Resilient Chain: AI-Enhanced Supply Chain Security and Efficiency Integration

Abstract

In most recent time, the global landscape of supply chain management has experienced unprecedented challenges during the COVID-19 pandemic, which has significantly impacted the routine and smooth operations of different firms in the United States. This paper explains about the importance of artificial intelligence (AI) and machine learning (ML) in the mitigation of these disruptions and possible ways of improving supply chain security and its efficiency. This research adopted a questionnaire survey involving 281 managers, with the aim to comprehensively examine the current state of AI integration across the U.S. supply chain sector, with focus on some key components like real-time tracking, cost optimization, and risk management. A mixed method approach was adopted for this research, utilizing both inferential and descriptive analyses to unravel insights and trends into the role of AI in enhancing supply chain security. The results indicate that integrating AI, most especially through cost optimization, real-time tracking, and risk management components, emerges as a significant determinant of supply chain security in the United States. Real-time tracking technologies are identified as crucial for monitoring shipments and assets, enabling quick responses to security incidents, and ensuring end-to-end visibility throughout the supply chain. Despite the potential benefits, the study highlights challenges that hinder the widespread integration of AI technologies in the U.S. supply chain. The high cost of AI adoption and the limited availability of skilled personnel are major obstacles. To address these challenges, the paper proposes practical recommendations. Firstly, real-time tracking technologies are recommended to monitor shipments and assets, facilitating rapid responses to security incidents, and ensuring visibility across the entire supply chain. Furthermore, the paper suggests optimizing costs by investing in cost-effective security solutions. This includes leveraging AI for automated monitoring systems and adopting secure packaging measures. These strategies aim to minimize vulnerabilities without compromising security standards, offering a balanced approach to enhancing supply chain security while mitigating the financial implications associated with AI adoption. In conclusion, this research sheds light on the pivotal role of AI in fortifying supply chain security in the United States. The findings and recommendations provide valuable insights for organizations seeking to navigate the complexities of modern supply chain management in an era of heightened disruptions.

Full text

Internaonal Journal of Scienc and Management Research
Volume 07 Issue 03 (March) 2024
ISSN: 2581-6888
Page: 46-65
Resilient Chain: AI-Enhanced Supply Chain Security and Efficiency
Integration
Nnaji Chukwu1, Simo Yufenyuy2, Eunice Ejiofor3, Darlington Ekweli4, Oluwadamilola
Ogunleye5, Tosin Clement6, Callistus Obunadike7, Sulaimon Adeniji8, Emmanuel Elom9,
& Chinenye Obunadike10
1,2,3,7,9Department of Computer Science and Quantitative Methods, Austin Peay State
University, Clarksville, USA
4 University of the Potomac, Washington DC, USA
5 George Washington University, Washington DC, USA
6 University of Louisville, Kentucky, USA
8University of Lagos, Lagos State, Nigeria
10 Anambra State University Uli, Anambra State, Nigeria
DOI - http://doi.org/10.37502/IJSMR.2024.7306
Abstract
In most recent time, the global landscape of supply chain management has experienced
unprecedented challenges during the COVID-19 pandemic, which has significantly impacted
the routine and smooth operations of different firms in the United States. This paper explains
about the importance of artificial intelligence (AI) and machine learning (ML) in the mitigation
of these disruptions and possible ways of improving supply chain security and its efficiency.
This research adopted a questionnaire survey involving 281 managers, with the aim to
comprehensively examine the current state of AI integration across the U.S. supply chain
sector, with focus on some key components like real-time tracking, cost optimization, and risk
management. A mixed method approach was adopted for this research, utilizing both inferential
and descriptive analyses to unravel insights and trends into the role of AI in enhancing supply
chain security. The results indicate that integrating AI, most especially through cost
optimization, real-time tracking, and risk management components, emerges as a significant
determinant of supply chain security in the United States. Real-time tracking technologies are
identified as crucial for monitoring shipments and assets, enabling quick responses to security
incidents, and ensuring end-to-end visibility throughout the supply chain. Despite the potential
benefits, the study highlights challenges that hinder the widespread integration of AI
technologies in the U.S. supply chain. The high cost of AI adoption and the limited availability
of skilled personnel are major obstacles. To address these challenges, the paper proposes
practical recommendations. Firstly, real-time tracking technologies are recommended to
monitor shipments and assets, facilitating rapid responses to security incidents, and ensuring
visibility across the entire supply chain. Furthermore, the paper suggests optimizing costs by
investing in cost-effective security solutions. This includes leveraging AI for automated
monitoring systems and adopting secure packaging measures. These strategies aim to minimize
vulnerabilities without compromising security standards, offering a balanced approach to
enhancing supply chain security while mitigating the financial implications associated with AI
adoption.

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In conclusion, this research sheds light on the pivotal role of AI in fortifying supply chain
security in the United States. The findings and recommendations provide valuable insights for
organizations seeking to navigate the complexities of modern supply chain management in an
era of heightened disruptions.
Keywords: Artificial Intelligence, Supply Chain, Supply Chain Security, Supply chain
Efficiency, Resilient Supply Chain.
1. Introduction
Supply chains are the complex web of interrelated processes and activities that enable the
manufacture, distribution, and delivery of goods and services from raw materials to end users.
They include a wide network of suppliers, manufacturers, distributors, retailers, and logistics
providers, each of whom plays an important part in maintaining the seamless flow of products
through the different phases of production and distribution [1]. The essential goal of serving
consumer demand efficiently and affordably is at the heart of any supply chain. Whether it's a
modest transaction at a local store or a complicated worldwide operation spanning continents,
supply chains are the foundation of business, propelling global economic progress and wealth
[1].
Furthermore, the value of supply chains cannot be emphasized since they allow firms to provide
goods and services to clients on time, meeting their demands and preferences. In today's hyper-
connected world, customers expect flawless experiences and quick delivery, putting enormous
pressure on businesses to optimize their supply chain processes to match these demands [2].
Efficient supply chains not only increase customer happiness, but they also improve overall
business performance by lowering costs and increasing profitability. Organizations may
improve their operational efficiency and competitiveness by simplifying operations,
minimizing waste, and optimizing inventory levels [3].
One option is to integrate artificial intelligence (AI) technology into supply chain management.
AI, with its capacity to analyze massive volumes of data, discover patterns, and make
predictions with amazing accuracy, has enormous potential for improving supply chain security
and efficiency. Organizations may use AI to not just increase their defenses against security
threats, but also to optimize their operations to meet the needs of an ever-changing marketplace
[1]. Furthermore, [4] said that the idea of resilience in supply chains encompasses a system's
capacity to foresee, adapt to, and recover from disturbances while retaining essential functions
and objectives. It represents a shift from traditional approaches that focus solely on efficiency
and cost optimization to a more holistic perspective that emphasizes flexibility, agility, and
robustness in the face of uncertainty and adversity.
Furthermore, resilience in supply chains acknowledges that disruptions are unavoidable and
can come from a variety of causes, including natural catastrophes, geopolitical conflicts,
economic downturns, pandemics, cyber-attacks, and supply shortages, among others. These
interruptions can have far-reaching effects, ranging from production delays and inventory
shortages to revenue loss, reputational harm, and even business collapse [5]. Furthermore, at
its foundation, resilience is about developing systems that can absorb shocks, adapt to changing
conditions, and recover rapidly when disruptions occur. To maintain continuity and
dependability in supply chain operations, it combines proactive planning, risk management,

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and responsiveness [6]. However, [5] stated that one of the major concepts of resilience is
redundancy, which entails incorporating redundancies and backups into supply chain networks
to reduce the effect of interruptions. This might include having numerous suppliers for crucial
components, keeping surplus inventory as a safety net against supply shortages, and
diversifying sourcing locations to decrease reliance on single points of failure.
Moreover, [2] said that another element is flexibility, which entails building supply chain
processes and systems that are flexible and sensitive to changing situations. It entails having
agile manufacturing processes that can rapidly ramp up or down in reaction to demand
variations, as well as flexible transportation and logistics networks that can reroute goods and
alter schedules as necessary. Resilience also requires collaboration and communication across
supply chain partners, as well as the development of strong connections and information-
sharing channels to aid in coordination and reaction to interruptions. Working closely with
suppliers, customers, and other stakeholders allows organizations to better anticipate risks,
coordinate response activities, and limit the impact of interruptions on operations [5].
Besides, resilience requires a proactive approach to risk management, which involves
identifying potential threats, assessing their likelihood and impact, and implementing measures
to mitigate or prevent them. It includes conducting risk assessments, developing contingency
plans, and investing in technologies and capabilities that enhance visibility and control across
the supply chain [6]. Importantly, resilience is not just about bouncing back from disruptions
but also about learning and adapting from them to improve future performance. It involves
conducting post-mortem analyses of disruptions to identify root causes, assess response
effectiveness, and identify opportunities for improvement. By leveraging insights gained from
past experiences, organizations can strengthen their resilience and enhance their ability to
withstand future challenges [7].
Integrating AI technologies into supply chain management holds immense importance for
enhancing both security and efficiency in today's dynamic business environment. [7], noted
that it can significantly bolster supply chain security by providing advanced threat detection
capabilities and enabling proactive risk mitigation strategies. AI-powered surveillance systems
can analyze vast amounts of data in real-time, detecting anomalies and potential security
breaches before they escalate. Also, machine learning algorithms can identify patterns
indicative of fraudulent activities or malicious intent, enabling organizations to take timely
action to prevent or minimize damage [8]. Additionally, AI can enhance cyber security by
continuously monitoring network traffic, identifying potential vulnerabilities, and
autonomously responding to cyber threats, thereby reducing the risk of data breaches and
cyber-attacks [9].
Similarly, AI can revolutionize supply chain efficiency by optimizing processes, automating
routine tasks, and enabling data-driven decision-making. Predictive analytics algorithms can
analyze historical data and market trends to forecast demand more accurately, enabling
organizations to optimize inventory levels and minimize stock outs or excess inventory [9]. AI-
powered predictive maintenance systems can monitor equipment performance in real-time,
predicting potential failures and scheduling maintenance activities proactively to minimize
downtime and disruptions [8]. Moreover, AI-driven automation technologies can streamline
logistics operations, from route optimization and load balancing to warehouse management
and order fulfillment, improving throughput and reducing operational costs [4].

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Additionally, by integrating AI technologies into supply chain management, organizations can
enhance their ability to respond to evolving security threats and operational challenges while
unlocking new opportunities for innovation and growth [9]. However, successful integration
requires careful planning, investment in technology infrastructure, and organizational readiness
to embrace change. Nevertheless, the benefits of AI integration improved security, enhanced
efficiency, and competitive advantage, and make it a worthwhile investment for organizations
seeking to thrive in an increasingly digital and interconnected world [10].
However, this report will explore the convergence of AI and supply chain management,
focusing specifically on how AI-enhanced systems can bolster the resilience of supply chains.
The report will delve into the myriad challenges facing traditional supply chains, from
disruptions caused by natural disasters and pandemics to the growing specter of cyber threats
and geopolitical instability. It will examine the role of AI in mitigating these challenges, from
bolstering security through risk assessment to improving efficiency through predictive
analytics and autonomous decision-making. Furthermore, it will explore the practical
implications of integrating AI into supply chain operations, considering the opportunities and
challenges that organizations may encounter along the way.
2. Literature Review
Artificial Intelligence (AI) is revolutionizing supply chain management by enabling
organizations to harness the power of data and automation to optimize processes, enhance
decision-making, and improve overall efficiency. In supply chain management, AI
encompasses a wide range of technologies and applications, including machine learning,
predictive analytics, natural language processing, and robotic process automation [11]. Also,
machine learning algorithms analyze large volumes of historical data to identify patterns,
trends, and correlations, enabling organizations to make more accurate predictions and
forecasts regarding demand, inventory levels, and market trends. Predictive analytics
algorithms leverage these insights to anticipate potential disruptions, such as supply shortages
or production delays, and develop proactive mitigation strategies.
Furthermore, natural language processing technologies enable organizations to extract valuable
insights from unstructured data sources, such as emails, customer reviews, and social media
posts, to gain a deeper understanding of customer preferences and market trends. Robotic
process automation streamlines repetitive tasks and workflows, such as order processing,
invoicing, and inventory management, freeing up human resources to focus on more strategic
activities [6]. Overall, AI empowers organizations to build more resilient, agile, and responsive
supply chains that can adapt to changing market conditions and emerging challenges,
ultimately driving greater efficiency, cost savings, and competitive advantage.
2.1 Role and Application of Artificial Intelligence in Supply Chain Management
In supply chain management, several AI technologies play pivotal roles in optimizing processes
and enhancing decision-making. [12], noted that machine learning, a subset of AI, involves
algorithms that enable systems to learn from data and improve performance over time without
explicit programming. In supply chains, machine learning algorithms analyze vast datasets to
uncover patterns, trends, and anomalies, aiding in demand forecasting, inventory optimization,
and predictive maintenance. Also, predictive analytics utilizes statistical algorithms and
machine learning techniques to forecast future outcomes based on historical data. In supply

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chain management, predictive analytics models are used to anticipate demand fluctuations,
identify potential disruptions, and optimize inventory levels, thereby improving operational
efficiency and reducing costs [13].
Furthermore, natural language processing (NLP) enables computers to understand, interpret,
and generate human language, allowing organizations to extract valuable insights from
unstructured data sources such as customer feedback, social media posts, and emails. NLP
technologies are used in supply chain management to analyze customer sentiment, extract
relevant information from documents, and enhance communication with stakeholders [13].
However, these AI technologies empower organizations to make data-driven decisions,
automate routine tasks, and optimize supply chain operations, ultimately driving efficiency,
reducing costs, and improving customer satisfaction [14]. [14], noted that as AI continues to
advance, its role in supply chain management is expected to expand, enabling organizations to
build more resilient and adaptive supply chains capable of navigating today's complex and
dynamic business environment.
In addition, Artificial Intelligence (AI) provides novel solutions to difficulties in supply chain
security and efficiency. AI-powered security systems can improve threat detection and risk
mitigation capabilities [11]. Furthermore, [15] said that modern AI algorithms can analyze
massive volumes of data from a variety of sources, including sensor data, transaction records,
and surveillance video, to discover patterns indicative of security dangers or abnormalities. It
lets organizations detect possible breaches or fraudulent activity in real time, allowing for
timely response to reduce risks and avoid interruptions.
Furthermore, artificial intelligence (AI) may improve supply chain efficiency by optimizing
processes, automating jobs, and facilitating data-driven decisions. Machine learning algorithms
can use previous data to estimate demand, allowing businesses to optimize inventory levels
and reduce stockouts or surplus inventory [13] better correctly. Furthermore, [16] said that AI-
powered predictive maintenance systems may monitor equipment performance in real time,
detecting possible failures and scheduling maintenance actions in advance to reduce downtime
and disturbances. Additionally, AI-powered automation solutions may optimize logistics
operations, from route optimization and load balancing to warehouse management and order
fulfilment, therefore increasing throughput and lowering operating costs.
However, by embracing AI, businesses may improve the security and efficiency of their supply
chains, allowing them to operate more successfully in today's dynamic and competitive
economic climate [11].
2.2 Security Enhancement with Artificial Intelligence in Resilient Chain
AI-powered surveillance and monitoring systems are critical for detecting and preventing
security breaches within supply chains. These systems use complex machine learning
algorithms to analyze massive volumes of data in real time, such as video feeds, sensor data,
and transaction records, in order to detect patterns indicating possible security threats or
abnormalities [17] Furthermore, by continually monitoring multiple components of the supply
chain, such as warehouse operations, transportation routes, and inventory movements, AI-
powered surveillance systems may detect unauthorized access, suspicious activity, or
deviations from typical behavior. These systems may detect abnormalities such as unusual

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deviations in shipping routes or unauthorized access to sensitive locations, allowing
organizations to investigate and respond to suspected security breaches quickly [18].
Furthermore, [19] said that AI algorithms may learn from previous data to increase their
accuracy over time, allowing them to adapt to changing threats and recognize new patterns that
indicate security problems. This proactive strategy enables organizations to anticipate possible
security risks and prevent breaches before they occur. Furthermore, AI-powered surveillance
and monitoring systems give organizations greater visibility and control over their supply
chains, allowing them to detect and prevent security breaches more effectively, protecting
critical assets, ensuring regulatory compliance, and protecting the supply chain ecosystem's
integrity [18].
Moreover, AI-driven risk assessment and mitigation techniques rely on artificial intelligence
tools to detect, analyze, and handle possible supply chain issues. These solutions use powerful
machine learning algorithms to handle large volumes of data and make data-driven decisions
to effectively manage risks [17]. However, one important part of AI-driven risk assessment is
predictive analytics, which entails analyzing past data to uncover patterns and trends that may
suggest possible hazards or disruptions. By analyzing historical data on supplier performance,
demand changes, market trends, and external variables like as geopolitical events or natural
catastrophes, AI algorithms may detect possible hazards and anticipate their likelihood and
influence on supply chain operations [19].
Furthermore, AI-powered risk assessment systems can continually monitor numerous risk
indicators in real time, allowing organizations to spot new dangers and manage them before
they become a problem. It may entail modifying inventory levels, diversifying sourcing tactics,
finding other transportation routes, or developing contingency plans to mitigate the effect of
probable interruptions [20]. Furthermore, AI-driven risk mitigation tactics may include the use
of scenario planning and simulation tools to model different risk scenarios and assess the
success of alternative mitigation solutions. Organizations may improve their understanding of
their vulnerabilities and establish comprehensive risk mitigation plans by modelling probable
hazards and their impact on supply chain operations [17]. Furthermore, AI-driven risk
assessment and mitigation techniques allow organizations to proactively identify and manage
possible supply chain risks, increasing resilience and assuring operational continuity in the face
of uncertainties and disruptions [4].
2.3 Integration of AI into Supply Chain Management
Implementing AI technology in supply chain operations brings possibilities and difficulties for
organizations to address. One of the most significant hurdles is integrating AI technologies into
current supply chain procedures and infrastructure. It entails overcoming technological
challenges such as data compatibility, system interoperability, and interaction with older
systems. Furthermore, organizations must provide enough data quality, accessibility, and
security to successfully enable AI algorithms [21]. Furthermore, the use of AI technology may
provide cultural and organizational issues. Resistance to change, lack of expertise, and
concerns about job displacement are common barriers that organizations may encounter when
implementing AI in supply chain operations. Addressing these challenges requires effective
change management strategies, investment in employee training and up-skilling, and fostering
a culture of innovation and continuous learning [20].

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Another factor to consider is the ethical and regulatory ramifications of artificial intelligence
in supply chain management. Organizations must traverse complicated ethical quandaries
including data protection, algorithmic bias, and the appropriate use of AI technology.
Furthermore, compliance with GDPR (General Data Protection Regulation) and industry
standards for data protection and transparency is required to assure legal and ethical AI
adoption [16]. Furthermore, scalability and sustainability are essential factors when
implementing AI in supply chain processes. As organizations expand their AI projects, they
must guarantee that their systems can manage greater data quantities and complexity while
retaining performance and dependability. Furthermore, organizations must address the
environmental effect of AI technology and work to reduce their carbon footprint by using
energy-efficient computing and sustainable data management techniques [22].
Overall, effective deployment of AI technologies in supply chain operations necessitates
meticulous planning, investment in technological infrastructure and personnel, and a dedication
to tackling technical, organizational, ethical, and environmental concerns. By addressing these
barriers, organizations may fully realize AI's promise to improve supply chain efficiency,
resilience, and competitiveness [17]. Furthermore, [23] said that effective integration and
deployment of AI technology in supply chain operations need meticulous planning, strategic
execution, and organizational alignment. One crucial method is to begin with a thorough
understanding of company objectives and supply chain constraints, then identify areas where
AI may provide value and efficiently solve pain points. To showcase AI's benefits to
stakeholders, [22] noted that organizations should choose pilot projects or use cases with
demonstrable ROI potential and low implementation complexity.
Furthermore, cultivating an environment of creativity and cooperation is critical for effective
AI integration. Organizations should form cross-functional teams, comprising supply chain
workers, data scientists, IT specialists, and business executives, to co-create AI solutions that
fulfil the demands of multiple stakeholders while also aligning with corporate objectives [22].
Furthermore, investing in human development and organizational competencies is important
for effective AI implementation. Organizations should give training and upskilling
opportunities to ensure that staff have the essential skills and knowledge to properly work with
AI technology. Furthermore, cultivating collaborations with technology suppliers, research
institutes, and industry peers can give access to knowledge, resources, and best practices to
speed AI adoption and implementation [21].
However, organizations should take an iterative and agile approach to AI integration, regularly
monitoring performance, gathering feedback, and adjusting plans based on real-world
deployment findings. Organizations may successfully integrate and implement AI technology
in their supply chain operations by using a strategic, collaborative, and flexible approach [20].
2.4 Theoretical Review
The study used the contingency theory to describe the use of AI-Enhanced supply chain
security to model the factor through resilient chains.
2.4.1 Contingency Theory
Contingency theory was proposed by organizational theorists Tom Burns and G.M. Stalker in
their book "The Management of Innovation" published in 1961. They introduced the idea that
organizational structures and management practices should be contingent upon various external

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and internal factors, such as the level of environmental uncertainty and the technology
employed by the organization. The theory proposes that there is no universal approach to
organizational management; instead, effective strategies depend on the specific context and
circumstances. This theory suggests that organizations must adapt their structures, processes,
and strategies to fit the unique demands of their environment, including factors such as
technology, market conditions, and organizational culture. Contingency theory acknowledges
that what works well in one situation may not be effective in another and emphasizes the
importance of flexibility and adaptation in organizational decision-making [12].
Furthermore, by considering environmental variables and aligning their strategy accordingly,
organizations may increase their performance and obtain better results. Contingency theory
emphasizes the importance of organizational responsiveness and agility to effectively traverse
the complexity and uncertainties of their contexts. According to the idea, there is no one-size-
fits-all approach to organizational management, and effective solutions vary depending on the
context and circumstances. In supply chain management, AI-enhanced security and efficiency
integration solutions may be adapted to each organization's specific demands, challenges, and
goals. Organizations that use flexible and adaptive techniques may construct resilient supply
chains that can withstand and recover from disturbances while remaining competitive [24].
Contingency theory is extremely significant to the Resilient Chain: AI-Enhanced Supply Chain
Security and Efficiency Integration. The idea emphasizes the significance of tailoring
organizational structures and methods to the specific circumstances and demands of the
environment. In the context of supply chain management, this means that organizations must
adjust their security and efficiency integration strategies to their individual issues, resources,
and goals. Similarly, while attempting to enhance efficiency, organizations must consider the
variables in their supply chain environment, such as market demand swings or regulatory
changes. AI technology may be used to optimize processes, automate regular jobs, and reduce
inefficiencies; however, the tactics used will differ based on each organization's unique
environment and requirements. By using contingency theory ideas, organizations may create
AI-enhanced supply chain management strategies that are flexible, adaptable, and suited to
their individual needs, thereby adding to supply chain resilience, security, and efficiency [25].
2.5 Empirical Studies
[20], seek to give insights and greater understanding into how using AI into SCM might
improve operations. The study found how the application of AI might enhance SCM as well as
its shortcomings by analyzing data collected using a qualitative technique and grounded theory.
The study's findings underline the need of corporations not only implementing, but also
integrating AI for successful use. Unlike previous studies, this study focuses on the dynamic
nature and fluctuations of SCM operations, as well as the problems that practitioners
experience. In addition, the study verifies prior results on AI's beneficial effects, such as
increased productivity, cost savings, and better decision-making. However, it emphasizes the
high costs and time commitments associated with using AI, which creates decision-making
challenges for businesses.
[6], study the direct and indirect impacts of AI, SCRes, and SCP in a supply chain characterized
by dynamism and unpredictability. The created framework was assessed using structural
equation modelling (SEM). Data for the survey were gathered from 279 enterprises of varying
sizes, working in a variety of industries and countries. Our findings indicate that, while AI has

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a direct influence on SCP in the near term, it is best to use its information processing
capabilities to create SCRes for long-term SCP.
[26], collected comments from 27 supply chain specialists to examine the link between various
aspects of an AI-enabled supply chain and how these factors contribute to its resilience.
Furthermore, to confirm the conclusions, an empirical study is undertaken, with answers from
231 supply chain specialists gathered. The findings show that the disruptive impact of an
incident is determined by the level of transparency maintained and offered to all supply chain
stakeholders. This is supported by empirical research, which shows that openness allows for
mass customization of the procurement approach to Last Mile Delivery, reducing the impact of
disruption. As a result, AI helps the supply chain be more resilient.
3. Methodology
The study is based on quantitative research design. This involves conducting field survey,
where participants are given research instrument containing relevant questions pertaining to
the subject matter under investigation. For the current study, a questionnaire was developed to
attend to the objectives of the study. The questionnaire contains two sections, with Section A
consisting of socio-demographic characteristics, while section B consists of the research
questions. The population of the study are managers and senior executives in large
organizations in the United States. More specifically, specific individuals who have substantial
knowledge about supply chain management and the growth of artificial intelligence and its
implementation. This is dictated by the context of the investigation, being enhancing supply
chain with artificial intelligence.
An accompanying statement outlining the study's goals and intended use of data, as well as a
rigorous assurance of data confidentiality, was sent with the questionnaire. The sample size for
the study was 281 professionals from randomly selected organization. The responses obtained
from these individuals is sufficient to provide insights into how AI can be employed for the
purpose of enhancing supply chain management in the United States.
surely! permits delve deeper into the method of the study:
3. 1. Survey layout:
The survey instrument applied in this paper was meticulously crafted to efficiently cope with
the research aims and objective. It is comprised of two segments:
- Segment A: Socio-demographic traits: This phase aims to acquire pertinent information
about the contributors, which includes their process roles, years of experience, instructional
history, and enterprise zone.
- Segment B: Research Questions: This section is committed to eliciting responses from the
participants regarding their perspectives, reports, and insights on the combination of artificial
intelligence (AI) within the supply chain management domain. The questions had been
designed to be clear, concise, and applicable to the research goals.
3. 2. Sampling techniques:
The sampling process combines several steps to ensure the representativeness and reliability of
the sample:

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- Population Definition: The sampling population are managers and senior executives
across various firms within the United States, whom have substantial know-how of supply
chain control and AI implementation.
- Random Selection: A random sampling method was adopted to select participants from
the sampling population. This method helps to mitigate biases and guarantees that each member
of the populace has an equal chance of being covered within the sample population.
- Determination of Sampling Size: The sampling size of 281 professionals was determined
based totally on statistical considerations, aiming to attain a stability among precision and
practicality.
- Inclusion criteria: Contributors are included in the study if they met the standards of
being managers or senior executives with relevant expertise in supply chain control and AI.
3.3. Data Analysis Methods:
- The data generated from the questionnaires were further analyzed using a statistical tool
known as SPSS.
- Data Collection: Upon receiving the finished questionnaires, the responses were compiled
into a dataset for evaluation.
- Data Cleaning: Priors to data analysis and evaluation, the dataset underwent thorough
cleaning so as to rectify any errors, inconsistencies, or missing values.
4. Results
This section discusses the outcome obtained from the analysis conducted based on the
responses from the participants obtained from questionnaire.
Table 1: Description of the Participants Socio-demographic characteristics
S/No
SOCIO-DEMOGRAPHIC
CHARACTERISTICS
FREQUENCY
PERCENTAGE
(%)
1
What is your current role in Supply Chain
industry?
Supply Chain Manager
99
35.2
Logistics Manager
92
32.7
Procurement Manager
73
26.0
Others
17
6.0
Total
281
100.0
2
How many years of experience do you have
in the supply chain industry?
Less than 1 year
35
12.5
1-5 years
57
20.3
6-10 years
108
38.4
More than 10 years
81
28.8
Total
281
100.0
3
Which sector best describes your
organization’s primary area of operation?
Manufacturing
75
26.7

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Retail
58
20.6
Healthcare
94
33.5
Transportation
46
16.4
Others
8
2.8
Total
281
100.0
4
What is the size of your organization in
terms of annual revenue?
< $1 million
80
28.5
$1 - $10 million
145
51.6
$10 - $100 million
47
16.7
> $100 million
9
3.2
Total
281
100.0
5
Which AI technologies is your organization
currently using or considering for use in the
supply chain
Machine Learning
113
40.2
Natural Language Processing
49
17.4
Predictive Analytics
83
29.5
Robotics
36
12.8
Total
281
100.0
6
How would you rate your organization’s
current level of AI adoption in the supply
chain?
Low
107
38.1
Moderate
76
27.0
High
22
7.8
Not applicable
76
27.0
Total
281
100.0
7
What are the main reasons for your
organization’s adoption or consideration of
AI in the supply chain?
Address Supply Chain Disruptions
87
31.0
Enhance Decision-Making
63
22.4
Improve Customer Services
45
16.0
Improve Efficiency
36
12.8
Reduce Costs
50
17.8
Total
281
100.0
Source: AI-Enhanced Supply Chain Security and Efficiency Integration, Chukwu (2024)
Table 1 presents the distribution of the participants based on specific information. In terms of
roles within the supply chain industry, most respondents (35.2%) identify as Supply Chain
Managers, followed closely by Logistics Managers (32.7%) and Procurement Managers
(26.0%). This distribution reflects the diverse range of roles and responsibilities within the
supply chain field, highlighting the importance of collaboration and coordination among
different functions. Regarding years of experience in the supply chain industry, a significant
proportion of respondents (38.4%) have 6-10 years of experience, indicating a considerable
level of expertise and knowledge among the professionals surveyed. Additionally, 28.8% of
respondents have more than 10 years of experience, further underscoring the depth of

57 | International Journal of Scientific and Management Research 7(3) 46-65
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experience within the supply chain industry. When asked about the primary sector of operation,
the highest percentage of respondents (33.5%) indicate healthcare as their organization's
primary area of operation, followed by manufacturing (26.7%), retail (20.6%), and
transportation (16.4%). This distribution reflects the diverse nature of the supply chain industry,
with professionals operating in a variety of sectors with unique challenges and opportunities.
In terms of organization size based on annual revenue, most respondents (51.6%) represent
organizations with annual revenues between $1 million and $10 million. This indicates that a
significant portion of the individuals works in small to medium-sized enterprises, which are
crucial players in the supply chain industry. Regarding the adoption of AI technologies,
machine learning emerges as the most widely used or considered technology, with 40.2% of
participants indicating its use or consideration. This reflects the increasing importance of
machine learning in enhancing supply chain operations through predictive analytics and
optimization. In terms of the current level of AI adoption, a substantial proportion of
respondents (38.1%) perceive their organization's AI adoption in the supply chain as low,
indicating a potential gap between the perceived potential of AI and its actual implementation.
However, it is encouraging to note that 27.0% of participants consider their AI adoption level
as high, suggesting a growing recognition of AI's value in the supply chain. When asked about
the main reasons for AI adoption or consideration in the supply chain, the most common
response was to address supply chain disruptions (31.0%). This aligns with the need for greater
resilience and agility in supply chain management, particularly in the face of global disruptions
such as the COVID-19 pandemic. Other key reasons cited include enhancing decision-making
(22.4%), improving customer services (16.0%), and reducing costs (17.8%), highlighting the
multifaceted benefits of AI adoption in the supply chain.
Table 2: AI Adoption in the Supply Chain Industry
S/No
Questions
Strongly
Agree –
SA
(%)
Agree
– A
(%)
Undecided
– U (%)
Disagree
– D (%)
Strongly
Disagree
– SD (%)
Mean
1
AI technology
has the potential
to improve
supply chain
efficiency.
48 (17.1)
132
(47)
23 (8.2)
60 (21.4)
18 (6.4)
3.47
2
AI can help in
forecasting
demand more
accurately,
leading to better
inventory
management.
78 (27.8)
158
(56.2)
22 (7.8)
18 (6.4)
5 (1.8)
4.02
3
AI can enhance
supply chain
visibility,
enabling real-
time tracking of
goods.
56 (19.9)
154
(54.8)
34 (12.1)
37 (13.2)
0 (0.0)
3.81

58 | International Journal of Scientific and Management Research 7(3) 46-65
Copyright © The Author, 2024 (www.ijsmr.in)
4
AI-powered
analytics can
optimize
transportation
routes and reduce
shipping costs.
58 (20.6)
154
(54.8)
29 (10.3)
30 (10.7)
10 (3.6)
3.78
5
AI can improve
risk management
in the supply
chain by
identifying
potential
disruptions early.
104 (37)
114
(40.6)
22 (7.8)
29 (10.3)
12 (4.3)
3.96
Source: AI-Enhanced Supply Chain Security and Efficiency Integration, Chukwu (2024)
Based on the mean rankings, the question "AI can help in forecasting demand more accurately,
leading to better inventory management" received the highest mean score of 4.02, indicating
that respondents strongly agree with this statement. This suggests that there is a strong belief
among respondents that AI can significantly improve demand forecasting and inventory
management practices in the supply chain. Similarly, the statement "AI can improve risk
management in the supply chain by identifying potential disruptions early" received a relatively
high mean score of 3.96, indicating strong agreement among respondents. This suggests that
there is a high level of confidence in the ability of AI to enhance risk management practices in
the supply chain by identifying and mitigating potential disruptions. On the other hand, the
statement "AI technology has the potential to improve supply chain efficiency" received a mean
score of 3.47, indicating agreement but with a slightly lower level of conviction compared to
the other statements. This suggests that while respondents see the potential for AI to improve
efficiency in the supply chain, there may be some skepticism or uncertainty regarding its actual
impact.
Overall, the rankings indicate a generally positive perception of the role of AI in addressing
key challenges in the supply chain industry, particularly in areas such as demand forecasting,
risk management, and inventory management. These findings suggest that there is a growing
recognition of the potential benefits of AI in enhancing supply chain operations and driving
efficiency and competitiveness in the industry.
Table 3: Challenges in AI Adoption in the Supply Chain Industry
S/No
Questions
Strongly
Agree –
SA
(%)
Agree
– A
(%)
Undecided
– U (%)
Disagree
– D (%)
Strongly
Disagree
– SD (%)
Mean
1
Lack of
understanding of
AI technology is
a barrier to its
adoption in the
supply chain.
92 (32.7)
114
(40.6)
25 (8.9)
28 (10)
22 (7.8)
3.80
2
The high cost of
implementing AI
104 (37)
109
(38.8)
25 (8.9)
32 (11.4)
11 (3.9)
3.94

59 | International Journal of Scientific and Management Research 7(3) 46-65
Copyright © The Author, 2024 (www.ijsmr.in)
solutions is a
major challenge
for many
organizations.
3
Concerns about
data security and
privacy hinder
the adoption of
AI in the supply
chain.
68 (24.2)
134
(47.7)
44 (15.7)
24 (8.5)
11 (3.9)
3.80
4
Resistance to
change among
employees is a
barrier to the
successful
implementation
of AI in the
supply chain.
26 (9.3)
66
(23.5)
26 (9.3)
101
(35.9)
62 (22.1)
2.62
5
Limited
availability of
skilled personnel
to manage AI
systems is a
challenge for
many
organizations.
70 (24.9)
117
(41.6)
28 (10)
38 (13.5)
28 (10)
3.58
Source: AI-Enhanced Supply Chain Security and Efficiency Integration, Chukwu (2024)
The highest mean score was recorded for the statement "The high cost of implementing AI
solutions is a major challenge for many organizations," with a mean score of 3.94. This
indicates that respondents strongly agree that cost is a significant barrier to AI adoption in the
supply chain. This finding suggests that the financial investment required for implementing AI
solutions is perceived as a major challenge for many organizations, potentially limiting their
ability to adopt AI technologies.
Similarly, the statement "Concerns about data security and privacy hinder the adoption of AI
in the supply chain" also received a relatively high mean score of 3.80, indicating strong
agreement among respondents. This suggests that data security and privacy concerns are
significant barriers to AI adoption in the supply chain, highlighting the importance of
addressing these issues to promote greater adoption of AI technologies.
The statement "Lack of understanding of AI technology is a barrier to its adoption in the supply
chain" also received a mean score of 3.80, indicating agreement among respondents. This
suggests that there is a perceived lack of understanding of AI technology among supply chain
professionals, which could be hindering its adoption.
In contrast, the statement "Resistance to change among employees is a barrier to the successful
implementation of AI in the supply chain" received a relatively low mean score of 2.62,
indicating less agreement among respondents. This suggests that while resistance to change is

60 | International Journal of Scientific and Management Research 7(3) 46-65
Copyright © The Author, 2024 (www.ijsmr.in)
recognized as a potential barrier, it may not be perceived as a major challenge compared to
other factors.
Overall, the mean rankings highlight the key challenges faced by organizations in adopting AI
technologies in the supply chain, including cost, data security and privacy concerns, and lack
of understanding of AI technology. Addressing these challenges will be crucial for promoting
greater adoption of AI in the supply chain industry.
Table 4: Future of AI in Supply Chain
S/No
Questions
Strongly
Agree –
SA
(%)
Agree
– A
(%)
Undecided
– U (%)
Disagree
– D (%)
Strongly
Disagree
– SD (%)
Mean
1
AI will play a
crucial role in
shaping the
future of the
supply chain
industry.
56 (19.9)
144
(51.2)
38 (13.5)
28 (10)
15 (5.3)
3.70
2
In the next 5
years, AI
adoption will
become more
widespread in the
supply chain
industry.
43 (15.3)
144
(51.2)
14 (5.0)
56 (19.9)
24 (8.5)
3.45
3
Organizations
that embrace AI
early will have a
competitive
advantage in the
supply chain
industry.
79 (28.1)
177
(63)
20 (7.1)
5 (1.8)
0 (0.0)
4.17
4
AI will lead to job
losses in the
supply chain
industry due to
automation.
69 (24.6)
156
(55.5)
32 (11.4)
24 (8.5)
0 (0.0)
3.96
5
AI will improve
overall
sustainability and
environmental
impact of the
supply chain
industry.
54 (19.2)
134
(47.7)
45 (16)
40 (14.2)
8 (2.8)
3.66
Source: AI-Enhanced Supply Chain Security and Efficiency Integration, Chukwu (2024)
The statement "Organizations that embrace AI early will have a competitive advantage in the
supply chain industry" received the highest mean score of 4.17, indicating strong agreement
among respondents. This suggests that there is a widespread belief that early adoption of AI

61 | International Journal of Scientific and Management Research 7(3) 46-65
Copyright © The Author, 2024 (www.ijsmr.in)
will confer a competitive advantage to organizations in the supply chain industry, highlighting
the strategic importance of AI adoption for staying ahead in the market.
Similarly, the statement "AI will lead to job losses in the supply chain industry due to
automation" received a relatively high mean score of 3.96, indicating agreement among
respondents. This suggests that there is a recognition of the potential impact of AI on job roles
in the supply chain, with some concerns about job losses due to automation.
The statement "AI will play a crucial role in shaping the future of the supply chain industry"
also received a relatively high mean score of 3.70, indicating agreement among respondents.
This suggests that there is a belief in the transformative potential of AI in reshaping the supply
chain industry and driving innovation and efficiency.
In contrast, the statement "In the next 5 years, AI adoption will become more widespread in the
supply chain industry" received a mean score of 3.45, indicating agreement but with a slightly
lower level of conviction compared to other statements. This suggests that while respondents
anticipate an increase in AI adoption in the supply chain industry, there may be some
uncertainty about the pace and extent of this adoption.
Overall, the mean rankings highlight the perceived impact and trends related to AI adoption in
the supply chain industry, including its potential to confer a competitive advantage, its impact
on job roles, and its role in shaping the future of the industry. These findings underscore the
importance of strategic planning and preparedness for AI adoption in the supply chain industry.
4.1 OLS Regression Analysis
OLS regression analysis is a statistical method used to estimate the relationship between one
or more independent variables and a dependent variable. It involves defining variables,
collecting data, specifying the regression model, estimating parameters using OLS, assessing
model fit, interpreting results, testing hypotheses, checking assumptions, and making
predictions. OLS regression is widely used for its simplicity and interpretability but requires
careful consideration of assumptions and context when interpreting results.
Table 5: Regression Analysis Showing the Effect of AI Integration on Supply Chain
Security
VARIABLES
COEFFICIENT
STD.
ERR.
T
P-
VALUE
Improve Efficiency
-0.073
0.046
-1.605
0.110
Accurate forecast
0.064
0.065
0.973
0.331
Real-time tracking
0.286
0.061
4.663
0.000
Cost optimization
0.394
0.054
7.281
0.000
Risk Management
0.102
0.052
1.977
0.049
Constant
3.664
.456
8.036
0.000
R
0.514
R-Squared
0.264
Adjusted R-Squared
0.251
F-Statistic (p-value)
19.749 (0.000)

62 | International Journal of Scientific and Management Research 7(3) 46-65
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Table 5 presents the regression result indicating how AI integration impacts supply chain
security in the United States. The independent variables considered are improved efficiency,
accurate forecast, real-time tracking, cost optimization, and risk management. The result in
Table 5 shows that only real-time tracking, cost optimization, and risk management are the
component of AI integration with positive and significant effect on supply chain security in the
United States.
Real-time tracking with a positive coefficient of 0.286 with an associated p-value of 0.000
implies that a percent point increase in real-time tracking will bring about increase in supply
chain security in the United States by 0.286 percent point. Also, cost-optimization with a
positive coefficient of 0.394 with an associated p-value of 0.000 implies that a percent point
increase in cost optimization through AI integration will lead to increase in supply chain
security by 0.394 percent points. Furthermore, risk management with a positive coefficient of
0.102 with an associate p-value of 0.049 implies that a percent point increase in risk
management through AI integration will bring about increase in supply chain security by 0.102.
The R-squared value of 0.264 indicates that 26.4 percent variation in supply chain security is
explained by all the independent variables (improved efficiency, accurate forecasting, real-time
tracking, cost optimization, and risk management). The F-statistic of 19.749 with an associated
p-value of 0.000 indicates that the model has overall significance. This implies that all the
variables are jointly significant in predicting supply chain security in the United States.
Table 5: Collinearity Statistics (Variance Inflator Factor)
Variables
Tolerance
VIF
Improved Efficiency
0.972
1.029
Accurate Forecasting
0.861
1.161
Real-time Tracking
0.931
1.074
Cost Optimization
0.960
1.041
Risk Management
0.855
1.169
Table 5 presents the Variance Inflator Factor tests for severe problem of multicollinearity. The
VIF values associated with all the variables are less than 5, which is the benchmark for
determining the multicollinearity. Also, the tolerance values are less than 1 but greater than 0,
which further indicates that the variables are not suffering from severe problems of
multicollinearity.
5. Conclusion and Recommendations
The outcome obtained from the analysis exercise shows that AI adoption among managers in
different industry is increasing, as majority of them acknowledge the potential of artificial
intelligence in restructuring the supply chain industry. It was deduced from the findings that
through adoption of AI, organizations, irrespective of the industry can increase the security of
supply chain in various ways, including tracking of good in real time, minimizing cost and
reducing redundancy, and effectively managing risk associated with their operations. In terms
of the challenges, it was widely accepted that there are several obstacles preventing adoption
of artificial intelligence by businesses. Among the identified challenges are limited knowledge

63 | International Journal of Scientific and Management Research 7(3) 46-65
Copyright © The Author, 2024 (www.ijsmr.in)
among supply chain personal regarding how AI can be integrated into their operations. From
another perspective of the professionals, the high cost of adopting AI technologies may also
prevent firms for opting for its integration. However, resistance to change among employees is
not considered as a barrier. The implication of this is that employees across these industries as
indicated by the participants are ready to embrace whatever changes is implemented by their
employers in relation to integrating AI for supply chain security. The regression analysis
revealed that real-time tracking, cost optimization, and risk management. Real-time tracking
in supply chains is vital for enhancing security by providing immediate visibility into the
location and status of goods. It enables proactive measures to be taken in response to potential
security threats, such as theft or tampering. Real-time data allows for quick identification of
anomalies, facilitating rapid decision-making and intervention to secure the supply chain and
minimize disruptions. Cost optimization in supply chains contributes to security by ensuring
resources are efficiently allocated to secure critical points. This includes investing in
technologies and personnel for security measures without unnecessary expenses. By
optimizing costs, supply chains can implement robust security measures, such as surveillance
systems and secure transportation, reducing vulnerabilities and enhancing overall security
posture. Risk management practices are integral to enhancing supply chain security by
identifying, assessing, and mitigating potential threats. By proactively addressing risks, such
as cyber threats, natural disasters, or supplier disruptions, supply chains can implement targeted
security measures to protect assets and ensure continuity.
There is need to implement real-time tracking technologies to monitor shipments and assets,
enabling quick response to security incidents and ensuring visibility throughout the supply
chain. Optimize costs by investing in cost-effective security solutions, such as automated
monitoring systems and secure packaging, to minimize vulnerabilities without compromising
security. Also, organizations must implement robust risk management practices, including
regular risk assessments and contingency planning, to identify and mitigate security risks,
ensuring the resilience of the supply chain against potential threats.
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