Building Resilient and Sustainable Supply Chains: A Distributed Ledger-Based Learning Feedback Loop
1
Department of Business Analytics & Information Systems, Quinnipiac University, Hamden, CT 06518, USA
2
Department of Computer Science, DePauw University, Greencastle, IN 46135, USA
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(20), 9023; https://doi.org/10.3390/su17209023 (registering DOI)
Submission received: 12 August 2025
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Revised: 8 October 2025
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Accepted: 10 October 2025
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Published: 12 October 2025
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Abstract
Global supply chains face increasing disruptions from cyber threats, geopolitical instability, extreme weather events, and a range of economic, social, and environmental sustainability challenges. As these disruptions intensify, enhancing Supply Chain Resilience (SCR) has become a strategic priority. This study investigates how Distributed Ledger Technology (DLT) can contribute to SCR by mitigating vulnerabilities and strengthening key capabilities within global supply chains. A qualitative research approach is employed, utilizing expert evaluations to examine DLT’s impact on supply chain vulnerabilities and capabilities. Five workshops were conducted with 25 industry professionals from logistics, IT, procurement, and risk management. Experts examined how DLT could address disruptions stemming from supplier instability, poor traceability, and regulatory and environmental pressures, while highlighting its potential to drive ethical sourcing and environmentally responsible practices. The structured discussions were guided by theoretical frameworks and expert evaluations were synthesized into two analytical matrices illustrating DLT’s influence on SCR. The findings reveal that the contribution of DLT to SCR and sustainability is highly context-dependent, with its effectiveness hinging on how it is embedded within governance structures and aligned with the interplay of complementary technologies. Building on these insights, the study presents the DLT-LFL (Distributed Ledger Technology–Learning Feedback Loop) framework, which integrates sensing, decision-making, adaptation, and predictive learning from distributed operational data, allowing supply chains to better anticipate disruptions, adjust processes dynamically, and continuously strengthen resilience and sustainable practices. The study also develops a practical checklist to assess how effective DLT applications and their integration with predictive and AI-driven analytics reduce vulnerabilities, strengthen capabilities, mitigate risks, and support adaptive decision-making.
1. Introduction
Global supply chains are under increasing strain due to geopolitical instability, cyber threats, extreme weather events, and sudden demand shocks [1]. According to a McKinsey report, supply chain disruptions lasting one month or longer occur every 3.7 years on average, with financial losses often amounting to 45% of annual EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) over a decade [2]. The 2021 semiconductor shortage, the 2023 Suez Canal disruptions, and cyberattacks targeting logistics firms all underscore the urgent need for greater supply chain resilience (SCR)—the ability to anticipate, withstand, and recover from disruptions while maintaining operational efficiency [3].
While platforms like TradeLens have been discontinued due to governance and profitability challenges, the need for decentralized, transparent, and tamper-proof supply chain solutions remains critical. Distributed Ledger Technology (DLT) continues to be recognized as a powerful tool to enhance traceability, security, and coordination across global supply chains [4,5]. By enabling real-time, immutable data sharing, DLT can help reduce fraud, improve operational efficiency, and build trust among supply chain participants. Despite the hurdles faced by early initiatives, companies like Walmart and Nestlé have successfully leveraged DLT to trace contaminated food sources within seconds rather than days, preventing costly recalls and mitigating public health risks. As supply chain vulnerabilities persist, the focus now shifts to scalable and resilient DLT applications that can overcome governance barriers while delivering long-term value [6].
Beyond logistics, cybersecurity threats have become a significant challenge for supply chains. According to a recent report, supply chain cyberattacks have surged by over 400%, with businesses generating over $50 million in revenue being two-and-a-half times more likely to experience such incidents [7]. These attacks often exploit vulnerabilities in software, hardware, and data-sharing networks, particularly those of third-party suppliers with weaker security controls. The financial impact is substantial; projections indicate that by 2025, software supply chain attacks could cost the global economy up to $60 billion. To mitigate these risks, DLT solutions offer a decentralized and cryptographic framework that can eliminate single points of failure and secure transaction records against tampering, thereby enhancing the overall security and resilience of supply chains [8,9].
This paper explores how and in which particular dimensions DLT can enhance SCR by providing greater visibility, security, and efficiency in global trade networks. It investigates the key benefits of DLT-driven solutions in mitigating disruptions caused by geopolitical risks, cyber threats, and demand fluctuations. Ultimately, we seek to answer the following question:
How do distributed ledger-based technologies impact supply chain resilience, and what factors determine their effectiveness in mitigating disruptions?
Therefore, this paper first provides a brief overview of the concepts of SCR and DLT before outlining the applied methods and presenting the key findings. It contributes to the resilience literature and capability theory by introducing the DLT-LFL framework, which integrates sensing, decision-making, adaptation, and predictive learning to advance both resilience and sustainability in supply chains. The paper also offers actionable guidance for practitioners on how to evaluate and implement DLT and complementary technologies to maximize their impact. It concludes with a discussion on future implications for both theory and practice.
2. Theoretical Background
2.1. Supply Chain Resilience
SCR has evolved from Supply Chain Risk Management (SCRM), which aims to safeguard business continuity by identifying and mitigating deviations from organizational goals [10]. SCRM follows a phased approach, addressing both internal and external supply chain risks to minimize overall vulnerability [11,12]. However, traditional risk management often falls short in assessing and responding to unpredictable disruptions [13]. SCR compensates for these shortcomings by emphasizing adaptability, defined as the supply chain’s ability to prepare for, respond to, and recover from unexpected events while maintaining operational continuity and control [14,15]. To assess the impact of DLT on SCR, the concept of “vulnerabilities” is used—disruptions affecting products, services, or resources, stemming from internal or external changes [16]. To counteract these vulnerabilities, supply chains must develop “capabilities”, which refer to the attributes necessary for resilience and long-term survival [17]. Capabilities enable organizations to anticipate, mitigate, and adapt to disruptions. They help prevent disruptions, reduce their impact, and facilitate post-disruption recovery. According to Kotzab et al. (2015), capabilities are defined as attributes necessary for performance or achievement, while in the supply chain context, they are described as features that enable an organization to anticipate and overcome disruptions [17]. Ultimately, a resilient supply chain is achieved by balancing capabilities and vulnerabilities, mitigating excessive risk and improving profitability for long-term performance (see Figure 1) [15].
2.2. Blockchain-Based Data Exchange in Supply Chains
DLT solutions promise to enable end-to-end traceability. Products can be tracked from their origin to the final consumer, with all relevant historical data securely stored and accessible in real time [18]. This increased visibility improves operational efficiency and helps businesses identify bottlenecks, mitigate disruptions, and optimize logistics [19]. Furthermore, the immutable nature of DLT records strengthens data integrity, reducing the risk of counterfeit goods and fraudulent activities. Every transaction recorded on the ledger is verifiable, making it more difficult for malicious actors to introduce falsified products into the supply chain. Beyond traceability, DLT facilitates secure and trusted data exchange across independent stakeholders, fostering collaboration between suppliers, manufacturers, and distributors [20]. Real-time customer feedback, for example, allows for more accurate demand forecasting, reducing excess inventory and enhancing responsiveness to market fluctuations [21]. Smart contracts further streamline operations by automating contractual obligations, eliminating intermediaries, and ensuring compliance with predefined conditions. By integrating DLT into supply chains, companies not only strengthen their traditional risk management approaches but also enhance supply chain resilience. DLT addresses previously hidden risks, such as counterfeit goods and cybersecurity threats, by providing verifiable data and reducing single points of failure [22].
3. Methodology
To assess the impact of Distributed Ledger Technology (DLT) on Supply Chain Resilience (SCR), a qualitative research approach was employed, focusing on expert evaluations of key factors that influence both “Vulnerabilities” and “Capabilities” within supply chains. These factors were derived from existing theoretical frameworks in the literature as described above and used to guide discussions during workshops with industry experts. The workshops aimed to explore how DLT could address specific vulnerabilities and enhance the capabilities of organizations in managing supply chain disruptions (see Table 1).
The workshops were conducted with a diverse group of professionals from different sectors of supply chain management, including logistics, IT, procurement, and risk management. Each workshop consisted of structured discussions around the theoretical concepts of “Vulnerabilities” and “Capabilities” in the context of SCR. The experts were tasked with evaluating the potential impact of DLT on these factors, drawing on their practical experience and expertise in managing real-world supply chain issues. For each vulnerability, the experts assessed how DLT could either mitigate or exacerbate the issue. Similarly, for each capability, the experts discussed how DLT could improve the flexibility, visibility, efficiency, and other characteristics required to enhance supply chain resilience.
To ensure the validity and relevance of the findings, the experts were selected for their involvement in DLT projects as well as expertise in supply chain operations, technology integration, or risk management across several industries. This diversity of perspectives was crucial for capturing how DLT interacts with different organizational contexts and supply chain challenges. Workshops, rather than individual interviews, were chosen because they allow for real-time interaction and debate among participants, fostering richer insights, cross-validation of opinions, and the emergence of shared or contrasting viewpoints [23]. This format also made it possible to test ideas collectively and identify consensus or divergence on how DLT could be applied to specific vulnerabilities and capabilities, which would have been less evident in isolated interviews [24].
In total, five workshops were conducted, with a collective group of 25 industry experts providing feedback and evaluations. Their insights were noted in writing, then aggregated, analyzed, and presented in two matrices that display the evaluation of DLT’s impact on SCR-related vulnerabilities and capabilities. To ensure academic rigor in developing the matrices, the study followed the guidelines proposed by Mostyn [25]. Two authors were directly involved in the coding process, first independently coding a subset of workshop transcripts to identify preliminary themes. We initially identified 14 themes related to vulnerabilities and 12 related to capabilities, which were iteratively refined through joint discussion and consolidation to produce the final coding scheme. Regular peer debriefings were used to resolve discrepancies and enhance consistency (for more details, see Appendix A) [26]. The discussions were mainly focused on specific supply chain issues, such as connectivity, supplier disruptions, external stressors, and cybersecurity risks, as well as on more general aspects like flexibility, collaboration, and market positioning. In cases of uncertainty, specific statements were shared with workshop participants to confirm whether particular categorizations or interpretations had been accurately understood. The results from these workshops provide a comprehensive overview of the factors contributing to SCR and how DLT may influence them, which is captured in the DLT-LFL framework and the accompanying checklist, both of which were shared with selected workshop participants for review and validation.
4. Findings
To explore how DLT can influence supply chain resilience in practice, the following section presents the findings from the expert workshops and introduces the DLT-LFL framework, which synthesizes these insights into a structured model for enhancing both resilience and sustainability in supply chains. The first matrix evaluates the impact of DLT on supply chain vulnerabilities, outlining the specific DLT applications that address each challenge and the integration hurdles that may limit their effectiveness. Insights from the first three workshops provide an understanding of how DLT interacts with these vulnerabilities in real-world scenarios (see Table 2). The second matrix focuses on supply chain capabilities, describing how DLT applications can enhance operational flexibility, visibility, efficiency, and other key resilience-related capabilities, along with the integration hurdles that may constrain their adoption (see Table 3).
How to read the tables: Each table lists specific DLT applications in the first column and their corresponding integration hurdles in the second column. The impact of each DLT application on the respective vulnerability (Table 2) or capability (Table 3) is indicated using a scoring system from 0 to 3 points, where 0 represents no impact and 3 indicates a strong positive influence. These scores were established through structured discussions with workshop participants, who assessed the potential effectiveness of each application based on their practical experience. The scoring reflects a consensus evaluation among the experts, capturing both the potential of the technology and the constraints arising from real-world implementation challenges.
4.1. Cybersecurity, Data Integrity, and Fraud Prevention
Cybersecurity and fraud are persistent challenges across industries, particularly in pharmaceuticals, logistics, and trade finance, where supply chain integrity is critical [22]. Experts from Workshop 1 emphasized that cryptographic encryption and immutable ledger records inherent to public blockchains could significantly enhance data security and fraud prevention. Similarly, Workshop 3 participants noted that DLT’s verification mechanisms and product provenance tracking offer substantial advantages in fraud prevention, particularly for high-value goods and financial transactions. However, scalability challenges and integration issues with legacy systems were identified as major obstacles by blockchain developers in Workshop 2. Additionally, compliance officers from Workshop 3 highlighted regulatory gaps, especially in jurisdictions with stringent data protection laws such as GDPR, which complicates broader implementation. Another challenge emphasized in all three workshops was the need for cross-organizational trust frameworks that not only rely on blockchain (particularly private blockchains) but are seamlessly integrated with public blockchains and other technologies.
4.2. Supply Chain Interdependence and Disruptions
Supply chains rely on highly interconnected global networks, making them prone to disruptions caused by supplier failures, demand fluctuations, and external shocks [10]. Supply chain managers and procurement specialists from Workshop 1 noted that DLT facilitates decentralized data sharing, reducing reliance on intermediaries and improving supply chain visibility. Additionally, Workshop 2′s logistics consultants emphasized that smart contracts could automate compliance checks and streamline supplier verification, enhancing reliability. Despite these benefits, complex coordination and insufficient governance approaches among multiple stakeholders remain significant challenges. Participants from Workshop 2 explained that without standardized protocols, different DLT implementations may not be interoperable, leading to fragmentation rather than improved coordination. Similarly, Workshop 3′s participants noted that high implementation costs and a need for further education challenge, especially small- and medium-sized enterprises (SMEs), in integrating DLT solutions, limiting the overall effectiveness of these technologies in addressing widespread disruptions.
4.3. Regulatory and External Stressors
Legal and regulatory uncertainties pose considerable risks for businesses considering DLT adoption [6]. While DLT offers audit trails and compliance monitoring, the absence of clear global legal frameworks hinders implementation. This concern was echoed by participants in Workshops 1 and 2, as they noted evolving regulations around data privacy and smart contract enforceability introduce risk, particularly for multinational supply chains. Additionally, while DLT can optimize logistics and inventory tracking, it does not address physical constraints such as shortages in raw materials or disruptions in production capacity. Supply chain managers from Workshop 1 pointed out that while DLT improves coordination long-term, the actual resolution of supply chain bottlenecks still requires investments in infrastructure and supplier diversification.
4.4. Operational Resilience and Information Transparency
The decentralized nature of DLT enhances operational resilience by reducing single points of failure, ensuring that supply chain networks remain functional even in the face of localized IT disruptions [10,27]. Participants from Workshop 3 highlighted that decentralization improves disaster recovery capabilities, as data is distributed across multiple nodes rather than stored in a single vulnerable location. This is particularly relevant in industries like automotive manufacturing, where just-in-time production models rely on real-time data coordination across multiple suppliers. For example, automotive companies leveraging blockchain-based supplier networks can maintain operational continuity even if one node in the network experiences downtime, preventing cascading failures across the supply chain.
4.5. Enhancing Operational Flexibility and Resource Efficiency
Workshop participants in Workshops 4 and 5 noted that DLT-driven automation—particularly through smart contracts—enables dynamic adjustments in workflows, reducing delays in procurement and order fulfillment, especially when combined with agentic AI in the future. In industries with complex supplier networks, such as automotive manufacturing, smart contracts can trigger automatic order modifications based on real-time data, ensuring efficient adjustments to fluctuating demand. However, many firms face resistance to change, as existing centralized Enterprise Resource Planning (ERP) systems are deeply embedded in supply chain operations and sometimes lack openness for DLT backends. Additionally, while process automation and streamlined documentation reduce administrative overhead, high initial deployment costs and scalability issues have hindered broader adoption, particularly in industries with thin margins, like retail logistics.
4.6. Improving Supply Chain Visibility and Proactive Risk Management
One of the most widely recognized benefits of DLT adoption is its ability to enhance supply chain visibility through real-time data sharing and decentralized tracking [6]. Workshop discussions highlighted how pharmaceutical companies leverage blockchain-based serialization systems to monitor drug distribution, reducing counterfeit risks and ensuring regulatory compliance. However, challenges remain in achieving data standardization across global supply chain partners, as interoperability between different DLT platforms is still evolving. Similarly, predictive analytics integrated with DLT-backed data and federated learning could enable proactive risk management, helping companies anticipate supply chain disruptions before they escalate. Federated learning, which allows multiple stakeholders to train machine learning models on decentralized data without sharing sensitive information, was highlighted in workshop discussions as a promising approach to enhancing predictive capabilities. In the logistics sector, this combination could enable firms to detect potential bottlenecks caused by geopolitical risks or supplier failures without exposing proprietary operational data.
4.7. Strengthening Supply Chain Resilience and Collaborative Cooperation
The decentralized and immutable nature of DLT fosters stronger data exchange mechanisms, which in turn enhance supply chain resilience [27]. Participants highlighted examples in the food industry, where companies use blockchain to create transparent audit trails, allowing for rapid identification of contamination sources and minimizing product recalls. However, achieving true resilience through DLT requires overcoming regulatory inconsistencies across jurisdictions, making implementation complex for multi-national organizations. Workshop participants from the pharmaceutical and healthcare industries noted that reducing disputes over contract terms and shipment discrepancies is the biggest motivator for blockchain projects. Nonetheless, governance difficulties remain a critical challenge. By aligning DLT applications with standardized operational protocols and shared data governance rules, organizations can systematically reduce cross-partner inefficiencies, improve coordination, and accelerate collective response to disruptions, thereby strengthening both resilience and collaborative capabilities across the supply chain.
4.8. Customer Loyalty, Information Security, and Organizational Adaptability
From a consumer perspective, DLT enhances customer loyalty and satisfaction through provenance tracking, enabling customers and end consumers to verify product authenticity and ethical sourcing [27]. This is particularly valuable in industries like luxury goods and organic food, where customers demand transparent supply chain records. However, participants noted that low consumer awareness of DLT’s benefits limits its impact on purchasing decisions, and integration challenges with existing customer service platforms slow adoption.
Information security remains one of DLT’s strongest capabilities, as tamper-proof records and cryptographic security mechanisms protect against fraud and cyber threats. In industries with sensitive intellectual property, such as automotive and pharmaceuticals, workshop participants emphasized the value of strict access controls in preventing unauthorized alterations.
Building on Pettit et al.’s foundational resilience framework and based on the findings of the research process, this study proposes the DLT-LFL (Distributed Ledger Technology—Learning Feedback Loop) Framework for Supply Chain Resilience (Figure 2). Synthesizing theoretical and empirical insights from DLT projects, the DLT-LFL introduces a dynamic, four-step loop: Sense, Decide, Adapt, and Learn, operationalizing DLT’s potential beyond just isolated use cases. Rather than treating the technology as a static tool, the framework suggests its use in an iterative, expanding, and self-reinforcing process. Data gathered through interconnected IoT devices inform decentralized decisions; these decisions trigger coordinated adaptations across supply chain partners; and the outcomes of these adaptations are continuously fed back into predictive learning mechanisms that refine subsequent sensing and decision-making. In this way, the framework links vulnerabilities and capabilities over time and allows both researchers and practitioners to re-design DLT solutions and evaluate their contributions to supply chain resilience with each iteration. For small and medium-sized enterprises in particular, the framework offers a structured pathway to test lightweight, lower-cost DLT applications for their specific needs, compare outcomes against industry benchmarks or best practices, and build resilience incrementally while avoiding large upfront investments.
In the Sense phase, DLT-based traceability systems and real-time monitoring IoT sensors and tools support joint early threat detection and proactive responses, thereby helping mitigate excessive risks arising from vulnerabilities. Workshop 4 on the Sense phase: “Real-time data feeds would let us flag anomalies instantly, instead of waiting for a quarterly audit to reveal the problem.” In the Decide phase, autonomous smart contracting and decentralized decision-support systems enable data-driven actions and optimized resource allocations, preventing eroded profitability by strengthening operational capabilities. Workshop 5 on the Decide phase: “In automotive, we repeat thousands of the same component orders each month—automating those with smart contracts saves days of paperwork and removes human error.” Adaptation emerges as the central resilience mechanism, where both vulnerabilities and capabilities are continuously adjusted in response to disruption signals—guided by the evolving state of the system and reinforced through feedback from resilience outcomes. Workshop 1 on the Adapt phase: “Even if it’s not real-time adaptation, but when sudden raw material shortages occur, and the DLT-platform shifts production schedules and sourcing priorities in hours instead of weeks, this keeps our output on track and is a drastic change of the current state”. Finally, the Learn phase is driven by predictive learning through decentralized approaches—such as federated learning—which reduces exposure to vulnerabilities, while adaptive learning from operational feedback loops accelerates capability development. Workshop 5 on the Learn phase: “Through federated learning, our demand forecasting model was trained on shipment […] and sales data from [multiple] suppliers without any of them sharing raw files—the algorithm travelled to their data, learned patterns, and sent back only the trained parameters. That way, we improved predictions for seasonal peaks without exposing sensitive commercial information.”
This learning–feedback loop operationalizes DLT’s potential by embedding intelligence, automation, and cross-organizational coordination into the ongoing processes of risk detection, response optimization, and performance adaptation across modern supply chains. The DLT-LFL framework serves as a dynamic analytical lens and tool for evaluating and guiding the design of distributed ledger technologies that enhance supply chain resilience by continuously linking sensing, decision-making, adaptation, and learning processes to evolving vulnerabilities and capabilities. In addition, the framework comes with a practitioner-oriented checklist (Table 4) that can be applied to assess current capabilities, identify resilience gaps, and prioritize interventions. The checklist systematically maps each phase of the DLT-LFL loop to concrete outcomes—risk mitigation, prevention of eroded profitability, reduction in vulnerabilities, and acceleration of capabilities—allowing organizations to evaluate which applications are most effective in their specific context. For small and medium-sized enterprises, the checklist also provides a low-barrier entry point to identify high-impact, resource-efficient DLT interventions, helping them prioritize incremental steps rather than costly full-scale implementations. By following this structured approach, practitioners can not only monitor the impact of solutions over time but also iteratively refine their technology strategies, ensuring they are aligned with operational needs, and continuously improving supply chain resilience and sustainability.
- Applying the Checklist
- Rate each criterion: 0 = absent, 1 = partial, 2 = fully implemented.
- Use the analysis questions to guide evaluation and provide evidence for the ratings.
- Identify gaps where risks, vulnerabilities, or capabilities are insufficiently addressed.
- Map described DLT applications to the identified gaps to prioritize interventions.
- Repeat assessments periodically to monitor improvements and maturity progression.
- Document key decisions and rationales for transparency and future review.
- Track which phases (Sense, Decide, Adapt, Learn) show the greatest maturity gaps.
- Adjust criteria or analysis questions as new DLT applications or industry practices emerge.
5. Conclusions
This study contributes to the theoretical understanding of Distributed Ledger Technology (DLT) as a tool for enhancing Supply Chain Resilience (SCR) by addressing both vulnerabilities and capabilities in global trade networks. The analysis of expert evaluations from multiple industry workshops provides empirical evidence for how DLT impacts key resilience factors, such as cybersecurity, data integrity, and operational flexibility. Additionally, this research advances existing theoretical frameworks in the SCR field by moving beyond static lists of vulnerabilities and capabilities, proposing a dynamic, iterative learning–feedback loop that links sensing, decision-making, adaptation, and predictive learning via federated learning. The findings underscore the importance of data standardization, interoperability, and governance as crucial components of DLT’s potential to transform supply chain resilience. To conceptualize these insights, the study proposes the DLT-LFL framework, which structures the contribution of DLT across four iterative phases—sense, decide, adapt, and learn—providing a dynamic model for building resilience.
For practitioners, this research offers actionable insights into how DLT can be strategically leveraged to improve supply chain operations. Industry experts emphasized DLT’s capacity to enhance real-time traceability, fraud prevention, and collaboration. To support immediate application, the study provides a practitioner checklist aligned with the DLT-LFL framework, enabling organizations to evaluate current capabilities, identify resilience gaps, prioritize interventions, and track improvements over time. However, the study is limited by the slow progress of active enterprise blockchain solutions and by the fact that many challenges were collected as practical advice, such as legacy system integration, regulatory compliance, and insufficient governance approaches. These insights can guide organizations in addressing hurdles before advancing DLT integrations. Furthermore, the study is limited by expert opinions and the few industries that participated in the workshops, making the implications particularly relevant for logistics, pharmaceuticals, and automotive sectors, where managing supply chain disruptions is critical for maintaining operational efficiency. The DLT-LFL framework, together with the checklist, can assist practitioners in aligning specific DLT solutions with resilience strategies by identifying where sensing, decision-making, learning, or adaptation processes require support.
In conclusion, DLT offers substantial potential for enhancing SCR by addressing critical vulnerabilities, such as cybersecurity risks, and by strengthening capabilities like collaborative learning, analytics, and decision-making. However, the research also highlights significant challenges, including governance and integration issues, transitions from private to public blockchains and regulatory uncertainties. Looking ahead, further research should focus on scalable, yet decentralized solutions that can accommodate the diverse needs of different industries, while also addressing the evolving regulatory landscape. Additionally, exploring the integration of AI-driven analytics and federated learning with DLT could pave the way for more sophisticated predictive capabilities. Future work could further refine the DLT-LFL framework by validating its applicability across different supply chain contexts and testing how specific DLT applications contribute to resilience-building over time. Research should also apply the checklist, develop quantitative measures, and assess the long-term impact of DLT implementations across different industries. Investigating the integration of complementary technologies such as IoT devices and edge computing, as well as federated learning, along with organizational and governance factors that influence adoption, will provide further guidance for practical implementation.
Author Contributions
Writing–original draft, T.G. and M.A.G.; Writing–review & editing, T.G. and M.A.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| AI | Artificial Intelligence |
| C | Capabilities |
| DLT | Distributed Ledger Technology |
| LFL | Learning Feedback Loop |
| SCR | Supply Chain Resilience |
| SCRM | Supply Chain Risk Management |
| V | Vulnerabilities |
Appendix A
| Initial Code | Consolidated Theme | Type | Example Statement |
| Security Flaws | Cybersecurity & Data Integrity | Vulnerability | “Sometimes we don’t manage to patch security issues fast enough across our supplier network, […] that could compromise our data.” |
| Late supplier shipments | Supplier/Customer Disruptions | Vulnerability | “Unexpected shipment delays force us to reroute inventory at the last minute.” |
| Regulatory reporting gaps | External Stressors (Legal/Regulatory) | Vulnerability | “Compliance requirements differ significantly across countries, […] creating gaps in our reporting.” |
| Predictive inventory planning | Proactive Risk Management | Capability | “Using data from multiple suppliers and in the future ideally also further partners along the way, we anticipate shortages […] before they happen.” |
| Resource optimization | Resource Efficiency | Capability | “Monitoring our stock with the Hyperledger Fabric helps us cut waste and use resources more efficiently.” |
| Collaborative dashboards | Supply Chain Visibility | Capability | “Shared dashboards across partners increase transparency and coordination.” |
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Figure 1.
Supply Chain Resilience Framework (Pettit et al., 2010, [15]).
Figure 1.
Supply Chain Resilience Framework (Pettit et al., 2010, [15]).
Figure 2.
DLT-LFL (Distributed Ledger Technology Learning Feedback Loop) for Supply Chain Resilience.
Figure 2.
DLT-LFL (Distributed Ledger Technology Learning Feedback Loop) for Supply Chain Resilience.
Table 1.
Workshop Iterations and Participants.
| # | Industry Sectors | Key Roles | Avg. Years of Experience | Main Expertise Areas |
|---|---|---|---|---|
| 1 | Logistics, Retail, Manufacturing | Supply Chain Managers, Blockchain Developers, Procurement Specialists | 8+ (9 participants) | Risk mitigation, real-time tracking, cybersecurity |
| 2 | Logistics, Pharmaceuticals | Supply Chain Consultants, Logistics Coordinators, Blockchain Architects | 13+ (5 participants) | Regulatory compliance, smart contracts, supplier risk |
| 3 | Logistics, Supply Chain Finance | COOs, Trade Finance Experts, Compliance Officers, Developers | 8+ (4 participants) | Fraud prevention, supplier risk |
| 4 | Healthcare, Automotive | Supply Chain Directors, Ethical Hackers, Warehouse Managers | 11+ (4 participants) | Resilience planning, threat detection |
| 5 | Automotive | Strategy Leads, Data Scientists, Risk Consultants | 11+ (3 participants) | DLT scalability, predictive analytics |
Table 2.
DLT-Impact on Vulnerabilities.
| Vulnerability Criteria | DLT Impact | DLT Applications | Integration Hurdles |
|---|---|---|---|
| Cybersecurity & Data Integrity | ●●● | Cryptographic encryption, immutable ledger records | Scalability challenges and integration issues with legacy systems; regulatory and interoperability gaps |
| Supply Chain Interdependence | ●● | Decentralized data sharing; reduced reliance on intermediaries | Complex coordination among multiple stakeholders; lack of standardized protocols |
| Supplier/Customer Disruptions | ●●● | End-to-end traceability; real-time monitoring | High initial implementation costs and educational efforts; limited adoption across fragmented industries |
| External Stressors (Legal/Regulatory) | ● | Audit trails; compliance monitoring | Unclear legal frameworks and rapidly evolving regulations create uncertainty |
| Resource Limits | ○ | Indirect support via improved data coordination, predictive analytics, asset sharing mechanisms | DLT does not address physical limitations such as raw material scarcity or labor shortages; impact limited to informational efficiency rather than physical resource optimization |
| Operational & System Failures | ●● | Decentralized architecture to avoid single points of failure | PoC stage solutions often struggle with network stability and performance under real-world loads |
| Transparency & Information Asymmetry | ●●● | Shared, immutable ledger providing full supply chain visibility | Data standardization and integration challenges across diverse systems |
| Fraud & Counterfeit Risk | ●●● | Verification mechanisms; product provenance tracking | Limited industry-wide adoption; need for cross-organizational trust and governance frameworks |
Table 3.
DLT-Impact on Capabilities.
| Capability Criteria | DLT Impact | DLT Applications | Integration Hurdles |
|---|---|---|---|
| Operational Flexibility | ●● | Automated workflows; smart contracts enabling dynamic adjustments | Integration with existing ERP systems; resistance to change in established processes |
| Resource Efficiency | ●●● | Process automation; streamlined documentation; reduced administrative overhead | High initial deployment costs; performance issues when scaling to large, complex, especially international networks |
| Supply Chain Visibility | ●●● | Real-time data sharing; decentralized tracking systems | Data standardization across partners; interoperability between different platforms, centralized and decentralized |
| Proactive Risk Management | ●● | Integration of predictive analytics and federated learning with real-time DLT data | Limited availability of high-quality, real-time data; lack of mature analytics frameworks on DLT infrastructure |
| Supply Chain Resilience | ●●● | Enhanced transparency and audit trails; robust data exchange mechanisms | Organizational reluctance to adopt radical changes; inconsistent global regulatory environment |
| Collaborative Cooperation | ●●● | Consensus-based decision-making; shared ledger for multi-party collaboration | Fragmented industry standards; trust issues and governance complexity between stakeholders hinder seamless collaboration |
| Customer Loyalty & Satisfaction | ●● | Provenance tracking; verifiable product quality and origin information | Low consumer awareness of DLT benefits; integration challenges with existing customer service platforms |
| Information Security | ●●● | Tamper-proof records; strict access controls; cryptographic security | Technical complexity and high energy requirements; slow transaction speeds compared to traditional databases |
| Production & Delivery Capacity | ○ | Minimal direct impact; potential for improved inventory management | DLT does not directly influence physical production; benefits are largely indirect through data optimization |
| Organizational Adaptability | ○ | Limited direct influence; supports process changes with reliable data | Cultural resistance to technology adoption; transformation requires broader change management initiatives |
Table 4.
Checklist for Evaluating Supply Chain Resilience Using the DLT-LFL.
| Phase 1: Sense Phase—Mitigating Risks |
| Objective: Detect threats early and reduce exposure to operational, regulatory, environmental, or supplier risks. |
| Assessment Criteria: |
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| Phase 2: Decide—Preventing Eroded Profitability |
| Objective: Make informed, timely decisions that protect operational performance and financial outcomes. |
| Assessment Criteria: |
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| Phase 3: Adapt—Reducing Exposure to Vulnerabilities |
| Objective: Adjust operational processes dynamically to respond to disruptions and strengthen resilience. |
| Assessment Criteria: |
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| Phase 4: Learn—Accelerating Capabilities |
| Objective: Embed predictive and operational learning to enhance future performance and capabilities. |
| Assessment Criteria: |
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Gürpinar, T.; Gulum, M.A. Building Resilient and Sustainable Supply Chains: A Distributed Ledger-Based Learning Feedback Loop. Sustainability 2025, 17, 9023. https://doi.org/10.3390/su17209023
AMA Style
Gürpinar T, Gulum MA. Building Resilient and Sustainable Supply Chains: A Distributed Ledger-Based Learning Feedback Loop. Sustainability. 2025; 17(20):9023. https://doi.org/10.3390/su17209023
Chicago/Turabian StyleGürpinar, Tan, and Mehmet Akif Gulum. 2025. "Building Resilient and Sustainable Supply Chains: A Distributed Ledger-Based Learning Feedback Loop" Sustainability 17, no. 20: 9023. https://doi.org/10.3390/su17209023
APA StyleGürpinar, T., & Gulum, M. A. (2025). Building Resilient and Sustainable Supply Chains: A Distributed Ledger-Based Learning Feedback Loop. Sustainability, 17(20), 9023. https://doi.org/10.3390/su17209023
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