# Enhancing Supply Chain Resilience Through Knowledge Management in the Metaverse

Abdellatif Ghorbal, Alexandre Philippot, Fouad Ben-Abdelaziz, Christophe

Jalaguier

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Abdellatif Ghorbal, Alexandre Philippot, Fouad Ben-Abdelaziz, Christophe Jalaguier. Enhancing Supply Chain Resilience Through Knowledge Management in the Metaverse. IFAC MIM 2025, NTNU Trondheim, Jun 2025, Trondheim (Norvège), Norway. hal-05153286

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# Enhancing Supply Chain Resilience Through Knowledge Management in the Metaverse

Abdellatif. GHORBAL*, Alexandre PHILIPPOT*, Fouad BEN-ABDELAZIZ** Christophe JALAGUIER*

*Université de Reims Champagne Ardenne, CReSTIC, Reims, France
(abdellatif.ghorbal/alexandre.philippot/christophe.jalaguier@univ-reims.fr)

**Neoma business school, Reims, France (fouad.ben.abdelaziz@neoma-bs.fr)**

**Abstract:** This paper examines how integrating knowledge management (KM) into the metaverse can enhance supply chain resilience. Using the Analytic Hierarchy Process (AHP), the study identifies and prioritizes key supply chain challenges, including limited visibility, agility and Resilience. Metaverse technologies such as digital twins, immersive environments, and blockchain are explored as solutions for fostering real-time monitoring, predictive analytics, and collaborative problem-solving. A proposed case study simulates disruptions in a multi-tier supply chain to evaluate the impact of these technologies on decision-making efficiency, visibility, and knowledge sharing. The findings underscore the metaverse's transformative potential for creating adaptive and resilient supply chains.

**Keywords:** Metaverse, Knowledge Management, Supply Chain Management, Resilience, AHP, Digital transformation.

## 1. INTRODUCTION

The rapid globalization of supply chain networks, coupled with heightened economic volatility, environmental challenges, and evolving customer expectations, has highlighted the urgent need for resilience in modern supply chains. Disruptions caused by geopolitical conflicts, natural disasters, pandemics, and technological vulnerabilities have exposed the limitations of traditional supply chain systems (kpmg2023). These disruptions, which ripple across global supply networks, underscore the importance of building robust frameworks that can adapt, recover, and thrive in the face of uncertainty.

In recent years, advancements in digital technologies such as artificial intelligence (AI), blockchain, and extended reality (XR) have redefined the landscape of supply chain management. Among these, the emergence of the metaverse—a virtual, interconnected digital ecosystem—presents unique opportunities to revolutionize supply chain operations. The metaverse enables immersive environments, real-time data integration, and seamless collaboration across geographically dispersed teams, offering innovative ways to address critical supply chain challenges. (Bhattacharya et al. 2023)

Knowledge management (KM) plays a pivotal role in leveraging the potential of the metaverse. As a process of creating, sharing, and applying knowledge, KM ensures that decision-makers across multi-tiered networks can access actionable insights when addressing disruptions (Alavi & Leidner, 2001). Within the metaverse, KM is significantly enhanced through tools such as digital twins for real-time monitoring and predictive analytics, blockchain for secure and transparent transactions, and immersive environments for virtual problem-solving. These technologies collectively strengthen supply chain resilience by fostering visibility, adaptability, and proactive decision-making. (Chen et al. 2023)

This study investigates the interplay between knowledge management and metaverse technologies in addressing the resilience challenges faced by supply chains. It identifies and prioritizes key challenges using the Analytic Hierarchy Process (AHP) and evaluates the potential of metaverse-enabled solutions to treat these challenges effectively. The study further proposes a case study framework to validate the hypotheses surrounding the impact of metaverse technologies on supply chain resilience. The research aims to explore how the metaverse can transform traditional supply chain systems into adaptive, future-ready networks.

The structure of this paper is as follows: Section 2 discusses the most pressing challenges in supply chain management, emphasizing the need for resilience. Section 3 details the application of AHP for prioritizing these challenges, while Section 4 explores metaverse technologies as enablers of resilience, highlighting their capabilities and applications. Section 5 presents the proposed case study framework, designed to evaluate the practical integration of these technologies. Finally, Section 6 concludes with a summary of contributions, implications for industry and academia, and directions for future research.

## 1. LITERATURE REVIEW

### 2.1 Knowledge Management in Supply Chains

PKM in supply chains refers to the systematic approach to capturing, sharing, and utilizing knowledge to optimize operations, decision-making, and collaboration among stakeholders. Traditional KM methods rely on centralized repositories, structured databases, and documented best practices, enabling organizations to standardize processes and enhance operational efficiency (Alavi and Leidner, 2001). These systems play a crucial role in improving information
accessibility, minimizing redundancies, and supporting supply chain agility through structured workflows and data-driven decision-making.

Despite their effectiveness, conventional KM frameworks face challenges in dynamic supply chain environments. The increasing complexity of global supply chains, rapid technological advancements, and unforeseen disruptions highlight limitations in traditional approaches. Key constraints include knowledge fragmentation, information silos, and difficulties in real-time collaboration across geographically dispersed networks (Shan et al., 2023). As supply chains become more interdependent and volatile, organizations require more adaptive and interconnected KM systems to support real-time decision-making and resilience-building efforts.

## 2.2 Knowledge Management in the metaverse

The emergence of the metaverse introduces a transformative shift in KM, transitioning from static, document-centric approaches to dynamic, immersive, and interactive knowledge ecosystems. Unlike traditional KM, which primarily focuses on storing and retrieving explicit knowledge, KM in the metaverse fosters a continuous exchange of tacit and explicit knowledge in real time. Through the integration of digital twins, decentralized knowledge-sharing platforms, and AI-driven analytics, KM in the metaverse enables a more fluid and responsive approach to managing supply chain knowledge (Chen et al., 2023).

This shift redefines the way supply chains manage knowledge by creating interconnected, persistent digital environments where decision-makers can engage with complex data in an interactive manner. The metaverse facilitates enhanced situational awareness, collaborative problem-solving, and proactive risk management by providing real-time insights into supply chain operations. Furthermore, blockchain-backed trust mechanisms ensure data security and transparency, fostering greater confidence among supply chain partners (Bhattacharya et al., 2023).

By transitioning from static repositories to immersive, real-time environments, KM in the metaverse enhances supply chain agility, resilience, and adaptability. This evolution marks a fundamental departure from traditional KM approaches, positioning the metaverse as a powerful enabler of knowledge-driven supply chain transformation.

## 2.3 Supply chain challenges

The challenges presented in this section were identified through an extensive analysis of multiple industry reports and publications on current supply chain trends. These reports highlight the most pressing issues organizations face in managing resilient and efficient supply chains in today's volatile and complex environment. By synthesizing insights from various sources, we outline the key challenges that impact supply chain performance and decision-making.

* G1: Operational Efficiency & Agility: Limited visibility, delays, and demand fluctuations necessitate more resilient systems (Surmacz and Szopinski, 2023).

* G2: Technology Integration & Risk Management: Increasing reliance on AI, IoT, and blockchain requires enhanced cybersecurity and risk mitigation strategies (Sourcemap, 2024).

* G3: Sustainability & Collaboration: The push for sustainable practices and stakeholder collaboration creates added complexity (Boudouaia et al., 2024).

* G4: External Pressures: Geopolitical tensions, economic instability, and inflation impact sourcing, logistics, and supply chain resilience (Rajah et al., 2018).

Having identified these key challenges, we applied Analytic Hierarchy Process (AHP) to rank these challenges based on a structured set of criteria. In the next section, we present the results of the AHP analysis.

## 3. PRIORITIZING CHALLENGES WITH AHP

The Analytic Hierarchy Process (AHP) was selected for this study due to its structured approach to decision-making, allowing for the prioritization of supply chain challenges based on multiple criteria. AHP provides a systematic framework to compare challenges in a hierarchical manner, ensuring consistency in evaluation.

Figure 1 illustrates the hierarchical decision-making structure used in this study. The hierarchy consists of four groups of challenges, as identified in the previous section. Each group contains a set of criteria used to assess its relevance to our research focus. The primary objective is to rank these challenges based on a predefined list of criteria, ensuring alignment with our study on how knowledge management (KM) in the metaverse can help address supply chain challenges.

<figure><img src="image_2.png" /><figcaption>Figure 1 Decision hierarchy of key challenges in SC</figcaption></figure>

### 3.1 Identification of key criteria

The selection of criteria resulted from discussions with my supervisors, drawing inspiration from previous studies while adapting them to maintain relevance to our research objectives. This tailored approach ensures that the evaluation process remains aligned with the intersection of KM, the metaverse, and supply chain resilience.

* Cr1: Alignment with KM Capabilities in the Metaverse: Assessing how well a challenge aligns
with metaverse-enabled KM capabilities such as real-time data sharing, collaboration, and decision-making.

* **Cr2** Impact on Supply Chain Performance: Evaluating the potential improvements in efficiency, resilience, agility, or sustainability when addressing the challenge.

* **Cr3** Feasibility of Solving the Challenge with Metaverse Technologies: Determining the practicality of leveraging metaverse tools like digital twins and immersive environments to address the challenge.

* **Cr4** Industry Relevance and Urgency: Prioritizing challenges that are highly relevant and urgent for industries adopting metaverse technologies.

* **Cr5** Ethical and Social Impact: Considering broader implications such as sustainability, environmental impact, and workforce conditions.

## 3.2 Pairwise comparison

The criteria were compared pairwise using a simplified Saaty scale to establish their relative importance. A consistency ratio (CR = 0.02) less than 0.10 validated the accuracy of judgments (Madaan and Mangla 2015), and the resulting weights reflected the priority of each criterion. Table 1 illustrates the pairwise comparison matrix and resulting weights.

<table><caption>Table 1. Pairwise comparison of Criteria</caption><thead><tr><th>Criteria</th><th>Cr1</th><th>Cr2</th><th>Cr3</th><th>Cr4</th><th>Cr5</th><th>Weight</th></tr></thead><tbody><tr><th>Cr1</th><td>1</td><td>0.2</td><td>0.2</td><td>0.33</td><td>3</td><td>0.08</td></tr><tr><th>Cr2</th><td>5</td><td>1</td><td>1</td><td>2</td><td>7</td><td>0.34</td></tr><tr><th>Cr3</th><td>5</td><td>1</td><td>1</td><td>2</td><td>7</td><td>0.34</td></tr><tr><th>Cr4</th><td>3</td><td>0.5</td><td>0.5</td><td>1</td><td>5</td><td>0.19</td></tr><tr><th>Cr5</th><td>0.33</td><td>0.14</td><td>0.14</td><td>0.2</td><td>1</td><td>0.05</td></tr></tbody></table>

Table 1 reveals that Cr2 “Impact on Supply Chain Performance” and Cr3 “Feasibility with Metaverse Technologies” are the most significant criteria, each assigned an equal weight of 0.344. The weights derived in this step will subsequently be applied to evaluate the challenge groups against these criteria. The next phase of the AHP process involves comparing the challenge groups based on each individual criterion.

## 3.3 Comparison of Challenge Groups

Tables 2 to 6 present the pairwise comparisons of the challenge groups with respect to each criterion Cr1 to 5. These comparisons provide the input values for Table 7, which is used to determine the final ranking.

<table><caption>Table 2. Pairwise comparison based on Cr1 Alignment with KM Capabilities</caption><thead><tr><th>Cr1</th><th>G1</th><th>G2</th><th>G3</th><th>G4</th></tr></thead><tbody><tr><th>G1</th><td>1</td><td>0.33</td><td>0.5</td><td>5</td></tr></tbody></table>

<table><caption>Table 3. Pairwise comparison based on Cr2 impact on SC performance</caption><tr><td>G2</td><td>3</td><td>1</td><td>5</td><td>7</td></tr><tr><td>G3</td><td>2</td><td>0.2</td><td>1</td><td>4</td></tr><tr><td>G4</td><td>0.2</td><td>0.14</td><td>0.25</td><td>1</td></tr></table>

<table><caption>Table 4. Pairwise comparison based on Cr3 on Feasibility with metaverse technologies</caption><tr><td>Cr2</td><td>G1</td><td>G2</td><td>G3</td><td>G4</td></tr><tr><td>G1</td><td>1</td><td>3</td><td>5</td><td>4</td></tr><tr><td>G2</td><td>0.33</td><td>1</td><td>3</td><td>2</td></tr><tr><td>G3</td><td>2</td><td>0.2</td><td>1</td><td>4</td></tr><tr><td>G4</td><td>0.25</td><td>0.5</td><td>2</td><td>1</td></tr></table>

<table><caption>Table 5. Pairwise comparison based on Cr4 industry relevance and urgency</caption><tr><td>Cr3</td><td>G1</td><td>G2</td><td>G3</td><td>G4</td></tr><tr><td>G1</td><td>1</td><td>0.33</td><td>0.5</td><td>3</td></tr><tr><td>G2</td><td>3</td><td>1</td><td>2</td><td>4</td></tr><tr><td>G3</td><td>2</td><td>0.5</td><td>1</td><td>5</td></tr><tr><td>G4</td><td>0.33</td><td>0.25</td><td>0.2</td><td>1</td></tr></table>

<table><caption>Table 6. Pairwise comparison based on Cr5 ethical and social impact</caption><tr><td>Cr4</td><td>G1</td><td>G2</td><td>G3</td><td>G4</td></tr><tr><td>G1</td><td>1</td><td>3</td><td>5</td><td>4</td></tr><tr><td>G2</td><td>0.33</td><td>1</td><td>3</td><td>2</td></tr><tr><td>G3</td><td>0.2</td><td>0.33</td><td>1</td><td>0.5</td></tr><tr><td>G4</td><td>0.25</td><td>0.5</td><td>2</td><td>1</td></tr></table>

<table><tr><td>Cr5</td><td>G1</td><td>G2</td><td>G3</td><td>G4</td></tr><tr><td>G1</td><td>1</td><td>1</td><td>0.2</td><td>0.33</td></tr><tr><td>G2</td><td>1</td><td>1</td><td>0.2</td><td>0.33</td></tr><tr><td>G3</td><td>5</td><td>5</td><td>1</td><td>3</td></tr><tr><td>G4</td><td>3</td><td>3</td><td>0.2</td><td>1</td></tr></table>

## 3.4 Final ranking of challenges :

<table><caption>Table 7. Final ranking pairwise comparison of criteria and challenge groups</caption><thead><tr><th>Ranking</th><th>Cr1</th><th>Cr2</th><th>Cr3</th><th>Cr4</th><th>Cr5</th><th>Weight</th></tr></thead><tbody><tr><th>G1</th><td>0.18</td><td>0.54</td><td>0.17</td><td>0.54</td><td>0.10</td><td>0.37</td></tr><tr><th>G2</th><td>0.56</td><td>0.23</td><td>0.45</td><td>0.23</td><td>0.10</td><td>0.33</td></tr><tr><th>G3</th><td>0.21</td><td>0.08</td><td>0.30</td><td>0.08</td><td>0.55</td><td>0.19</td></tr><tr><th>G4</th><td>0.05</td><td>0.14</td><td>0.08</td><td>0.14</td><td>0.25</td><td>0.11</td></tr></tbody></table>

The ranking highlights Operational Efficiency and Agility as the top priority, reflecting its critical role in addressing immediate supply chain disruptions and inefficiencies. This is followed by Technological Integration and Risk Management, emphasizing the need for digital tools to enhance visibility and mitigate risks. These findings guide the study's focus on leveraging metaverse-enabled KM strategies to tackle the most pressing challenges, ensuring both practical relevance and academic rigor.

Resilience and agility represent key concerns within the first group of challenges. Given that our decision-making model
identified this group as the most important, the subsequent
section will explore how metaverse technologies and
knowledge management can contribute to addressing supply
chain resilience.

4. TREATING SUPPLY CHAIN CHALLENGES THROUGH KM IN THE METAVERSE

Supply chain resilience, defined as the ability to anticipate,
prepare for, and adapt to unexpected disruptions, is
increasingly critical in today's interconnected and volatile
global markets. The integration of metaverse technologies and
advanced knowledge management systems within the
metaverse provides a transformative approach to enhance
supply chain resilience.

1. **Digital Twins for Real-Time Monitoring and Predictive Capabilities:** Digital twins serve as virtual replicas of physical supply chain entities, enabling real-time synchronization of operations. Powered by AI, these twins facilitate proactive risk assessment and mitigation strategies, providing organizations with the ability to simulate various disruption scenarios and optimize decision-making processes. Through continuous monitoring and predictive analytics, digital twins enhance visibility across the supply chain, minimizing downtime and ensuring operational continuity(Dolgui and Ivanov 2023).

2. **Immersive Environments for Collaborative Problem Solving:** Extended Reality (XR) technologies, including augmented reality (AR) and virtual reality (VR), create immersive environments that enable stakeholders to visualize, simulate, and address potential disruptions collaboratively. AI-driven XR tools offer interactive platforms for scenario planning, training, and real-time decision-making, fostering seamless communication across geographically dispersed teams. These environments allow for enhanced stakeholder engagement, ensuring preparedness for unexpected challenges(Hussain et al. 2023).

3. **Blockchain for Secure and Transparent Transactions:** The integration of blockchain within the metaverse enhances supply chain resilience by providing immutable records of transactions, improving traceability, and ensuring data security. AI-powered blockchain systems automate processes such as fraud detection and contract execution, reducing vulnerabilities and ensuring trust among supply chain partners (Bhattacharya et al. 2023).

4. **Knowledge Management and Real-Time Insights:** Knowledge management in the metaverse leverages AI to process and analyze vast datasets, transforming raw information into actionable insights. This capability supports dynamic decision-making and real-time adaptation to disruptions. For example, AI algorithms facilitate data cleaning, fusion, and visualization, enabling supply chain managers to identify patterns and predict outcomes effectively. Knowledge-sharing platforms within the metaverse also ensure that critical information is accessible to all stakeholders, enhancing coordination and response times(Chen et al. 2023).

**5. Training and Workforce Optimization:** The metaverse provides a risk-free, interactive environment for workforce training and skill development. Virtual simulations allow employees to engage in realistic supply chain scenarios, improving their ability to manage disruptions effectively. These training modules, powered by AI and digital twins, enhance knowledge retention and preparedness, contributing to overall supply chain resilience(Kye et al. 2021).

**6. Enhanced Networking and Communication:** AI-driven improvements in networking, particularly through 5G and emerging 6G technologies, enable real-time communication and coordination across supply chain networks. Low-latency and high-throughput connectivity support the seamless integration of metaverse applications, ensuring that critical decisions can be made swiftly during crises(Dolgui and Ivanov 2023).

The integration of metaverse technologies and advanced knowledge management frameworks offers a robust foundation for enhancing supply chain resilience by addressing critical challenges such as real-time monitoring, collaboration, and data security. These innovations pave the way for a more adaptive and prepared supply chain ecosystem, capable of withstanding and recovering from disruptions. To further explore and validate these contributions, the next section will outline a proposed case study framework. This framework aims to test the hypotheses surrounding the metaverse's impact on supply chain resilience through a structured protocol, focusing on key scenarios and methodologies for evaluating its effectiveness.

5. DISCUSSION

This study examined how knowledge management (KM) in the metaverse can help address key supply chain challenges, particularly in enhancing resilience. Through the application of the Analytic Hierarchy Process (AHP), we identified and prioritized major supply chain challenges, ensuring that our selection aligns with the objectives of KM in the metaverse. The study then explored how metaverse technologies, such as digital twins, blockchain, and immersive environments, can enhance knowledge-sharing, decision-making, and collaboration within supply chains.

Our findings highlight that metaverse-enabled KM presents a transformative approach to optimizing supply chains by fostering real-time visibility, predictive analytics, and adaptive decision-making. The structured prioritization of supply chain challenges ensures that the study remains focused on the most pressing issues that KM in the metaverse can effectively address. Additionally, the proposed integration of digital tools such as AI-driven analytics and decentralized KM platforms presents new opportunities for building more resilient and adaptive supply chains.

5.2 *Future research Directions*

While our study provides a conceptual foundation for
understanding the role of the metaverse in KM-driven supply
chain management, several areas require further investigation
to ensure its practical applicability and effectiveness. First, empirical validation remains a major gap, as most existing research is theoretical. Future studies should focus on real-world case studies, pilot programs, and industry collaborations to assess the impact of metaverse-enabled KM solutions. Additionally, interoperability and standardization pose challenges, as integrating metaverse platforms with existing supply chain management systems requires seamless data exchange and system compatibility across digital environments. Security and trust mechanisms also warrant further exploration, particularly in leveraging blockchain for secure knowledge management, data governance, and decentralized knowledge-sharing frameworks. Moreover, as metaverse adoption scales, evaluating the performance and computational efficiency of KM solutions in large-scale supply chain operations becomes essential, necessitating research into cloud-based architectures and AI-driven optimizations. Finally, despite technological advancements, human-centric approaches remain crucial, emphasizing the need for intuitive, user-friendly interfaces that enhance knowledge creation, transfer, and utilization within metaverse-driven KM systems. Addressing these research directions will help bridge the gap between conceptual advancements and practical implementation, ensuring that KM in the metaverse can effectively support resilient and adaptive supply chains.

<figure><img src="image_3.png" /><figcaption>Figure 2 Research Methodology</figcaption></figure>

## 5.3 Case Study proposal

To advance the understanding of metaverse-enabled KM in supply chains, a structured case study is proposed as part of future research efforts. The objective is to test how digital twins, immersive collaboration, and blockchain-backed knowledge-sharing impact supply chain resilience.

**Step 1:** Scenario Definition: A multi-tier supply chain model will be simulated to evaluate how metaverse-driven KM enhances decision-making in disruption scenarios.

**Step 2:** Data Integration and Knowledge Management: Digital twins and AI-driven insights will be used to analyze real-time supply chain disruptions, with knowledge-sharing mechanisms enabled through blockchain.

**Step 3:** Virtual Collaboration: Stakeholders will engage in immersive problem-solving using VR platforms to assess how real-time knowledge-sharing improves agility and responsiveness.

**Step 4:** Performance Metrics Evaluation: Key performance indicators (KPIs), such as decision-making speed, supply chain visibility, and risk mitigation effectiveness, will be measured.

**Step 5:** Comparative Analysis: The metaverse-based KM system will be compared with traditional supply chain management approaches to quantify the benefits of immersive, real-time collaboration.

# 6. CONCLUSIONS

This study investigated how metaverse technologies can optimize knowledge management (KM) to enhance supply chain resilience. By integrating digital twins, blockchain, and immersive environments (Dolgui and Ivanov 2023), metaverse driven KM addresses key challenges such as fragmented knowledge flows, limited real-time collaboration, and inefficient decision-making. These technologies enable real-time visibility, predictive analytics, and adaptive learning, contributing to more agile and resilient supply chains.

Using the Analytic Hierarchy Process (AHP), we systematically prioritized supply chain challenges, ensuring a structured approach to aligning KM solutions with industry needs. The proposed case study framework further demonstrates how metaverse technologies can enhance decision-making speed, collaboration, and risk mitigation.

Despite its contributions, this study is primarily conceptual and requires empirical validation through industry case studies and pilot implementations. Future research should explore interoperability challenges, security concerns, and human-centric approaches to ensure the seamless adoption of metaverse-enabled KM. Addressing these areas will further establish the metaverse as a transformative tool for building intelligent, resilient, and adaptive supply chains in an increasingly digitalized world.

# ACKNOWLEDGEMENTS

The work presented in this article was carried out as part of DeMETeRE, the winning project of the PIA4 DE-MOES (Higher Education Demonstrator) funded under the France 2030 plan - ANR-21-DMES-0011..
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