Tipwadee Leala, Krist Thamniyom, Thawatchai Chomsiri
Cryptocurrency investments have grown exponentially, but the rapid expansion of decentralized finance (DeFi) ecosystems has been accompanied by the rise of sophisticated fraud schemes, particularly Rug Pulls. These scams occur when developers deliberately withdraw liquidity or sell large amounts of tokens, leaving investors with worthless assets. This research presents a machine learning-based framework for detecting rug-pull-prone projectson the Binance Smart Chain (BSC). A comprehensive dataset was constructed by aggregating transactional and smart contract features from reliable sources such as BscScan, TokenSniffer, DEXTools, and PeckShield Alerts. Data preprocessing included handling missing values, removing duplicates, detecting and mitigating outliers, and addressing severe class imbalance using Synthetic Minority Oversampling Technique (SMOTE). Seven machine learning algorithms were compared: Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). The top-performing models, Random Forest and XGBoost, were further validated using stratified holdout testing. Results demonstrate that XGBoost achieved the highest overall performance$(\mathrm{F1} = 0.82,\ \text{ROC-AUC} = 0.90,\ \text{PR-AUC} = 0.994)$confirming the model's robustness in identifying fraudulent patterns. This approach offers a scalable framework for blockchain fraud detection on BSC, with potential applicability to other networks such as Ethereum and Polygon.
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.
Jay Daniel, Elias Abou Maroun, Jose Arturo Garza-Reyes, Asmae El Jaouhari · 5 authors
Purpose Blockchain serves as a vital technology for digital transformation within manufacturing supply chains through its improved security and transparent tracking capabilities. Blockchain technology emerges as a promising solution for supply chain stakeholders who face ongoing information asymmetries and trust deficits amidst growing demands for sustainable practices and ethical sourcing from consumers and regulators. This paper aims to explore the potential of blockchain technology to improve transparency within supply chain operations, particularly in the Australian electrical manufacturing industry. Design/methodology/approach This research uses a multimethod research design encompassing three phases: Phase 1, a comprehensive literature review; Phase 2, semistructured interviews; and Phase 3, a case study to explore the application of distributed ledger technology for secure real-time supply chain activity monitoring. Findings The results suggest that integrating blockchain with Internet of Things technologies and sensor-based data leads to substantial improvements in data integrity while reducing fraud risks and enabling more efficient supply chain actor collaboration. Practical implications Manufacturing firms can benefit from our research findings, which provide actionable steps for using blockchain to achieve sustainable operations and improved efficiency. The authors offer strategic guidance for firms to develop supply chains that ensure transparency and resilience, along with alignment to environmental, social and governance goals. Originality/value The study advances digital supply chain transformation research by showing how blockchain serves as an essential technology to improve transparency and trust while boosting performance in manufacturing supply chains.
Abstract This study aims to provide a detailed bibliometric examination of the progression and prospect of research on blockchain-driven technology for business models in effective business practices. The study reviews a sample of 100 journal articles published between 2017 and 2023, according to the Scopus database index. The study presents the most influential articles, as well as the top contributing journals, authors, institutions, and countries. The major publications are from China, Germany, and the United Kingdom. Furthermore, using bibliographic coupling, the study identifies six key topical clusters within the existing body of literature: Industry 4.0 and circular economy practices for environmental sustainability; blockchain technology framework for the tourism industry; blockchain-enabled supply chain design; blockchain-supported business model design and supply chain resilience; impact of blockchain technologies on business models; and smart contracts for sustainable business models, respectively. The key limitation of this study is relying only on the Scopus dataset and missing some emerging trends such as decentralized finance, supply chain sustainability, and regulatory frameworks in the context of blockchain-driven business models. There is a need for continued exploration of these emerging trends. A closer qualitative examination of the clusters helped in mapping the progression of current research in the domain to suggest strategic directions for future research and suggested a framework.
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.
Muhammad Zeeshan Ullah Khan, Syed Imran Zaman, Sharfuddin Ahmed Khan
This chapter explores the transformative potential of blockchain technology in the context of future supply chain finance (SCF). It begins by illustrating how blockchain’s decentralized and tamper-evident ledger architecture can address traditional inefficiencies in SCF. By recording each transaction in an immutable chain, blockchain not only improves transparency but also reduces fraud risk, particularly in complex supply chains involving multiple intermediaries. The discussion highlights smart contracts as a key innovation, capable of automating routine processes. Real-world implementations in areas ranging from e-commerce logistics to agricultural finance underscore the wide applicability of these concepts. Building on blockchain fundamentals, the chapter details how next-generation supply chains can harness decentralized data sharing and automated contract enforcement to boost operational efficiency, reduce counterfeiting, and manage disruptions more proactively. It examines how blockchain-based solutions can be enhanced by emerging technologies, which together enable real-time monitoring and predictive analytics. However, despite these advantages, organizations must carefully evaluate feasibility concerns. Issues of scalability, interoperability, and energy consumption persist, especially in public blockchains reliant on intensive consensus mechanisms.
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.
This study and implementation focus on the complex healthcare supply chain, encompassing resource procurement, supply management, and service delivery to all the stakeholders without any geographical boundaries. It introduced a novel approach utilising Ethereum blockchain technology to establish a track-and-trace mechanism for healthcare supply chains, bolstered by smart contracts and data immutability. By leveraging smart contracts, contractual obligations are automatically executed, ensuring prompt results without intermediaries or time delays. The proposed solution addresses the prevalent issues of transparency and monitoring within conventional supply chains. The method, rooted in solidity smart contracts, undergoes rigorous testing across various inputs, culminating in an average gas cost evaluation for various functionalities of system. This innovative system meticulously tracks the lineage of goods, with an average gas cost of 18,027 for all accounts. Notably, the process incurs a gas cost of 292,000 for all operations.
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.
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.
Roshan Jahan, Abdul Majid, Alina Khan, Aksha Malik · 5 authors
Blockchain has emerged as a revolutionary alternative for ensuring authenticity, fostering trust, and improving transparency in supply chain management, particularly within the rapidly expanding secondary markets for luxury and collectible items. This article provides a comprehensive assessment and analysis of blockchain applications specifically designed to address significant issues, including counterfeit products, provenance verification, and consumer trust in the resale market. Users may access the transfer history, ownership history, and validity of a product due to the blockchain's decentralized and unchangeable record properties. Blockchain significantly enhances item traceability, elevates consumer confidence, and increases resale values due to the emphasis on verified authenticity in both empirical and theoretical research. While implementing the blockchain technology users faces considerable challenges that includes data security concerns, high infrastructure costs, extensive interoperability issues, and technological complexities across several sectors. This article focuses on critical implementation approaches via qualitative secondary research and methodical examination of academic publications, industry reports, and case studies, notably featuring the Aura Blockchain Consortium. The research shows that blockchain has the potential to transform the resale business by reducing transaction costs, enhancing efficiency, and fostering a reliable marketplace; nevertheless, ongoing developments and standardization are essential for wider use and integration. It encompasses smart contract-based Non-Fungible Tokens (NFTs), public versus private blockchain topologies, and the secure association of tangible items with their NFTs.
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.
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.