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Literature Review: Supply Chain Digitalization
Emmanuel Estrada
School of Business, Liberty University
BUSI 740 – Managing the Supply Chain
Dr. Shelly Zaldivar
October 13, 2023
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Abstract
Supply chain digitalization has rapidly expanded in recent years. Traditional analog processes
are transitioned to digital. The evolution of the supply chain is driven by advancements in IT, AI,
Machine Learning, Big Data, Blockchain, analytics, and IoT. Digitalization improves the
efficiency of the supply chain by leveraging artificial intelligence, resources, and data to make
better decisions. The interest in accelerating supply chain digitalization is partially attributed to
lessons learned from global supply chain interruptions during the COVID-19 lockdowns.
COVID-19 exposed the weaknesses within the supply chain. The same technologies that have
contributed to the development of supply chain digitalization have also sparked the Fourth
Industrial Revolution and helped create “smart” factories. Supply chain digitalization comprises
several dimensions that are comparable to the dimensions of wisdom as described in the bible.
Organizations can build robust supply chains by implementing digitalization and God’s wisdom.
Keywords: digitalization, digitization, supply chain, artificial intelligence, analytics,
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Literature Review: Supply Chain Digitalization
Supply Chain digitalization utilizes data, advanced analytics, and automation to optimize
supply chain performance (Simchi-Levi et al., 2021). It upgrades old analog processes with
digital processes. Digitalization is the collection of aggregate data across the supply chain.
Advanced analytics is the collection of three levels of analytics: statistics, machine learning, and
optimization. Automation uses advanced analytics and digitization to optimize processes across
the supply chain. Supply chain digitization is reported to increase revenue and profit while
reducing costs and improving the customer service experience (Simchi-Levi et al., 2021).
Researchers have also found that digitalization has significantly accelerated the growth of
resilient supply chains (Zhao et al., 2023).
Purpose and Layout
This literature review aims to learn how technological advancements have influenced the
advancement of the supply chain. In this study, current literature on the topic of supply chain
digitalization will be reviewed. It is important to note that the literature researched uses the terms
supply chain digitization and supply chain digitalization to describe the same area of supply
chain management. For the sake of consistency, only supply chain digitalization will be used.
The paper is outlined by first going over a brief history of the supply chain, how digitalization
was introduced, and what advancements have been made in recent years, like the introduction of
machine learning, artificial intelligence, and the Internet of Things. A section on integrating
biblical principles into work will also be included. The paper will conclude by discussing the
findings and recommendations for future research.
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Methodology
The methodology used for the literature review is to use only recently published peer-
reviewed journal articles. Journal article publication dates will be limited to the most recent five
years. Because supply chain digitization centers around technology, peer-reviewed journal
articles written within the last one to three years were favored when conducting research. This
decision ensured that the most current information was being gathered for review. For the
biblical integration section, the King James Bible was chosen along with the book Every Good
Endeavor by Keller and Alsdorf.
History of Supply Chain Management
A supply chain is a logistics network that connects suppliers, manufacturers, distribution
centers, warehouses, and retail outlets. The goal of a supply chain is to transport raw materials
and goods in the most efficient manner possible (Simchi-Levi et al., 2021). As supply chains
have evolved and grown in scope and scale, they have become increasingly challenging to
manage. Supply chain digitization became increasingly necessary to support organizations
restructuring their supply chains to increase transparency (Seyedghorban et al., 2018). The
implementation of supply change management systems can improve interdepartmental
communication and collaboration. The same systems can be used to integrate suppliers,
transportation companies, distribution centers, and all business partners with the supply chain.
With improved communication and visibility, each supply chain node can be optimized for
performance in its role. These minor improvements collectively build up a supply chain that is
more resilient and robust (Perano et al., 2023).
As technology has continued to evolve, so have supply chain management systems and
processes. In the 1990s, the use of the World Wide Web, also known as the Internet, became
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more widespread. The internet increased the speed at which firms could communicate with each
other; this also contributed to the growth of Information Technology (IT). Many digitalization
systems require the Internet to access data and communicate with corresponding nodes in the
supply chain. As internet communication increased, firms could leverage IT systems to develop
systems to exploit its capabilities. IT systems are essential to an effective management system as
they communicate information across the supply to its internal and external business partners.
Supply chain resilience, agility, and overall performance are all significantly improved by a well-
implemented IT system (Cherian et al., 2023).
Digitalization and Analytics
Information and data are collected from nodes across the supply chain. Advanced
analytics systems can then use this data. Advanced analytics comprises several analytical tools,
including statistical, machine learning, and optimization models. Digitized information acquired
through improved IT systems combined with innovations in mobile computing, the internet, and
IT infrastructure allow the advanced analytics programs to take full advantage of information
from across the supply chain; this increase in IT capability allows organizations to be agile,
responsive, and efficient (Cherian et al., 2022).
Artificial Intelligence
Artificial intelligence (AI) can be defined as computers or machines with the ability to
imitate the capabilities of humans, typically at higher speeds and with a greater degree of
accuracy (Toorajipour et al., 2021). The term AI broadly includes machine learning and
cognitive computing that address various operations and supply chain aspects. The idea of using
AI is familiar; however, developments in technology, computing power, and increased supply
chain digitization have made it more feasible to implement AI systems within processes. AI
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systems were once considered an excellent addition to an organization's IT system, but AI has
become necessary to maintain competitive advantages. AI has been recognized as a direct
contributor to significant improvements in the robustness of supply chains and processes across
different industries, including healthcare and manufacturing, for its ability to make intelligent
and timely decisions (Kumar et al., 2023). Forecasting is another area where AI systems have
shown significant improvement. Supply chain efficiency is dependent upon accurate demand
forecasts. In situations with limited data, like with a new product, AI can form models based on
learned patterns from similar products and historical data that it then uses to form demand
forecasts (Zhu et al., 2021).
Machine Learning
One subgroup of AI is machine learning; it uses algorithms, statistics, established rules,
and instructions to look for patterns in data sets. Once the patterns are recognized, they can be
used to make predictions and forecasts (Simchi-Levi et al., 2021). The algorithms can be tailored
to search for the desired outcome, like finding the optimal transportation routes to optimize
speed and minimize fuel expenses and downtime.
Many transportation companies are adopting electric vehicle (EV) trucks to meet
increasingly strict green energy regulations. A significant challenge transportation companies
will face is finding the best balance between moving freight and charging the EV truck’s electric
batteries. Additional factors must be considered when creating charging schedules, including the
electrical grid demand, operating hours, and differences in charging costs based on timeframes.
Researchers concluded that machine learning algorithms need to be implemented to address
Minimum Distance (MD), Minimum Travel Time (MTT), and Minimum Wait Time (MWT)
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strategies. These three factors will determine total driving time and contribute to lead times that
can influence customer satisfaction (Lee et al., 2020).
The effectiveness of humanitarian supply chains has significantly benefited from machine
learning management systems. A humanitarian supply chain can be described as the planning
and implementation of the transportation of materials and goods into areas facing natural
disasters (Shakibaei et al., 2023). Humanitarian supply chains can vastly differ from commercial
supply chains because they face additional challenges when responding to disasters. The
efficiency of humanitarian supply chains is more critical as human lives may be directly affected
by its success. The location of storage facilities can significantly impact the ability, speed, and
sustainability to support disaster relief operations. Also, humanitarian supply chains must operate
efficiently in constantly changing, fast-paced, chaotic environments. Humanitarian supply chains
must also be able to operate in varying types of disasters, such as fires, floods, landslides, and
earthquakes.
Machine learning systems have been successfully implemented to determine the optimal
number of forces, goods, and supplies for a given region and situation. Before a disaster occurs,
several relief center locations are considered. Algorithms assist in decision-making to minimize
travel distance and costs, determine the number of personnel, and minimize casualties (Shakibaei
et al., 2023).
The Internet of Things
The Internet of Things (IoT) describes objects that contain networked sensors through
software and IT systems. IoT facilitates the collection and exchange of data across networks. In
the supply chain, IoT devices include radio frequency identification (RFID), global positioning
system (GPS) trackers, supporting software, and IT systems to identify and track packages and
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freight through the supply chain. Data and location information are collected in real-time and can
be used to track shipment progression. IoT significantly reduces the time it takes to capture data,
allowing for faster decision-making that enables supply chains to react in real-time, creating a
level of responsiveness and agility that had not been previously experienced (Ben-Daya, 2019).
Supply chain visibility is a set of organizational, technical, and managerial practices
aided by tools to monitor the movement of goods and information throughout the supply chain.
IoT has been instrumental in improving supply chain visibility; it transforms the traditional
supply chain management system into a “smart” supply chain management system. When
applied to manufacturing operations, referred to as the Industrial Internet of Things (IIoT), the
movement of resources, finished goods, and information across the supply chain enhances the
capability of supply chain management visibility (Al-Khatib, 2023).
Blockchain
Organizations use supply chain quality management systems to provide the best products
and services. One significant challenge supply chain management systems face is sharing data
that can be interpreted and utilized across organizations. Blockchain technology has been found
to drive the development of improved supply chain quality management systems (Zheng et al.,
2023). Blockchain is the term used to describe the advanced database system that allows
transparent information exchange across business networks. Data is grouped into sets called
blocks and linked together in chronological order. The data cannot be modified or deleted and is
chronologically consistent without network consensus. Blockchain systems facilitate the creation
of tracking orders, ledgers, accounts, transactions, and payments that cannot be altered. Studies
show that blockchain technology is instrumental in improving supply chain quality and making
pricing decisions (Zheng et al., 2023).
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Big Data
Supply chains have vast amounts of data that vary in volume, velocity, and variety, which
is typically too complex for traditional IT systems to process. Big Data is collecting and
processing large amounts of information for use in analytics and machine learning. The data is
collected from various sources throughout the supply chain, like IoT. Using Big Data can
improve supply chains' performance, visibility, transparency, agility, and responsiveness (Lixu et
al., 2022).
Covid Supply Chain
In 2020, the COVID-19 pandemic caused a series of worldwide government-initiated
lockdowns previously unseen. The lockdowns led to a massive disruption of the global supply
chain that had lasting consequences after they had been lifted. The supply chain disruptions were
a rude awakening to the necessity of IT capability, supply chain agility, and supply chain
resilience to respond quickly to sudden changes in the market by leveraging technological
capabilities (Cherian et al., 2022).
Supply chains are one of the most sensitive and critical units for organizations. Black
swan events like the COVID-19 pandemic or the Suez Canal blockage can expose and
exacerbate the supply chain’s weaknesses. The COVID-19 pandemic has provided a pathway for
a digital transformation in how supply chains operate and cope with interruptions, restricted
movement, and office closures (Tiwari et al., 2023). Post-COVID-19 supply chain processes
cannot be the same as pre-pandemic processes. They require more technological integration,
leveraging digitalization processes, and sustainability considerations (Dwivedi et al., 2022).
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Digitalization
Organizations have adopted digitization to prevent and minimize the negative impacts of
supply chain interruptions like those during the COVID-19 pandemic. Various technologies play
a role in transitioning traditional analog systems to digital systems. Digitalization is the process
in which traditionally physical or analog processes, objects, and content are transformed entirely
or primarily digital (Tiwari et al., 2023). Some technologies that enable the growth of supply
chain digitalization are RFID, GPS tracking, blockchain, big data prediction, AI planning, and
analytics-driven forecasting. Digitalization goes beyond the adoption of these and similar
technologies. To leverage the full benefit of digitalization technology, they must be accompanied
by organizational style and management changes.
Transformation Factors
Supply chain digitalization is also impacted by factors outside the organization that have
hindered its adoption and growth. Digitalization readiness is heavily dependent on organizational
readiness and people readiness. For digitalization to be as effective as possible, multiple
organizations throughout the supply chain must adopt compatible systems (Tiwari et al., 2023).
A successful digitalization transformation will improve transparency, visibility, and
agility. Aamer et al. (2023) found that an organization’s readiness for supply chain digitalization
is dependent on several factors, including its innovation strategy, management support, supply
chain integration, IT infrastructure, cybersecurity systems, extensive data management,
digitalization reskilling and upskilling, digitalization culture, and government regulation. Many
organizations have conducted supply chain digitalization studies and pilot programs, but as many
as 70% fail to implement digitalization fully. This failure is partly attributed to a lack of proper
assessment and preparation of the organization’s readiness to transition to digitalization. Because
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digitalization relies on several technologies and systems syncing together in relative harmony,
there must be diligent preparation before implementation so managers can embrace the
challenges of digitalization.
Industry 4.0
The Fourth Industrial Revolution, or Industry 4.0, results from digitalization through
advancements in Big Data, machine learning, cloud computing, blockchain IT, AI, IoT, and
robotics (Rymarczyk, 2022). Industry 4.0 has revolutionized how organizations plan,
manufacture, and distribute their products. “Smart factories” have advanced software, sensors,
and robotics. Data is collected and analyzed to optimize maintenance, automation, lead times,
and inventory stocking levels. Supply chain digitalization is interconnected to Industry 4.0 as
they share the same systems.
The data produced by industry and supply is mutually beneficial and can be used to
improve each other’s efficiency. For example, systems within a “smart” factory can
communicate data to the supply chain that can assist in determining transportation schedules to
optimize lead time, reduce fuel costs, and reduce inventory holding times and driver wait times.
The supply chain can communicate transportation status and more precise delivery times of raw
materials and components to intelligent factories to optimize production schedules. These
systems' enhanced value, communication, and capabilities are essential to continuously
improving sustainable supply chains with a global competitive advantage (Acioli et al., 2021).
Biblical Integration
The Genesis model is a framework comprised of lessons from the Bible that discuss the
creation, fall, redemption, and restoration of man. The story begins with God’s perfect creation
of the world and man. The fall of Adam and Eve occurs when they are tempted into sin that
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impacts the world. The story unfolds God’s plan for the redemption of his fallen creations. The
story concludes with God’s plan to restore all to his original intentions. Keller and Alsdorf
(2015) talk about how people have a calling to work and can find fulfillment if they perform
well. In the book of Genesis, God created the universe, animals, plants, and man through six
days of work and rested on the seventh. People should not consider work a necessary evil or
something forced upon them but a source of potential fulfillment and satisfaction.
Vocation
This is especially obtainable when performing one’s vocation. Keller and Alsdorf
describe a vocation as work that one feels called to do by God. Vocations come in all forms and
are not limited to prestigious, glamorous, or high-paying work. Everyone from the humble auto
mechanic to Fortune 500 CEO can find fulfillment in their calling if they perform it in service to
God. The Bible states, “WhateverKyour hand finds to do, do it with all your might,Kfor in the
realm of the dead,Kwhere you are going, there is neither working nor planning nor knowledge nor
wisdom” (NIV Bible. Ecclesiastes, 9:10, 2023).
Wisdom
The Bible has many lessons surrounding the use and application of wisdom. The book of
1 Kings tells the story of two women fighting over a baby. One woman’s baby had died in the
middle of the night and switched its body with the baby of the second woman. They went before
King Solomon to ask for child custody, but neither woman had evidence that she was the baby’s
mother. After hearing their arguments, King Solomon ordered that the baby be cut in half and
given equally to each woman. The first woman agreed to King Solomon’s decision, while the
second woman cried out to give the baby to the first. This act proved to King Solomon that the
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woman who cried out was the baby’s mother, who preferred to have her baby taken away so it
would not be harmed (NIV Bible, 1 Kings 3:16-28, 2023).
In this story, King Solomon listened to the women (collected data), considered the
possibilities (analyzed the data), predicted how the woman would react (forecast), observed their
reaction to his order (collected and analyzed additional data), then gave his final order
(implementation). This parable, albeit harsh, is a good model for applying wisdom in decision-
making when dealing with complex problems with ambiguous data. Wisdom is complex; it is not
mere intelligence or book smarts. Some knowledgeable people are lacking in wisdom. Merida
(2015) says wisdom comprises worship, insight, discernment, moral, justice, and skill
dimensions. Worshiping God is the starting point for learning wisdom. Wise people are
insightful through spiritual truth and have a thirst for wisdom. Wisdom has a dimension of
discernment. Wisdom allows a person to process and analyze a situation and make the correct
decision. Wisdom also has the dimension of morality, the preference of doing good over evil.
Good leaders use their wisdom to do justice. Lastly, wisdom requires skills (Merida, 2015).
Some of the aspects of supply chain digitalization are in line with Merida's explanation of
wisdom. The purpose of digitalization is to collect data, analyze it, and make decisions that will
best serve its stakeholders and customers. Insight is the representation of the data being
collected. Discernment is the processing and analyzing of data. These actions require the skills of
everyone working within the supply chain. With morality and justice in mind, forecasts,
schedules, and operational decisions should be made. What is best for the stakeholders and
customers is what is morally right and just.
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Discussion and Future Research
Most of the literature researched for this review agreed that digitalization is a positive
evolution for supply chains. Digitalization is very complex; each branch has its own set of
complex systems. For example, the Internet of Things utilizes RFID, which requires radio
receivers to read, and GPS trackers linked to outer space. The rapid advancement of technology
has made digitalization possible but has also hindered it. Most organizations lack the readiness to
adopt digitalization technologies. The lack of readiness has contributed to the failure of
digitalization pilot and implementation initiatives.
Digitalization is still in its infancy; future research is needed on how organizations can
best prepare themselves to adopt digitalization. One of the merits of digitalization is the
conversion of analog tasks to digital tasks, some of which are robotic labor. Future research is
alsoKneeded on how digitization may displace human workers and what skills they require to
remain competitive.
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