Dr. K. Pushpa Latha, Meghana Reddy, Dr. B. Rajalingam, Malleswari Akurati · 6 authors
The pharmaceutical supply chain is a network of various stakeholders such as manufacturers, distributors, logistics providers, pharmacies, and regulatory authorities. It is essential to ensure transparency, security, and trust among them to prevent counterfeit drugs, data manipulation, and financial fraud. Drug supply chains using traditional trade finance processes are heavily dependent on paper, based documentation and centralized systems. These practices often cause delays, result in high transaction costs, and reduce traceability. This article introduces a Trade Finance Framework Powered by Blockchain for enhancing security in the drug supply chain transactions. The framework utilizes blockchain technology to offer decentralized, tamper, proof record keeping and real, time transaction verification. Smart contracts facilitate the automation of trade finance procedures such as letter of credit validation, payment release, and compliance verification, thus cutting down on processing time and human intervention. A distributed ledger keeps a record of each transaction, which is accessible to all and cannot be altered, all while hiding the sensitive data through encrypting mechanisms. The system put forward bolsters the mutual trust of the different parties involved, increases the ability to trace pharmaceutical products, decreases the risks of fraud, and makes regulatory compliance easier. The framework, by combining blockchain with trade finance, enables secure, effective, and transparent drug supply chain management, thus leading to higher patient safety and better financial accountability.
Naim Ayadi, Syed Arshad Hussain, Arif R. Deen, Asadullah Ullah · 9 authors
There is diminished transparency, fragmented information exchange, and lack of trust among geographically dispersed stakeholders, which increasingly challenge global supply chains. The classic centralized systems of supply chain management are not always capable of being able to offer real-time traceability and data integrity which is dependable and effective in contract enforcement. The proposed study is a blockchain-based smart contract design that is focused on ensuring increased transparency, traceability and trust in global supply chain management. The suggested framework will combine automated smart contracts, cryptographic provenance tracking, permissioned blockchain consensus, and a decentralized trust score evaluation mechanism to overcome some of the major operation and governance challenges. A simulated assessment with a multi-tier global supply chain setting of 15 blockchain nodes and 12,000 transactions was performed through experimentation. The findings show that the proposed system attained an average transaction delay of 210 ms, which is very low compared to centralized systems (520 ms), with throughput being raised to 120 transactions per minute. End-to-end traceability performance also improved significantly, with a reduction in trace-back time to 8 s compared with 95s this represents a 100% tampering detection rate. The consensus mechanism ensured that the ledger integrity failed only at a rate of less than 1.1%, even when more than 30% of nodes were faulty. Risk-wise, the trust evaluation algorithm dynamically enhanced reliable supplier scores up to 12%, which facilitated the selection of reliable partners. On the whole, the results prove that smart contracts based on blockchains can drastically enhance the efficiency of operations, data integrity, and confidence in global supply chains, with the platform capable of providing a resilient and scalable backbone for the future supply chain management model.
Blockchain technology has been widely heralded as a transformative tool capable of establishing trust in digitized supply chains through cryptographic finance, immutability, and decentralized ledgers. However, empirical evidence and recent analyses suggest that these technological mechanisms alone are insufficient to generate holistic trust among supply chain stakeholders. This study critically examined why blockchain adoption often failed to produce sustained trust, despite enhancing transparency, traceability, and data integrity. A qualitative, theory-driven methodology was employed, analyzing peer-reviewed literature across supply chain management, financial technology, and digital governance domains. The findings revealed that trust remained deeply rooted in social, relational, and institutional dimensions, which blockchain technologies could not replace. Off-chain data dependencies, governance gaps, regulatory ambiguities, and power asymmetries emerged as key factors undermining trust formation. Furthermore, blockchain often displaced trust from human and institutional actors to opaque technical systems, reducing accountability and stakeholder confidence. The study concluded that blockchain should be conceptualized as a supportive infrastructure for trust rather than a substitute for relational and institutional mechanisms. Recommendations included integrating blockchain with hybrid governance models, legal frameworks, and inclusive participation strategies to enhance trust resilience. The study also identified future research directions focusing on cross-industry comparisons, socio-technical interactions, and emerging blockchain alternatives. These insights contribute to a more nuanced understanding of the socio-technical limits of blockchain in supply chain digitization and highlight the critical role of governance and institutional alignment in sustaining trust.
Diplomska naloga obravnava blockchain tehnologije s poudarkom NFT-jev (non-fungible tokens) in nelegitimnih kriptovalut. Raziskava vključuje primerjalno analizo dveh skupin blockchain projektov: uveljavljenih kriptovalut z lastno verigo blokov in problematičnih projektov brez realne vrednosti. Z analizo tehničnih, ekonomskih in socialnih dejavnikov smo identificirali ključne indikatorje, ki razlikujejo legitimne projekte od prevar. Raziskali smo vzorce cenovnih manipulacij, mehanizme odklepanja žetonov, distribucijo lastništva in marketinške taktike. Analiza 30 projektov je pokazala trimodalno porazdelitev ocen legitimnosti, kjer vsi projekti z oceno nad 75 ostajajo operativni, medtem ko vsi projekti z oceno pod 40 predstavljajo dokumentirane prevare. Končni rezultat je metodologija za prepoznavanje tveganih projektov in smernice za varnejše sodelovanje v blockchain ekosistemu, kar bo prispevalo k večji ozaveščenosti uporabnikov in izboljšanju varnostnih praks.
Jauhar Abbas, Syed Shameel Ahmed Quadri, Adeel Ansari, Seema Ansari
This study examined the impact of blockchain integration on supply chain finance (SCF) performance, transparency, and trust. With traditional SCF systems facing challenges such as delayed payments, information asymmetry, and transaction inefficiencies, blockchain technology offers decentralized, immutable, and real-time data sharing capabilities to enhance financial operations. A quantitative cross-sectional research design was employed, and data were collected from 312 professionals working in manufacturing, retail, and logistics sectors. Descriptive analysis, exploratory factor analysis, and structural equation modeling (SEM) were applied to assess relationships among blockchain adoption, SCF performance, transparency, and trust. Results indicated that blockchain adoption significantly improved SCF performance (mean = 4.08), transaction verification speed (mean = 4.02), and cost efficiency (mean = 3.95). Transparency increased as stakeholders accessed real-time and verifiable financial data (mean = 4.05), while trust among supply chain partners was strengthened (mean = 4.04) due to the system’s immutable and auditable records. These findings demonstrated that blockchain acts as a strategic enabler for enhancing operational efficiency, information sharing, and stakeholder confidence in SCF operations. The study contributes to theory and practice by providing empirical evidence of blockchain’s role in fostering performance and relational benefits in supply chains. Recommendations include strategic blockchain implementation, employee training, governance alignment, and continuous monitoring of performance metrics. Future research could explore cross-border adoption, integration with emerging technologies, and long-term impacts across different industries.
This paper explores how Zero-Knowledge Proofs (ZKPs) can enhance the privacy and security of decentralized supply chains. Although blockchain technology enhances supply chain transparency, it also reveals sensitive information, including supplier identities, pricing strategies, and transaction volumes. ZKPs offer a feasible approach in that subjects can authenticate data without revealing the underlying data, whilst keeping the information confidential and maintaining trust. In this study, the main performance indicators, including the time to verify a transaction (0.48 seconds), communication overhead (1.3 KB proof size), and privacy (95) in the ZKP-based system, are examined. ZKPs can enhance economic security by eliminating risks, such as industrial espionage and counterparty fraud, that can arise from publicly accessible data in historical blockchain systems. The performance of ZKP-enabled networks is also compared with that of traditional transparent blockchain systems. The major benefits are data privacy (95 % in ZKPs and 40 % in traditional systems) and scalability (80 % high and 60 % moderate). The paper also discusses how AI-based ZKP generation can speed up proof generation and automated compliance auditing to uphold regulatory compliance, including the General Data Protection Regulation (GDPR) and Anti-Money Laundering (AML). By incorporating AI into the ZKP procedure, proof generation can be sped up, yielding significant improvements in efficiency. This study finds that ZKPs can provide an effective approach to decentralized supply chain security, privacy, efficiency, and regulatory compliance, thereby making global trade activities more secure, transparent, and efficient.
Dr Mamata Jagannathji Rathi, Miss. Sanika Yogiraj Parankar
This research investigates the role of supply chain automation through the integration of Blockchain technology, Smart Contracts, and the Internet of Things (IoT). The study explores how decentralized ledgers can automate critical workflows, including instant payment release, real-time inventory reconciliation, and automated compliance auditing. By utilizing IoT sensors to feed environmental data into a blockchain-backed system, companies can trigger automated responses that reduce human error and operational overhead. The findings indicate that this transition accelerates "speed-to-market" and fosters a self-correcting, autonomous ecosystem capable of responding to disruptions in real-time. While the research acknowledges significant implementation barriers—such as high initial capital expenditure, cybersecurity risks, and the "SME gap"—it concludes that the evolution toward an automated, transparent, and green supply chain is essential for resilience. Ultimately, the paper argues that automation should not be viewed as a replacement for human labor, but as a tool to liberate workers for high-level system orchestration. This study provides a strategic roadmap for organizations navigating the shift from Industry 4.0 to a human-centric, sustainable Industry 6.0 framework
Abstract We examine prospective classification of crypto currencies risks within the ISDA Standardized Initial Margin Model (SIMM) framework for calculation of initial margin on trades sensitive to cryptocurrencies’ risk factors in the uncleared market. Consistent with the view that cryptocurrencies are digital assets that fundamentally rely on distributed ledger technology (DLT) and induce financial risks that are significantly different from those in traditional risk classes like commodities or FX, we find that cryptocurrencies are best classified into a distinct risk class within SIMM that is split into two buckets – pegged and floating (unpegged) crypto currencies as risk factors - and suggest risk weights’ calibration methodology within the cryptocurrencies risk class that is consistent with the existing approaches adopted in SIMM.
The integration of blockchain technology into supply chain management represents a fundamental shift in how goods are tracked, verified, and transferred across global networks. This comprehensive research examines the implementation, impact, and challenges of distributed ledger technology across diverse supply chain ecosystems, with particular focus on transparency enhancement, counterfeit prevention, process efficiency, and stakeholder collaboration. Through a mixed-methods approach analyzing deployment data from 127 organizations across 18 industries over a four-year period, this study demonstrates that blockchain-enabled supply chains achieve an average improvement of 41.3% in traceability accuracy, reduce documentation processing times by 67.8%, and decrease disputes among supply chain partners by 52.4%. The research further reveals that smart contract implementations automate approximately 38.6% of routine supply chain transactions, reducing administrative costs by an average of 31.7% while minimizing human error in compliance verification. Counterfeit detection capabilities improve by 89.2% in pharmaceutical and luxury goods sectors through immutable product provenance tracking. However, the study identifies significant implementation barriers including interoperability challenges with legacy systems, scalability limitations during peak transaction periods, regulatory uncertainty across jurisdictions, and substantial upfront investment requirements averaging $2.3 million per enterprise implementation. The carbon footprint of certain consensus mechanisms, particularly proof-of-work, presents environmental concerns that necessitate alternative approaches for sustainable adoption. This paper proposes a phased implementation framework emphasizing pilot testing, stakeholder education, hybrid architecture models, and regulatory engagement to balance innovation with operational stability. The findings indicate that while blockchain technology offers transformative potential for supply chain transparency and efficiency, successful adoption requires strategic alignment with business objectives, collaborative ecosystem development, and measured progression from discrete applications to integrated systems. The research contributes to both academic understanding and practical implementation guidelines for distributed ledger technology in complex supply chain environments.
Since the emergence of the blockchain and the uprising of ChatGPT, the Distributed Ledger Technology (DLT) and Artificial Intelligence (AI) are well-discussed topics both in public and professional circles, but especially in the domain of Supply Chain Management (SCM). These subjects are tech-savvy, complicated to explain and even more complex to use. On top of that, there is a scientific discussion around synergies in combining both technologies. Together they can be useful in engaging current challenges in SCM, where transparency-related data has to be generated, processed and formed into decisions and reports. To investigate the potentials of these technologies working together in a non-financial reporting environment, we performed a systematic literature review. We also included literature focusing solely on the technological perspective. The objective is a comprehensive overview on how a combination of DLT and AI could help to solve current challenges arising from sustainability related regulations. Further, we discussed ideas around Internet of Things applications or Federated Learning approaches, that use data from different entities and can be used in sustainability reporting, exploring possibilities to enhance compliance and responsible business conduct in SCM.
Effective risk management has grown more and more crucial in the complex world of international trade finance, bolstered by security, trust, and openness. By creating an integrated system that blends Hyperledger Fabric blockchain technology, Supply Chain Finance (SCF) protocols, and Generative Adversarial Networks (GANs), this study seeks to improve the intelligence and dependability of financial risk assessment. Four interrelated steps make up the suggested approach: (1) preprocessing and encoding SCF datasets; (2) creating synthetic risk data with GANs to mimic uncommon or dishonest trade behaviors; (3) using Hyperledger Fabric to execute smart contracts and log transactions decentralized; and (4) using real-time SCF compliance modeling for dynamic risk assessment. While blockchain guarantees the transparency, immutability, and auditability of financial records, GAN integration improves the prediction model by adding value to the training corpus. Comparative studies show that the suggested system considerably lowers the likelihood of data tampering and improves risk prediction accuracy by 12% when compared to traditional machine learning models. The results demonstrate that integrating generative modeling with blockchain technology can significantly improve financial risk management, transparency, and adaptability in global trade settings.
1. Improving transparency, authenticity, and traceability in the Agricultural Supply Chain with a Blockchain Model (FarmTrace) Farm Trace Blockchain (Farm Trace) has been developed to give farmers and agribusinesses (such as wholesalers and retailers) a better view of their entire supply chain from when the product is harvested to when it is sold to a customer, assuring the integrity of the data entered into the blockchain and providing a means for trust between all stakeholders involved (Blade; 2022). This application allows stakeholders (including consumers) easy access to the traceability history of their products stored on the blockchain permanently, thus allowing all parties involved to verify their product’s history and status via the Web3 access created within the FarmTrace application. It is important to note that Farm Trace has two entries into the same user interface (i.e., farmer entry and retailer entry) and both are designed to allow for better management of the supply chain by providing the ability to track producers, distributors, retailers, and customers in real-time.
Christian Finke, Tamino Marahrens, Matthias Schümann
As supply chains (SCs) face increasing pressure from ecological demands, ethical expectations, and global disruptions, Distributed Ledger Technology (DLT) is gaining attention as a potential enabler of transparent, secure, and automated processes, helping to meet the expectations of customers and regulatory authorities. Nevertheless, the lack of generally valid design recommendations hinders its implementation. Therefore, we adopted grounded theory principles within a design science research approach to address this gap. Subsequently, we derived 11 overarching expert insights for developing a DLT operating model in SCs and 15 for its implementation by conducting 16 expert interviews. These insights were finally used to extract 19 generally valid design recommendations for applying DLT in SC processes that contribute to practical implementations and the framing of realistic adoption expectations by guiding researchers and practitioners.
The proliferation of counterfeit products in various industries, including pharmaceuticals, electronics, and luxury goods, poses a significant threat to consumer safety, brand reputation, and economic integrity. Traditional verification methods often fail due to centralized control and limited traceability. This research proposes a block chain-based system to identify fake products by leveraging the decentralized, immutable, and transparent nature of block chain technology. The system records product information such as manufacturing details, origin, and ownership history on a distributed ledger, ensuring secure and tamper-proof tracking across the supply chain. Each product is tagged with a unique QR code that links to its block chain record, allowing end-users to verify authenticity through a mobile application. The system incorporates distinct login modules for administrators, sellers, and customers to ensure secure interactions and streamline product management. Simulation results validate the system’s capability to detect counterfeit products with high accuracy and real-time verification speed. The proposed solution provides a scalable and efficient framework for enhancing supply chain integrity and protecting consumers against fake goods
Abstract Counterfeit and stolen goods seriously threaten the reliability of modern supply chains. They affect consumer trust, brand reputation, and economic stability. To tackle this issue, this paper presents a blockchain-based smart supply chain framework. It combines Non-Fungible Tokens (NFTs) with dual-layer Anti-counterfeiting mechanisms such as RFID tags and holographic labels [2], [6]. Each physical product connects to a unique NFT, creating a secure digital twin on a private blockchain network [3], [8]. This setup ensures traceability, verifies authenticity, and keeps transaction records safe from tampering [1], [5].. The proposed system includes a new Supply Chain Consensus (SCC) algorithm, designed specifically for supply chains. It classifies nodes by trust and stake to allow for efficient and scalable transaction validation. Also, a collateral-based incentive mechanism encourages honest participation among all involved, including manufacturers, transporters, buyers, and arbitrators [7]. Furthermore, a decentralized dispute resolution model features a transparent voting process that ensures fairness and accountability during conflicts [8]. A conceptual framework and simulation-based analysis were carried out to assess the system's performance in terms of transaction efficiency, security, and counterfeit reduction [1], [5]. The findings show that this approach significantly boosts supply chain transparency, lowers verification costs, and improves product authentication compared to traditional centralized systems [4]. This framework provides a scalable and secure solution for the next generation of supply chains, particularly in sectors like pharmaceuticals, luxury goods, and electronics. Keywords: Blockchain, Smart Supply Chain, Non-Fungible Tokens (NFTs), Anti-Counterfeiting, Digital Twin, RFID, Smart Contracts, Supply Chain Security.
Demand volatility, logistical interruptions, and linked worldwide networks define the remarkable complexity of modern supply chains. Classic centralized management solutions find difficulty in offering real-time solutions to changing operational problems. For designing distributed, intelligent, and self-organizing supply chain ecosystems, artificial intelligence agents combined with Model-Control-View (MCV) architectures provide transformational possibilities. These autonomous computational entities span three functional layers: view interfaces enable monitoring and interaction, control mechanisms govern decision-making and optimization, and model components represent digital twins of supply chain entities. Multi-agent coordination enables decentralized yet coherent operations through the negotiation and collaboration of agents representing suppliers, production, logistics, and retail, all of which adhere to standardized protocols. Applications include demand forecasting, intelligent logistics, stock optimization, supplier partnering, and flexible disruption response. While reducing reliance on centralized control systems, the framework enhances resilience, scalability, openness, and operational efficiency. Challenges in implementation include organizational adaptation needs, cybersecurity vulnerabilities, and data integration complexity. Future advances in autonomous and cooperative supply chain systems will include explainable artificial intelligence, quantum-enhanced optimization, edge computing powers, and blockchain-enabled trust mechanisms.
The convergence of Artificial Intelligence (AI) and Blockchain Technology (BCT) is transforming supply-chain ecosystems by enhancing transparency, intelligence, and automation. However, existing research lacks a unified theory explaining how these technologies jointly create resilience across organizational levels. This paper extends the Strategic–Decentralized Resilience Theory (SDRT), originally developed to guide effec-tive blockchain implementation, by integrating Agentic AI capabilities to form the SDRT–Agentic AI framework. The framework conceptualizes how predictive, adaptive, and agentic (autonomous) AI capabilities reinforce SDRT’s three pillars: Strategic, Or-ganizational, and Decentralized Resilience. The framework draws on three AI modali-ties—predictive AI for strategic foresight and agility, adaptive AI for organizational learning and flexibility, and agentic AI for self-governed, trustless coordination within blockchain ecosystems. Together, these mechanisms explain how intelligent and de-centralized systems co-evolve to generate dynamic, multi-level resilience. This con-ceptual paper develops a comprehensive model and propositions describing interac-tions between AI capabilities and blockchain-based organizational structures. It con-tributes to information systems and supply-chain research by unifying two fragmented domains, AI and blockchain, under a resilience-oriented mid-range theory. Practically, the framework provides managers with a roadmap to align AI investments with de-centralized governance mechanisms, enabling proactive decision-making, adaptability, and sustainable competitiveness in increasingly autonomous digital environments.
Quang Huy Duong, Carlos F.A. Arranz, Mao Xu, Li Zhou · 5 authors
The rapid transition to electric vehicles has intensified challenges in electric vehicle battery (EVB) closed-loop supply chains (CLSC), particularly regarding material traceability, supply chain transparency, and recycling efficiency. While decentralised technologies, particularly Web3 and Metaverse, offer promising solutions, their integration into EVB CLSC remains fragmented and insufficiently examined. We introduce an Operational Decentralisation Framework enabling a systematic analysis of centralised operations and a critical evaluation of decentralised alternatives as transformational forces. By adopting a holistic perspective, the framework equips firms with strategic guidance for transitioning from centralised structures to decentralised ecosystems. We analyse 588 academic articles and 1,168 industry documents through two advanced text mining techniques – Dynamic Latent Dirichlet Allocation and Burst Detection. Web3 and metaverse can potentially reconfigure the design, manufacturing, end-of-life diagnostics, procurement, waste management, load balancing, capacity planning, inventory management and service operations of two key areas: (1) EVB CLSC operations and (2) EVB circular energy/grid operations. We also found that while blockchain and digital twins show established applications, Web3 and Metaverse applications face significant barriers, including scalability, technology complexity, and expertise gaps, despite their great potentials. Therefore, we propose four visionary models integrating Web3, Metaverse, and AI technologies that have the potential to overcome existing barriers and enable transformative decentralisation. Extending the TOE framework, the study contributes to the theory by developing an integrated framework for evaluating decentralised technology adoption in EVB CLSCs. For practitioners, we provide actionable insights and pathways for technology implementation across different CLSC stages and guidance for addressing key adoption barriers.
Background Global supply chains are increasingly challenged by disruptions, environmental pressures, and evolving market demands, necessitating a strong digital transformation. This study explores how the integration of Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) is revolutionizing supply chain management (SCM) by improving operational efficiency, transparency, resilience, and sustainability. Methods Adhering to the PRISMA framework, a systematic review of literature published between 2010 and 2024 was undertaken. Comprehensive searches were conducted in Scopus database. The collected literature was rigorously screened and analyzed using Atlas-ti software to identify recurring themes and assess the synergistic impact of AI, Blockchain, and IoT on supply chain operations. Results The review reveals that digital transformation significantly improves SCM through improved demand forecasting, optimized inventory management, and real-time decision-making capabilities. AI provides predictive insights that mitigate risks and streamline processes, Blockchain offers secure, transparent, and immutable records that improve trust and traceability, and IoT enables real-time monitoring and connectivity across the supply chain network. Despite these benefits, challenges remain, including cybersecurity vulnerabilities, interoperability with legacy systems, and the need for workforce upskilling. Conclusion The integration of AI, Blockchain, and IoT into SCM presents a compelling pathway toward creating more resilient and sustainable supply chains. The paper offers a comprehensive analysis of the benefits and challenges associated with these digital technologies and provides strategic recommendations for practitioners and policymakers to encourage a balanced, technology-driven, and sustainable supply chain ecosystem. JEL codes O33, M11, M15
The complexity of modern supply chain networks requires sophisticated approaches to inventory management that can effectively handle demand uncertainty and coordinate decisions across multiple organizational levels. This paper proposes a novel hierarchical multi-agent reinforcement learning framework for dynamic inventory allocation in multi-echelon supply chains facing stochastic demand patterns. The hierarchical architecture decomposes the inventory control problem into strategic and operational decision layers, where high-level agents coordinate allocation policies across distribution networks while low-level agents optimize local replenishment decisions. The framework integrates Centralized Training with Decentralized Execution paradigm, enabling autonomous agents to learn coordinated policies through shared experience while maintaining operational independence during deployment. Experimental results demonstrate that the proposed approach achieves significant reductions in total system costs compared to traditional base-stock policies and single-agent reinforcement learning methods, while effectively mitigating the bullwhip effect in supply chains with high demand variability.
As global pressure increases for sustainable and transparent supply chains, logistics organisations are exploring ways to strengthen environmental, social and governance (ESG) performance. This article examines how artificial intelligence (AI) and distributed ledger technologies (DLT) contribute to ESG integration in logistics. The study applies a qualitative desk research approach based on secondary data from 2017–2025, including sustainability reports, port authority publications, and the industry press. The comparative case analysis covers four Baltic logistics actors: the Port of Klaipėda (Lithuania), Vlantana (Lithuania), the Freeport of Riga/ Baltic Container Terminal (Latvia), and HHLA TK Estonia (Estonia). The findings show that Klaipėda’s LNG, OPS, and hydrogen projects enhance environmental outcomes; the Vlantana Norge case exposes social and governance compliance risks; and Riga and Tallinn demonstrate governance-oriented digitalisation through 5G networks and blockchain documentation. AI primarily supports efficiency and risk detection, while DLT secures the transparency and auditability of ESG data. Together, they function as complementary enablers of ESG reporting, though broader adoption requires regulatory alignment, interoperability, and investment in the digital infrastructure.
Abstract The digital transformation of global supply chains presents unprecedented opportunities, yet it concurrently exacerbates the existing gap in financial inclusion for Micro, Small, and Medium Enterprises (MSMEs). Traditional supply chain finance (SCF) models often fail to serve these small suppliers due to high information asymmetry, lack of verifiable collateral, and manual, paper-intensive processes, leading to significant liquidity constraints. This study proposes and empirically investigates blockchain technology as a foundational solution to mitigate these challenges. Specifically, it examines how blockchain-enabled traceability fosters greater trust and transparency, which in turn facilitates more accessible and inclusive supplier financing mechanisms. Employing a mixed-method approach (Quantitative N=150−180 survey and Qualitative interviews) with a cross-sectional design, the research analyzes relationships using descriptive statistics, regression, and factor analysis. Preliminary findings are expected to demonstrate a significant positive impact of blockchain adoption on financial inclusion metrics for MSMEs. The research contributes by providing a rigorous framework for practitioners and policymakers aiming to leverage decentralized technology to create a more equitable and sustainable global trade ecosystem. Keywords: financial inclusion, blockchain enabled supply, micro, small, and medium enterprises,
Global supply chains are essential to world trade, but they harbor profound inequities- challenges that are manifestations of, and exacerbate, social inequalities. Lack of information, ambiguous procurement practices, and biased risk models relegate small suppliers, developing states, and underrepresented laborers. Artificial intelligence and blockchain with data governance come together in the form of AI-Driven Access and Transparency Networks (AI-ATNs), which make global value networks more equitable and accountable. Explainable AI is used together with fairness-conscious optimization and distributed ledger transparency in AI-ATNs.The outcome? Supply chain participation based on merit and need rather than location or connections. Agriculture, manufacturing, and humanitarian logistics provide real examples of AI systems turning equity into both a social goal and economic necessity. The digital revolution needs to move past efficiency targets and embrace equity intelligence, transforming global supply chains into ethical systems that balance business success with social justice.
The competitive hospitality sector faces a growing credibility crisis, where rising consumer skepticism regarding "greenwashing" severely limits the ability of hotels to capture the Sustainable Revenue Premium. This research addresses a critical gap in Sustainable Supply Chain Management (SSCM) literature by empirically modeling the "Credibility Mechanism"—the process by which digital technology resolves information asymmetry to monetize sustainability claims. Focusing on the complex Food and Beverage (F&B) supply chains of emerging archipelagic economies, the study employs a rigorous sequential mixed-methods design. First, Design Science Research was utilized to architect a permissioned cross-chain blockchain framework integrating Zero-Knowledge Proofs (ZKPs) for verifiable, private provenance. Subsequently, Partial Least Squares-Structural Equation Modeling (PLS-SEM) confirmed that blockchain-enabled transparency significantly mitigates perceived greenwashing risk, which in turn fosters Customer Trust. Critically, the study validates financial outcomes using a Stochastic Frontier Bayesian Model (SFBM) applied to longitudinal hotel data. Results demonstrate that adopting this traceable framework yields an 8.4% increase in F&B revenue efficiency and sustains a 5.1% price premium for ethically sourced items. These findings provide profound theoretical advancements by redefining SCM risk mitigation through Information Governance rather than material redundancy. Managerially, the research offers a data-driven justification for high-tech investment, proving that verifiable transparency is a direct revenue driver essential for competitive advantage in opaque markets.