# Supply Chain Risk Management: Leveraging AI for Risk Identification, Mitigation, and Resilience Planning

Charles Onyeka Nwamekwe, Nkemakonam Chidiebube Igbokwe

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Charles Onyeka Nwamekwe, Nkemakonam Chidiebube Igbokwe. Supply Chain Risk Management: Leveraging AI for Risk Identification, Mitigation, and Resilience Planning. International Journal of Industrial Engineering, Technology & Operations Management, 2024, 10.62157/ijietom.v2i2.38. hal-04883513

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# Supply Chain Risk Management: Leveraging AI for Risk Identification, Mitigation, and Resilience Planning

Charles Onyeka Nwamekwe a,* and Nkemakonam Chidiebube Igbokwe a

**Article History**

Received 6 October 2024

Revised 2 December 2024

Accepted 20 December 2024

Available Online 31 December 2024

**Keywords:**

Supply chain risk management

Artificial intelligence

Risk identification

Resilience planning

Predictive analytics

Disruption management

**Abstract**

This study explores the critical role of Supply Chain Risk Management (SCRM) in today's interconnected and dynamic global economy, focusing on leveraging Artificial Intelligence (AI) for risk identification, mitigation, and resilience planning. As supply chains face increasing vulnerabilities due to geopolitical tensions, natural disasters, and technological disruptions, traditional risk-management approaches have proven insufficient in addressing these challenges. This paper comprehensively analyses how AI, through predictive analytics, machine learning, and autonomous systems, transforms SCRM by enabling real-time risk detection and response capabilities. The study also examines AI applications across various industries, including manufacturing, retail, and logistics, showcasing its potential in optimizing operational efficiency, enhancing supply chain visibility, and improving decision-making processes. Furthermore, the paper highlights the benefits and limitations of integrating AI with emerging technologies such as IoT and blockchain to enhance supply chain resilience. The findings contribute to understanding AI's growing impact on global supply chain management, providing insights into future trends and practical recommendations for managers seeking to strengthen their risk management strategies.

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BY

Copyright: © 2024 by the authors. Submitted for possible open-access publication under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

## 1. Introduction

The globalization of supply chains has increased their complexity and interdependence, exposing organizations to many risks, including geopolitical tensions, natural disasters, and health crises such as the COVID-19 pandemic. These disruptions can cascade, leading to delays and increased costs across the supply chain (Modgil et al., 2021). Organizations are increasingly leveraging Artificial intelligence (AI) technologies. AI can enhance risk identification, assessment, and mitigation processes, providing real-time insights crucial for maintaining supply chain resilience (Kanti et al., 2022; Modgil et al., 2021).

Research indicates that AI facilitates proactive risk management by analyzing vast datasets to predict potential disruptions and suggest mitigation strategies (Baryannis et al., 2018; Paul et al., 2022). Furthermore, integrating AI into supply chain management improves operational efficiency and fosters agility, enabling firms to adapt quickly to changing conditions (Modgil et al.,

2021). This strategic application of AI is essential for developing a robust supply chain risk management that can withstand the complexities of modern global supply chains (Paul et al., 2022).

SCRM has emerged as a pivotal aspect of contemporary business strategy, particularly considering the increasing frequency of unpredictable events such as trade wars, extreme weather, and cyber-attacks. Effective SCRM enables organizations to anticipate, identify, and mitigate potential risks, thereby minimizing disruptions and ensuring operational continuity (Reynolds, 2024). Integrating AI into SCRM processes is particularly beneficial, as AI technologies can enhance risk identification and mitigation through real-time data analysis and predictive analytics (Lokanan and Maddhesia, 2022; Modgil et al., 2021).

AI applications in SCRM facilitate improved decision-making by providing insights that help organizations navigate complex supply chain dynamics (Younis et al., 2021; Modgil et al., 2021). For instance, machine

<footnote>a Department of Industrial and Production Engineering, Faculty of Engineering, Nnamdi Azikiwe University, P.M.B. 5025 Awka, Anambra State, Nigeria.</footnote>

<footnote>* Correspondence: co.nwamekwe@unizik.edu.ng (C.O.N.)</footnote>
learning algorithms can analyze historical data to forecast potential disruptions, allowing companies to develop proactive strategies (Abaku, 2024). Furthermore, Al-driven tools can enhance collaboration across supply chain partners, fostering resilience and adaptability in the face of unexpected challenges (Kanti et al., 2022). As organizations increasingly rely on Al for SCRM, robust frameworks incorporating these technologies become essential to address the evolving landscape of supply chain risks.

Al fundamentally transforms supply chain management by enhancing decision-making processes, optimizing operations, and improving efficiency. In risk management, Al's ability to process vast amounts of data, recognize patterns, and provide predictive insights is particularly valuable (Modgil et al., 2021). Al-driven tools can analyze historical and real-time data to predict potential risks, allowing businesses to implement preventive measures before disruptions occur (Younis et al., 2021). This proactive approach is crucial in today's volatile environment, characterized by trade wars, extreme weather, and cyber threats.

Moreover, Al can automate decision-making processes, enabling organizations to respond swiftly to risk events, thereby fostering more resilient supply chains. For instance, machine learning algorithms can identify emerging risks and suggest optimal responses, enhancing operational agility (Nayal et al., 2021). Integrating Al streamlines risk identification and mitigation and supports strategic planning and resource allocation, ultimately improving supply chain resilience (Joel, 2024). As organizations increasingly adopt Al technologies, the potential for enhanced risk management capabilities becomes critical for future research and application in supply chain contexts (Abaku, 2024).

This study explores the transformative potential of Al in SCRM, focusing on risk identification, mitigation, and resilience planning. Al technologies, including machine learning, predictive analytics, and autonomous systems, are increasingly being utilized to enhance the effectiveness of risk management strategies. By processing large volumes of data and recognizing patterns, Al can provide predictive insights that enable companies to anticipate and address potential risks before they escalate into significant disruptions.

Integrating Al into SCRM allows real-time historical and current data analysis, facilitating proactive decision-making (Yamin, 2021). For instance, machine learning algorithms can identify emerging risks and suggest optimal responses, improving operational agility and resilience (Xu, 2023). However, while Al offers substantial benefits, such as enhanced efficiency and faster response times, it also presents limitations, including the need for high-quality data and potential biases in algorithmic decision-making (Xu, 2023).

Future trends in SCRM will likely increase reliance on Al-driven analytics to foster collaboration among supply chain partners and enhance overall resilience. As organizations navigate complex global supply chains, leveraging Al for risk management will ensure operational continuity and competitive advantage in an increasingly unpredictable environment (Ummi et al., 2018).

## 2. Overview of SCRM

### 2.1. Definition and Types of Supply Chain Risks

Supply chain risks encompass disruptions or uncertainties that can negatively impact the seamless operation of supply chains. These risks can be classified into two primary categories: external and internal. External risks arise from factors beyond a company's control, such as political instability, economic downturns, natural disasters, and pandemics. For instance, trade restrictions or severe weather can halt production and delay shipments, significantly affecting supply chain performance. The COVID-19 pandemic exemplifies how external shocks can disrupt global supply chains, leading to widespread operational challenges.

Conversely, internal risks originate within a company's supply chain and can stem from operational inefficiencies, supplier reliability issues, or technological disruptions. Examples include equipment failures, poor quality control, and supplier insolvency. These internal vulnerabilities can exacerbate the impact of external risks, creating a compounded effect on supply chain resilience. Therefore, Effective SCRM strategies must address external and internal risks to enhance overall supply chain resilience (Pournader et al., 2021).

Leveraging Al technologies can significantly improve risk identification and mitigation processes. Al tools can analyze vast amounts of data to detect patterns and predict potential disruptions, enabling companies to take preventive actions before risks materialize. Furthermore, Al can facilitate real-time monitoring and decision-making, allowing organizations to respond swiftly to emerging threats and maintain operational continuity. As supply chains become increasingly complex, integrating Al into SCRM practices will be essential for building resilient supply chains capable of withstanding various disruptions.

### 2.2. Current Approaches to Risk Management

Traditional methods of risk management in supply chains are predominantly reactive, relying heavily on risk assessments and contingency planning. Companies often utilize risk matrices to identify potential risks, rank them by likelihood and severity, and develop corresponding mitigation strategies. However, these conventional approaches frequently fail to provide real-time risk identification and are often inadequate for managing the complexities of dynamic global supply chains (Reynolds, 2024; Nikookar and Yanadori, 2021).

The reliance on static assessments can lead to delayed responses to emerging threats, which is
particularly problematic in today's fast-paced business environment characterized by rapid changes and unforeseen disruptions, such as those experienced during the COVID-19 pandemic (Yamin, 2021; Nikookar and Yanadori, 2021). For instance, while risk matrices can effectively categorize risks, they may not capture the interdependencies and cascading effects arising from a single disruption, such as a natural disaster or geopolitical event (Aggarwal and Srivastava, 2019).

In contrast, leveraging AI technologies can significantly enhance risk management capabilities. AI can facilitate real-time monitoring and analysis of supply chain data, allowing for proactive identification of potential risks before they escalate into significant disruptions (Hussain et al., 2022). By employing machine learning algorithms and predictive analytics, organizations can better understand risk patterns and develop more agile responses, ultimately fostering greater resilience in their supply chains (Pu, 2024).

As supply chains evolve and face increasing uncertainties, transitioning from traditional reactive methods to AI-driven proactive strategies will be essential for organizations aiming to maintain operational continuity and competitive advantage (Golan et al., 2021; Hajarath, 2024).

## 2.3. Challenges in Traditional SCRM

Some of the key challenges in traditional SCRM include:

a. Lack of Real-Time Data: The traditional supply chain risk management approaches often rely on periodic risk assessments rather than real-time monitoring, significantly limiting the ability to respond swiftly to emerging risks. This reactive stance can lead to vulnerabilities, particularly in complex and dynamic global supply chains where conditions change rapidly (Reynolds, 2024; Modgil et al., 2021). For instance, risk matrices are commonly employed to identify and rank potential risks based on their likelihood and severity, allowing companies to develop mitigation strategies accordingly. However, these methods often fail to provide the timely insights necessary for effective risk management, especially during unforeseen disruptions such as natural disasters or geopolitical tensions (Modgil et al., 2021).

The lack of real-time data hampers organizations' capacity to monitor supply chain conditions continuously, thereby delaying their response to potential threats (Nayal et al., 2021). In contrast, leveraging AI technologies can significantly enhance risk management capabilities by facilitating real-time data analysis and predictive analytics. AI can process vast amounts of data from various sources, enabling organizations to identify risks as they emerge and respond proactively (Golan et al., 2021; Hajarath, 2024). For example, AI-driven tools

can analyze patterns in historical data and current market conditions to forecast potential disruptions, allowing firms to implement preventive measures before risks materialize (Reynolds, 2024).

Moreover, the integration of AI into supply chain management not only improves risk identification but also enhances overall operational efficiency and resilience. By automating decision-making processes, AI enables faster responses to risk events, fostering a more agile supply chain (Dong, 2020). As organizations increasingly adopt AI technologies, the shift from traditional reactive methods to proactive, data-driven strategies will be crucial for building resilient supply chains capable of withstanding various disruptions.

b. Siloed Systems: Siloed systems within supply chains often lead to fragmented data, significantly inhibiting effective risk identification and management. Supply chains frequently involve multiple partners and systems, each operating independently and without seamless integration. This lack of collaboration can result in critical information being trapped within organizational silos, preventing timely access to data necessary for comprehensive risk assessments (Reynolds, 2024). Consequently, organizations may struggle to identify emerging risks and respond promptly, exacerbating vulnerabilities during disruptions (Nikookar and Yanadori, 2021).

The fragmentation of data not only limits visibility across the supply chain but hinders the ability to leverage advanced analytics for proactive risk management. Traditional risk management approaches rely on periodic assessments and are insufficient in dynamic environments where conditions can change rapidly (Nikookar and Yanadori, 2021). For example, during the COVID-19 pandemic, many organizations faced challenges adapting to sudden shifts in demand and supply chain disruptions due to their inability to access real-time data across their networks (Zhao, 2023).

To address these challenges, leveraging AI technologies can facilitate data integration across various systems and partners. AI can analyze large datasets from disparate sources, providing organizations real-time insights into potential risks and enabling more agile decision-making (Aggarwal and Srivastava, 2019; Golan et al., 2021). By breaking down silos and fostering collaboration among supply chain partners, organizations can enhance their resilience and improve their capacity to respond to disruptions effectively (Júnior et al., 2023). Ultimately, adopting AI-driven solutions will be crucial for organizations seeking to build more resilient supply chains capable of navigating the complexities of today's global marketplace (Hussain et al., 2022; Hajarath, 2024).

c. Reactive Measures: Traditional supply chain risk management strategies are often characterized by
reactive measures, focusing on addressing disruptions only after they have occurred rather than proactively preventing them. This approach can lead to significant vulnerabilities, particularly in today's complex and interconnected global supply chains, where the speed and unpredictability of disruptions can severely impact operational continuity (Reynolds, 2024; Yamin, 2021). For instance, many organizations rely on risk assessment frameworks that evaluate potential risks periodically, which can result in delayed responses to emerging threats (Nikookar and Yanadori, 2021).

The reliance on reactive measures can be detrimental, especially during crises such as the COVID-19 pandemic, where rapid changes in market conditions and supply chain dynamics necessitate immediate action (Zhao, 2023). Research has shown that organizations that adopt proactive risk management practices—such as continuous monitoring and real-time data analysis—are better positioned to identify and mitigate risks before they escalate into significant disruptions.

<figure><img src="image_7.png" /><figcaption>**Figure 1.** A Flowchart Showing the Two Broad Supply Chain Risks</figcaption></figure>

Leveraging AI technologies can transform traditional risk management approaches by enabling real-time data integration and analysis across the supply chain. AI can facilitate the identification of patterns and anomalies in data, allowing organizations to anticipate potential disruptions and implement preventive measures (Aggarwal and Srivastava, 2019; Pu, 2024). For example, AI-driven predictive analytics can analyze historical data and current market trends to forecast risks, enhancing decision-making processes and improving overall supply chain resilience (Júnior et al., 2023).

# 3. Role of Artificial Intelligence in Supply Chain Risk Management

## 3.1. AI and Risk Identification

### 3.1.1. Predictive Analytics and Big Data

AI-driven predictive analytics enhances supply chain risk management by leveraging historical and real-time data to forecast potential risks. By analyzing factors such as past disruptions, weather patterns, geopolitical events, and supplier performance, AI can identify trends that may lead to future risks, enabling organizations to take proactive measures (Reynolds, 2024; Lokanan and Maddhesia, 2022). For instance, AI can predict demand spikes during holiday seasons or foresee supplier delays based on historical performance data, allowing companies to adjust their operations accordingly (Zhao, 2023).

The integration of AI in supply chain management not only improves risk identification but also enhances overall operational efficiency. Organizations can analyze vast datasets by utilizing machine learning algorithms to uncover hidden patterns and correlations that traditional methods may overlook (Modgil et al., 2021). This capability is particularly valuable in dynamic environments where rapid changes occur, as it allows for timely interventions to mitigate potential disruptions' impact (Hajarath, 2024; Modgil et al., 2021).

Moreover, AI-driven predictive analytics can facilitate better decision-making by providing insights that inform strategic planning and resource allocation. For example, companies can optimize inventory levels based on predicted demand, reducing the risk of stockouts or excess inventory (Reynolds, 2024; Joel, 2024). This proactive approach enhances resilience, cost savings, and improved customer satisfaction (Dong, 2020).

### 3.1.2. Machine Learning Models for Pattern Recognition

Machine learning (ML) algorithms are pivotal in enhancing supply chain risk management by identifying patterns that signal potential risks, such as transport delays, raw material shortages, or supplier reliability issues. These algorithms are trained to detect anomalies in data, providing early warnings that allow businesses to take preventive measures before disruptions occur. For example, ML models can analyze historical data to identify trends and correlations, enabling organizations to predict demand spikes during peak seasons or foresee delays based on past supplier performance (Wang et al., 2018).

The application of ML in supply chain management improves risk identification and enhances operational efficiency. By processing large datasets from various sources, ML algorithms can uncover hidden patterns that traditional methods may overlook, thus facilitating a more proactive approach to risk management. This capability is particularly valuable in dynamic environments where rapid changes occur, as it allows
organizations to adapt quickly to emerging threats (Pellegrino et al., 2023).

Moreover, integrating ML into supply chain processes can lead to significant cost savings and improved service levels. For instance, by predicting potential disruptions, companies can optimize inventory levels and adjust their logistics strategies, reducing the risk of stockouts or excess inventory (Ada et al., 2021). This proactive approach enhances resilience and contributes to overall supply chain performance.

## 3.2. Al for Risk Mitigation

### 3.2.1. Automated Decision-Making Systems

Al-powered decision-making systems are revolutionizing supply chain risk management by automating risk mitigation strategies and enabling organizations to respond swiftly to disruptions. These systems leverage advanced algorithms to analyze vast data, providing actionable insights that facilitate timely interventions. For instance, Al can automatically reroute shipments in response to transport disruptions, adjust inventory levels anticipating demand fluctuations, or shift production to alternative suppliers when risks are identified (Modgil et al., 2021).

The ability of Al to process real-time data allows for a more dynamic approach to risk management. Traditional methods often rely on static assessments and historical data, which can be insufficient in rapidly changing conditions (Modgil et al., 2021). In contrast, Al systems continuously learn from new data, enabling them to adapt and refine their predictions and recommendations (Modgil et al., 2021). This capability is particularly valuable during crises like the COVID-19 pandemic, where supply chains faced unprecedented challenges and required agile responses (Modgil et al., 2021).

Moreover, Al-driven systems enhance collaboration among supply chain partners by providing a shared platform for risk assessment and mitigation. By integrating data from various stakeholders, these systems can offer a holistic view of the supply chain, identifying vulnerabilities that may not be apparent when analyzing data in isolation (Modgil et al., 2021). This collaborative approach improves risk visibility and fosters a culture of proactive risk management across the supply chain network (Modgil et al., 2021).

### 3.2.2. Al in Supply Chain Visibility

Enhanced visibility is one of the most significant contributions of Al to supply chain risk management. Al-powered Internet of Things (IoT) devices can monitor the status of shipments in real-time, providing critical insights into potential risks such as temperature deviations, delays, or damage during transport (Modgil et al., 2021; Younis et al., 2021). This real-time monitoring capability allows organizations to respond promptly to issues as they arise, thereby minimizing the impact of disruptions on the supply chain.

For instance, Al systems can continuously collect and analyze data from IoT sensors embedded in shipping containers, tracking variables such as location, temperature, and humidity (Lokanan and Maddhesia, 2022). If a temperature deviation is detected, the system can alert stakeholders immediately, allowing them to take corrective actions, such as rerouting the shipment or adjusting storage conditions to prevent spoilage. This level of visibility enhances operational efficiency and significantly reduces the risk of financial losses associated with damaged goods.

Additionally, when combined with blockchain technology, Al enhances transparency within the supply chain by ensuring that every transaction is recorded and traceable. Blockchain provides a secure and immutable ledger of all transactions, which can be accessed by all parties involved in the supply chain (Wang, 2022). This transparency fosters trust among partners and enables better collaboration, as stakeholders can verify the authenticity and condition of goods at any point in the supply chain (Nayal et al., 2021). The integration of Al and blockchain thus creates a robust framework for managing risks, as it allows for real-time tracking and verification of shipments, ultimately leading to improved resilience.

## 3.3. Al in Resilience Planning

### 3.3.1. Scenario Planning and Simulations

Al can significantly enhance resilience planning in supply chain management through scenario-based simulations that model various disruption scenarios, such as supplier failures, natural disasters, or demand spikes. These simulations enable businesses to assess the potential impact of different risks and develop strategies to minimize disruptions effectively (Modgil et al., 2021). By employing Al algorithms, organizations can create dynamic models that simulate the behaviour of supply chains under various conditions, allowing them to identify vulnerabilities and test the effectiveness of different mitigation strategies (Yamin, 2021).

For example, Al-driven simulations can analyze historical data on supplier performance and external factors like weather patterns to predict how a disruption might affect the supply chain. This predictive capability allows organizations to prepare for potential issues by adjusting inventory levels, identifying alternative suppliers, or rerouting shipments before disruptions occur (Nikookar and Yanadori, 2021). Such proactive measures are crucial for maintaining operational continuity and enhancing overall supply chain resilience (Zhao, 2023).

Moreover, integrating Al with technologies like the Internet of Things (IoT) and blockchain further strengthens resilience planning. IoT devices can provide real-time data on shipment conditions, while blockchain ensures transparency and traceability of transactions throughout the supply chain. This combination of
technologies allows organizations to respond more effectively to disruptions, as they can access accurate and timely information that informs decision-making (Golan et al., 2021).

### 3.3.2. Autonomous Systems and Supply Chain Adaptability

AI-driven autonomous systems transform supply chain risk management by enabling real-time adaptability to changing conditions. These systems leverage advanced algorithms and data analytics to facilitate dynamic decision-making processes that enhance operational resilience. For instance, autonomous robots in warehouses can adjust their operations to meet demand fluctuations, while AI-powered logistics systems can dynamically reroute shipments based on real-time data, thereby improving resilience against unforeseen disruptions (Ben-Faress et al., 2019; Wang et al., 2018).

<figure><img src="image_8.png" /><figcaption>Figure 2. The Role of SCRM</figcaption></figure>

Integrating AI in supply chain operations allows for continuous monitoring and analysis of various factors, including inventory levels, transportation routes, and supplier performance. This capability enables organizations to respond swiftly to emerging risks, such as transport delays or unexpected demand spikes. For example, suppose an AI system detects shipment delays due to traffic congestion. In that case, it can automatically reroute the delivery to an alternative route, minimizing the impact on the overall supply chain.

Moreover, the use of AI in autonomous systems extends beyond logistics and transportation. In manufacturing settings, AI can optimize production schedules by reallocating resources and adjusting workflows in response to real-time demand signals. This flexibility is crucial in maintaining operational efficiency and reducing the risk of overproduction or stockouts.

Additionally, AI can facilitate collaboration among supply chain partners by providing a shared platform for data exchange and decision-making, further enhancing the overall resilience of the supply chain (Saglam et al., 2020).

## 4. Case Studies: AI in Supply Chain Risk Management

### 4.1. AI in Retail Supply Chains

Retailers like Walmart and Amazon increasingly leverage AI to enhance demand forecasting, identify supplier risks, and improve inventory management. AI-powered systems utilize historical sales data and external factors—such as holidays, promotions, and market trends—to predict changes in consumer demand. This capability allows retailers to adjust their supply chains, ensuring they meet customers proactively needs while minimizing excess inventory and associated costs (Modgil et al., 2021).

For instance, AI algorithms can analyze vast datasets to identify patterns and correlations that inform demand forecasts. By considering factors such as seasonal trends and promotional events, these systems can generate more accurate predictions, enabling retailers to optimize their inventory levels and reduce the risk of stockouts or overstock situations (Younis et al., 2021). This proactive approach enhances operational efficiency and improves customer satisfaction by ensuring that products are available when consumers want them.

Moreover, AI can identify supplier risks by analyzing historical performance data and external risk factors. For example, AI systems can monitor supplier reliability and assess the potential impact of geopolitical events or natural disasters on supply chain operations (Lokanan and Maddhesia, 2022). By providing early warnings of potential disruptions, AI enables retailers to take corrective actions, such as sourcing from alternative suppliers or adjusting order quantities, thereby enhancing supply chain resilience.

### 4.2. AI in Manufacturing Supply Chains

SCRM increasingly integrates AI to enhance risk identification, mitigation, and resilience planning. In manufacturing, AI technologies, such as predictive maintenance models, foresee machinery failures, optimize inventory management and minimize downtime risks. Companies like Siemens and GE exemplify this trend by utilizing AI to monitor equipment performance and predict maintenance needs, ensuring seamless supply chain operations (Baryannis et al., 2018).

Moreover, the systematic approach of SCRM involves identifying, evaluating, and mitigating risks across the supply chain. It includes a comprehensive analysis of various processes, from material acquisition to product distribution (Ben-Faress et al., 2019). AI's role in this context is pivotal, as it enables real-time data analysis and decision-making, which are essential for enhancing
supply chain resilience against disruptions. The integration of machine learning techniques further supports the identification of potential risks, allowing companies to develop proactive strategies to address vulnerabilities.

# 5. Significant and Limitations of AI in Supply Chain Risk Management

## 5.1. Significant

### 5.1.1 Real-Time Risk Monitoring

Al's application in SCRM transforms how businesses monitor and mitigate risks. Al enables real-time monitoring of supply chain activities, allowing organizations to identify potential risks as they emerge and respond proactively. This capability significantly reduces the impact of disruptions, such as natural disasters or supply shortages, by facilitating immediate corrective actions (Modgil et al., 2021; Baryannis et al., 2018). For example, Al systems can analyze data from various sources to detect anomalies and predict potential disruptions, enhancing decision-making processes (Baryannis et al., 2018; Younis et al., 2021).

Moreover, Al-driven predictive analytics can enhance the resilience of supply chains by providing insights that inform risk mitigation strategies. By leveraging historical data and machine learning algorithms, companies can forecast potential risks and develop contingency plans (Modgil et al., 2021; Belhadi et al., 2021). This proactive approach minimizes operational disruptions and fosters a culture of continuous improvement within supply chains as organizations learn from past incidents to refine their risk management practices (Pellegrino et al., 2023). Ultimately, integrating Al into SCRM frameworks equips businesses with the tools necessary to navigate the complexities of modern supply chains effectively.

### 5.1.2. Improved Predictive Accuracy

Integrating Al into SCRM significantly enhances predictive capabilities, enabling organizations to analyze vast amounts of data accurately. This advancement surpasses traditional methods, leading to more informed decision-making and improved risk mitigation strategies (Younis et al., 2021; Modgil et al., 2021). Al models, particularly those utilizing machine learning algorithms, can identify patterns and anomalies in real-time, facilitating proactive responses to potential disruptions (Lokanan and Maddhesia, 2022; Modgil et al., 2021). For instance, organizations can leverage Al to forecast demand fluctuations and supply chain bottlenecks, optimize inventory levels, and reduce the likelihood of stockouts or overstock situations.

Moreover, the application of Al in SCRM fosters a culture of resilience by equipping businesses with the tools necessary to adapt swiftly to changing market conditions. By employing predictive analytics, companies can simulate various scenarios and assess the potential impact of different risks, allowing them to develop robust contingency plans (Modgil et al., 2021).

This proactive approach minimizes operational disruptions and enhances overall supply chain performance as firms become more agile and responsive to external challenges (Modgil et al., 2021; Golan et al., 2021). Consequently, adopting Al technologies in SCRM is not merely a trend but a critical evolution in how organizations manage risks and ensure continuity in their operations.

### 5.1.3. Enhanced Decision-Making

Integrating Al into SCRM revolutionizes decision-making processes, particularly in dynamic and fast-paced environments. Al automates these processes, enabling organizations to respond more swiftly and efficiently to disruptions, which is crucial given that delays in decision-making can lead to significant financial losses (Wang et al., 2018). By leveraging Al, companies can analyze large datasets in real-time, allowing for rapid identification of potential risks and implementing mitigation strategies before issues escalate (Baryannis et al., 2018).

Moreover, Al's predictive capabilities enhance the accuracy of risk assessments, enabling firms to anticipate disruptions related to supply chain uncertainties such as demand fluctuations, supplier failures, or geopolitical tensions (Liang and Liu, 2017). This proactive approach minimizes the impact of disruptions and fosters a culture of resilience within organizations as they become better equipped to adapt to changing circumstances (Saglam et al., 2020). Consequently, deploying Al in SCRM streamlines operations and significantly enhances overall supply chain performance by ensuring timely and informed decision-making.

## 5.2. Limitations

### 5.2.1. Data Availability and Quality

Data quality and integration significantly influence Al's effectiveness in SCRM. Al systems rely heavily on large datasets to function optimally; however, poor data quality or fragmented data from disparate systems can severely limit the performance of Al models (Baryannis et al. (2018). Many organizations face challenges consolidating data from various sources, leading to inefficiencies and inaccuracies in risk identification and mitigation processes (Belhadi et al., 2021). For instance, integrating Al into supply chains necessitates a seamless flow of information across all levels of the supply chain. When data is siloed or inconsistent, it hampers the Al's ability to generate reliable insights, ultimately affecting decision-making capabilities. Furthermore, organizations must invest in data governance and management practices to ensure that the data fed into Al systems is accurate, complete, and timely. It is particularly crucial in
dynamic environments where rapid decision-making is
essential for maintaining supply chain resilience.

5.2.2. Algorithm Biases and Limitations

The application of AI in SCRM holds significant
promise for enhancing risk identification, mitigation, and
resilience planning. However, the effectiveness of AI
models is contingent upon the quality of the data used
for training these algorithms. Incomplete or skewed
datasets can lead to biased or inaccurate predictions,
which may result in misguided decisions that exacerbate
supply chain risks (Goisauf and Abadía, 2022; Baryannis
et al., 2018). For instance, if an AI system is trained on
historical data that does not adequately represent
current market conditions or emerging risks, it may fail to
identify critical vulnerabilities within the supply chain
(Baryannis et al., 2018).

Moreover, the reliance on AI for decision-making can
amplify existing biases in the data, reflecting implicit
human biases in the training datasets (Goisauf and
Abadía, 2022). This phenomenon underscores the
importance of data governance and quality assurance in
AI applications within SCRM. Organizations must
prioritize integrating comprehensive, high-quality data
from various sources to enhance the reliability of Al-
driven insights (Kanti et al., 2022). Consequently,
addressing these data challenges is essential for
organizations aiming to leverage AI effectively for risk
management and resilience planning, ensuring that
decisions based on AI predictions are sound and
beneficial (Baryannis et al., 2018).

5.2.3. Cost and Implementation Barriers

Integrating AI into SCRM presents opportunities and
challenges, particularly for smaller enterprises. While AI
can enhance risk identification, mitigation, and resilience
planning by leveraging predictive analytics and machine
learning, the initial costs associated with implementation
can be prohibitive. Smaller businesses often struggle
with the significant investments required for technology
and skilled personnel, which can hinder their ability to
adopt AI solutions effectively (Paul et al., 2022).
Moreover, the complexity of existing supply chain
systems necessitates a tailored approach to AI
integration, which may involve additional costs and
expertise. Despite these challenges, the potential
benefits of AI, such as improved efficiency and reduced
operational risks, underscore the importance of strategic
planning and investment in AI technologies for long-term
resilience in supply chains (Modgil et al., 2021; Nayal et
al., 2021).

6. Future Trends and Developments

6.1. AI-Enhanced Supply Chain Resilience Post-Pandemic

The COVID-19 pandemic has significantly
accelerated the adoption of AI in SCRM as organizations
strive to build more resilient supply chains capable of
anticipating and responding to future disruptions. Al
technologies like machine learning and predictive
analytics enable companies to analyze vast amounts of
data, identify potential risks, and enhance decision-
making processes (Baryannis et al., 2018; Pournader et
al., 2021). This shift is particularly crucial as businesses
face increasing uncertainties and complexities in their
supply chains, which were starkly highlighted during the
pandemic (Kanti et al., 2022).

AI's role in SCRM extends beyond risk identification and facilitates proactive mitigation strategies and resilience planning. By leveraging AI, organizations can develop early warning systems that provide timely alerts about potential disruptions, allowing for swift responses. Furthermore, AI can optimize inventory management and logistics, ensuring supply chains remain agile and responsive to changing market conditions (Pournader et al., 2021). As companies continue to integrate AI into their operations, the potential for enhanced supply chain resilience becomes increasingly evident, positioning AI as a central component in the future of SCRM (Baryannis et al., 2018; Pournader et al., 2021).

6.2. Integration of AI with Other Technologies

Integrating AI with complementary technologies such as the Internet of Things (IoT) and blockchain is poised to enhance SCRM significantly. This convergence allows for real-time insights, improved transparency, and enhanced decision-making capabilities, which are critical in navigating the complexities of modern supply chains. AI algorithms can analyze data collected from IoT devices, providing predictive analytics that helps organizations anticipate disruptions and respond proactively (Singh, 2024).

Blockchain technology further complements this integration by ensuring data integrity and traceability throughout the supply chain. It enables secure and transparent transactions, allowing stakeholders to trace the origin of products and monitor their journey in real-time (Hasan et al., 2023). The synergy between AI, IoT, and blockchain streamlines operations and fosters a more resilient supply chain capable of adapting to unforeseen challenges (Nazir, 2024). As companies increasingly adopt these technologies, they can expect enhanced operational efficiency and a stronger competitive edge in the marketplace (Obaid, 2024).

6.3. AI Role in Ethical and Sustainable Supply Chains

AI has become pivotal in monitoring ethical and
sustainability risks across global supply chains. By
leveraging AI technologies, businesses can effectively track compliance with Environmental, Social, and Governance (ESG) standards, ensuring their supply chains are resilient and responsible. The integration of AI facilitates real-time data analysis, enabling organizations to identify potential risks related to labour practices, environmental impact, and governance issues (Baryannis et al., 2018).

Moreover, Al's ability to process large datasets enhances transparency throughout the supply chain, allowing companies to monitor supplier practices and ensure adherence to ethical standards. This capability is particularly crucial in today's globalized economy, where supply chains often span multiple countries with varying regulations and standards. By employing Al-driven analytics, businesses can proactively address compliance issues, mitigating risks associated with unethical practices and fostering a sustainable supply chain (Liang and Liu, 2017).

# 7. Conclusion

## 7.1. Summary of Key Findings

In summary, the findings from this research collectively demonstrate that Al is a transformative force in SCRM. Across these studies, it is evident that Al-driven tools significantly enhance three critical areas: risk identification, mitigation, and resilience planning. Through its ability to process vast amounts of data in real-time, Al improves visibility into potential disruptions, allowing companies to predict and address risks before they escalate into major operational challenges.

Al's predictive capabilities are particularly valuable in an increasingly volatile global environment, where supply chain risks are becoming more complex and frequent. Machine learning algorithms, predictive analytics, and data-driven simulations enable companies to anticipate disruptions more accurately than traditional methods. This proactive approach helps mitigate risks related to supplier failures, transport delays, and geopolitical uncertainties, ensuring that supply chains remain flexible and responsive to change.

Moreover, integrating Al into decision-making processes automates risk mitigation strategies, reducing human error and speeding up response times. This automation enhances the efficiency and effectiveness of supply chain operations, providing companies with the tools to swiftly reroute shipments, adjust inventory levels, or engage alternate suppliers when risks arise. Additionally, Al's role in resilience planning, including scenario-based simulations, empowers companies to develop robust strategies to adapt to real-time disruptions.

While the research highlights Al's vast potential, it also acknowledges the challenges associated with its implementation, such as data quality issues, algorithm biases, and high initial investment costs. Nevertheless, the consensus across the three papers is clear: Al offers

a path forward for businesses to navigate an increasingly unpredictable global supply chain landscape, positioning them for greater agility, resilience, and long-term success.

## 7.2. Implications for Supply Chain Managers

In light of the findings from the research, it is evident that supply chain managers must prioritize adopting Al technologies to navigate the growing complexity and volatility of global supply chains. Integrating Al into risk management processes offers a transformative opportunity to shift from reactive to proactive strategies in mitigating supply chain risks. Al's ability to process large volumes of real-time and historical data, predict disruptions and suggest timely interventions provides supply chain managers with the tools to foresee risks, such as geopolitical tensions, natural disasters, or operational inefficiencies, and take swift, data-driven actions.

Moreover, as Al improves supply chain visibility, facilitates autonomous decision-making, and enhances scenario-based resilience planning, it enables managers to optimize their operations and maintain continuity in the face of disruptions. Al-driven insights improve day-to-day operational efficiency and allow for long-term strategic planning, helping businesses stay ahead of emerging risks. This proactive capability is crucial in an era of fast-paced technological advancements and unpredictable global challenges.

However, the adoption of Al is not without challenges. Supply chain managers must be prepared to overcome barriers related to data quality, algorithm biases, and the significant upfront investment required to implement Al systems. Ensuring that the organization has access to clean, structured data and is equipped with the technical expertise to manage Al models will be essential to leverage these technologies successfully.

Ultimately, the implications for supply chain managers are clear: embracing Al is no longer a luxury but a necessity for staying competitive and resilient in today's dynamic business environment. By integrating Al into their risk management frameworks, supply chain managers can enhance their organizations' agility, responsiveness, and overall supply chain resilience, securing a competitive advantage in the market.

## 7.3. Recommendations for Future Research

Future research should address Al's limitations, particularly regarding data quality and algorithm biases. Additionally, there is a need for further exploration of Al's role in promoting ethical and sustainable supply chains. While Al has demonstrated significant potential in improving supply chain risk management, several limitations warrant further investigation. One key area for future research is improving the quality of data that Al systems rely on. Supply chains operate across various industries and regions, often leading to fragmented and
inconsistent data. Developing standardized data formats and protocols to ensure cleaner, more accessible, and more accurate data will enhance the effectiveness of AI models. Research should also explore strategies for integrating diverse data sources, including real-time and historical data, to improve AI's predictive accuracy.

Another pressing issue is algorithm bias. If trained on biased or incomplete data, AI systems may produce skewed predictions that could mislead supply chain decision-makers. Further exploration is needed to develop methodologies for identifying and mitigating these biases. It can involve improving training datasets, creating more transparent AI models, and developing robust algorithms for diverse and dynamic supply chain environments.

Moreover, there is a growing need to examine Al's role in promoting ethical and sustainable supply chains. As businesses increasingly prioritize environmental, social, and governance (ESG) goals, Al can be leveraged to track and manage compliance with ethical and sustainability standards across global supply chains. However, more research is required to understand how Al can balance optimizing for both efficiency and sustainability, ensuring its use promotes socially responsible practices without compromising economic goals. It includes investigating Al's potential to enhance transparency, reduce carbon footprints, and monitor labour practices throughout the supply chain. In summary, future research should address Al's data challenges and algorithmic limitations while exploring its capacity to contribute to global supply chains' ethical and sustainable transformation.

**Author Contributions:** Conceptualization, C.O.N. and N.C.I.; methodology, C.O.N.; software, C.O.N.; validation, N.C.I.; formal analysis, C.O.N. and N.C.I.; investigation, C.O.N. and N.C.I.; resources, C.O.N.; data curation, N.C.I.; writing—original draft preparation, C.O.N. and N.C.I.; writing—review and editing, C.O.N. and N.C.I.; visualization, C.O.N.; supervision, N.C.I.; project administration, N.C.I.; funding acquisition, C.O.N. All authors have read and agreed to the published version of the manuscript.

**Author Initials**

C.O.N., - Charles Onyeka Nwamekwe
N.C.I., - Nkemakonam Chidiebube Igbokwe

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Inform Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank Nnamdi Azikiwe University, Nigeria, for supporting this research and publication. We also thank the reviewers for their constructive comments and suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

Abaku, E. (2024). Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience.

International Journal of Science and Technology Research Archive, 6(1), 92-107.
https://doi.org/10.53771/ijstra.2024.6.1.0033

Ada, N., Kazançoğlu, Y., Sezer, M., Ede-Senturk, C., Ozer, I., & Ram, M. (2021). Analyzing barriers of circular food supply chains and proposing Industry 4.0 solutions. *Sustainability, 13*(12), 6812. https://doi.org/10.3390/su13126812

Aggarwal, S., & Srivastava, M. (2019). A grey-based DEMATEL model for building collaborative resilience in supply chains. *International Journal of Quality & Reliability Management, 36*(8), 1409-1437. https://doi.org/10.1108/ijqrm-03-2018-0059

Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2018). Supply chain risk management and artificial intelligence: State of the art and future research directions. *International Journal of Production Research, 57*(7), 2179-2202.
https://doi.org/10.1080/00207543.2018.1530476

Belhadi, A., Mani, V., Kamble, S., Khan, S., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. *Annals of Operations Research, 333*(2-3), 627-652.
https://doi.org/10.1007/s10479-021-03956-x

Ben-Faress, M., Elouadi, A., & Gretete, D. (2019). Global supply chain risk management. *American Journal of Engineering and Applied Sciences, 12*(2), 147-155.
https://doi.org/10.3844/ajeassp.2019.147.155

Dong, W. (2020). Research on supply chain resilience of agricultural products based on AHP-FCE model. *Learning & Education, 9*(3), 114. https://doi.org/10.18282/l-e.v9i3.1594

Goisauf, M., & Abadía, M. (2022). Ethics of AI in radiology: A review of ethical and societal implications. *Frontiers in Big Data, 5*.
https://doi.org/10.3389/fdata.2022.850383

Golan, M., Trump, B., Cegan, J., & Linkov, I. (2021). Supply chain resilience for vaccines: Review of modeling approaches in the context of the COVID-19 pandemic. *Industrial Management & Data Systems, 121*(7), 1723-1748.
https://doi.org/10.1108/imds-01-2021-0022

Hajarath, K. (2024). Enhancing supply chain resilience: Proactive strategies for disruptive events. *International Journal of Supply Chain Management, 9*(3), 1-11.
https://doi.org/10.47604/ijscm.2633

Hasan, I., Habib, M., & Mohamed, Z. (2023). Blockchain database and IoT: A technology-driven agri-food supply chain. *International Supply Chain Technology Journal, 9*(3), 40-45.
https://doi.org/10.20545/isctj.v09.i03.01

Hussain, G., Nazir, M., Rashid, M., & Sattar, M. (2022). From supply chain resilience to supply chain disruption orientation: The moderating role of supply chain complexity. *Journal of Enterprise Information Management, 36*(1), 70-90.
https://doi.org/10.1108/jeim-12-2020-0558

Joel, O. (2024). Leveraging artificial intelligence for enhanced supply chain optimization: A comprehensive review of current practices and future potentials. *International Journal of Management & Entrepreneurship Research, 6*(3), 707-721.
https://doi.org/10.51594/ijmer.v6i3.882

Júnior, L., Frederico, G., & Costa, M. (2023). Maturity and resilience in supply chains: A systematic review of the literature. *International Journal of Industrial Engineering and Operations Management, 10*(3), 101-110.
https://doi.org/10.53771/ijstra.2023.10.3.101
*Management*, 5(1), 1-25. https://doi.org/10.1108/ijieom-08-2022-0035

Kanti, P., Sadia, R., & Das, S. (2022). Artificial intelligence adoption in supply chain risk management: Scale development and validation. *Ho Chi Minh City Open University Journal of Science - Economics and Business Administration, 12*(2), 15-32. https://doi.org/10.46223/hcmcoujs.econ.en.12.2.2142.2022

Liang, Y., & Liu, Y. (2017). The risk control evaluation for supply chains based on knowledge management. *International Journal of Computational Science and Engineering, 14*(1), 74. https://doi.org/10.1504/ijcse.2017.081170

Lokanan, M., & Maddhesia, V. (2022). Supply chain fraud prediction with machine learning and artificial intelligence. https://doi.org/10.21203/rs.3.rs-1996324/v1

Modgil, S., Singh, R., & Hannibal, C. (2021). Artificial intelligence for supply chain resilience: Learning from COVID-19. *The International Journal of Logistics Management, 33*(4), 1246-1268. https://doi.org/10.1108/ijlm-02-2021-0094

Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B., Kazançoğlu, Y., & Narwane, V. (2021). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. *The International Journal of Logistics Management, 33*(3), 744-772. https://doi.org/10.1108/ijlm-12-2020-0493

Nazir, H. (2024). Revolutionizing retail: Examining the influence of blockchain-enabled IoT capabilities on sustainable firm performance. *Sustainability, 16*(9), 3534. https://doi.org/10.3390/su16093534

Nikookar, E., & Yanadori, Y. (2021). Preparing supply chains for the next disruption beyond COVID-19: Managerial antecedents of supply chain resilience. *International Journal of Operations & Production Management, 42*(1), 59-90. https://doi.org/10.1108/ijopm-04-2021-0272

Obaid, M. (2024). From field to fork: The role of AI and IoT in agriculture. *E3S Web of Conferences, 491*, 02006. https://doi.org/10.1051/e3sconf/202449102006

Paul, S., Riaz, S., & Das, S. (2022). Adoption of artificial intelligence in supply chain risk management. *Journal of Global Information Management, 30*(8), 1-18. https://doi.org/10.4018/jgim.307569

Pellegrino, R., Gaudenzi, B., & Zsidisin, G. (2023). Mitigating foreign exchange risk exposure with supply chain flexibility: A real option analysis. *Journal of Business Logistics, 45*(1). https://doi.org/10.1111/jbl.12338

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. *International Journal of Production Economics, 241*, 108250. https://doi.org/10.1016/j.ijpe.2021.108250

Pu, G. (2024). Management antecedents of supply chain resilience: An integrating perspective. *Journal of Contingencies and Crisis Management, 32*(1). https://doi.org/10.1111/1468-5973.12551

Reynolds, S. (2024). Exploring the implications of supply chain disruptions on organizational resilience. https://doi.org/10.20944/preprints202406.0563.v1

Saglam, Y., Sezen, B., & Çankaya, S. (2020). The inhibitors of risk information sharing in the supply chain: A multiple case study in Turkey. *Journal of Contingencies and Crisis Management, 28*(1), 19-29. https://doi.org/10.1111/1468-5973.12285

Singh, P. (2024). Measuring the broader value proposition of digital transformation in supply chains. *International Journal of Supply Chain Management, 13*(1), 16-24. https://doi.org/10.59160/ijscm.v13i1.6222

Ummi, N., Ferdinant, P., Irman, A., & Gunawan, A. (2018). Integration house of risk and analytical network process for supply chain risk mitigation of cassava opak chips industry. *MATEC Web of Conferences, 218*, 04022. https://doi.org/10.1051/mateconf/201821804022

Vishwakarma, L., Mishra, R., & Kumari, A. (2023). Application of artificial intelligence for resilient and sustainable healthcare system: Systematic literature review and future research directions. *International Journal of Production Research, 1-23*. https://doi.org/10.1080/00207543.2023.2188101

Wang, H. (2022). Linking AI supply chain strength to sustainable development and innovation: A country-level analysis. *Expert Systems*, **41**(5). https://doi.org/10.1111/exsy.12973

Xu, T. (2023). Achieving manufacturing supply chain resilience: The role of paradoxical leadership and big data analytics capability. *Journal of Manufacturing Technology Management*, 35(2), 205-225. https://doi.org/10.1108/jmtm-05-2023-0206

Yamin, M. (2021). Investigating the drivers of supply chain resilience in the wake of the COVID-19 pandemic: Empirical evidence from an emerging economy. *Sustainability, 13*(21), 11939. https://doi.org/10.3390/su132111939

Younis, H., Sundarakani, B., & Alsharairi, M. (2021). Applications of artificial intelligence and machine learning within supply chains: Systematic review and future research directions. *Journal of Modelling in Management, 17*(3), 916-940. https://doi.org/10.1108/jm2-12-2020-0322

Zhao, Y. (2023). The impact of resilient supply chain on enterprise supply chain management—based on the analysis of e-commerce enterprises under the COVID-19. 59-67. https://doi.org/10.2991/978-94-6463-142-5_7