarize papers in the Special Issue, and project some future research directions.