The digital transformation of agricultural supply chains requires efficient coordination among heterogeneous stakeholders and reliable information exchange across distributed logistics networks. As a key component linking agricultural production and downstream distribution, collaboration between agricultural product distribution and textile packaging enterprises has become increasingly dependent on intelligent communication and data-sharing infrastructures. This study systematically investigates the strategic management mechanisms and implementation pathways for collaborative development by integrating transaction cost economics, complex adaptive systems theory, and network effects theory. A four-dimensional management framework encompassing technological support, organizational coordination, benefit distribution, and risk prevention is established, in which entropy weight–TOPSIS is employed for strategic objective alignment, blockchain-based architectures enable trusted information sharing, Shapley value optimization supports dynamic benefit allocation, and Value-at-Risk (VaR) models facilitate quantitative risk control. The proposed framework further incorporates smart contracts and permission-controlled data interaction to improve collaboration efficiency while preserving data security. The resulting management architecture provides a quantitative and scalable solution for digital supply chain coordination and demonstrates practical value for intelligent logistics systems. Moreover, its distributed information-sharing mechanisms and network-oriented optimization strategies offer methodological references for communication-enabled industrial ecosystems, wireless sensing infrastructures, and electromagnetic information transmission environments requiring reliable multi-node coordination and secure data exchange.
With the rapid advancement of industrial Internet technologies and intelligent wireless sensing infrastructures, efficient data acquisition and information transmission have become fundamental to modern textile supply chain management. The integration of electromagnetic-enabled Internet of Things (IoT) devices, RFID technologies, and intelligent communication networks provides essential support for real-time financial monitoring and digital taxation services. Against this background, this paper investigates the application of intelligent finance and taxation in textile industry supply chains by proposing an integrated framework based on artificial intelligence, blockchain, cloud computing, and IoT technologies. The framework enables transparent financial management, automated tax compliance, dynamic supply chain finance, and end-to-end traceability through seamless integration of operational, financial, and logistics data. Key applications, including blockchain-based material provenance verification, AI-driven credit assessment, automated customs and tax processing, and intelligent risk management, are systematically analyzed. The proposed architecture improves supply chain transparency, operational efficiency, sustainability, and resilience while facilitating data-driven decision-making across textile production and distribution processes. Furthermore, the study demonstrates that intelligent finance and taxation can establish a unified digital ecosystem for financial governance and supply chain collaboration, providing valuable technical references for wireless industrial information acquisition, smart sensing, and communication-assisted digital management in future intelligent manufacturing environments.
A transformação digital tem ampliado a adoção de tecnologias capazes de fortalecer a eficiência, a transparência e a confiabilidade dos processos organizacionais. Nesse contexto, este estudo propõe um modelo conceitual de integração entre a tecnologia blockchain e o Índice de Resiliência Organizacional (IRO), com o objetivo de fortalecer a produtividade, a eficiência empresarial, a governança corporativa e a confiabilidade dos indicadores utilizados na gestão organizacional. A pesquisa possui abordagem qualitativa, exploratória e descritiva, fundamentada em revisão bibliográfica e análise documental sobre blockchain, transformação digital, produtividade, eficiência organizacional, governança corporativa e resiliência organizacional. O modelo proposto incorpora o blockchain como uma camada tecnológica de confiança aplicada ao IRO, possibilitando maior integridade, autenticidade, rastreabilidade, transparência e auditabilidade dos dados utilizados na avaliação da resiliência organizacional. A integração também considera o potencial dos contratos inteligentes para automatizar procedimentos, validar evidências, atualizar indicadores e fortalecer mecanismos de controle interno. A análise demonstra que informações organizacionais mais seguras e verificáveis podem contribuir para decisões baseadas em evidências, redução de vulnerabilidades, melhoria dos processos, fortalecimento da governança e maior capacidade adaptativa das organizações. Como contribuição teórica, o estudo aproxima dois campos ainda pouco integrados na literatura: blockchain e avaliação da resiliência organizacional. Conclui-se que a integração entre blockchain e IRO constitui uma proposta inovadora para ampliar as aplicações da tecnologia blockchain na Administração e apoiar o desenvolvimento de organizações mais produtivas, eficientes, transparentes, resilientes e sustentáveis.
In blockchain-enabled supply chain finance, traditional credit risk assessment models suffer from conflicts between data sharing and privacy protection, reliance on static evaluation methods, and limited data credibility. To overcome these challenges, this paper proposes a blockchain-based dynamic credit risk assessment model that integrates privacy computing and intelligent risk monitoring. First, blockchain’s immutability and traceability ensure the authenticity and transparency of supply chain transaction data, effectively mitigating information asymmetry and data tampering. Second, privacy-preserving technologies, including homomorphic encryption based on the Paillier algorithm and zk-SNARKs, enable secure data sharing and validity verification without exposing sensitive enterprise information, thereby improving assessment reliability. Third, a dynamic risk monitoring framework is constructed by combining smart contracts, long short-term memory (LSTM) networks, and an improved dynamic graph neural network (DGNN). LSTM models temporal risk evolution in transaction data, while DGNN captures risk propagation among upstream and downstream enterprises. Smart contracts synchronize transaction states in real time, allowing continuous updates of credit risk levels. The proposed secure information processing and dynamic graph modeling strategy also provides a valuable reference for trustworthy data interaction and intelligent decision-making in distributed electromagnetic sensing and communication networks, where reliable information propagation and adaptive resource management are essential. Experimental results based on a textile supply chain dataset show that the proposed model achieves approximately 94% credit assessment accuracy, outperforming traditional static models by 15%–20%, while maintaining excellent response speed and throughput for dynamic financial decision-making. The proposed framework provides a practical and secure solution for blockchain-based credit risk management and offers methodological insights for data-driven engineering systems requiring secure information fusion and dynamic network analysis.
Background: Despite the growing adoption of hybrid contract models in construction, energy, and agricultural procurement, there remains a significant gap in understanding how lump-sum and unit-price contracts differentially allocate risk across sectors and country contexts. This study addresses this gap by examining risk mitigation strategies through document analysis and thematic synthesis. Objective: The aim of this study was to identify key risk allocation strategies, contractual mechanisms, and the effectiveness of hybrid models in managing uncertainty across developed and developing country contexts. Methods: A qualitative approach based on thematic analysis and cross-case comparison was applied, drawing on 48 peer-reviewed sources published between 2015 and 2025, alongside relevant sector documents and procurement reports. Results: The analysis identified that hybrid contracts reduced cost overrun variability by incorporating performance-based incentives aligned with Expected Utility Theory and Principal-Agent Theory, while developing economies such as Indonesia and Bangladesh exhibited distinct risk profiles requiring adaptive contract mechanisms. However, significant gaps remain, particularly regarding the empirical validation of blockchain-enabled contract enforcement and AI-driven risk prediction, as well as the underrepresentation of developing economy contexts in existing research. Conclusion: The findings carry both scientific and practical implications. Theoretically, this study advances an integrative multi-theory framework combining Expected Utility Theory, Game Theory, and Principal-Agent Theory to analyse contract risk across diverse contexts. Practically, the results provide evidence-based guidance for procurement professionals and policymakers in selecting and designing contract structures that balance cost certainty with adaptive flexibility.
Economic Guarantees of Security (EGS), Internal Resistance Series, Working Paper No. 4 Work in Progress —August, 2026 Affiliation: International Institute of Political Philosophy (Kyiv, Ukraine) Author: Prof. Aleksandr Rozenfeld Contact: aleksrozenfeld2021@gmail.com Abstract This paper is part of the research series Internal Economic Resistance within the broader research program Economic Guarantees of Security (EGS). It develops the concept of business economic resistance as an endogenous constraint on military aggression and examines its role within a two-loop model of deterrence. Unlike conventional approaches that regard business primarily as a passive object of wartime mobilization, this study conceptualizes business as a decentralized network of autonomous economic agents possessing independent objectives, assets, contractual obligations, and decision-making authority. The paper argues that the principal source of business resistance lies not merely in expected financial losses but in the anticipated erosion of entrepreneurial freedom, property rights, contractual stability, market access, and institutional predictability. These institutional threats generate rational behavioral responses, including reduced investment, capital flight, production adjustment, contract restructuring, market reallocation, informal economic activity, and business exit. Although these responses rarely take the form of organized political protest, their diffusion through production, financial, contractual, and logistical networks gradually reduces the fiscal, technological, and organizational capacity of the state. The paper introduces the concept of an economic mobilization limit, defined as the point beyond which additional state pressure no longer increases, but instead diminishes, the effective resources available for military mobilization. Particular attention is devoted to the anticipatory nature of business behavior. Economic resistance frequently begins before the outbreak of war, as firms respond to expected sanctions, mobilization measures, regulatory restrictions, and institutional uncertainty. Consequently, well-designed systems of Economic Guarantees of Security can influence expectations at the decision-making stage, activating endogenous economic constraints before military aggression occurs. The proposed framework extends traditional deterrence theory by integrating external economic measures with internally generated behavioral responses of business. It demonstrates how decentralized economic decisions can complement international sanctions and other preventive mechanisms, thereby strengthening both the prevention of aggression and the conditions for its termination. One of the key conclusions of this work is that military aggression can be not only prevented but even stopped not only by external pressure measures but also by the economic behavior of businesses. The work presents and expands on a two-loop deterrence model that links international pressure measures with the internal disobedience of economic agents.
Open access
2 source records
Infrastructure Resilience and Vulnerability Analysis
Ms. Gunavarthani S, Dr. Princy J, Ms. Samyuktha S K
The textile industry has undergone a dramatic change in recent times because organizations are incorporating digital technology solutions for addressing issues related to sustainability and fast-tracking the journey toward a circular economy. These include Digital Product Passports (DPP), blockchain, Radio Frequency Identification (RFID), the Internet of Things (IoT), Artificial Intelligence (AI), and Industry 4.0 technologies, among others. The current research intends to conduct a systematic review of the literature on the topic of digital transformation and sustainability in the textile industry. A Systematic Literature Review (SLR) was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. In all, 55 peer-reviewed journals from 2020 to 2026 have been reviewed based on a structured selection process and analyzed using the thematic analysis approach. Six themes have been identified in the literature, which are as follows: Digital Product Passport, Digital Traceability Technologies, Industry 4.0 & Artificial Intelligence, Circular Economy Practices and Circular Supply Chains, Sustainability and Environmental, Social & Governance (ESG), and Barriers, Challenges and Future Research Directions. The results show that digital technology greatly improves the traceability of products, efficiency, and resource recycling, facilitating sustainability along the supply chain. Yet, issues such as costly digital technology implementation, inadequate digital infrastructure, the absence of standardization in digital data structures, and organizational readiness hinder digital technologies' broader application. This research fills a gap in the literature in that it identifies a consolidated thematic framework explaining the role of digital technologies in transforming the industry sustainably. The results provide insights useful for academic studies, industry professionals, and policymakers working on sustainable textile ecosystems powered by digital technology.
This study develops and validates an ecological economics framework integrating green logistics practices to enhance supply chain resilience in Southeast Asia's automotive sector. Using mixed-methods analysis of 272 firms across Thailand, Indonesia, and Malaysia, structural equation modelling confirms three hypotheses: green logistics adoption significantly predicts resilience (β=0.38, p 4.0 thresholds flipping green logistics from cost to profit centre. Findings advance dynamic capabilities theory with biophysical limits, resolve triple bottom line tensions, and deliver managerial roadmaps (rail pilots→FCA training→blockchain Scope 3) plus ASEAN policy blueprints (CBAM harmonisation, $500M capacity fund). The framework positions the ASEAN automotive sector for regenerative leadership, converting natural capital from externality to competitive asset amid global decarbonisation pressures. These findings offer actionable insights for managers, investors, and policymakers seeking to align profitability with ecological resilience in emerging economies.
Open access
Supply Chain Resilience and Risk Management
Sustainable Supply Chain Management
Infrastructure Resilience and Vulnerability Analysis
This study investigates the multidimensional impacts of the Industry 5.0 paradigm on logistics and supply chain management, focusing primarily on human-centric digitalization and sustainability dynamics. Within this scope, aspects of operational efficiency, resilience, and financial performance are analysed as complementary dimensions within the thematic synthesis. Emphasising human-centric digitalization, it examines how the integration of advanced technologies with social responsibility principles reshapes supply chain strategies, fosters organisational transformation, and creates competitive advantages in the context of sustainable development. Following the PRISMA protocol, and using the Web of Science Core Collection as the primary database, a systematic literature review of 47 peer-reviewed studies was conducted, mapping thematic linkages among digitalization, resilience, financial outcomes, and sustainability. The synthesis identifies a conceptual framework that positions human–machine collaboration as a central enabler for sustainable transformation, enhancing decision-making, adaptability, energy efficiency, carbon footprint reduction, green innovation, and financial outcomes. Findings highlight interconnected pathways through which digitalization generates both operational gains and long-term strategic resilience. This study contributes an original analytical lens that unites human-centric digitalization, sustainability, resilience, and financial performance within a single framework, offering actionable insights for aligning technological innovation with sustainable supply chain strategies. In particular, the study points to practical pathways such as the use of digital twins for resource optimisation, blockchain for supply chain transparency, and AI-driven solutions for emission reduction.
Digital and intelligent fresh-product supply chains increasingly rely on third-party logistics providers (TPLs) to record and disclose transport-process information. However, the TPL bears data-collection and digital-governance costs while capturing only part of the market value created by credible disclosure. This study develops a supplier-led Stackelberg game for a supplier–TPL–retailer supply chain. Contractual terms are negotiated before operation. Conditional on the negotiated contract, the supplier sets the wholesale price, the TPL selects the disclosure level, and the retailer determines the retail price. We derive decentralized equilibria under blockchain and non-blockchain regimes and compare cost-sharing and joint cost-sharing/revenue-sharing contracts. The results show that cost-sharing increases the TPL’s optimal disclosure level, but disclosure upgrades occur through discrete threshold jumps. Blockchain adoption depends jointly on fixed implementation costs and reliability improvements, and cost-sharing alone may not ensure both adoption and high-level disclosure. Introducing revenue-sharing allows the TPL to internalize part of the demand-side value generated by credible disclosure, leading to a Pareto-improving coordination interval for all supply-chain members. The findings provide a mathematical basis for designing incentive-compatible contracts for blockchain-enabled disclosure in digital fresh product supply chains.
Perkembangan Artificial Intelligence (AI) telah mendorong transformasi dalam Supply Chain Management (SCM) melalui peningkatan efisiensi operasional, kualitas pengambilan keputusan, dan ketahanan rantai pasok. Penelitian ini bertujuan untuk mengidentifikasi perkembangan penelitian AI dalam SCM, teknologi AI yang dominan, manfaat dan tantangan implementasinya, serta peluang penelitian pada periode 2021–2026. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan menganalisis sepuluh artikel yang relevan dari berbagai sumber ilmiah. Data dianalisis menggunakan pendekatan narrative synthesis untuk mengidentifikasi pola, persamaan, dan perbedaan hasil penelitian. Hasil kajian menunjukkan bahwa Machine Learning merupakan teknologi AI yang paling banyak diterapkan, terutama pada demand forecasting, inventory management, optimasi logistik, dan manajemen risiko. Selain itu, perkembangan penelitian juga mengarah pada pemanfaatan Deep Learning, Computer Vision, Natural Language Processing, Digital Twin, dan integrasi AI dengan Internet of Things serta Blockchain untuk meningkatkan transparansi, fleksibilitas, dan ketahanan rantai pasok. Meskipun implementasi AI memberikan manfaat yang signifikan, masih terdapat tantangan berupa kualitas data, kesiapan infrastruktur digital, keamanan siber, dan kompetensi sumber daya manusia. Penelitian ini memberikan gambaran mengenai tren perkembangan AI dalam SCM sekaligus menjadi referensi bagi pengembangan penelitian dan implementasi AI pada berbagai sektor industri.
Scalable event-driven architectures are now the focus of enterprise supply chain and logistics research as this information is surfaced from transport assets, warehouses, suppliers, platforms and risk environments at a more frequent rate to allow for faster decision making. This review looks at the concepts of peer-reviewed studies of 2015–2025 that have focused on architectures that have the ability to transform distributed events into traceability, resilience, visibility, and automated coordination. The review of the literature shows that there is no single concept but rather scattered concepts in the domain of scalable event-driven logistics within the fields of Internet of Things (IoT) in logistics, Logistics 4.0, big data analytics, blockchain traceability, multi-agent control and digital supply chain twins. These streams have significant challenges around event capture, real time analytics, decentralized provenance, and disruption response. Key gaps remain in latency benchmarking, cross-enterprise semantic interoperability, governance of shared event streams, and validated architecture-level performance evidence. The field is significant due to the increased reliance on enterprise architectures that extend beyond the organizational boundary that are also responsive, auditable and resilient.
This research paper aims to investigate the applications of blockchain technology and smart contracts in managing global supply chains, as well as their financial and economic implications. The study addressed the concept of supply chain management and the technologies of blockchain and smart contracts, with a focus on their applications in validating and tracking transactions, facilitating the flow and storage of information in international trade, supporting customs, shipping, and container management operations, and increasing transparency in commercial transactions. The most prominent digital supply chain platforms based on blockchain were also presented. The results showed that the application of these technologies contributes to reducing costs and fees, improving cash management, increasing transparency and reducing risks, and enhancing the economic and operational efficiency of exporting and importing companies. It also provides administrative bodies with the ability to track and verify transactions instantly, which supports more accurate financial and strategic decision-making.
Purpose This study develops a pricing and contract design framework for cryptocurrency catastrophe (CAT) bonds to transfer extreme crypto-native risks, including protocol exploits, exchange breaches and decentralized finance (DeFi) failures, to capital markets. The paper aims to address arbitrage-free valuation, sponsor-optimal contract design and trustless settlement under the unique informational and operational features of blockchain systems. Design/methodology/approach We propose a multi-trigger crypto CAT bond structure that jointly captures short-term catastrophic shocks and long-term systemic deterioration through oracle-reported loss metrics. An arbitrage-free valuation framework is developed under an incomplete market setting using the minimal martingale measure, while sponsor-optimal contract design is formulated under a dual-measure framework. Empirically, crypto loss dynamics are modeled using generalized extreme value distributions and copula-based dependence structures, whereas financial risk factors are modeled through ARIMA–GARCH and vine copulas. A smart-contract-enabled on-chain settlement architecture is further introduced to automate trigger evaluation and cash-flow execution. Findings Empirical results based on REKT crypto incident data demonstrate strong dependence between monthly extreme and aggregate losses, with heterogeneous dependence structures across blockchain ecosystems. Simulation studies show that trigger and principal repayment designs substantially affect bond price distributions and tail risk exposures. Conservative trigger structures generate more stable bond valuations, whereas aggressive structures exhibit greater downside dispersion. The proposed framework supports economically viable risk transfer while enabling transparent and timely settlement through blockchain-based execution. Originality/value This study develops, to the best of our knowledge, the first integrated framework for crypto native catastrophe bonds that combines arbitrage-free pricing, sponsor optimal contract design and smart contract-based on-chain settlement. Unlike traditional CAT bonds or cyber insurance-linked securities the proposed framework explicitly incorporates oracle-based observability, crypto-specific dependence structures and automated settlement, providing a novel mechanism for transferring systemic digital asset risks to capital markets.
Blockchain technology has emerged as one of the most transformative innovations of the digital economy, extending far beyond cryptocurrencies into sectors such as finance, healthcare, logistics, governance, and intelligent automation. This study critically examines the role of blockchain technology in enhancing global economic growth through decentralization, transparency, cybersecurity, smart contracts, and digital trust mechanisms. Drawing upon contemporary literature and emerging industrial applications, the paper explores how blockchain contributes to economic resilience, operational efficiency, supply chain optimization, decentralized finance (DeFi), central bank digital currencies (CBDCs), and AI-integrated digital ecosystems. The study adopts a conceptual and analytical approach to evaluate blockchain’s macroeconomic implications and institutional challenges in the context of Industry 4.0. Findings suggest that blockchain has the potential to reduce transaction costs, enhance cross-border economic integration, improve governance transparency, and facilitate sustainable digital transformation. However, regulatory uncertainty, scalability limitations, cybersecurity concerns, and energy consumption remain significant barriers to global adoption. The paper contributes to the literature by proposing an integrated framework linking blockchain innovation with economic sustainability, digital governance, and technological resilience. Policy implications and future research directions are also discussed.
Few forces have reshaped organizational life as quickly as digital transformation. The way firms create value, manage risk, and hold their competitive ground now depends on systems that grow more entangled with one another every year. A typical enterprise sits at the center of a constant flow of data drawn from its operations, its cloud platforms, the sensors embedded in its products, its planning systems, and the many places where it meets its customers. Artificial intelligence (AI), machine learning, big data analytics, blockchain, and cybersecurity have each made it easier to turn that flow into useful judgment. Yet most organizations still adopt these tools one at a time, and the habit quietly erodes the strategic payoff that integration could deliver. This paper sets out an Autonomous Decision Intelligence (ADI) framework that gathers AI, cybersecurity, big data analytics, blockchain, and management information systems (MIS) into one coherent architecture built for resilient digital enterprises. The argument rests on a synthesis of recent work in decision intelligence, predictive analytics, business intelligence, federated learning, cloud computing, blockchain governance, cyber threat intelligence, and enterprise risk management. From that body of evidence, we construct a conceptual model for organizational decision-making that is trustworthy and capable of improving itself over time. The framework gives weight to secure data governance, explainable AI, privacy-preserving analytics, blockchain-based trust, cyber-resilience, and intelligent automation. It then asks how such a design might reinforce critical infrastructure protection, supply chain resilience, economic sustainability, IT project governance, and day-to-day agility. The contribution is at once theoretical and practical, because it shows how converging technologies can turn conventional decision-support systems into adaptive ecosystems that learn. Organizations that pair AI-driven analytics with strong security and decentralized trust look best placed to absorb uncertainty, keep operating under stress, and pursue digital transformation that lasts.
Blockchain Technology is a Distributed Ledger Technology (DLT) where the data (digital information) is stored in multiple computers and not in a centralized one [1]. Each system would store a copy of the distributed ledger to avoid pitfalls. The information persists as blocks and gets updated simultaneously on all environments after being validated. Four main types of Blockchain, as described: private/permissioned, public/permissionless, hybrid, and consortium [2]. Corda is an open-source platform of a distributed ledger founded by R3 Consortium (R3CEV LLC). DLT is based on peer-to-peer connections with an agreement, and it is not part of the public. Corda architecture is non-native to cryptocurrencies. The platform is based on top of the Java Virtual Machine (JVM), written in Kotlin. Overall, it explains how Corda can be implemented in a wide range of industries with private/permissioned networks. Earlier, blockchain technology was public and permissionless, which posed a little challenge to many industries to adapt, even in the Supply Chain Management system (SCM) and healthcare. Corda is an open-source and DLT concept with private and permissioned features that make it easy to use in industries like SCM, and how that can be achieved.
Supply Chain Finance (SCF) enhances financial liquidity, optimizes operational efficiency, and strengthens collaboration among supply chain stakeholders. Traditional SCF systems are vulnerable to economic risks, fraud, and a lack of transparency due to centralized data control and limited real-time monitoring capabilities. Current systems rely on slow central financial bodies and static credit evaluations, lacking dynamic risk assessment and mechanisms to build stakeholder trust. This research proposes Bi-MATRA, a Blockchain-Integrated Multi-Agent Trust and Risk Assessment system. The goal is to create a decentralized, intelligent risk evaluation system for real-time trust computation and automated decision-making in SCF contexts. Bi-MATRA combines blockchain's immutability and transparency with a multi-agent system that automatically monitors and assesses risk. Bi-MATRA uses an adapted Eigen Trust algorithm on the blockchain to calculate trust. Agents rate each other based on interaction history, and transitive trust linkages determine global trust ratings. Smart contracts for dynamic trust-based decision-making update these scores. Smart contracts enforce predefined financial agreements, automating processes and reducing the need for intermediaries. Agent-based modelling was employed to develop a blockchain-based simulation testbed for assessing Bi-MATRA's responsiveness, accuracy, and resilience. The framework was more efficient at trust validation and risk identification than typical SCF systems. Key studies reveal that Bi-MATRA saves transaction clearance time by 35% and improves early risk detection by 28%. EigenTrust-based trust computation builds agent trust and accountability, enhancing financial interactions. In conclusion, the Bi-MATRA framework offers a scalable, decentralized approach to intelligent risk assessment and trust-building in blockchain-enabled supply chain finance systems.
This paper explores contemporary blockchain applications in the supply chain and logistics sector, focusing on how distributed ledger technology enhances transparency, traceability, security, and operational efficiency across complex global supply networks. The study reviews key blockchain concepts, including consensus mechanisms, smart contracts, and tokenization, and examines their role in improving coordination among stakeholders. Real-world case studies from organizations such as Walmart, Nestlé, Pfizer, Moderna, and Maersk demonstrate practical implementations of blockchain for product traceability, anti-counterfeiting, and shipment tracking. The paper also analyzes major challenges facing blockchain adoption, including scalability limitations, regulatory uncertainty, interoperability issues, and data reliability concerns. Furthermore, it highlights emerging trends such as integration with artificial intelligence, Internet of Things technologies, digital twins, sustainability tracking, and cross-chain platforms. The findings suggest that blockchain plays a critical role in supporting digital transformation initiatives aligned with Industry 4.0 and enables the development of more resilient, transparent, and trustworthy supply chain ecosystems.
Ethereum's EIP-1559 fee mechanism was designed under the assumption of homogeneous, myopic agents responding to a single congestion signal. We examine how this assumption interacts with the heterogeneous demand structure of real-world Ethereum users. Analyzing 62,142 confirmed transactions from seven operational firms across seven industries (January--March 2026), we document significant intraday gas-fee variation: fees peak at hour~12 UTC (7\,AM ET, $\hatβ_{12}=\$0.054$ above the U.S.\ evening baseline, $p<0.001$) and are associated with periods of elevated speculative-arbitrage activity. Operational firms exhibit heterogeneous scheduling responses moderated by transaction deferrability and gas intensity. Residual cost floors, i.e. the gap between observed expenditure and the counterfactual under perfect off-peak scheduling, range from 40.7\% to 92.5\% of actual expenditure, and persist even during the lowest-cost hours ($h\in\{20,21,22,23\}$ UTC, 3--6\,PM ET). We introduce an On-Chain Scheduling Matrix that maps firms to four scheduling regimes as a practical framework for managing gas-fee exposure under the current mechanism.
Abstract It is becoming more difficult for global supply chain networks to be governed because of issues such as a lack of transparency, data fragmentation, regulatory divergence, and the likelihood of having more than one supplier. This paper introduces an innovative Blockchain-Enabled Governance Framework (BEGF) that amalgamates distributed ledger technology with an ensemble machine learning (ML) risk-scoring engine and a multiobjective linear programming (LP) optimizer to enable supply chain decisions that are transparent, real-time, and verifiable. We use the framework in five long-term industrial case studies: automotive (Toyota), pharmaceutical (Merck), electronics (Samsung), apparel (Zara), and food and drink. These studies include 1,840 supplier nodes in 37 countries over 36 months. The BEGF can predict disruptions with an average AUC of 0.947. It can also reduce the number of supply disruptions by 38.5% and save each business $13.8 million a year. The Pareto-optimal optimizer lowers costs all along the supply chain.
O presente artigo examina técnicas de compressão de dados em blocos de blockchain e sua relação com a redução de custos de armazenamento on-chain, considerando tanto o consumo de espaço em disco pelos nós da rede quanto o custo econômico de inclusão de dados em transações. Em plataformas como o Ethereum, parcela significativa do custo de transações – especialmente para rollups e aplicações que utilizam calldata de maneira extensiva – está associada ao armazenamento e à propagação de bytes de dados na camada base, o que tem motivado a adoção de estratégias de compressão de calldata, ajuste de parâmetros de gas e a introdução de novas formas de armazenamento temporário, a exemplo de blobs de dados. A metodologia empregada baseia-se em revisão bibliográfica de trabalhos sobre estratégias de armazenamento on-chain, análise de propostas de melhoria do ecossistema (Ethereum Improvement Proposals – EIPs) voltadas à redução de custos de calldata, exame de relatórios técnicos sobre compressão de dados em soluções de camada 2 e análise recente do impacto de mecanismos como o EIP-4844 (proto-danksharding) sobre a estrutura de custos de rollups. As evidências levantadas indicam que técnicas de compressão aplicadas ao payload de transações podem reduzir da ordem de cinco vezes o tamanho efetivo dos dados enviados, traduzindo-se em economias de aproximadamente 50% no gasto de gas para determinadas operações em redes de segunda camada, sem exigir mudanças disruptivas no protocolo subjacente. Paralelamente, abordagens estruturais – entre as quais a separação de dados de disponibilidade em blobs não permanentes e o emprego de rollups que publicam apenas provas e raízes de estado, em vez de dados completos – contribuem para reduzir a pressão de armazenamento permanente sobre os nós da camada 1. Conclui-se que a compressão de dados em blocos, combinada a ajustes de modelo de dados (blobs, rollups, off-chain storage) e a técnicas de compressão clássicas (run-length, delta, dicionário, entropia), constitui peça central na estratégia de escalabilidade e sustentabilidade econômica da Web3, não obstante levante desafios relevantes quanto à complexidade de implementação, compatibilidade entre clientes e preservação da verificabilidade de longo prazo.
O presente artigo examina as distinções entre finalidade probabilística e finalidade absoluta em sistemas blockchain, bem como suas implicações para o desenho e a operação de aplicações financeiras que visam a replicar ou substituir infraestruturas tradicionais de liquidação. Em cadeias que operam sob finalidade probabilística – modelo historicamente associado a protocolos baseados em Prova de Trabalho (Proof-of-Work) – o grau de irreversibilidade de uma transação cresce à medida que novos blocos são adicionados sobre o bloco que a contém, de modo que a probabilidade de reversão tende assintoticamente a zero sem, contudo, alcançar garantia determinística, o que justifica a prática de mercado de aguardar múltiplas confirmações antes de considerar a liquidação efetivamente concluída. Em contrapartida, cadeias dotadas de finalidade absoluta – também denominada finalidade instantânea – usualmente implementadas sobre protocolos de tolerância a falhas bizantinas (BFT) ou em arquiteturas híbridas que combinam Prova de Participação (PoS) e BFT, oferecem irreversibilidade assim que um bloco é atestado por um superconjunto qualificado de validadores, aproximando-se das expectativas de definitividade inerentes a sistemas de liquidação financeira tradicionais. A metodologia adotada combina revisão conceitual das diferentes acepções de finality em mecanismos de consenso, análise de documentação técnica de protocolos BFT – a exemplo de Tendermint, IBFT e QBFT – e discussão de relatórios recentes sobre risco de liquidação e finality aplicáveis à tokenização de ativos do mundo real (Real World Assets – RWA) em infraestruturas on-chain. Os resultados obtidos sinalizam que, embora a finalidade probabilística se mostre adequada a pagamentos de varejo e transferências de valor moderado, aplicações financeiras de maior montante, processos de tokenização de ativos e infraestruturas de mercado requerem, na prática, garantias mais robustas de irreversibilidade, com frequência combinando finalidade técnica e mecanismos jurídicos de mitigação de risco de liquidação. Conclui-se que a opção entre os dois modelos de finalidade encerra trade-offs relevantes entre segurança, velocidade de confirmação, complexidade de protocolo e conformidade regulatória, e que o desenho de aplicações financeiras em ambiente Web3 deve considerar explicitamente essas diferenças ao definir janelas de liquidação, políticas de gerenciamento de risco e estratégias de integração com o sistema financeiro tradicional.