Impact of Digital Technology on Supply Chain Efficiency in Manufacturing Industry

Abstract

With the advancement of digital technologies, supply chain management is changing dramatically. However, the practice and application of digital supply chains are complex and challenging. Some studies claim that technology is the core element, while others believe that efficient configuration and collaboration of technical functions assure successful applications. To address this gap, this research conducts a systematic literature review to analyze how digital technologies particularly the Internet of Things (IoT) and Artificial Intelligence (AI) impact supply chain efficiency in Manufacturing Industry. The study also identifies some challenges of digital supply chain (DSC) implementation. Analysis of this study is based on a systematic literature review of 59 studies that were selected using a combination of relevant keywords and specified inclusion and exclusion criteria. The results show that both IoT and AI are the closest technologies related to the autonomy and predictive power of future supply chain expectations. The convergence of the two tech-nologies optimizes all aspects of manufacturing and opens up more possibili-ties for smart factories. This research also explored DSC challenges and problems that take into consideration to expand the approaches to DSC success factors derived from existing literature. Many papers have discussed DSC technology from the perspective of the ap-plication. They demonstrate the positive impact of those digital technologies to successfully achieve digital and intelligent supply chains on firm perfor-mance by improving the efficiency of SCM.

Full text

Impact of Digital Technology on Supply Chain
Efficiency in Manufacturing Industry
Xuan Wang, Vikas Kumar
*
, Archana Kumari, and Evgeny Kuzmin
Abstract With the advancement of digital technologies, supply chain manage-
ment is changing dramatically. However, the practice and application of digital
supply chains are complex and challenging. Some studies claim that technology is
the core element, while others believe that efficient configuration and collabora-
tion of technical functions assure successful applications. To address this gap, this
research conducts a systematic literature review to analyze how digital technolo-
gies particularly the Internet of Things (IoT) and Artificial Intelligence (AI) im-
pact supply chain efficiency in Manufacturing Industry. The study also identifies
some challenges of digital supply chain (DSC) implementation. Analysis of this
study is based on a systematic literature review of 59 studies that were selected us-
X. Wang
School of Management
University of Bristol, Bristol, BS8 1QU, UK
e-mail: 18895636939@163.com
https://orcid.org/0000-0001-8746-5182
*
V. Kumar (✉)
Bristol Business School
University of the West of England, Bristol, BS16 1QY, UK
e-mail: Vikas.Kumar@uwe.ac.uk
https://orcid.org/0000-0002-8062-7123
A. Kumari
Gloucestershire Business School
University of Gloucestershire, Oxstalls Campus, Gloucester GL2 9HW, UK
e-mail: akumari@glos.ac.uk
https://orcid.org/0000-0003-1005-9543
E. A. Kuzmin
Institute of Economics of the Ural Branch Russian Academy of Sciences
Moskovskaya St. 29, 620014, Ekaterinburg, Russia
e-mail: kuzmin.ea@uiec.ru
https://orcid.org/0000-0002-8805-135X

2
ing a combination of relevant keywords and specified inclusion and exclusion cri-
teria. The results show that both IoT and AI are the closest technologies related to
the autonomy and predictive power of future supply chain expectations. The con-
vergence of the two tech-nologies optimizes all aspects of manufacturing and
opens up more possibili-ties for smart factories. This research also explored DSC
challenges and problems that take into consideration to expand the approaches to
DSC success factors derived from existing literature. Many papers have discussed
DSC technology from the perspective of the ap-plication. They demonstrate the
positive impact of those digital technologies to successfully achieve digital and in-
telligent supply chains on firm perfor-mance by improving the efficiency of SCM.
Keywords Digital Technology · Supply Chain Efficiency · Manufacturing
1 Introduction
With the continuous expansion and development of the manufacturing industry, an
increasing number of enterprises begin to devote themselves to supply chain effi-
ciency [1]. It depends on a smooth network chain structure formed by the up-
stream and downstream organisations in the process of production and transporta-
tion through organizational control of capital flow, information flow and logistics
[2]. Specifically, it is aimed at optimizing the process, improving production
quality and reducing unnecessary costs to satisfy customs and achieving the max-
imum economic benefit [1]. Mangan and Lalwani [3] claimed that effective supply
chain management in the manufacturing industry is especially important to deliver
the right product to consumers at the right time, in the right quantity, in the right
quality, and at the right state. However, the rapidly changing market and diverse
customer needs have brought unprecedented challenges to manufacturing supply
chains, because it relies heavily on timely and accurate data analysis in a complex
business environment [4]. Meanwhile, a dynamic and uncertain competitive envi-
ronment, unpredictable social factors and other random environmental changes
aggravate the complexity of supply chain management, leading to high production
costs [2]. For example, COVID-19 has seriously affected the manufacturing sup-
ply chain on a global scale, such as labour shortage, material shortage, delivery
delays and logistics stagnation and so on. The COVID-19 pandemic however has
spawned the widespread use of digital technologies to improve the ability of the
economy and society to respond to the impact of the pandemic.
Digital supply chain (DSC) is an intelligent, data-driven technology network
that is based on massive real-time data processing, excellent collaboration, and
communication capabilities to achieve information transparency, advanced plan-
ning, demand patterns prediction as well as maximizing the availability of assets

3
[5]. Many studies have acknowledged the positive effects of digital technologies
on the efficiency of supply chains. Chase [6] pointed out that DSC has become an
inevitable choice for enterprises to improve the ability to anticipate demand and
risk through the application of digital technology, which improves not only the
market response speed and operational efficiency but also the service level and
economic benefits of enterprises. Similarly, Preindl et al. [7] claimed that the DSC
enhances the information-sharing capability, which plays a vital role in this coop-
eration aimed at processing large amounts of information to coordinate related op-
erations to improve efficiency. In addition, Banerjee & Mishra [8] argued that dig-
ital technologies improve supply chain collaboration, which requires internal and
external coordination and unification amongst suppliers, manufacturers, retailers
and customers. However, the practice and application of the digital supply chain
are complex and challenging, which requires cross-industry, cross-sector and
cross-field cooperation. Although the application of digital technology in the sup-
ply chain would effectively improve its flexibility and adaptability theoretically,
there are still many obstacles in the practical process. According to the data, 80%
of practices related to digital transformation failed [9]. Reyes et al. [10] argued
that enterprises should identify whether these technologies could achieve their
strategic and operational aims rather than blindly following technology for tech-
nology's sake. Tjahjono et al. [11] believed that improvement of organizational
ability assures successful applications through efficient configuration and collabo-
ration of technical functions. However, there is little theoretical research in this
field. Therefore, it is imperative to explore challenges and the key factors that af-
fect the successful application of digital technologies as well as potential risks to
better adapt to enterprises’ strategic goals to achieve the desired results after
weighing those digital technologies’ pros and cons [2].
This study, therefore, discusses and analyses the impact of digital technologies
focusing on AI and IoT on the supply chain efficiency in the manufacturing con-
text. AI and IoT are considered to have significant potential amongst the eight
most influential digital technologies. IoT currently has a high utilization rate in the
manufacturing industry, and its application is becoming mature and has achieved
good results in terms of information communication, and AI is the most promising
technology based on the Internet of Things. This research will use the systematic
literature review (SLR) method to collect existing literature to answer the two re-
search questions. The first one is ‘How do AI and IoT improve supply chain effi-
ciency?’, aimed at identifying the principles working on SCM performance. The
second one is ‘What factors influence the successful implementation of the digital
supply chain?’, aimed at exploring the challenges and obstacles of the digital sup-
ply chain. The next section briefly introduces the three theoretical bases (IoT, AI
and SCM) related to the topic. Section 3 describes the details of the SLR method-
ology for looking for publications that explicitly study IoT, AI, and the manufac-
turing supply chain. Section 4 presents the analysis of research results and find-
ings and makes a comprehensive and critical analysis of the two research

4
questions. Finally, section 5 concludes this study by recommending some future
research directions.
2 Background
2.1 Internet of Things (IoT)
Ashton [12] came up with the term Internet of Things (IoT) firstly, which refers to
identify an object uniquely by connecting physical things through technology and
its virtual network [13]. Three elements are required to achieve the function of
IoT, and they are data acquisition technology, data transfer technology and data
analysis techniques [14]. Thus, different layers of sensors, storage and data trans-
mission with the function of recognition, processing, communication and connec-
tion are imperative to achieve connections amongst things [15]. Gnimpieba et al.
[16] argued that the connections and communication between physical and virtual
'things' enable transparency and collaboration between them by clear information
network. Each sensing device is uniquely addressable by IoT Internet infrastruc-
ture, which has dynamic self-configuration capabilities with standard and interop-
erable communication protocols, standardized communication protocols [17], be-
cause each physical and virtual object is given a unique identity, physical
attributes and virtual personalities [14]. Similarly, Reaidy et al. [18] argued that
IoT is an intelligent internet-based network, which enables devices to collect, pro-
cess, and transfer data within an interconnected physical global infrastructure.
Meanwhile, it could identify, track, and manage products by transmitting real-time
information through advanced technologies. Based on collected information by
devices, it can not only trace, manage and control the internal and external state of
an object but also continuously observe its surroundings [19]. Furthermore, it is
expected to anticipate and perceive dynamic changes in the supply chain man-
agement to facilitate supply chain management effectiveness and sustainability
with its information transparency, tracking and agility [2]. In the same way, this
type of control and monitor reduce risks by timely response, control and adaption
of the supply chain activities in an uncertain environment [20]. Nevertheless, a
large amount of information obtained may hinder supply chain management effi-
ciency due to the amount of useless information [21]. Therefore, information
management and effective control are vital for making reasonable predictions for
the future and reacting to achieve sustainable development and competitive ad-
vantages. Yet, the existing researches are limited and need to be expanded.

5
2.2 Artificial Intelligence (AI)
Artificial intelligence (Al) can think like humans and mimic human behaviour by
simulating human intelligence characteristics in machines such as learning and
self-thinking and problem-solving. By absorbing large amounts of unstructured
data, such as text, images or video, the ideal characteristic of AI is that it can ra-
tionalize and take actions automatically without the help of humans to have the
best chance of achieving specific goals. Calatayud [22] pointed out that AI is not a
monolithic technology, which depends on the cooperation of various advanced
technologies with hardware and software. For example, AI is effectively coupled
with the large numbers of data collected by other digital technologies, especially
the Internet of Things, to predict the future trends with advanced algorithms and
take measures to avoid any deviations from expected performance with minimum
error [23]. On the other hand, it focuses on developing computer programs that
can learn, understand, reason, plan and act on their own to make decisions auto-
matedly in SCM through the use of simulation models and powerful analytics
[24]. Meanwhile, software algorithms automate complex decision-making tasks by
analysing real-time data, anticipating and identifying risks as well as automatically
taking action to continuously control supply chain performance and prevent risks
before they occur. Robots are an excellent product of this combination. For exam-
ple, in automated warehouses, robots help supply chain material handling automa-
tion through planned algorithms to pick, stack and unload after learning. The high-
ly mechanized environment can change the supply chain and create a faster, safer
and more efficient logistics supply chain [23]. Meanwhile, they are installed with
planning programmes to monitor inventory status and order progress with high ac-
curacy. These robots are safe and agile in predicting the relationship between de-
mand and inventory in the warehouse to achieve higher productivity and lower
costs, avoiding the risk of goods disruption with faster service and higher quality
[1].
AI is primarily a smarter algorithm that learns more and more by itself through
mimicking human thought processes and feelings. Its applications in supply chain
management are in transportation, predictive maintenance and demand forecasting
facilities autonomous prediction and decision-making of supply chain [25]. AI
technology has huge potential in the manufacturing supply chain. Koot et al. [2]
argued that AI applications would enable automated production systems and au-
tomated logistics systems to change decisions flexibly like the selection of suppli-
ers when they receive real-time information about supply shortages or disruptions.
However, there are some sceptical voices about AI like ethical questions. Ta-
zhiyeva [26] claimed that the question of whether intelligent systems such as ro-
bots should be given the same rights as humans is controversial. In addition, the
extraction of sensitive information may violate privacy security and even break
politics [27].

6
2.3 Supply Chain Management (SCM)
A supply chain is a functional network structure that connects suppliers, manufac-
turers, distributors and consumers, starting from parts production, making inter-
mediate products and final products, and finally sending the products to users
through the sales network [1]. Alternatively, a supply chain is an interface formed
between supply chain members through activities so that organizations can meet
the internal and external requirements. As for Supply chain management (SCM), it
includes a series of activities and processes that plan, control, coordinate and op-
timizes the whole supply chain system, so that enterprises can integrate and coop-
erate to address the volatile and complex situation of the external market [11].
SCM is an integrated and coordinated management mode, which requires the
members of the supply chain system to cooperate to achieve corporate goals. The
practice of SCM was widely used in the manufacturing industry firstly, focusing
on logistics management tasks to reduce transportation costs, and over time it ex-
tends to all aspects of the supply chain [4]. The data shows that a company spends
a lot on its supply chain, accounting for nearly 25% of its operating costs [10].
Thus, effective supply chain management is critical in reducing unnecessary costs
to achieve better performance. Many researchers have been working on the effi-
ciency of SCM. Singh et al. [28] agreed that effective supply chain management
can help firms to improve their competitive advantages, which promote their per-
formance and lead their competitors. In particular, it can help achieve four goals:
shorten cash flow times, reduce the risks faced by organizations, achieve earnings
growth and provide predictable revenue [29].
However, it is not easy to achieve efficient supply chain management in the
challenging and complex market environment in the era of economic globaliza-
tion. A large amount of information appears in the market all the time, which pro-
vides rich opportunities but also foreshadows risks. For instance, enterprises are
unable to timely identify the changes and collect useful information in the supply
chain accurately, leading to difficulty in making the right choice and failure of de-
cision-making. Alternatively, in a traditional supply chain is hard to carry out suf-
ficient information sharing to eliminate the communication barriers within the
member enterprises. Thus, information development of the supply chain is appear-
ing, which is a grand solution to this problem contribute to the digital supply chain
(DSC). Nowicka [30] believes that the digital supply chain can effectively im-
prove information transparency among supply chain members, which greatly pro-
motes information communication efficiency by reducing the time cost, shortening
the flow cycle, and reducing unnecessary costs. Effective and accurate communi-
cation ensures collaboration and trust among suppliers, manufacturers, and dis-
tributors, especially in the application of inventory and logistics, which greatly re-
duces unnecessary costs, which are not value-added activities [10]. On the other
hand, the access of information and the speed of information feedback are also
important to the efficiency of SCM due to a large amount of data available in and

7
out of the market [31]. The digital supply chain can improve the traceability, reli-
ability and response speed of the supply chain through real-time monitoring capa-
bilities [32]. At the same time, this ability can predict the risk of the supply chain
and make decisions through the high-speed collection, analysis and processing of
relevant information organizations and supply chains can realize higher levels of
efficiency by responding more quickly to observed internal and external disrup-
tions [20].
3 Methodology
3.1 Systematic Literature Review (SLR)
This study aims to analyse the impact of digital technology on supply chain man-
agement efficiency in the manufacturing industry. Practitioners have long been en-
thusiastic about the impact of digital technology on the efficiency of supply chain
management. Thus, a Systematic Literature Review (SLR) is used in this study fo-
cusing on the IoT, AI and SCM to show an overview of current related research
studies by using search strings, keywords and statistics within the specified data-
base and give an objective assessment by conducting qualitative and quantitative
reviews of the existing literature to answer the research questions.
This study identified keywords and they are AI, IoT, efficiency, challenge and
SCM. According to those keywords in different categories, combining the most
common grouping of keywords carefully to identify the search string to screening
in a search engine is vital, which ensures proper select coverage. After filtering,
four types of search strings are selected and they are “Artificial Intelligence or In-
ternet of Things AND supply chain”, “Artificial Intelligence or Internet of Things
AND supply chain AND efficiency”, “Artificial Intelligence AND Internet of
Things AND supply chain AND efficiency”, “Artificial Intelligence or Internet of
Things AND supply chain AND challenge”. For the source selection, this research
selected Springer, Wiley, Scopus, Web of Science, ScienceDirect, and Taylor&
Francis, which are trustworthy and authoritative. The following Table 1 displays
the initial number of papers in each elected database.
Table 1. The initial number of papers in each elected database
Databases
Entries
Initial number
Springer
44,500
207,800
Wiley
32,600
Scopus
21,000
Web of Science
63,700
ScienceDirect
21,000
Taylor& Francis
25,000

8
3.2 Selection Criteria
To accurately locate the useful literature in the rough search of the specified
databases based on different combinations of search strings, this research needs to
further develop selection criteria to refine the “sample poor” by evaluating and
discovering the relevance degree and potential contribution of each publication to
the research problem. There are three layers of inclusion and exclusion criteria.
First, the initial research results should meet the following three inclusion criteria.
(1) The articles should be fully published and written in English.
(2)The articles’ subject topics should be consistent with the academic area of
study being considered such as Supply Chain Management, Computer Sciences,
Decision Sciences, Business Management, Technology Science, and Engineering.
(3) The articles are academic document types such as Doctoral Dissertation,
Books (Chapters), Academic Journal/ Articles/ Papers, Review Articles and so on.
Further selection and screening are carried out for the articles that have met the
above three requirements to evaluate the usefulness of the content itself. Articles
that do not conform to the following three additional exclusion criteria are
screened by title, abstract, keywords, and introduction screening.
(4) Focus on specific actions or applications or challenges of AI or IoT that af-
fect supply chain efficiency. Specifically, remove all improved performance in
SCM that is unrelated to the implementation of AI or IoT technology. Two rea-
sons are provided. Firstly, the improvement in supply chain performance reflected
in the article may be the result of iterative improvements of the covered technolo-
gies themselves. Secondly, the application of digital technologies may not be in-
dependent. So, it may be necessary to judge whether the application of these com-
prehensive technologies is causally related to those two technologies.
(5) The initial data should be processed to show a critical analysis of the impact
on the efficiency of SCM.
(6) Besides theoretical support, the factors affecting the implementation of the
digital supply chain should provide clear points or specific cases.
The third level is only consistent with exclusion criteria. The following criteria
are defined to improve the articles’ sample accuracy.
(7) Remove all duplicated articles from different databases and choose the lat-
est version for further reading only.
(8) Remove all case study articles that are not in the manufacturing industry.
(9) The article should be available online (either open access or through a sub-
scription).
3.3 Sample Selection
Following the selection criteria outlined in the previous section, 59 articles were
selected from seven databases for the final bibliographic study. They are grouped
and classified according to the two research questions posed and divided into two

9
broad areas. The first category is to explore the specific application of the IoT and
AI in supply chain management and there are 42 papers. It includes specific im-
plementation cases of IoT and AI in manufacturing supply chains and perfor-
mance evaluation related to the first research question. The focus of the study is
on digital technology with specific enterprise implementation activities and impact
on results. The second category is 18 papers (because some articles cover both ar-
eas) to identify causes of failure implementation and explore the reasons. This part
concentrate on the challenges of implementing digital technology with analyses
different angles of causation. This study conducted simple statistics based on the
different keywords involved in each article to show the specific research areas and
research issues, which provides convenience for the following research.
The scrutiny of the sample illustrates that most of the articles are from engi-
neering and management. IoT is widely used in management accounting for 39%
and most of them come from supply chain logistics management A small portion
is computer science, focusing on the development and integration of technologies.
it shows that most of the current researches on digital technology focus on tech-
nology development and innovation rather than application and practice realiza-
tion. This provides a guide for research to fill in the gaps in the application of digi-
tal technology at present.
4 Findings and Discussions
A large number of academic literature has acknowledged that the application of
digital technology in SCM has made great breakthroughs in supply chain man-
agement efficiency. The network is an imperative element of competition ability
and the efficiency of SCM depends on the network connections and coordinated
operation, which are based on information sharing and information technology
applications between each member [30]. Paul [33] reported that nearly half of sur-
veyed supply chain leaders have significantly accelerated their investments in
digital technology to make their businesses more responsive and forward-looking
during the pandemic. IoT and AI are the two most promising digital technologies
in the manufacturing supply chain of the future [34]. IoT now accounts for 27%
and tends to grow to 73% in the next 3 to 5 years. Similarly, AI is promising to in-
crease dramatically from 17% to 62% [35]. Thus, the following sub-sections will
focus on IoT and AI’s applications using the selected 59 papers in response to the
two research questions raised earlier.
4.1 How do AI and IoT improve supply chain efficiency
Calatayud et al. [36] argued that the development of AI and IoT has greatly pro-
moted the emergence of industry 4.0 and intelligent factories. Those two technol-

10
ogies complement each other and are closely linked. AI is equivalent to applica-
tion software, which needs IoT as the foundation. IoT is like hardware that needs
AI to drive it [27]. Specifically, the Internet of Things produces a large amount of
data collection with perception devices. Subsequently, Artificial Intelligence pro-
vides the suitable optimum proposal for the Internet of Things by using data and
analysing data with its powerful data analysis capabilities. Tjahjono et al. [11]
agreed with the argument and claimed that technologies are closely related, with-
out boundaries or priorities, so it is important to work together to promote supply
chain development, especially in supply chain management. [1] pointed out that
the realization of DSC needs to integrate multiple technological advantages to ob-
tain competitive advantages. In addition to improving the efficiency of SCM and
reducing organization operating costs, DSC also provides a full-fledged decision
foundation for enterprises to formulate the overall industrial development strategy.
Similarly, Preindl et al. [7] agreed that the digital supply chain has broken the
communication barriers of previous supply chain links, and improved the infor-
mation transparency and reliability of the supply chain. Furthermore, the applica-
tion of digital technologies has greatly changed the process of supply chain man-
agement, which collects and analyses large amounts of real-time data intelligently
and then uses that information to make decisions and implement them [37]. This
transformation improves the connectivity and collaboration of all parts of the sup-
ply chain members by sharing information more accurately and in real-time, im-
proving the efficiency of supply chain management by making accurate decisions
and optimizing operations [38]. At the same time, the prediction of potential risk
and simulation of the feasible adoption of risk mitigation measures ensure sustain-
able and stable performance improvement in an increasingly changeable supply
chain management surroundings [22]. This ability to monitor and predict not only
improves flexibility and agility on risk management in the supply chain efficiency
but also gain sustainable competitive advantages [39].
Nevertheless, Cui et al. [40] pointed out that digital technologies are not the on-
ly key elements contributing to the improvement of the efficiency of SCM. In-
stead, the improvements of collaboration between the supply chain members, the
ability to collect and process information and the integration of management in-
formation systems are the basic reasons due to the application of those digital
technologies. Thus, technology provides only one possible way for improvement.
The important thing is how to obtain key capabilities to realise these expected
benefits through integrating digital technologies with the realities of enterprises.
Similarly, [41] believes that the essence of the Internet of things is to provide in-
formation and integrate information.
However, what really improves operational performance is data transmission
speed and data transparency level, which promote cooperation between supply
chain members and optimize the supply chain information flow, logistics, capital
to gain competitive advantages of enterprise's key abilities [42]. The Internet of
Things effectively provides a large amount of data, but the transformation of data
into information still requires judgment, data processing, and autonomous decision

11
making. Although IoT currently allows decisions to be made by machines without
human participation [43], there are still few studies on autonomous supply chain
decision-making and smart factories, which combine the Internet of things with
autonomous decision-making, are novel and nascent in the field. Meanwhile, [44]
argued that an intelligent supply chain needs the combination of different technol-
ogies to realize informatization and automation, which is the foundation for
achieving a high degree of network-physical interconnectivity. In this case, Artifi-
cial intelligence plays a vital role to realize a smart supply chain and push the flex-
ibility and agility of the supply chain to the undiscovered limit [22]. It makes deci-
sions and takes actions to adapt to a rapidly changing environment by analysing
information in real-time and monitoring operations on a global scale with predict-
ing the future with a minimum error rate [36]. While artificial intelligence (AI) is
on the rise, it also needs to consider how other technologies are coming together in
valuable new ways [35].
4.2 IoT application in SCM & the impact
IoT technologies are widely used in the manufacturing industry like radio fre-
quency identification, wireless communication technology, laser scanning, etc.
Those information and communication technologies (ICTs) could improve supply
chain connectivity due to the integration and visibility of information [45]. Supply
chain connectivity reflects the impact of digital connections between stakeholders
on and off the supply chain, which relies on the extraction of information around
all related aspects [36]. [46] claimed that IoT achieves collaboration to optimise
accuracy, integration and transparency of the information with its four aspects of
characteristics and there are extracting data, transferring data, storing data and
processing data. [40] identified 16 factors that have impacts on the supply chain
and quantified them. Consistent with other researchers, IoT has made outstanding
contributions to the supply chain in information processing capacity, information
transparency, management system integration, industry standards.
The application in the automotive manufacturing industry provides automobile
manufacturers with opportunities to optimize. A large amount of money has been
invested in the digitization of various manufacturing processes from design to ve-
hicle production, including design, manufacturing, quality inspection, logistics and
inventory management [13]. According to [47], 60% of manufacturers worldwide
used data generated by connected devices to analyse processes and make decisions
in 2017. General Motors is a good example. In terms of manufacturing, it uses
sensor data to determine the humidity of a car's paint environment. The sensor
first detects humidity data by sensing the external environment and then transmits
the detected data to an algorithmic device that has been set up in advance to de-
termine if it is within the reasonable range. If the system decides the environment
is inappropriate, it sends the car to another area in the manufacturing process,

12
thereby minimizing repainting and maximizing plant uptime. This innovation
alone saved GM millions of dollars per year [48].
In terms of logistics, [11] pointed out that information transparency and con-
nectivity effectively enable timely and accurate information sharing, which elimi-
nates communication and technical barriers across departments and areas by en-
hancing the transparency of the operation between suppliers, manufacturers and
customers. There are many examples of this in logistics and inventory manage-
ment. The cost of inventory management accounts for a large proportion of the
cost of automobile manufacturers, which directly influences organizations’ normal
operations [41]. The poor communication among various departments of the en-
terprise will lead to the waste of resource deployment and other phenomena
caused by the lack of real-time coordination and control of resources of all mem-
bers in the supply chain of the automobile manufacturing enterprise, which will
affect the benefits. IoT improves the inventory management level of automobile
manufacturing enterprises from two perspectives. Firstly, establishing the invento-
ry circulation network based on the Internet of Things technology facilitates the
circulation of idle inventory among enterprises. The other one is to use IoT to
monitor the whole process of materials in automobile manufacturing enterprises to
improve the inventory management level. For example, providing available real-
time data in SC by monitoring the movement of materials, equipment, and prod-
ucts through the supply chain efficiently allocates resources between inventories
[16]. In addition, with Enterprise Resource Planning (ERP), Product Lifecycle
Management (PLM) and other systems that effectively collect and deliver infor-
mation to connect factories and suppliers,
All empowered departments in the SC can track inventory, product flow, and
product cycles times. That information will help manufacturers reduce inventories
and capital requirements by anticipating problems. According to data, smart facto-
ry penetration is expected to grow by 35% by 2025 through investments in real-
time analytics and dynamic supply chain tracking [11].
On the other hand, IoT is used in the production process to upgrade manufac-
turing processes through monitoring and tracking. It will not only reduce unneces-
sary waste, contributing to the sustainable development of the factory but also im-
prove the profit space in the long term. Moreover, information is important to
improving supply chain operations [34]. A two-way flow of information not only
improves the supply chain collaboration but also achieve the operational excel-
lence of sustainable supply chain management by rapid feedback, interruption re-
duction, process optimization [40]. For instance, in terms of quality testing and
monitoring, the automotive aftermarket has always been a core part of the automo-
tive industry, covering all the services consumers need after buying a car [49]. The
physical nature of a vehicle means that it is subject to unpredictable wear and tear
and the hardware facilities face functional degradation, inflexibility and other po-
tential problems. By implementing IoT components, vehicle status can be contin-
uously monitored, enabling anticipation of potential damage or failure and then
preventive measures. In addition, it can trace back to the production assembly pro-

13
cess for optimization through manufacturing data on systems and components
[50].
4.3 AI application in SCM & the impact
Michel [51] pointed out that with the rise of globalization and the continuous de-
velopment of science and technology, supply chain management has gradually be-
come a priority for all enterprises. Meanwhile, as sensor costs fall and the Internet
of things advances, Al is expected to be one of the most promising technologies in
the future of supply chain management due to the increasing access of data
throughout the supply chain and improvement in computing technology [52]. A
large amount of data is accumulated and deposited in the cloud. Thus, how to
make use of big data and give full play to the value of data to predict market de-
mand, assist the decision-making, optimize the operation process, and predict the
risk points of each link in SCM are the new challenges. Some pieces of literature
have acknowledged the fact that Artificial Intelligence has helped addressed those
problems, which results in the in-depth, predictive, and credible understanding of
business partners and even competitors in a complex and sprawling supply chain.
Moreover, [52] argued that AI facilitates the sustainability of supply chain man-
agement in pursuit of improving quality, reducing cost and increasing efficiency.
Hassija [27] supported those arguments by quantifying the benefits of AI for or-
ganizational in ten aspects of supply chain management.
So far, a lot of AI technology has been applied to supply chain management
[53] including Artificial Neural Networks (ANN), Artificial Immune Systems
(AIS), Virtual Reality (VR), Genetic Algorithms (GA) and so forth [54]. In supply
chain management, these applications are significant for supply chain activities in
terms of demand prediction, marketing decision support systems, pricing, product
manufacturing and supplier selection. The most common and influential is ANNs,
which is a data analysis technique mainly relying on a large amount of experi-
mental data [29]. Li [55] believes that artificial neural networks are becoming
more and more important in a changeable competitive environment because they
can solve data-intensive problems in the era of big data to discover knowledge,
rules or models [56]. Compared with human beings, AI is the computational intel-
ligence related to the input and output streams of processing units, which can ef-
fectively solve the problems with complex and difficult algorithms that human be-
ings are incomparable [57].
Amirkolaii et al. [58] claimed that AI effectively helps managers to make pre-
dictions and adjust the plan in SCM, which avoids the waste of resources and
business risks like the “Bullwhip Effect”. Specifically, when the information is
transferred from the upstream and downstream of the supply chain without infor-
mation-sharing in real-time, the information will be gradually distorted and ampli-
fied, which results in the increasing fluctuation of demand and supply information
and the formation of false bubble space. However, AI can help companies accu-

14
rately determine supply and demand relationships ahead of time and develop dy-
namic operational strategies by using historical data analysis, real-time data analy-
sis, prediction model programming and other analysis measures [59]. At the same
time, AI makes the optimal distribution of limited resources in the supply chain to
achieve the maximum benefit and formulates mitigation strategies for changes
[60]. In addition, Smart supply chains harness the power of AI and other emerging
technologies to help companies make predictions and risk analyses to maintain
business continuity in chaotic and volatile situations. This efficient supply chain
control tower system based on artificial intelligence can respond quickly, smooth
through or even completely avoid risk with minimal loss or minimize disruption
damage when a disaster occurs [61]. Similarly, AI can also be used in daily opera-
tions through real-time Omni-directional monitor risks to promote the agility of
SCM to respond quickly and mitigate risks by predicting supply chain disruptions
risks and recommending solutions. For example, more than 30 counties in Thai-
land were hit by the worst flooding in 40 years overnight in August 2017. IBM's
Singapore factory's supply chain division was immediately clear that the floods
would have a huge impact on Thailand's hard drive makers from the purchase or-
ders being executed and pending approval. Based on the potential risks in short
supply, the Singapore factory quickly select the Singapore hard disk from the re-
pository suppliers to place orders, lock and prepare goods and coordinate and de-
ploy available transportation ensuring dedicated hard disk supply is in place and
the production line is not interrupted [62].
However, correct forecasting is a complex process that depends on many inter-
nal and external aspects, such as an accurate database, highly integrated algo-
rithms, market stability and so on [27]. And the wrong prediction will be a signifi-
cant financial loss to the organization [63]. For example, Nike introduced a
demand prediction programme but failed to implement it in 2001, which leads to
insufficient inventory in Air Jordans and an excess of less popular types. This fail-
ure experience took an unimaginable financial hit to Nike, costing it around $100
million in lost sales [64]. In the process of production and transportation, AI im-
proves the effectiveness and accuracy of logistics decisions by tracking the flow
patterns of goods and services, simplifying activities to achieve efficient and
transparent partnerships [25]. For example, in terms of inventory siting, planning
and cost minimization, and supplier selection issues, Artificial intelligence can
eliminate human conditions and personal feelings to build models by analysing
historical stock data. The warehouse is the foundation of the development of mod-
ern logistics and warehousing location determines the efficiency of logistics. Ac-
cording to simulations and the filter criteria, Artificial intelligence systems fore-
cast future data to select the most appropriate choices and make a decision [65]. In
addition, the stand or fall of transport line planning can directly affect the opera-
tion of the modern material filling system of the whole with the introduction of
AI, which greatly improves the efficiency of delivery batch business and expresses
sorting business. There are many application scenarios of AI in the field of logis-
tics, such as packaging material box algorithm recommendation, cargo space

15
planning, vehicle and cargo matching, AGV scheduling, automatic intelligent
storage and so on. Klumpp [25] believed that AI and algorithms occupy an in-
creasingly crucial status in logistics. For example, intelligent logistics technology
represented by digital and intelligent heavily rely on the core elements of intelli-
gent storage including unmanned distribution, AGV (Automated guided Vehicle)
and logistics robots [66].
Furthermore, AI can assist with cruise control, lane-keeping and collision
avoidance to achieve high safety of unmanned driving to the destination of goods.
It also offers machine learning, sensor fusion, computer vision technology, motion
planning and control to autonomously select and improve road safety [48].On the
other hand, AI effectively liberates part of human activity. Specifically, AI can
maintain efficient operation for a long time for some simple programmed tasks
[29]. For difficult and complex tasks, AI can objectively and intelligently perform
fast calculations. Take Audi’ parts logistics as an example, which is the key to en-
sure the efficient production of the whole factory. The Tungsten Network [67] re-
ported that they waste an average of one hundred and twenty-five hours per week
on trivial businesses like repeat and simple routines, dealing with supplier inquir-
ies, accounting audits and so on. There are about 6,500 hours a year wasted on in-
effectual work. Thus, some organizations have begun to adopt advanced AI appli-
cations like robots to complete repetitive activities automatically. Those AI
applications also reinforce each other to optimise the real-time strategies and au-
tomatically adjust them according to the surroundings, which enables robots
smarter and faster. Audi's intelligent factory is a typical example. The logistics
and transportation of parts are all completed by the unmanned driving system. The
forklift truck that transfers material also realizes automatic driving, realize true au-
tomatic factory [24]. Not only will unmanned vehicles be involved in material
transportation, but drones will also play an important role [4]. In Audi's smart fac-
tory, miniaturized and lightweight robots replace manual labour to install and fix
trivial parts. Flexible assembly cars will replace manual screw tightening. Many
mechanical arms are arranged in the assembly trolley. These mechanical arms can
be identified and screwed according to the established procedures. The assembly
assistance system can inform workers where to assemble and can check the final
assembly result. In some wiring harness assembly work also need manual partici-
pation. The assembly auxiliary system can prompt workers which positions need a
manual assembly, and display whether the final assembly is qualified on the dis-
play screen to prevent defective products [68]. The flexible grasping robot invent-
ed by Audi Intelligent Factory is different from the current grasping robot. The
biggest feature of this robot is the flexible tentacles, which are similar to the
tongue of a chameleon and have more flexible grasping parts. In addition to grab-
bing ordinary parts, the flexible grasping robot can also grab nuts, gaskets and
other fine parts.

16
4.4 Factors influencing the successful implementation of the digi-
tal supply chain
A digital supply chain is expected to strengthen competitive advantages through
improved product quality, lower operational costs, faster market response and
higher collaboration between supply chain members [69]. However, the introduc-
tion of digital technologies recently is exposed to various difficulties and obstacles
that arise from internal and external factors such as increasing internationalization
and interconnection of companies, uncertain demand changes and faster produc-
tion cycles time [36]. Definitely, digital technologies can greatly improve supply
chain management efficiency by avoiding the problems of errors, losses, and costs
associated with manual management to achieve better business performance. Ac-
cording to [70], organizations with a digital supply chain and highly digital opera-
tions are expected to improve efficiency by 4.1% per year and increase revenue by
2.9% per year. Based on these obvious motivations, manufacturing supply chains
are investing more in digital technologies and obtain great results. Around three-
quarters of manufacturing, enterprises tend to speed up their digitalization process
and will achieve comprehensive and basic digital advances by 2020 around the
world [71]. Taking China as a specific example, The Ministry of Industry and In-
formation Technology proposed that digitized manufacturing enterprises above the
scale will be popular, and intelligent transformation will be preliminarily realized
in key industries by 2025 (more than 2,000 smart scenarios for the application of
new technologies, more than 1,000 smart workshops, and more than 100 bench-
mark smart factories leading the development of the industry). By 2035, digitisa-
tion will be universal in all manufacturing sectors above scale [72]. In terms of
performance returns, the results are remarkable. The income of the intelligent
manufacturing business gradually increased from 73.467 million yuan in 2017,
147.12 million yuan in 2018, and 264.348 million yuan in 2018 to 413.252 million
yuan in 2020. Smart manufacturing in 2020 operating revenue growth of 56% and
revenue rose to 29.83% from 24.58% a year earlier [73].
However, in practical application, many enterprises, which spend high cost and
are equipped with the most advanced automation technology such as Manufactur-
ing Execution System (MES), Web Mapping Service (WMS), Transport Monitor
System (TMS) and other information systems are still not satisfied with the output
results in SCM. Furthermore, there are obviously greater difficulties and obstacles
to further digitization and intelligent process. Although many enterprises have re-
alized the importance of DSC, only 5% were satisfied with their digital transfor-
mation approaches. Thus, it is important to explore how smart supply chains can
be successfully applied and what reasons influence their implementation [35]. Ag-
eron et al. [74] believed that technological, organisational and strategic challenges
remain to overcome to achieve the success of the DSC implementation. Agrawal
et al. [75] identified 12 barriers to the implementation of DSC including the fear
of loss of confidential information, lack of budget, lack of digital skills and talents,

17
lack of strategic guidance and so on. From a quantitative perspective, [27] ana-
lysed and presented different 13 possible obstacles. The next section explores
some of the potential obstacles.
4.4.1 Lack of budget and management support
The lack of sufficient financial support will greatly hinder the digitalization pro-
cess of DSC such as the improvement of infrastructure, the cultivation of corre-
sponding talents, the integration of introduced digital technologies and so on. Ac-
cording to the data, the information technology (IT) system is critical to DSC, so
improving infrastructure is an effective way to promote the digital supply chain.
However, since enterprise on large-scale information technology and equipment
needs a large number of investments to support, improvement of the infrastructure
is a huge barrier to the implementation of the digital supply network [76]. In addi-
tion, new digital technologies and resources need to be constantly updated and in-
tegrated to drive connectivity of various technologies with appropriate organiza-
tional structure [26], which requires financial support setting the threshold for the
productization of digital technologies. Those changes also face huge risks such as
structure, culture, capabilities, policies and so forth. Specifically, DSC requires
organizational structure transformation to provide effective means of communica-
tion between organizational members and between the organization and its envi-
ronment through creating and sharing knowledge. With greater data transparency
and synchronization, machines are empowered to make operational decisions to
allocate resources for optimal utilization as well as capturing the highest utiliza-
tion value of investment [77]. Although those activities are designed to improve
the efficiency and accuracy of information communication, they may cause dis-
ruptions due to the need to redesign the supply chain operation process [1]. In ad-
dition, they do not directly present the most direct revenue return, because their re-
turn on investment is difficult to calculate [68]. So, they are often underfunded by
trade-offs between implementation and running costs and their Return on Invest-
ment.
Another important barrier is the lack of relative talents. [74] argued that the
talent gap is huge and the scarcity of talent and human consumption may lead to
the failure of digital transformation. Large sums of money have been paid to find
potential talents since digital transformation requires the training of new talents re-
lated to digital technology including data analytics, cloud computing, data securi-
ty, mobile technology and so forth. Similarly, [75] claimed that new talents such
as advanced engineers, data scientists and software programmers in information
and communication technology need to be trained in the latest programming lan-
guages. At the same time, it is vital to attract experienced software developers,
product managers and other technical specialists from Apple, Google, or Facebook
to ensure the speed and quality of reform. In addition, behavioural competencies
related to personality traits such as business process management, responsibility,

18
social acceptability, innovation, and negotiation are also important, which adapt to
the characteristics of the digital supply chain such as agility and flexibility. All of
those activities require sustainable purchasing ability and are fundamental to the
success of the digital supply chain.
4.4.2 Lack of guidelines, strategic orientation and knowledge
Although many enterprises are equipped perfect in terms of infrastructure (MES,
WMS, TMS, OMS information systems), there are greater difficulties and obsta-
cles to further digitization and intelligent process in DSC due to the lack of indus-
try-specific guidelines, strategic orientation and relevant knowledge. [75] pointed
out that there's not just one way to digitize a supply chain and companies at differ-
ent stages have different digital goals. specifically, each enterprise should be
based on their specific needs, according to the existing infrastructure and talent re-
serves, corporate culture and technical requirements for formulation and imple-
mentation of the corresponding digital supply network planning [9]. From selected
researches, we found that there are many studies on how digital supply chains
achieve specific advantages (collaboration, information transparency and so on)
from a broad perspective. However, few papers have explicitly focused on the
specific activities and implementation steps of the digital supply chain in the in-
dustry through case studies [75]. Therefore, the lack of specific route guidance has
influenced the successful implementation of DSC. Many organizations recognized
the purpose and significance of digital supply chain transformation, but do not
have a specific map explaining the approach and sequence in terms of ways of co-
operation, internal and external operations and so on. Organizations must thor-
oughly consider existing procedures and processes to identify areas of improve-
ment to address digital transformation, then define their digital strategy and
develop appropriate actions [78]. In this case, the digital strategic plan provides a
clear roadmap for DSC adoption and helps managers identify the stages and loca-
tions of DSC deployment in the supply chain to avoid inconsistent, fragmented
and ineffective activities. organisations could make the cost of input to get the
greatest economic benefit [79].
In addition, the lack of professional knowledge and digital vision among lead-
ers and employees about digital means is part of the reason for the slow adoption
of the digital supply chain [76]. DSC is a way of managing core operations in the
supply chain rather than owning digital products and services. In other words, the
decision point that determines the efficiency of SCM is not the application of digi-
tal technology, but the matching degree of operation mode and information tech-
nology [5]. Several well-known research institutions, such as Accenture, IDC,
Deloitte, etc., pointed out that the transformation of business model is the founda-
tion of the success of digital enterprises. A successful digital transformation
should leverage emerging technologies to increase stickiness and strengthen con-
nectivity as well as creating an integration platform for digital and non-digital

19
technologies. Therefore, technology is not the end, but the capabilities of real-time
visibility, continuous collaboration, organizational flexibility, enhanced respon-
siveness and prevention. For example, most manufacturing enterprises tend to
choose to invest in advanced technology, which is the most popular but may not
be the most appropriate choice [80]. When they embrace shiny new technologies
such as Robots, machine learning tools, etc., these technologies act as separate
parts rather than putting them together to deliver value, which puts companies in
an integration dilemma [81]. These technologies become fragmented and fail to
build the enterprise into a cohesive platform to obtain the advantages of DSC. Fur-
thermore, a deficient view on the integration of digital technologies may even
damage their adaptive strategies [76].
4.4.3 Social human rights and environment
The MIT Sloan Management Review [75] conducted a survey aimed at answering
the question of why most companies are failing to reap business benefits from dig-
itization. They found that people play a key role in digital transformation. On the
one hand, some business leaders like to be in their comfort zone and resist change,
in which case change can be challenging. Hudnurkar [82] claimed that it's hard for
people to change because they develop vision and power with familiar things. In
fact, 43% of 4,500 CIOs in the Harvard Nash/KPMG CIO survey identified re-
sistance to change as the biggest obstacle to a successful digital strategy.
On the other hand, people are unwilling to share information due to safety, se-
curity privacy, interests and other restrictions. Digital supply chains rely on infor-
mation transparency and cross-departmental collaboration. To achieve the smooth
operation of the supply chain, a data-sharing system is needed. However, for some
sensitive data, such as inventory, life cycle, etc., it damages the privacy of the
supply chain and even makes it vulnerable to malicious attacks. Therefore, net-
work security risks become one of the obstacles to the promotion of the digital
supply chain [83]. Due to business interests or narrow thinking, departments and
companies are not willing to share information, which artificially brings "depart-
ment wall" and "enterprise gap". For example, to leave room for cost reduction,
purchasing fails to share the true cost with finance and sales, sales exaggerate the
forecast, and purchasing overstates the demand for suppliers, etc. In this case,
when they passing information adding their understanding and selfishness, the in-
formation will be distorted and the digital supply chain would be out of shape. All
of these have brought obstacles to digitization. Thus, it is imperative to establish a
holistic view, more synergy thinking to promote the flow of information.
Munirathinam [84] claims that more than 25% of cyber-attacks come from IoT
devices. Although AI has greatly increased the capability of processing data and
increased the reliability of decision making, the available way of collecting data
can be a security risk. In addition to ensuring the quantity and quality of data, Eth-
ics and a sound sense of responsibility are important for digital development. For

20
example, some applications and devices have been empowered to make decisions
automatically through algorithm settings, model parameters, data permissions,
etc., which have become “black boxes” [27]. In this case, transparency and ac-
countability are very low. Security loopholes are existing and connections be-
tween devices are still immature, which will give many criminals many opportuni-
ties to commit crimes. Therefore, there is still a lack of relevant laws and
regulations to ensure security issues and increase trust.
In summary, there are three levels of obstacles to the implementation of the
digital supply chain. Financial support is the key factor from the first beginning to
the whole process, which is the premise and basic guarantee to ensure continuous
follow-up. The lack of clear guidance and management knowledge is the root
cause why the implementation effect is not achieved in the process of transfor-
mation. The lack of acceptance from human beings and the imperfection of legal
protection aftermath would hinder the sustainable development of DSC.
5 Conclusions
Many papers have discussed DSC technology from the perspective of the applica-
tion. They demonstrate the positive impact of those digital technologies to suc-
cessfully achieve digital and intelligent supply chains on firm performance by im-
proving the efficiency of SCM. However, few studies have explored the causes
and mechanisms that promote this effect. This study found that information trans-
parency, accuracy information and decision-making, and collaboration amongst
supply chain members directly affect the efficiency of SCM. The realization of
these functions depends on the innovative advantages brought by digital technolo-
gy. The study shows that digital technology is key to the improvement of man-
agement efficiency through bringing about the change of supply chain mode, func-
tional advantages, ability improvement and so on. This research selected two key
technologies (IoT & AI) to discuss their specific contributions to supply chain
management efficiency in the manufacturing industry. The results show that both
IoT and AI are the closest technologies related to the autonomy and predictive
power of future supply chain expectations. IoT promotes supply chain collabora-
tive management by improving real-time information transparency, information
systems integration, and big data processing capabilities. In addition, the ability to
track, predict and independent decisions provide suggestions and guidance for en-
terprises to make decisions. Al accompanied with its analysis, learning capabilities
enhance the accuracy of autonomous prediction, classification, decision-making
and risk aversion. It enables IoT to generate index values, while IoT will provide
AI with an information base and a basis for autonomous decision-making, among
other things. The convergence of the two technologies optimizes all aspects of
manufacturing and opens up more possibilities for smart factories.

21
This research also explored DSC challenges and problems that take into con-
sideration to expand the approaches to DSC success factors derived from existing
literature. One is the capital barrier, which includes the improvement of infrastruc-
ture, the introduction of new technology and the training of talents. These are im-
portant foundations of the digital supply chain and require substantial ongoing fi-
nancial support. The second is the management implementation barriers such as
the lack of knowledge of relevant personnel, no clear guidelines, no strategic plan
and so on. These are the most common difficulties in the process of digital transi-
tion as well as key reasons why companies have invested so much money while it
doesn't work. In this case, organizations recognize the necessity of change, but
lack specific implementation methods and steps, resulting in the waste of a large
number of financial, material and human resources in worthless activities. In addi-
tion, they may be eager to achieve financial returns without objectively combining
their circumstances and strategic planning. The last one is personal barriers includ-
ing fear of changing from the comfort zone, inadaptability to changes in working
style and reluctance to divulge privacy due to security concerns. Privacy security
remains a huge challenge due to technological uncertainty, lack of trust, unwill-
ingness to give up power.
However, our study has certain limitations. In terms of the type of research it-
self, a systematic literature review is a research method that provides an overview
and critical discussion of previous studies and may not cover the details of every
research question. Therefore, such analysis from a relatively macro perspective
lacks in-depth survey and research. Although it identified the importance of digital
technology efforts to improve performance and how they improve supply chain
capabilities, there is no specific quantitative analysis to delve into the impact of
digital technology on management efficiency into quantitative relationships. All
discussions are based on secondary data from cited samples, without collection
and analysis of primary data. This study noted the importance of having clear
guidelines and roadmaps for the supply chain during the implementation phase.
But these need to be developed in the context of a large number of specific enter-
prises to guide DSC adoption. By understanding the strategic purpose, the differ-
ent stages and locations of DSC implementation to deploy and develop a clear
roadmap. The study also does not examine the status quo of the current supply
chain to identify the gaps of practices as well as predicting potential risks.
Based on these research limitations, the following suggestions are proposed for
future study. Firstly, design and calibrate specific digital supply chain transfor-
mation strategies and routes according to different environments, different indus-
tries and different enterprises' specific digital stages; In addition to researching the
creation and development of models, frameworks, methods, solutions, etc., atten-
tion should also be paid to researching and testing their usability, application or
generalization, and possible risks as well as corresponding solutions and optimiza-
tions. Secondly, any progress could not be achieved by a single technology, but by
their cooperation to improve the level of information system integration. Alterna-
tively, it is the unavailability of a single technology. Thus, a quantitative evalua-

22
tion framework is needed, which enables us to monitor, control and reflect the per-
formance of digital supply chain implementation in the different applications by
combining technologies together in valuable new ways. Thirdly, Strengthen the
research on how to solve the supply chain information security. At present, the in-
tegration of many technologies has been developed to a certain extent, such as the
contribution of blockchain technology to information security compared with AI
& IoT [28]. Fourthly, it is necessary to anticipate future developments. For exam-
ple, the transparency of information between supply chain partners is not only for
commercial interests but also provides supervision for the social responsibility of
enterprises, which reflects the social responsibility of enterprises. This transparen-
cy encourages supply chain partners to develop and share best practices for green
operations and logistics. Supply chain partners can demonstrate compliance with
industry best standards for worker safety, environmental protection and business
ethics [85]. In addition, anticipate potential risks and threats in advance is impera-
tive. Those issues, for example, how to strengthen the improvement of policies
and relevant laws in the context of gradually transparent information to prevent
criminals from speculating in crimes and how to guarantee people's basic em-
ployment, technical authorization and other sensitive issues regarding human
rights are expected to be noticed.
References
1. Attaran, M. (2012). Critical Success Factors and Challenges of Implement-
ing RFID in Supply Chain Management. Journal of Supply Chain and Op-
erations Management, 10 (1), 114–167.
2. Koot, M., Mes, M. R. K., & Iacob, M. E. (2021). A systematic literature
review of supply chain decision making supported by the Internet of
Things and Big Data Analytics. Computers & Industrial Engineering, 154,
107076.
3. Mangan, J. and Lalwani, C. (2016). Global Logistics and Supply Chain
Management, 3rd ed., Wiley.
4. Speranza, M. (2018). Trends in transportation and logistics. European
Journal of Operational Research, 830–836.
5. Büyüközkan, G., & Göçer, F. (2018). Digital Supply Chain: Literature re-
view and a proposed framework for future research. Computers in Indus-
try, 97, 157–177.
6. Chase, C. (2019). How the Digital Economy Is Impacting the Supply
Chain. Journal of Business Forecasting, 38 (2): 16–20.
7. Preindl, R., K. Nikolopoulos, and K. Litsiou. (2020). Transformation
Strategies for the Supply Chain: The Impact of Industry 4.0 And Digital
Transformation. Supply Chain Forum: An International Journal, 2 (1): 26–
34.

23
8. Banerjee, M., & Mishra, M. (2017). Retail supply chain management prac-
tices in India: A business intelligence perspective. Journal of Retailing and
Consumer Services, 34, 248–259.
9. ChuangLian (2021). How to avoid the failure of digital transformation of
supply chain? [online]. [Viewed 21 July 2021]. Available at:
https://weibo.com/ttarticle/p/show?id=2309404645830090162388.
10. Reyes, P., Visich, J., & Jaska, P. (2020). Managing the dynamics of new
technologies in the global supply chain. IEEE Engineering Management
Review, 1–1.
11. Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What does In-
dustry 4.0 mean to Supply Chain? Procedia Manufacturing, 13, 1175–
1182.
12. Ashton, K. (2009). That “Internet of things” thing. RFID Journal, 22(7),
97-114.
13. Li, S., Xu, L., & Zhao, S. (2015). The internet of things: A survey. Infor-
mation Systems Frontiers, 17(2), 243–259.
14. Dweekat, A., Hwang, G. and Park, J. (2017). A supply chain performance
measurement approach using the internet of things: toward more practical
SCPMS, Industrial Management & Data Systems. 117(2), 267-286.
15. Yan, R. (2017). An optimization approach for increasing revenue of per-
ishable product supply chain with the internet of things. Industrial Man-
agement & Data Systems, 117(4), 729-741.
16. Gnimpieba, D., Nait-Sidi-Moha, A., Durandb, D. and Fortina, J. (2015).
Using internet of things technologies for a collaborative supply chain: ap-
plication to tracking of pallets and containers. Procedia Computer Science,
56, 550-557.
17. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A sur-
vey. Computer Networks, 54(15), 2787–2805.
18. Reaidy, P., Gunasekaran, A. and Spalanzani, A. (2015). Bottom-up ap-
proach based on internet of things for order fulfilment in a collaborative
warehousing environment. International Journal of Production Economics,
159, 29-40.
19. Macaulay, J., Buckalew, L., & Chung, G. (2015). Internet of Things in Lo-
gistics. Troisdorf: DHL Customer Solutions & Innovation.
20. Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and
supply chain management: a literature review. International Journal of
Production Research, 57(15-16), 4719-4742.
21. Lambert, D. M., & Cooper, M. C. (2000). Issues in Supply Chain Man-
agement. Industrial Marketing Management, 29(1), 65–83.
22. Calatayud, A. (2017). The connected supply chain: enhancing risk man-
agement in a changing world. Inter-American Development Bank, Wash-
ington, DC.
23. Bonkenburg, T. (2016). Robotics in logistics. March. DHL Trend Research
[online]. [viewed 14 August 2021]. Available at: https://

24
www.dhl.com/content/dam/downloads/g0/about_us/logistics_insights/dhl_
trendreport_robotics
24. Kern, Johannes & Wolff, Pascal. (2019). The digital transformation of the
automotive supply chain -an empirical analysis with evidence from Ger-
many and China: Case study contribution to the OECD. TIP Digital and
Open Innovation project.
25. Klumpp, M. (2018), Automation and artificial intelligence in business lo-
gistics systems: human reactions and collaboration requirements, Interna-
tional Journal of Logistics Research and Application, 21(3), 224-242.
26. Tazhiyeva, A. (2018). Challenges and opportunities of introducing Internet
of Things and Artificial Intelligence applications into Supply Chain Man-
agement.
27. Hassija, V., Chamola, V., Gupta, V., Jain, S., & Guizani, N. (2020). A sur-
vey on supply chain security: Application areas, security threats, and solu-
tion architectures. IEEE Internet of Things Journal, 8(8), 6222-6246.
28. Singh, R. K., Kumar, P., & Chand, M. (2019). Evaluation of supply chain
coordination index in context to Industry 4.0 environment. Benchmarking:
An International Journal, 28 (5), 1622-1637.
29. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M.
(2021). Artificial intelligence in supply chain management: A systematic
literature review. Journal of Business Research, 122, 502–517.
30. Nowicka, K. (2018). Trust in Digital Supply Chain Management. Logistics
and Transport, 3 (39): 59–64.
31. Closs, D. and Swink, M. (2005). The role of information connectivity in
making flexible logistics programs successful. International Journal of
Physical Distribution and Logistics Management, 35, 259-277.
32. Queiroz, M. M., Pereira, S. C. F., Telles, R., & Machado, M. C. (2019).
Industry 4.0 and digital supply chain capabilities. Benchmarking: An In-
ternational Journal, 28(5), 1761-1782.
33. Paul W. (2019). Deloitte and MAPl Smart Factory Research: Capturing
Value through Digital Journey. Deloitte insight & MAPI.
34. Pettey, C. (2019). Gartner top 8 supply chain technology trends for 2019
[online] Available at: https://www.gartner.com/smarterwithgartner/gart
ner-top-8-supply-chain-technology-trends-for-2019/.
35. PWC (2020). Eight emerging technologies and six convergence themes
you need to know about. Emerging Technology [online]. [Viewed 11 Au-
gust 2021]. Available at: https://www.pwc.com/us/en/tech-
effect/emerging-tech/essential-eight-technologies.html.
36. Calatayud, A., and Mangan, J. (2018). The self-thinking supply chain.
Supply Chain Management: An International Journal, 24 (1), 22-38
37. Holweg, M., Disney, S., Holmström, J., & Småros, J. (2005). Supply
Chain Collaboration: European. Management Journal, 23(2), 170–181.

25
38. MacCarthy, B. L., C. Blome, J. Olhager, J. S. Srai, and X. Zhao. (2016).
Supply Chain Evolution – Theory, Concepts, and Science. International
Journal of Operations & Production Management, 36 (12), 1696–1718.
39. Nyaga, G.N., Whipple, J.M. and Lynch, D.F. (2010). Examining supply
chain relationships: do buyer and supplier perspectives on collaborative re-
lationships differ? Journal of Operations Management, 28(2), 101-114.
40. Cui, L., Gao, M., Dai, J., & Mou, J. (2020). Improving supply chain col-
laboration through operational excellence approaches: an IoT perspective.
Industrial Management & Data Systems.
41. Haddud, A., DeSouza, A., Khare, A. and Lee, H. (2017). Examining poten-
tial benefits and challenges associated with the internet of things integra-
tion in supply chains. Journal of Self-thinking supply chain.
42. Fernie, J., & Sparks, L. (Eds.). (2018). Logistics and retail management:
emerging issues and new challenges in the retail supply chain. Kogan page
publishers. 233-274.
43. Zhou, L., Chong, A. and Ngai, E. (2015). Supply chain management in the
era of the internet of things. International Journal of Production Econom-
ics, 159, 1-3.
44. Wu, K.J., Tseng, M.L., Chiu, A.S.F. and Lim, M.K. (2017). Achieving
competitive advantage through supply chain agility under uncertainty: a
novel multi-criteria decision-making structure. International Journal of
Production Economics, 190, 96-107.
45. Chae, B., Yen, H. R., & Sheu, C. (2005). Information Technology and
Supply Chain Collaboration: Moderating Effects of Existing Relationships
Between Partners. IEEE Transactions on Engineering Management, 52(4),
440–448.
46. Pasi, Bhaveshkumar & Rane, Santosh. (2020). Smart Supply Chain Man-
agement: A Perspective of Industry 4.0.
47. IDC (2017). Digital Transformation Drives Supply Chain Restructuring
Imperative, IDC White Paper [online]. [Viewed 15 August 2021]. Availa-
ble at: idc-insights-community.com www.idc.com
48. Jujjavarapu, G., Hickok, E., Sinha, A., Mohandas, S., Ray, S., Bidare, P.
M., & Jain, M. (2018). AI and the Manufacturing and Services Industry in
India. The center for Internet and Society, India. URL:
https://cisindia.org/internetgovernance/files/AIManufacturingandServices_
Report_02.pdf
49. Farahani, C. (2019). Gartner top 8 supply chain technology trends for 2019
[online]. [Viewed 1 September 2021]. Available at:
https://www.gartner.com/smarterwithgartner/gart ner-top-8-supply-chain-
technology-trends-for-2019/
50. Arora R., Haleem A., Arora P.K. (2020). Impact of IoT-Enabled Supply
Chain—A Systematic Literature Review. Smart Innovation, Systems and
Technologies. Springer, 174 (3), 513-519.

26
51. Michel, R. (2017). The evolution of the digital supply chain. Supply Chain
247 [online]. [Viewed 29 July 2021]. Available at:
https://www.supplychain247.com/article/the_evolution_of_the_digital_sup
ply_chain/capgemini.
52. Merlino, M., & Sproģe, I. (2017). The Augmented Supply Chain. Procedia
Eng., 178, 308–318.
53. Kasabov, N. (2019). Evolving and Spiking Connectionist Systems for
Brain Inspired Artificial Intelligence. In R. Kozma, C. Alippi, Y. Choe, &
F. C. Morabito (Eds.), Artificial Intelligence in the Age of Neural Net-
works and Brain Computing, 111–138. Academic Press.
54. Vassiliadis, V. and Dounias, G. (2009). Nature-Inspired intelligence: a re-
view of selected methods and applications. International Journal on Artifi-
cial Intelligence Tools, 18(4), 487-516.
55. Li, E. Y. (1994). Artificial neural networks and their business applications.
Information and Management, 27, 303–313.
56. Aleksendri´c, D., & Carlone, P. (2015). Soft computing techniques. In D.
Aleksendri´c, & P. Carlone (Eds.), Soft Computing in the Design and
Manufacturing of Composite Materials, 4, 49–60. Oxford: Woodhead Pub-
lishing.
57. Chen, S. H., Jakeman, A. J., & Norton, J. P. (2008). Artificial Intelligence
techniques: An introduction to their use for modelling environmental sys-
tems. Mathematics and Computers in Simulation, 78, 379–400.
58. Amirkolaii, K. N., Baboli, A., Shahzad, M. K., & Tonadre, R. (2017). De-
mand forecasting for irregular demands in business aircraft spare parts
supply chains by using artificial intelligence (AI). IFAC-Pap., 50, 15221–
15226.
59. Küfner, T., Uhlemann, T.H.-J., Ziegler, B. (2018). Lean Data in Manufac-
turing Systems: Using Artificial Intelligence for Decentralized Data Re-
duction and Information Extraction. Procedia CIRP, 51st CIRP Conference
on Manufacturing Systems, 72, 219–224.
60. Min, H. (2009). Artificial intelligence in supply chain management: theory
and applications. International Journal of Logistics Research and Applica-
tions, 13(1), 13–39.
61. Tsafarakis, S., Saridakis, C., Baltas, G., & Matsatsinis, N. (2013). Hybrid
particle swarm optimization with mutation for optimizing industrial prod-
uct lines: An application to a mixed solution space considering both dis-
crete and continuous design variables. Industrial Marketing Management,
42, 496–506.
62. Frederico, G. F., Garza-Reyes, J. A., Anosike, A., & Kumar, V. (2019).
Supply Chain 4.0: concepts, maturity and research agenda. Supply Chain
Management. An International Journal, 25(2), 262–282.
63. Camarillo, A., Ríos, J., & Althoff, K.-D. (2018). Knowledge-based multi-
agent system for manufacturing problem solving process in production
plants. Journal of Manufacturing Systems, 47, 115–127.

27
64. Ramanathan, U. (2012). Supply chain collaboration for improved forecast
accuracy of promotional sales. International Journal of Operations & Pro-
duction Management, 32(6), 676–695.
65. Townsend, D. M., & Hunt, R. A. (2019). Entrepreneurial action, creativity,
& judgment in the age of artificial intelligence. Journal of Business Ven-
turing Insights, 11, e00126.
66. Accenture (2018). Grow in the new: Accenture China Digital Transfor-
mation Index. Strategy & Consulting. Digital Transformation [online].
[Viewed 18 August 2021]. Available at: https://www.accenture.com/cn-
en/insights/digital/digital-transformation-index.
67. Tungsten Network (2017). Supply chain losing hours, money to poor fi-
nancial systems [online]. [Viewed 29 August 2021]. Available at:
https://www.mhlnews.com/global-supply-chain/article/22054569/ supply-
chain-losing-hours-money-to-poor-financial-systems.
68. Hofmann, E., Sternberg, H., Chen, H., Pflaum, A. and Prockl, G. (2019).
Supply chain management and Industry 4.0: conducting research in the
digital age. International Journal of Physical Distribution & Logistics
Management, 49(10), 945-955.
69. Pradabwong, J., Braziotis, C., Tannock, J. D. T., & Pawar, K. S. (2017).
Business process management and supply chain collaboration: effects on
performance and competitiveness. Supply Chain Management: An Interna-
tional Journal, 22(2), 107–121.
70. Schrauf, S., Berttram, P. (2016). Industry 4.0: How Digitization Makes the
Supply Chain More Efficient, Agile, and Customer-focused. Strategy
[online]. [Viewed 19 August 2021]. Available at: http://
www.strategyand.pwc.com/reports/industry4.0.
71. Reinsel, D., Gantz, J., & Rydning, J. (2018). Data age 2025: The digitiza-
tion of the world from edge to core. Framingham: IDC. Retrieved from.
http://cloudcode.me/media/1014/idc.pdf
72. GOV (2021). Ministry of Industry and Information Technology (MIIT):
Digitalization will be popularized in manufacturing enterprises above des-
ignated size by 2025. Ministry of Industry and Information Technology
(MIIT) [online]. [Viewed 23 August 2021]. Available at:
http://www.gov.cn/xinwen/2021-04/14/content_5599586.htm
73. Pacific Securities (2021). Industrial software enterprise in transformation
[online]. [Viewed 29 August 2021]. Available at:
https://www.hanghangcha.com/pdf.html
74. Ageron, B., Bentahar, O. & Gunasekaran, A. (2020). Digital supply chain:
challenges and future directions, Supply Chain Forum: An International
Journal, 21(3), 133-138.
75. Agrawal, P., Narain, R., & Ullah, I. (2019). Analysis of barriers in imple-
mentation of digital transformation of supply chain using interpretive
structural modelling approach. Journal of Modelling in Management,
15(1), 297–317.

28
76. Fitzgerald, M., Kruschwitz, N., Bonnet, D. and Welch, M. (2014). Em-
bracing digital technology: a new strategic imperative. MIT Sloan Man-
agement Review, 55(2), 1-12.
77. Ivanov, D., Dolgui, A. and Sokolov, B. (2018). The impact of digital tech-
nology and industry 4.0 on the ripple effect and supply chain risk analytics.
International Journal of Production Research. 1-18.
78. Bienhaus, F. and Haddud, A. (2018), Procurement 4.0: factors influencing
the digitisation of procurement and supply chains, Business Process Man-
agement Journal, 24(4), 965-984
79. Meffert, I. (2017). How to make sure your digital transformation succeeds
[online]. [Viewed 1 September 2021]. Available at:
https://www.mckinsey.com/business-functions/mckinsey-digital/our-
insights/how-to-make-sure-your-digital-transformation-succeeds
80. Bentahar, O., S. Benzidia, and R. Fabbri. (2016). Traceability Project of a
Blood Supply Chain.” Supply Chain Forum: International Journal, 17 (1),
15–25.
81. Shenhar, A. J. (2001). One Size Does Not Fit All Projects: Exploring Clas-
sical Contingency Domains. Management Science, 47(3), 394–414.
82. Hudnurkar, M., Jakhar, S., & Rathod, U. (2014). Factors Affecting Collab-
oration in Supply Chain: A Literature Review. Procedia - Social and Be-
havioral Sciences, 133, 189–202.
83. Dudukalov, E. V., Terenina, I. V., Perova, M. V., & Ushakov, D. (2021).
Industry 4.0 readiness: the impact of digital transformation on supply chain
performance. In E3S Web of Conferences, 244, p. 08020. EDP Sciences.
84. Munirathinam, S. (2019). Industry 4.0: Industrial Internet of Things
(IIOT). Advances in Computers, 117(1), 129-164.
85. Aslinda Abu Seman, N., K. Govindan, A. Mardani, N. Zakuan, M. Zameri
Mat Saman, R. E. Hooker, and S. Ozkul. (2019). The Mediating Effect of
Green Innovation on the Relationship between Green Supply Chain Man-
agement and Environmental Performance. Journal of Cleaner Production,
229, 115–127.