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REVIEW ARTICLE
Lu WANG, Tianhu DENG, Zuo-Jun Max SHEN, Hao HU, Yongzhi QI
Digital twin-driven smart supply chain
© The Author(s) 2022. This article is published with open access at link.springer.com and journal.hep.com.cn
Abstract Today’s supply chain is becoming complex
and fragile. Hence, supply chain managers need to create
and unlock the value of the smart supply chain. A smart
supply chain requires connectivity, visibility, and agility,
and it needs be integrated and intelligent. The digital twin
(DT) concept satisfies these requirements. Therefore, we
propose creating a DT-driven supply chain (DTSC) as an
innovative and integrated solution for the smart supply
chain. We provide background information to explain the
DT concept and to demonstrate the method for building a
DTSC by using the DT concept. We discuss three research
opportunities in building a DTSC, including supply chain
modeling, real-time supply chain optimization, and data
usage in supply chain collaboration. Finally, we highlight a
motivating case from JD.COM, China’s largest retailer by
revenue, in applying the DTSC platform to address supply
chain network reconfiguration challenges during the
COVID-19 pandemic.
Keywords digital twin, supply chain management
*
1 Introduction
Today’s supply chain is becoming complex and fragile.
Hence, supply chain managers need to create and unlock
the value of the smart supply chain. For example, the
2020 Coronavirus outbreak threatened the supply chain
domestically and globally and significantly affected the
supply chain, including supply shortages, sourcing limita-
tions, logistical delays, and demand reductions. Auto-
makers, such as General Motors, Nissan Motor, and Fiat
Chrysler, halted production in other countries due to the
supply shortage caused by China’s manufacturing shut-
down (Wayland, 2020). Apple has also reported that it does
not expect to achieve its second quarter in 2020 forecasts
for revenue (Lucas, 2020). Given that most iPhones are
assembled in China, the Coronavirus outbreak has led to
Apple’s global constraints on the supply side. On the
demand side, closed Apple stores have resulted in lower
demand in China.
To protect the supply chain from this type of disruption,
firms must continuously monitor the outbreak, check
inventory and logistical hubs, and respond rapidly to
changing circumstances. In addition, to evaluate the
overall risk and impact, firms must assess upstream
suppliers and downstream customers. In the long term,
firms should rethink their supply chain structure and
strategy and conduct simulation exercises to prevent future
incidents (Hippold, 2020). The challenges faced by supply
chain managers in the pandemic demonstrate the require-
ments of the smart supply chain:
Connectivity. Connectivity is the ability to connect
all enterprises, products, properties, and other valuable
items in the supply chain in order to provide comprehen-
sive information and to monitor the marketing status, intra-
enterprise operations, and inter-enterprise communications
(Butner, 2010; Wu et al., 2016).
Visibility. Visibility is the ability to keep track of the
flow of materials, finances, and information throughout the
supply chain (Butner, 2010; Busse et al., 2021). Supply
chain managers must have access to real-time data related
to production, inventory, logistics, and marketing in order
to identify where and how products are stocked and when
and how products are sold to consumers.
Agility. Agility is the quick ability to detect changes,
collect relevant data, analyze opportunities and threats,
make optimal decisions, implement these decisions,
Received September 13, 2021; accepted December 21, 2021
Lu WANG, Tianhu DENG (✉)
Department of Industrial Engineering, Tsinghua University, Beijing
100084, China
E-mail: deng13@mail.tsinghua.edu.cn
Zuo-Jun Max SHEN
Faculty of Engineering and Faculty of Business and Economics,
University of Hong Kong, Hong Kong 999077, China; Department of
Industrial Engineering and Operations Research and Department of Civil
and Environmental Engineering, University of California, Berkeley,
Berkeley, CA 94720, USA
Hao HU, Yongzhi QI
JD.COM, Beijing 100101, China
The authors are grateful for the financial support from the National Key
R&D Program of China (Grant No. 2018YFB1700600).
Front. Eng. Manag. 2022, 9(1): 56–70
https://doi.org/10.1007/s42524-021-0186-9
and modify operations accordingly (Gligor et al., 2019;
Seyedghorban et al., 2020).
Integrated. An integrated supply chain shares
information and makes decisions jointly across different
stages of the supply chain (Wu et al., 2016).
Intelligent. An intelligent supply chain makes large-
scale, optimal decisions and uses predictive analytics to
protect the supply chain from future risks (Butner, 2010;
Wu et al., 2016; Busse et al., 2021).
Many organizations desire to unlock the value of the
smart supply chain but do not know how to begin. Creating
a digital twin-driven supply chain (DTSC) is an innovative
and integrated solution for building a smart supply chain
(Barykin et al., 2020; 2021; AlMulhim, 2021; Busse et al.,
2021; Ivanov and Dolgui, 2021). The digital twin (DT)
concept uses advanced digitalization to mirror the physical
world in the virtual world and has been named one of
Gartner’s Top 10 Strategic Technology Trends from 2017
to 2019 (Panetta, 2017; 2018; 2019). Gartner predicts that
half of the world-famous industrial companies will
implement DTs by 2021, thereby improving the compa-
nies’effectiveness by 10%(Pettey, 2017). According to
Thomas Kaiser, SAP Senior Vice President of the Internet
of Things (IoT), “DTs are becoming a business imperative,
covering the entire lifecycle of an asset or process and
forming the foundation for connected products and
services; companies that fail to respond will be left
behind”(Marr, 2017).
The DT concept was developed to enable smart
manufacturing (Glaessgen and Stargel, 2012; Shafto
et al., 2012; Grieves, 2015), but the DT’s ability to mirror,
predict, and optimize complex systems allows it to be
effective even beyond manufacturing systems. As
explained in Section 3, the DT concept and the smart
supply chain perfectly complement each other. Therefore,
building a DTSC is the solution to realize the smart
supply chain. In a DTSC, the virtual supply chain and the
physical supply chain are entangled (Rehana, 2018). All
stakeholders can access the real-time status of physical
entities (e.g., stocks, procurement, and sales). Managers
can simulate and implement decisions in the virtual world
before executing them in the physical supply chain. The
primary objective is to enable the supply chain to operate
with better performance and higher efficiency.
This article contributes to the literature by investigating
the following questions:
(1) What is the DT concept and how is it being used and
exploited in supply chain management (SCM)? Section 2
discusses these topics.
(2) What are the basic components to build a DTSC?
Why do these components satisfy the requirements of the
smart supply chain? Section 3 discusses these topics.
(3) How can the existing SCM theories be applied, and
what are the new research opportunities in the DT context?
Section 4 presents these topics.
(4) What are the implications and future challenges
when implementing the DTSC in actual business
environments? These matters are analyzed in Section 5 by
presenting a motivating case of JD.COM.
2 Literature review
In this section, we first review the concept development of
the DT and the properties of an efficient DT. Then, we
proceed to review the current DT applications in SCM.
2.1 History of the DT concept
The DT concept has been developed by practitioners and
researchers after 20 years of effort. Table 1 summarizes the
conceptual development from 2002 to 2020. Two notable
trends have reshaped the understanding of a DT. The first
trend is a shift from viewing the DT concept as a single
methodology to a multidisciplinary integration. The key
driver behind this change was the gradual broadening of
the scope of the DT application. The original concept was
based on product lifecycle management (PLM) in smart
manufacturing and aerospace engineering (Grieves, 2005;
2006; 2011; Glaessgen and Stargel, 2012; Shafto et al.,
2012). As the application scope widened, the specific
application domain lost emphasis. Researchers and practi-
tioners proposed general concepts, such as “virtual
information constructs”(Grieves and Vickers, 2017) and
“the linked collection of the relevant digital artifacts”
(Boschert and Rosen, 2016). Therefore, simulation alone is
incapable of building all the required virtual models and
incorporating multidisciplinary approaches as needed. The
paradigm’s organic connection to new and emerging
technology enables the DT concept to play a pivotal role
in developing intelligent enterprise and supply chains
(Anasoft, 2019).
The second trend involved a shift in the focus of the DT
concept from being “tech-oriented”to “decision-oriented”.
Although the DT concept originally focused on virtual
modeling (Grieves, 2015; Boschert and Rosen, 2016;
Grieves and Vickers, 2017), new approaches have
emphasized the paradigm’s capability to provide optimal
decisions and prospective insight that is data-driven
(Stanford-Clark et al., 2019; Stark and Damerau, 2019;
Olcott and Mullen, 2020). As the Acatech Industrie 4.0
Maturity Index (Schuh et al., 2017) suggested, predictive
capacity and adaptability —including automated decision-
making and actions —are based on connectivity and
visibility that result from creating connected virtual
counterparts.
We summarize the properties of a DT on the basis of
these principles. First, the core property of an efficient DT
is to establish digital representations of physical entities
and processes at different scopes (Boschert and Rosen,
2016; Stark and Damerau, 2019; Qi et al., 2021). A DT
overcomes data silo issues in traditional enterprise
Lu WANG et al. Digital twin-driven smart supply chain 57
management systems and provides a holistic understand-
ing (Olcott and Mullen, 2020). Second, the availability of
real-time data enables high-frequency synchronization
between physical entities and processes and digital
representation. A DT can leverage historical and real-
time data to simulate the system’s past, present, and future
behavior. Finally, armed with predictive analysis and
optimization tools, a DT can provide useful information
and knowledge, allowing intelligent responses to sudden
and unexpected situations.
2.2 State-of-the-art DTSC
We search publications on the Web of Science database
with search terms including “digital twin(s)”and “supply
chain”from January 2016 to October 2021. Retrieved
articles have discussed the following issues: (1) What is a
DTSC? (2) What are the advantages that the DT concept
brings to SCM? And (3) is there any case to illustrate the
value of implementing a DTSC?
What is a DTSC? The concept of DTSC has been
considered by several articles. According to Busse et al.
(2021), a DTSC is “a digital simulation model of a real
logistics system, which features a long-term, bidirectional
and timely data-link to that system”. The authors propose a
general framework, including modules of supply chain,
optimization, simulation, reporting, and interface. Ivanov
and Dolgui (2021) identify a DTSC as “a computerized
model that represents network states for any given
moment in real time”. Haag and Simon (2019) suggest
that implementing DTs provides the possibility for
companies to model real-world assets and their interactions
in arbitrary magnitude and level of detail. Li et al. (2020)
propose a five-dimensional framework of a sustainable
business model under the DTSC concept. Recently,
Kalaboukas et al. (2021) propose the concept of cognitive
DTs for SCM, which are capable of predicting trends and
flexible enough in dynamic environments. In addition to
these general conceptual development, the DTSC concept
has been discussed in several specified application fields,
including circular supply chain (Preut et al., 2021), food
supply chain (Smetana et al., 2021; Shoji et al., 2022),
global port management (Wang et al., 2021), and logistics
(Lee and Lee, 2021; Moshood et al., 2021; Park et al.,
2021). In summary, the core functionality of a DTSC is to
provide an integrated and holistic view, which enables all
stakeholders to contribute jointly to the creation across
different supply chain stages (Ducree et al., 2020; Yang
et al., 2020).
What are the advantages that the DT concept brings
to SCM? A number of articles suppose that the DT
strategy is expected to lead the digital transformation of the
supply chain to the next stage (Cozmiuc and Petrisor,
2018; Ghobakhloo, 2018; Reeves and Maple, 2019;
Marmolejo-Saucedo et al., 2019; Beltrami et al., 2021;
Kenett and Bortman, 2021). The first advantage that the
DT concept brings to SCM is improved connectivity.
Sharma et al. (2021) suppose that the DT concept connects
different life stages in the chain. The authors review
strategies to balance efficiency and cost to enable such
Table 1 History of the digital twin (DT) concept
Year Event
2002 Conceptual ideal for product lifecycle management (PLM) (a presentation by Dr. Grieves)
2005 Mirrored Spaces Model (Grieves, 2005)
2006 Information Mirroring Model (Grieves, 2006)
2011 Digital twin (Grieves, 2011)
2012 A DT is an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models,
sensor updates, and fleet history to mirror the life of its corresponding flying twin. The DT is ultra-realistic and may consider one or more important
and interdependent vehicle systems, including airframe, propulsion and energy storage, life support, avionics, and thermal protection. The concept is
proposed by NASA and the US Air Force (Glaessgen and Stargel, 2012; Shafto et al., 2012)
2015 A virtual representation of what has been produced (Grieves, 2015)
2016 A set of virtual information constructs (Grieves and Vickers, 2017) (first online in 2016)
2016 The linked collection of the relevant digital artifacts including engineering data, operation data, and behavior descriptions via several simulation
models (Boschert and Rosen, 2016)
2019 A DT is a digital representation of an active unique product (real device, object, machine, service, or intangible asset) or unique product–service
system (a system consisting of a product and a related service) that is composed of selected characteristics, properties, conditions, and behaviors by
means of models, information, and data within a single or even across multiple life cycle phases. The concept is proposed by the International
Academy for Production Engineering Encyclopedia of Production Engineering (Stark and Damerau, 2019)
2019 A DT is a dynamic virtual representation of a physical object or system, usually across multiple stages of its lifecycle. It uses real-world data,
simulation, or machine learning models, combined with data analysis, to enable understanding, learning, and reasoning. DTs can be used to answer
what-if questions and present insights in an intuitive way. The concept is proposed by IBM (Stanford-Clark et al., 2019)
2020 A DT is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. The concept is proposed by
the Digital Twin Consortium (Stanford-Clark et al., 2019)
58 Front. Eng. Manag. 2022, 9(1): 56–70
connectivity. According to Pehlken and Baumann (2020),
DTs help provide production information to a variety of
stakeholders in the recycling industry and therefore
increase the impact on sustainability. Zafarzadeh et al.
(2021) focus on production logistics. The authors analyze
142 articles and conclude that the application of the DT
concept that provides a connected and holistic view of
production logistics is important. To realize connectivity
successfully, a DTSC needs a unified data management
approach to create a new integrated vision of the supply
chain (Avventuroso et al., 2017; Landolfiet al., 2017;
Gupta et al., 2020; Autiosalo et al., 2021).
The second advantage that DTs bring to SCM is
improved end-to-end visibility (Moshood et al., 2021).
As mentioned by Hegedus et al. (2019), a DTSC can serve
as a retrofittable and cost-effective solution for asset
tracking across supply chain, which is a highly desirable
functionality in SCM. Wang et al. (2020) conceptualize the
idea of DTSC for overcoming the limited visibility of the
logistics process in non-high-tech industries. Tozanlıet al.
(2020) study how the DT concept facilitates a data-driven
trade-in pricing policy in fully transparent platforms. Wang
et al. (2021) present information visualization for using
DTs in global port management. Chen and Huang (2021)
identify information asymmetry as the main challenge that
restricts the development of closed-loop supply chain. The
authors analyze 288 articles and find that the DT concept is
conducive to solving problems with information asymme-
try in remanufacturing supply chains. In particular,
blockchain technology for SCM in the DT context can be
used for security and privacy (Kanak et al., 2019; 2020;
Greif et al., 2020; Joannou et al., 2020; Leng et al., 2020;
Deng et al., 2021; Ho et al., 2021).
The third advantage is DTs’capability of increasing
supply chain agility and resilience. Several researchers
have discussed the concept of DTSC in managing
disruption risks (Ivanov and Dolgui, 2019; 2021; Ivanov
et al., 2019). Seif et al. (2019) note that the DT concept
enables agility because a modular way of modeling is
applied. Golan et al. (2021) address gaps in modeling
supply chain resilience in general and specifically for
vaccine production. The authors suggest that adopting a
DTSC helps supply chain managers to quantify tradeoffs
between efficiency and resilience under disruption better.
Barykin et al. (2020) review the optimization and
simulation methods that are used to evaluate the impact
of potential failure on supply chain performance in the
DTSC context.
Considering that supply chains are becoming increas-
ingly complex, several researchers discuss how to deal
with the complexity and enable intelligent supply chain in
the DT context. Simulation tools are useful to represent the
interdependencies in complex supply chains in the DT
context, including discrete event simulation (D’Angelo
and Chong, 2018; Dobler et al., 2020; Dutta et al., 2021;
Pilati et al., 2021; Wilson et al., 2021), agent-based
simulation (Gorodetsky et al., 2019; Clark et al., 2020;
Orozco-Romero et al., 2020), and hybrid simulation
models (Makarov et al., 2021). Frazzon et al. (2020)
address distributed decision-making under a socio–cyber–
physical system perspective in the DTSC context. Shen
et al. (2020) discuss how the DT concept supports
collaborative intelligent manufacturing and the SCM,
including resilient collaborative supply network design,
collaborative planning of production and distribution,
dynamic reconfiguration and rescheduling, and remote
maintenance. Baruffaldi et al. (2019) exploit optimization
and simulation techniques to quantify how information
availability impacts warehousing operations, which results
in the creation of a DT of the warehouse. Cavalcante et al.
(2019) combine machine learning and simulation to create
a DTSC for improving sourcing decisions. As mentioned
by Barykin et al. (2020), no single approach builds the
concept of a DTSC, and hybrid and data-driven decision-
making algorithms are identified as a key enabler to realize
the value of the DT concept in SCM (de Paula Ferreira
et al., 2020; Andronie et al., 2021).
Is there any case to illustrate the value of implement-
ing a DTSC? The value of DTSC implementation has
been validated conceptually and tested in several experi-
mental environments in a variety of application domains.
A number of articles have investigated how the DTSC
concept improves supply chain agility under COVID-19
pandemic (Ivanov, 2020; Burgos and Ivanov, 2021;
Ivanov and Dolgui, 2021; Nasir et al., 2021). In particular,
creating a DTSC helps policymakers deal with situations
in the COVID-19 pandemic (Nasir et al., 2021). Another
application domain is the food supply chain, in particular,
fresh food delivery (Defraeye et al., 2019; 2021; Onwude
et al., 2020). With the help of a DTSC, producers, retailers,
and consumers can continuously monitor and control
quality evolution during fresh food postharvest life. The
DTSC concept has also been applied in the literature on
pharmaceutical (Marmolejo-Saucedo, 2020; Santos et al.,
2020), semiconductor (Ehm et al., 2019; Moder et al.,
2020a; 2020b), aircraft manufacturing supply chain
(Mandolla et al., 2019; Heim et al., 2020), the coal
industry (Semenov et al., 2020), and tobacco industry
(Shen et al., 2021).
We identify the gaps in the literature and try to address
the following issues in this article. First, in spite of the
advanced conceptual development of the DTSC, questions
about the actual detailed architecture of a DTSC have to be
studied (Busse et al., 2021). We discuss the basic
components to build a DTSC in Section 3. Second, the
existing literature has discussed how to use mature
methods to enable intelligent decision-making in a DTSC
(e.g., simulation and machine learning), whereas we
extend the discussion by showing how existing SCM
theories can be applied. We also discuss new research
opportunities in the DT context in Section 4. Third, the
proposed frameworks are validated either conceptually or
Lu WANG et al. Digital twin-driven smart supply chain 59
in experimental environments. We illustrate how the DTSC
concept helps in large-scale real-life business environment
in Section 5.
3 Building a DTSC
In this section, we describe how to build a DTSC. To
begin, we briefly review the definition of supply chain and
SCM. Then, we explain the structure of a DT and identify
the corresponding components in a DTSC. Finally, we
discuss how considering these components satisfies the
properties of the smart supply chain.
According to Christopher (2011), a supply chain is “a
network of organizations that are involved, through
upstream and downstream linkages, in the different
processes and activities that produce value in the form of
products and services in the hands of the ultimate
customer”. Generally, a supply chain consists of suppliers,
manufacturers, distributors, retailers, and customers.
Products or services flow downstream to end customers.
The flow of information and funds is bidirectional between
supply chain nodes. Therefore, the purpose of SCM is to
manage the flow of products, information, and finances
associated with a series of inter- and intra-enterprise
activities. Such activities typically include procurement
planning, manufacturing strategy, customer order manage-
ment, inventory management, logistics management, and
marketing (Chandra and Kumar, 2000; Min and Mentzer
2000; Lummus et al., 2001).
Considering the properties of an efficient DT and the
definitions of supply chain and SCM, stakeholders must
identify (1) the scope of physical entities that should be the
focus of the supply chain; (2) the fidelity applied to
understand the current status of these physical entities in
virtual space; and (3) the key sources of data used to
synchronize the physical supply chain and the virtual one.
Once these information are defined, a DTSC prototype
may be built.
In a DTSC, we focus on two types of physical entities.
First, a DTSC must include the supply chain nodes, which
are enterprises involved in the supply chain. Given that the
aim of a DTSC is to enhance the performance and
competitiveness of the entire chain, the physical entities
must include all the enterprises, beginning with suppliers
and ending with customers. Second, products, properties,
and other valuable items should be considered. Products
are the core output that brings profit and competitiveness to
the chain, and it is an indispensable part of the physical
entities in a DTSC. Properties are the items needed to
produce and deliver products, and they belong to
enterprises in the supply chain. For example, a manufac-
turing company owns the raw materials and equipment
needed to produce its goods. Other valuable items are
facilities that add value to the business but belong to
external organizations, such as the trucks of a third-party
logistics company. By including these two types of
physical entities, we can mirror the status, the intra-
enterprise operations, and the inter-enterprise communica-
tions of a firm, and therefore, mirror the entire business
operation in virtual space.
Having clarified the two types of physical entities
considered in a DTSC, we must discuss the fidelity that
should be used to analyze these physical entities in virtual
space. We suggest creating a virtual or digital supply chain
that reflects the physical supply chain in the following two
aspects: (1) the basic properties of the physical entities and
(2) the inter- and intra-enterprise business processes. The
basic properties are the static attributes of the physical
entity, such as organizational structure, strategic objec-
tives, and location. For the product, we consider attributes
such as appearance, size, and functions. For manufacturing
equipment, the relevant properties usually include function
and power consumption. As an example, for a truck used in
transportation, endurance and capacity are taken into
consideration.
The key steps in mirroring the physical supply chain in
virtual space is to define the inter- and intra-enterprise
business processes, such as procurement, production,
distribution, and marketing. To define a business process
clearly, we suggest asking the following questions:
What are the goals of the business process?
Is it an intra-enterprise business or an inter-enterprise
business?
What is the standard procedure of the business
activity?
At each step, which enterprise or department makes
decisions or takes action?
What policies, regulations, and limitations must be
considered before decisions are made or actions are taken?
How should the performance of the business process
be evaluated?
If both basic properties and business processes are
properly defined, then we can develop a virtual supply
chain that exactly mirrors the structure and functions of the
physical supply chain.
The synchronization refers to the transmission of
bidirectional information. Generally, the data used in the
physical supply chain fall into one of the following two
categories: (1) static data related to the basic properties of
physical entities or (2) real-time, dynamic data generated in
business operations. Static data are used to ensure that the
virtual supply chain shares the same structure and proper-
ties as the physical supply chain. Real-time, dynamic data
are used to synchronize the status and processes in the
virtual world. For example, the location of a truck and
traffic conditions are updated in real time; hence, the esti-
mated arrival time is updated continuously and is precise.
Real-time data are collected not only by sensors and
other IoT equipment, which is typical with manufacturing
systems, but also by online systems, such as procurement
management systems and order management systems. In
60 Front. Eng. Manag. 2022, 9(1): 56–70
the virtual world, data are simulated. On the basis of the
analysis of the actual data in the physical supply chain and
the simulated data in the virtual supply chain, we obtain
results that provide valuable information and knowledge.
Information and knowledge are then transferred to the
physical world to support intelligent decision-making and
the efficient implementation of decisions.
A DTSC is proposed as a solution to the smart supply
chain, because the features of a DT and the requirements of
the smart supply chain match each other. In a DTSC, the
physical supply chain is connected by smart sensors or
online systems and collects specific data and information
that enable the virtual supply chain to mirror the static
properties and dynamic business processes of the physical
supply chain. Connectivity is therefore achieved. As the
most important features in the DT, real-time data
acquisition and implementation allow the connection
between the physical and virtual supply chain to
synchronize operation dynamics, which increases the
supply chain visibility. The synchronized data provide
opportunities to monitor, analyze, control, and optimize the
supply chain and results in up-to-date virtual simulation
and optimization that provide agility. A DTSC actively
operates in the entire business process throughout the
supply chain. Therefore, a DTSC jointly optimizes the
supply chain across different stages and establishes an
integrated supply chain. Predictive analytics are “the
capabilities used to model and simulate current and future
conditions, considering operating conditions to test future-
state scenarios”(Klappich, 2019). Consequently, predic-
tive analytics of a DTSC allow decision-makers to look
forward instead of backward and allow the supply chain
to be intelligent.
4 Digital twin-driven smart supply chain:
New research opportunities
Building on the explanation of a DTSC, we use this
section to discuss new research opportunities to implement
a smart DTSC. In Section 4.1, we focus on supply chain
modeling, including demand, supply, and risk modeling.
In Section 4.2, we center our discussion on making real-
time decisions in SCM, and in Section 4.3, we explain data
usage in supply chain collaboration.
4.1 Supply chain modeling
A DTSC enables an accurate and timely representation of
the supply chain status. Considering analytical tractability
and that data may be limited, partial, or difficult to access,
simplified assumptions about demand, supply, and risks
are used in supply chain modeling rather than an accurate
characterization of reality. With the DTSC’s capability to
mirror dynamics, assumptions that were commonly and
previously used need to be reviewed, and a structural and
theoretical analysis is required.
First, to take full advantage of real-time demand signals
and supply conditions in a DTSC, novel methods that
model supply and demand in a nonlinear way are required.
For example, to model random supply and demand, Feng
and Shanthikumar (2018) propose a new approach that
transforms nonlinear supply and demand functions into
linear functions on a higher dimension. Data-driven
optimization is another research tool that addresses the
complex supply and demand data by solving mathematical
programming problems directly using observed data
(Bertsimas and Thiele, 2006). Without assuming a
particular distribution, one-step optimization is used with
the collected data rather than the traditional two-stage
approach of optimization after estimation (Liyanage and
Shanthikumar, 2005).
Levi et al. (2015) illustrate an application of this idea in
modeling demand with unknown distribution. The authors
study the newsvendor problem in which the only infor-
mation is a random sample of demand. The accuracy of
the proposed sample average approximation approach is
ensured by a tight bound. Compared with demand signals,
more research is needed to understand the modeling
capacity from data-driven approaches. Currently, most
studies assume the infinite capacity according to de Kok
et al. (2018). Feng and Shanthikumar (2018) consider
capacitated supply chains and concluded that three ways
can be used to model capacity: (1) deterministic supply,
(2) all-or-nothing supply, and (3) proportional random
yield.
Second, building a DTSC can innovate the approach to
risk modeling. Generally, discrete events are used to model
risks, and risks are assumed to propagate between adjacent
nodes in supply networks. Garvey et al. (2015) apply
Bayesian networks to measure risk propagation in a supply
network. These measures can be combined with other
problems (e.g., inventory management and network
design) to develop globally optimal solutions. The authors
focus on binary risk and assume that all conditional
probability distributions are well-defined. In a DTSC
context, the propagation of risk signal (information) and
the actual risk (event) can be asynchronous. The risk signal
can be synchronized instantaneously throughout the entire
supply chain, but the actual influence of the risk needs time
to propagate. New modeling approaches are required for
companies to understand asynchronous signal and event.
Analytics need to be developed to predict when actual risk
happens to allow companies to prepare.
Third, model adaptability with real-time data requires
future research. Supply chains today are facing a changing
environment. Simulation models in a DTSC are a
promising way to manage the complexity and uncertainty
of reality (Tohamy, 2019). According to Rajagopal et al.
(2017), simulation methods that consider disruption and
supply risks in a multi-period setting are applied in supply
chain network design, risk propagation analysis (Wu et al.,
Lu WANG et al. Digital twin-driven smart supply chain 61
2013; Bueno-Solano and Cedillo-Campos, 2014; Chen
et al., 2015), facility location, and inventory management
(Colicchia et al., 2010; Schmitt and Singh, 2012; Sarkar
and Kumar, 2015). Despite these advances, we still require
novel approaches to model adaptability, that is, the ability
to learn continuously from real-time status and to update
models constantly.
Currently, data-driven approaches have been widely
adopted to enable demand learning. To name a few, Deng
et al. (2014) use the Bayesian method in statistically
learning service-dependent demand. In Cao et al. (2019),
customers’arrival rate (demand) is learned in a Bayesian
method. Harrison et al. (2012), Ghate (2015), and Chen
et al. (2017) propose other Bayesian demand learning
methods. Ma et al. (2020) propose a framework of data-
driven sustainable smart manufacturing based on demand
response for energy-intensive industries. Pereira and
Frazzon (2021) propose a data-driven approach that
combines machine-learning demand forecasting and
operational planning simulation-based optimization to
synchronize demand and supply adaptively in omni-
channel retail supply chains. In spite of the advanced
data-driven methods, leveraging real-time data to update
simulation models in a changing environment remains an
unresolved problem (Hong and Jiang, 2019). Moreover,
little has been done to identify how to leverage demand
and supply information through product lifecycle rather
than a single demand/supply data point (Ma et al., 2020).
In summary, to take full advantage of real-time data and
information in a DTSC, we need novel methods to capture
supply, demand, and risks accurately in supply chain
modeling. To manage the changing environment, increas-
ing model adaptability remains an important issue for
future research.
4.2 Real-time supply chain optimization
In a DTSC, the physical status and the digital model are
frequently synchronized, which requires real-time and
system-level instantaneous optimization of available
information (Olsen and Tomlin, 2020). Using offline
results directly is a promising way to respond to
environmental changes under tight computation budgets.
Lowrey et al. (2018), Hong and Jiang (2019), and Jiang
et al. (2020) explain this idea. A simple illustration of this
principle in inventory management is the use of radio-
frequency identification technology to collect real-time
data and inventory policies predefined at the retail store
(Bottani et al., 2017). The real-time data are used to track
product traceability, product history, amount of sales,
product availability, and inventory. With the inventory and
product availability information, a store can implement
inventory policies with continuous review as soon as an
inventory status change is detected in real time.
Making all decisions in real time is not necessary, and
choosing the right synchronization rate is required. In
practice, “real-time”means that the time between real-
world changes is negligible with respect to the need and
intended usage of the digital models by applications or
users (Minerva et al., 2020). Generally, two strategies are
applied to trigger the algorithms in real-time problems:
Time trigger strategy (e.g., making decisions every minute)
and event trigger strategy (e.g., making decisions when a
new demand arrives). Heemels et al. (2012) conduct a
debate on time trigger (periodic) and event trigger in
system control. Despite the computation delays explicitly
considered by the authors, more types of delay occur in
practice. Figure 1 shows a basic decision scheme in a
DTSC. According to Power (2011), “real time”in practice
always has latency between (1) physical supply chain
changes, (2) the reflection of physical supply chain change
in data in one or more systems of record (the availability of
real-time data), and (3) the availability of changed data to
current optimization models. The above summary con-
siders only the process from the real world to virtual
representations. If the feedback process is taken into
consideration, additional latency occurs between (4) the
availability of decisions proposed by the optimization
Fig. 1 Basic real-time decision scheme in a DTSC.
62 Front. Eng. Manag. 2022, 9(1): 56–70
model and (5) the implementation of the decision by an
actuator or an operator and the actual changes.
In a DTSC context, the length of the decision period
varies among different applications. For example, Guo
et al. (2017) focus on the global network configuration
problem and provide an online-learning approach of joint
optimization to change a labor-intensive and error-prone
configuration to one that is optimally designed. Ulmer
(2019) reoptimizes vehicle routing as a reaction to new
customer requests and in anticipation of future requests.
Sung et al. (2021) apply two different offline path planning
algorithms to generate different training path data sets and
to improve the online path planner performance. In some
contexts, the length of a decision period may be short. For
instance, automated guided vehicles are controlled at the
millisecond level, and the reconfiguration of a production
line to respond to urgent orders must be determined in light
of the takt time. An open question is how to combine
different trigger strategies, considering the above latency,
to achieve timely synchronization and response in a prac-
tical context. Moreover, closing the loop between offline
models and online feedback has unanswered questions.
For example, considering the time to (6) validate perfor-
mance and (7) update optimization models, how often
should offline models be checked and updated? The
discussion on model adaptability in the previous section
is closely related to this question.
4.3 Data usage in supply chain collaboration
We discuss data-driven modeling and decisions in previous
sections. In this section, we focus on the proper data usage
in supply chain collaboration. According to the Accenture
Technology Vision report (Daugherty et al., 2021), 87%of
executives identify that the value of a DTSC is in their
organization’s increased ability to reflect information
about their supply chains, instead of their own organiza-
tions. Inevitably, data usage issues in a DTSC must be
addressed.
Questions related to data ownership and privacy remain
unanswered. When a company builds a DTSC using a DT
vendor and allows their supply chain partners to upload
data to the platform, ownership (i.e., vendors, organiza-
tions, or both) of this industrial data is unclear (Internet of
Business, 2017). Companies face the risk of inadvertently
signing imbalanced agreements with DT vendors. Further-
more, companies must carefully manage sensitive custo-
mer information, which is increasingly exposed to criminal
threats (Fuller et al., 2020) and regulated by laws. For
example, the European Union’s General Data Protection
Regulation regulates personal data privacy and security.
In particular, these new regulations require data con-
trollers (companies) to explain their data use to data
subjects (customers) (European Union, 2018). China’s
government also has proposed new data security laws to
regulate Internet information services and algorithmic
recommendations (Wang, 2021).
In addition, supply chain collaboration with real-time
information and the use of new technologies require more
research. The existing literature considers the communica-
tion of demand and inventory data, but other types of data
have not been fully analyzed. A DTSC synchronizes not
only demand and inventory data, but also dynamic
behaviors, including manufacturing, distribution, market-
ing, and logistics. Should the effect of this data sharing on
the behaviors and costs be studied as well? Should real-
time data be shared in the form of statistical information or
the original data? How often should real-time information
be communicated? How does the automated consensus
mechanism provided by blockchain affect the coordination
behaviors in the supply chain? These questions need to be
answered to improve the application of DTSC.
5 Case introduction
In this section, we provide a motivating case study from
JD.COM, China’s largest retailer by revenue, in reconfi-
guring the supply chain network during the COVID-19
pandemic using a DTSC platform. As a result, applying
a DTSC platform significantly improves the response
efficiency of JD.COM.
5.1 Background
Nowadays, different emerging trends, such as retail
decentralization, community group buying, social shop-
ping, and live commerce, are accelerating the interaction
and integration of retail and manufacturing. As a result, the
supply chain structure is becoming highly complicated.
JD.COM operates 41 mega “Asia No. 1”logistics parks in
China, with nearly 1300 warehouses and over 9 million
self-operated stock keeping units (SKUs). The scope of
products covers consumer packaging goods, information
appliances, home appliances, clothing, fresh food, books,
and automobiles. The transportation network includes
multiple transportation methods, containing land transpor-
tation and shipping. JD.COM extends the supply chain
planning and operation to both upstream and downstream,
by using digital and intelligent technologies and multi-
channel models. To manage and optimize the operations,
traditional supply chain planning methods and algorithms
are facing increasing challenges.
The challenge was amplified by the COVID-19 outbreak
from the end of 2019. First, the pandemic caused
exceptional demand for masks, alcohol, household clean-
ing products, and food. Second, transportation networks
were interrupted due to lockdowns. Third, products were
out of stock because of the shortage of labor and raw
materials and logistical disruptions. To address these
problems, JD.COM applied a DTSC platform to support
its business. According to Curtis Liu, Vice President of
Lu WANG et al. Digital twin-driven smart supply chain 63
JD.COM and President of JD Intelligent Supply Chain,
crises such as epidemics have highlighted the importance
of a smart supply chain. Through the applications of a
DTSC, data-driven models combined with simulation
facilitate the quick evaluation and adjustment of supply
chain planning strategies. Building a DTSC will become
an important trend for managing future supply chains.
5.2 The DTSC platform in JD.COM
JD.COM’s DTSC platform builds end-to-end digital
representations for the entire supply chain. Compared
with traditional retail supply chains, which consist of
several distinct stages, JD.COM has an integrated supply
chain structure, where products are directly delivered from
factories and manufacturers to consumers through a single
jd.com platform. This integrated supply chain structure has
a higher level of collaboration, more effective information
sharing, and a higher level of agility than existing supply
chain structure (Shen and Sun, 2021). The DTSC platform
is well established to support this integrated supply chain
structure by effectively connecting internal systems in
JD.COM and external systems to offer a holistic value
throughout the supply chain, as shown in Fig. 2. The data
sources used to synchronize the physical supply chain and
the digital models include network configuration platform,
procurement system, transfer system, and fulfillment
system. Optimization algorithms and simulation algo-
rithms work together to support integrated supply chain
design and intelligent operations. By directly connecting
with decision and execution systems, these optimized
insights can be quickly implemented to respond to
changes. Meanwhile, insights are visualized for better
understanding. The DTSC platform is expected to support
a wide range of business decision-makings in JD.COM,
from long-term strategies, to mid-term plans and short-
term operations. The next subsection takes the supply
chain network reconfiguration as an example.
5.3 The DTSC platform for enabling supply chain network
reconfiguration
In this section, we illustrate how the DTSC platform
improves operational efficiency to address the challenges
during the COVID-19 pandemic. In a typical two-level
supply chain network, upstream distribution centers might
suddenly be unable to distribute goods to warehouses
because regional outbreaks resulted in travel restrictions.
In such cases, JD.COM had to reconfigure its supply chain
network in response to the disruptions. Orders could be
replenished by other alternative distribution centers or the
backorder strategy could be used. Adopting alternative
distribution centers negatively affected the order fulfill-
ment rate in its original service area and lead to significant
additional transportation costs. Considering the unpredict-
able outbreaks and varying sales and inventory situations
of massive SKUs in different regions, whether and how to
reconfigure the supply chain network for cross-regional
distribution were difficult to assess. The DTSC platform
is capable of comprehensively and deeply considering
conflicting objectives and quickly simulating and optimiz-
ing different strategies. Using the DTSC platform entails
the following steps:
Internalizing simulation models in the DTSC plat-
form. The JD.COM DTSC platform supports the creation
of models from the physical level, time level, process level,
and cost level to map the real environment as accurately as
possible. The platform uses significant data technology to
Fig. 2 Framework of JD.COM’s DTSC platform.
64 Front. Eng. Manag. 2022, 9(1): 56–70
create the basic data automatically for simulation models
from the production system. The basic information
includes the scope of products, the scope of the region,
the distribution center and warehouse information, and the
customer type.
Mirroring the current business. After initialization,
simulation models require detailed structure and para-
meters to mirror the real system accurately. Additional
settings include the current network structure and replen-
ishment strategy. Taking one of the cases during the
COVID-19 pandemic as example, the demand in Shijia-
zhuang area was replenished by two distribution centers in
Shijiazhuang and Beijing. By carefully calibrating the
parameters, the simulation models in the platform were
able to capture the physical supply chain in terms of
some key indicators (e.g., local order fulfillment rate and
transportation costs) with an average accuracy of 96%.
Setting candidate plans. Taking the same example,
owing to travel restrictions in Beijing, using the distribu-
tion centers in Tianjin was set as the alternative plan.
Analyzing and visualizing the results. JD.COM
applied advanced algorithms to improve the delivery
network design (Kang et al., 2021). In the above case, the
alternative solution was evaluated by the inventory
turnover rate, inventory availability, order split rate, local
order fulfillment rate, ratio of same day delivery, and total
costs. As a result, only part of the SKUs was replenished
by the distribution center in Tianjin to remain at a high
service level.
In summary, JD.COM’s DTSC platform significantly
improves operational efficiency in response to sudden
disruptions in the supply chain during the COVID-19
pandemic. The platform supports integrated supply chain
planning instead of optimizing the single supply chain
stage independently. The platform achieves a breakthrough
in the end-to-end supply chain representation in the virtual
space. Facilitated by data-driven optimization tools, the
response time to disruptions is shortened by an average of
50%. Previously, the evaluation of supply chain reconfi-
guration with nearly 200000 SKUs took several days.
The running time is reduced to less than an hour with the
help of big data analytics and optimization tools in the
DTSC platform.
5.4 Discussion
Encouraged by the successful application in the supply
chain network reconfiguration during the COVID-19
pandemic, JD.COM is enthusiastic about applying the
DTSC platform on a large scale to realize the smart supply
chain. First, the DTSC platform is developed to model
JD.COM’s nationwide supply chain network, which
requires large-scale simulation and optimization tools.
Second, the platform is expected to realize high-frequency
synchronization between physical supply chain and digital
models. The daily operational efficiency can be improved
by exploring real-time data. Third, analytics will be used
proactively to foresee potential disruptions and to get
JD.COM fully prepared.
6 Conclusions
Given the innovative DT concept, the opportunities to
build and improve the smart supply chain are considerable.
Practitioners should embrace the innovative DT tech-
nology to build a DTSC from the ground up. Realizing
the smart supply chain vision takes time, but the trans-
formation is worthwhile. For future study, researchers are
encouraged to solve new problems presented by the smart
supply chain. We need new modeling approaches and
decision-making patterns to make full use of real-time data
and adapt to the changing environment. We must explore
using a DTSC to transform data and information into
knowledge and value. To reduce risks and increase agility,
new problems related to predictive analytics for the smart
supply chain must be addressed. Our discussion is not
exhaustive. We expect more practical and academic focus
on the DT concept in order to fully realize the benefits of
the DTSC.
Open Access This article is licensed under a Creative Commons
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