The digital economy is rapidly transforming the global landscape by integrating technology, entrepreneurship, and innovation across every sector. Startups have become the key drivers of this transformation, enabling new models of production, finance, and governance. By 2047, the digital economy is expected to evolve into a deeply interconnected system powered by artificial intelligence, blockchain, decentralized finance, and sustainable technologies. These advancements will reshape industries, empower small enterprises, and foster inclusive growth. This paper explores how startups will act as engines of innovation, leveraging digital tools to solve complex social and economic challenges. It highlights emerging trends such as AI-driven decision-making, edge computing, green technologies, and decentralized governance models that will redefine the global business environment. At the same time, the paper acknowledges the challenges of data privacy, cybersecurity, skill development, and environmental sustainability. Through policy analysis and strategic recommendations, the study emphasizes the importance of strong digital infrastructure, ethical data practices, and inclusive innovation ecosystems to ensure balanced growth. By 2047, success in the digital economy will depend not only on technological advancement but also on human creativity, collaboration, and sustainable practices.
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.
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
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.
In the context of Industry 4.0, industrial firms are encountering new challenges related to data management, flow traceability, security and process transparency. Blockchain, as a distributed ledger technology, offers innovative solutions to meet these challenges. This study proposes a systematic literature review (SLR) on the recent contributions of blockchain in industrial environments. A total of 20 scientific articles, published over the last ten years, were analyzed to better understand how this technology is being integrated into production processes and supply chains. The analysis identified four major areas in which blockchain is being mobilized: traceability of production processes, transparency of supply chains, integration into digital industrial systems, and its role in decision support. The results show that blockchain enables reliable, real-time monitoring of industrial operations, particularly when coupled with technologies such as IoT, smart contracts or event-driven databases. It also promotes better coordination between players, reinforces trust, and facilitates audits in complex or multi-actor environments. However, despite its potential, several limitations remain. Barriers related to scalability, implementation costs, system interoperability and the integration of manual tasks still limit its widespread adoption. Furthermore, in many cases, blockchain is treated as a secondary technology, reducing the depth of analysis available. This review offers a structured vision of the contributions and limitations of blockchain in industry while identifying future research prospects, particularly around hybrid models and concrete implementation cases.
This paper investigates how Web3 technologies, such as blockchain, NFTs, and the metaverse, can drive Business Model Innovation (BMI) by enabling new forms of value creation, delivery, and capture. While the strategic potential of Web3 has been widely discussed, there remains a lack of operational tools to guide its implementation in real-world business contexts. To address this gap, we introduce the Web3 Value Exploitation De sign Model (Web3 VEDM), a step-by-step framework grounded in the GUEST methodology. The model is designed to support engineering managers in assessing Web3 readiness, aligning stakeholders, and developing decentralized business models. The framework is empirically validated through a real-world case study in the agri-food sector, offering actionable insights into how organizations can leverage Web3 to transition from centralized to decentralized, participatory ecosystems. The study contributes both theoretically and practically by bridging the gap between conceptual exploration and structured application of Web3 in business transformation.
Purpose Cement manufacturing is a vital yet emission-intensive industry that faces challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. This paper provides a blockchain-based certification framework to enhance traceability and sustainability in cement production. Design/methodology/approach Leveraging Ethereum smart contracts (SCs) and blockchain technology, our solution ensures decentralized, immutable tracking of emissions data, production processes and compliance through secure interactions among regulators, manufacturers and auditors. The framework facilitates deployment, registration, reporting, auditing and certification. Detailed insights into system architecture, algorithms, SC implementation and validation are provided. Security analysis evaluates access controls, data privacy and vulnerability mitigation, while cost analysis highlights the framework's economic feasibility by examining gas costs for key functions. Findings The blockchain-based framework successfully produced a scalable solution with a modular design to allow for flexible deployment by different regulatory bodies. Transparency was achieved through blockchain events announcement, and accuracy was ensured through periodic auditing rounds. Security analysis results show no serious vulnerabilities. The developed framework proves a cost-effective solution for certifying sustainable production practices in the cement industry, with potential applications across other heavy manufacturing sectors. Research limitations/implications Cement manufacturing is a vital yet emission-intensive industry that faces significant challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. Practical implications We believe that this paper provides the following practical implications: Innovative Framework: A blockchain-based certification system promoting adherence to sustainable processes in cement manufacturing. SC Implementation: Detailed insights into the system architecture, implementation and validation of algorithms and SCs. Comprehensive Analysis: A thorough security analysis of SC coding and a cost analysis highlighting the economic feasibility of our proposed framework. Social implications The proposed methodology will help all stakeholder involved in the cement production and supply chain have a better understanding and a robust tool to observe and learn about operations, tasks and other activities made through sustainable cement production Originality/value The study contributes a novel blockchain-based method to certify sustainable production in energy-intensive industries, especially cement, offering a valuable tool that ensures transparency and immutability.
Ningshuang Zeng, Xuling Ye, Shiqi Chen, Yan Liu · 5 authors
Although existing models and theories have explained systemic behaviors such as demand amplification and disruption propagation, practical challenges in Modular Construction Supply Chains (MCSC) remain unresolved due to production heterogeneity, geographic dispersion, and conflicting stakeholder interests. In addition, the lack of digital infrastructure and process-level data integration continues to hinder the development of automation and intelligent decision-making. To address these issues, this study develops an MCSC coordination system informed by industrial input. The system features a novel dual-engine architecture that integrates blockchain-enabled smart contracts and Robotic Process Automation (RPA). It also incorporates a practice-oriented approach to MCSC Supply Batch (MSB)-based management, using industrial insights to define the MSB as the fundamental coordination unit in process execution. The automatic triggering mechanism enabled by MSBs and dual-engine enables task-to-task transitions while maintaining traceability and operational clarity across supply chain nodes. A real-world case study validates the effectiveness of the proposed system in enhancing traceability, automation, and stakeholder collaboration within MCSC environments.
Dun Li, Dezhi Han, Noël Crespi, Roberto Minerva · 8 authors
Digital twin (DT) technology integrates Internet of Things (IoT), communication networks, and sensor systems through high-fidelity modeling and multi-dimensional simulation, enabling dynamic mapping and real-time optimization of physical objects. However, DT development still faces several challenges, including cross-platform interoperability limitations, excessive latency in real-time scenarios, security vulnerabilities in distributed deployments, and the complexity of accurately modeling multi-modal systems. Blockchain (BC) enhances the security and functional scope of DTs across diverse applications. This survey begins by introducing the core principles of BC and DT, and then investigates the rationale and benefits behind their integration. From a data-centric perspective, we explore how Blockchain-empowered Digital Twins (BCDTs) enhance data storage, secure exchange, privacy protection, and system interoperability. The survey further explores the architecture of BCDT systems, covering network topology, functional modules, platform design, and representative prototypes, offering insights into real-world applications. In addition, we survey how BCDT supports the convergence of key Industry 4.0 technologies, including the Internet of Things, vehicle networks, unmanned aerial systems, artificial intelligence, federated learning, 5G mobile networks, and software-defined networking. Industrial-grade quality BCDT-supported applications are highlighted, providing a solid foundation for further research. Finally, we analyze the challenges faced by BCDT and offer some optimistic suggestions for further research in the field of BCDT.
Slow and disputed progress payments undermine contractor liquidity and project schedules due to manual verification, fragmented data, and limited transparency. This paper presents a prototype system that incrementally integrates digital twin (DT), building information modeling (BIM), and blockchain to automate milestone-based payments. The continuously updated DT is conceptualized as a dynamic oracle, capturing real-time site conditions and comparing them with structured BIM milestones. Verified achievements trigger Ethereum smart contracts referencing Merkle-proofed evidence stored off-chain in InterPlanetary File System (IPFS), with disbursements authorized via Gnosis Safe multi-signature wallets. A prototype on a police station project shortened verification to payment from several days to minutes and eliminated disputes across all milestones. A survey of industry professionals confirmed gains in efficiency, transparency, and trust. The proposed system provides a practical foundation for transparent, automated payments and offers pathways for future adoption such as stablecoin settlement and public sector integration. • Digital twins resolve blockchain oracle challenges in construction payments. • BIM-blockchain integration reduces payment verification from days to minutes. • Smart contracts with multi-signature governance secure transaction integrity. • Stakeholders confirm enhanced payment speed, transparency, and trust.
This paper explores the integration of Blockchain technology into Structural Health Monitoring (SHM) to enhance data traceability, integrity, and automation in infrastructure asset management. Traditional SHM approaches, including Digital Twin-based systems, often face limitations related to data tampering, sensor unreliability, and the lack of transparent and verifiable data workflows. To address these challenges, the SHERPA framework is proposed. SHERPA leverages decentralized storage via the InterPlanetary File System and three Smart Contracts dedicated to data validation, anomaly flagging, and automated workflow execution. Rather than focusing on the structural interpretation of data, SHERPA establishes a secure and auditable backbone for SHM data governance. A prototype implementation on the Canalone Viaduct in Italy demonstrated the feasibility of the system, showcasing automated response to threshold violations and immutable data registration. The framework proved effective in enhancing transparency, traceability, and stakeholder confidence, positioning SHERPA as a promising enabler of more trustworthy and accountable SHM systems.
Andrés Fernández‐Miguel, Susana Ortíz-Marcos, Mariano Jiménez, Alfonso Pedro Fernández del Hoyo · 6 authors
This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm.
Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.
The major challenges faced by confectionery supply chain are lack of information, traceability, managing the ownership of goods across supply chains, inability to track vendors in real-time. Blockchain and Internet of Things (IoT) in Industry 4.0 era help organisations to overcome these challenges by guaranteeing authentic information, real-time visibility, and transparency across the supply chain management. The extant literature has revealed that blockchain and IoT technologies are in their early stages of information management for the supply chain. This study explores the potential opportunities available with Blockchain and IoT in the confectionery supply chain. Utilising the inputs from the survey and interviews, the article identifies to present the gaps in the supply chain and proposes a new blockchain and IoT-based architecture of the food safety system for the confectionery supply chain. Typical blockchain architecture is designed for a food safety system, and the technical specifications required to implement blockchain are evaluated. Further, blockchain-assisted distribution information management is proposed to bring more visibility to the shipment of goods. Implications of deploying blockchain and IoT in the supply chain from a management perspective are discussed. The study distinguishes itself from existing literature in two ways: first, by introducing a tailored blockchain and IoT architecture specifically designed for food safety in the confectionery supply chain; and second, by demonstrating its practical implications in addressing real-time traceability, transparency, and operational efficiency in line with Industry 4.0 goals. This integrated approach helps organisations make informed decisions, reduce supply chain risks, and improve regulatory compliance across the confectionery value chain.
The sustainable and efficient management of the built environment is a crucial challenge in the increasingly digitalized AEC sector. Innovative technologies such as Building Information Modeling (BIM) and Digital Twin (DT) offer significant opportunities to enhance the operational efficiency and sustainability of physical assets. However, digitalization generates vast amounts of Big Data, and their handling through centralized architectures leads to risks of fragmentation, lack of transparency, and vulnerability to manipulation. In response to these challenges, this study presents an innovative Proof of Concept (PoC) that integrates Blockchain (BT), Digital Twin (DT), and Non-Fungible Token (NFT) technologies to promote decentralized and sustainable data management in the construction industry. The application, called dDT (decentralized Digital Twin), was initially deployed on the Solana blockchain and later integrated with Polygon to leverage EVM compatibility and the ERC-721 standard for NFTs. The platform enables the tokenization of data flows generated by physical assets, ensuring traceability, security, and transparency throughout the entire asset lifecycle. The dDT system represents a sustainable innovation as it creates a secondary data market, fostering collaboration among industry stakeholders and financing new developments through the sale of data-linked NFTs. This decentralized solution addresses fragmentation and transparency issues, promoting more secure, resilient, and sustainable data management practices. The PoC demonstrates how the integration of BT, DT, and NFT can accelerate the transition toward more efficient and innovative practices, with positive impacts on sustainability and technological advancement in the AEC sector.
Rahanatu Suleiman, Akshita Maradapu Vera Venkata Sai, Wei Yu, Chenyu Wang
Digital Twins (DTs) have become essential tools for improving efficiency, security, and decision-making across various industries. DTs enable deeper insight and more informed decision-making through the creation of virtual replicas of physical entities. However, they face privacy and security risks due to their real-time connectivity, making them vulnerable to cyber attacks. These attacks can lead to data breaches, disrupt operations, and cause communication delays, undermining system reliability. To address these risks, integrating advanced security frameworks such as blockchain technology offers a promising solution. Blockchains’ decentralized, tamper-resistant architecture enhances data integrity, transparency, and trust in DT environments. This paper examines security vulnerabilities associated with DTs and explores blockchain-based solutions to mitigate these challenges. A case study is presented involving how blockchain-based DTs can facilitate secure, decentralized data sharing between autonomous connected vehicles and traffic infrastructure. This integration supports real-time vehicle tracking, collision avoidance, and optimized traffic flow through secure data exchange between the DTs of vehicles and traffic lights. The study also reviews performance metrics for evaluating blockchain and DT systems and outlines future research directions. By highlighting the collaboration between blockchain and DTs, the paper proposes a pathway towards building more resilient, secure, and intelligent digital ecosystems for critical applications.
Supply chain finance (SCF) plays a key role in easing financing difficulties for small and medium-sized enterprises, but it also comes with risks such as information asymmetry, fraud involving pledged assets, and delays in credit evaluation.In this study, we introduce a dynamic risk management framework driven by IoT and enhanced by the integration of multiple technologies.Built on a four-layer IoT structure, comprising perception, network, processing, and application layers, the framework combines blockchain for secure and trusted data sharing, federated learning for collaborative data processing, and digital twin models for real-time risk simulation.At the perception level, 5th-Generation Mobile Communication Technology (5G)enabled low-power sensors ensure comprehensive and tamper-proof data collection.The network layer uses blockchain techniques such as sharding and zero-knowledge proofs to safeguard data privacy and institutional trust.In the processing layer, federated learning combined with edge and cloud computing enhances credit evaluation.On the other hand, the application layer employs smart contracts and feedback mechanisms to enable real-time responses and adaptive risk strategies.To put this framework into practice, we propose a phased approach: first building a real-time data ecosystem, then deploying secure risk control systems, optimizing distributed computing, and finally integrating a closed-loop risk control mechanism.This modular, collaborative strategy ensures that technological systems align with actual business needs.Ultimately, the research demonstrates how IoT, blockchain, and AI can work together to create a scalable and practical model for managing risk dynamically in SCF.
The Agentic Service Ecosystem consists of heterogeneous autonomous agents (e.g., intelligent machines, humans, and human-machine hybrid systems) that interact through resource exchange and service co-creation. These agents, with distinct behaviors and motivations, exhibit autonomous perception, reasoning, and action capabilities, which increase system complexity and make traditional linear analysis methods inadequate. Swarm intelligence, characterized by decentralization, self-organization, emergence, and dynamic adaptability, offers a novel theoretical lens and methodology for understanding and optimizing such ecosystems. However, current research, owing to fragmented perspectives and cross-ecosystem differences, fails to comprehensively capture the complexity of swarm-intelligence emergence in agentic contexts. The lack of a unified methodology further limits the depth and systematic treatment of the research. This paper proposes a framework for analyzing the emergence of swarm intelligence in Agentic Service Ecosystems, with three steps: measurement, analysis, and optimization, to reveal the cyclical mechanisms and quantitative criteria that foster emergence. By reviewing existing technologies, the paper analyzes their strengths and limitations, identifies unresolved challenges, and shows how this framework provides both theoretical support and actionable methods for real-world applications.
<ns3:p>The emergence of Web 3.0 and the Metaverse marks a transformative shift in the evolution of the internet and digital ecosystems. This paper explores the foundational principles of decentralization, user autonomy, and data transparency that underpin Web 3.0 technologies, including blockchain, smart contracts, and digital wallets. We analyze how these innovations are reshaping business models, enabling new forms of value creation, and redefining digital ownership and governance. In parallel, we examine the Metaverse as a virtual, immersive environment integrating Web 3.0 infrastructure, and its potential to revolutionize sectors such as logistics, education, finance, and data management. The study also highlights the critical role of a holistic framework encompassing technological, economic, and legal pillars. A special focus is given to data provenance, privacy-preserving computation, and the need for coherent regulatory strategies in light of GDPR, the AI Act, and the Data Act (European Parliament, 2016; European Parliament, 2023; European Parliament, 2024). Finally, we identify emerging challenges related to NFT authenticity, system sustainability, and user experience, proposing a multidisciplinary and lean governance approach to guide future developments.</ns3:p>
Ningshuang Zeng, Xuling Ye, Shiqi Chen, Yan Liu · 6 authors
The integration of off-site manufacturing with onsite construction in the Modular Construction Supply Chain (MCSC) presents significant challenges due to the differences in production methods and geographical distances.While various technologies have transformed construction practices, substantial improvements are still needed in on-and off-site coordination to mitigate delays, ensure quality, and lower costs.Blockchain-enabled smart contracts offer a promising workflow engine for enhancing processoriented MCSC coordination.Additionally, Robotic Process Automation (RPA) holds the potential to further refine the modules within MCSC processes and automate tasks.This paper, therefore, aims to develop a new workflow engine that combines blockchain-enabled smart contracts, RPA, and related visualization technologies to address the coordination challenges inherent in MCSC.Demanddriven process modeling and smart contracts conversion with RPA actions are conducted with the MCSC coordination unit configuration, and the initial system is developed and validated in a realworld case study.Industrial requirements and feedback are collected and analyzed via questionnaires and interviews.Finally, practical implications are discussed, and the integration of RPA and AI is identified as a promising exploration for future work in MCSC coordination.
Qazi Muhammad Osama, Usman Ali, Tahira Ali, Danish Ali
The rapid rate of technological development necessitates a comprehensive and morally sound framework to guarantee acceptable integration between commercial innovation, mechanical systems, and artificial intelligence (AI). This review article presents a unified approach to integrated technology management by examining the intersection of future business models, mechanical sustainability, and AI ethics. The study emphasizes the significance of matching innovation with long-term environmental and societal objectives by analyzing the ethical ramifications of AI deployment—such as algorithmic bias, transparency, and accountability—as well as developments in sustainable mechanical engineering, such as eco-efficient machinery, lifecycle optimization, and circular manufacturing. Additionally, it explores revolutionary business models that are changing value generation and operational efficiency in the contemporary technology ecosystem, including platform economies, digital twins, decentralized finance (DeFi), and Industry 5.0 frameworks. Cross-sectoral synergies that support scalability, equality, and resilience are given particular attention. In order to balance the ethical use of AI with sustainable engineering methods and progressive entrepreneurship, the assessment also emphasizes the crucial roles that legislative frameworks, data governance, and stakeholder collaboration play. In the end, this integrated viewpoint offers practical advice to academics, politicians, and business executives working to create technology systems that are nimble, sustainable, and responsible in a time of complexity and ongoing innovation.
Giovanni De Gasperis, Sante Dino Facchini, Asif Saeed
In recent years, numerous regions worldwide have experienced devastating natural disasters, leading to significant structural damage to buildings and loss of human lives. The reconstruction process highlights the need for a reliable method to document and track the maintenance history of buildings. This paper introduces a novel approach for managing and monitoring restoring interventions using a secure and transparent digital framework. We will also present an application aimed at improving building structures with respect to earthquake resistance. The proposed system, referred as the “Building Ledger Dossier”, leverages a Digital Twin approach applied to blockchain to establish an immutable record of all structural interventions. The framework models buildings using OpenSees, while all maintenance, repair activities, and documents are registered as Non-Fungible Tokens on a blockchain network, ensuring timestamping, transparency, and accountability. A Decentralized Autonomous Organization oversees identity management and work validation, enhancing security and efficiency in building restoration efforts. This approach provides a scalable and globally applicable solution for improving both ante-disaster monitoring and post-disaster reconstruction, ensuring a comprehensive, verifiable history of structural interventions and fostering trust among stakeholders. The proposed method is also applicable to other types of processes that require the aforementioned properties for document monitoring, such as the life-cycle management of tax credits and operations in the financial or banking sectors.
The article investigates the problem of ensuring the veracity and traceability of production data in digital factories, where EU regulatory requirements and a high level of counterfeiting create a critical deficit of trust in source information. The objective of this study is to analyze the architectures of blockchain solutions for data verification in supply chains and to develop a phased implementation plan that considers regulatory obligations and the protection of trade secrets. The novelty lies in a combined approach: classification of DLT networks according to scalability, cost, and privacy criteria; use of Merkle trees and zero-knowledge proofs to preserve confidential data while proving authenticity; and justification of architecture choice through practical case studies (IBM Food Trust, VeChain, Airbus/Circularise, SAP). The study demonstrates that blockchain enables a reduction in batch traceability time from days to seconds, a reduction in manual operations to 67%, and an increase in data matching accuracy to 92%. However, the immutability of the ledger does not eliminate the immutable garbage problem: the veracity of records depends on sensor calibration and procedural control, which requires preliminary semantic normalization and master data management. A phased implementation enables the minimization of risks and the assessment of economic impacts. To comply with DPP, it is recommended to introduce decentralized identifiers (DID) and Verifiable Credentials, as well as the integration of zero-knowledge proofs. Thus, blockchain transitions from a trial technology to a necessary component of the practical infrastructure for sustainable production chains. This article will help managers and experts in digitalization and supply chain management.
Ranjit Kannappan, Julien Hatin, E. Bertin, Noël Crespi
The Digital Product Passport (DPP) is a key enabler of the European Union’s vision for a circular economy. Achieving the full potential of DPP requires addressing the challenges of traditional product lifecycle systems (PLM). Traditional PLM focuses on streamlining data management and decision making. However, their centralized architecture limits transparent, crossorganizational collaboration, impacting the circular economy efforts. This paper proposes a blockchain based framework, tailored to support DPP implementation by enabling the creation and sharing of lifecycle data using digital twin technology. The proposed architecture implements two types of digital twins - Component Digital Twin and Product Digital Twin modeled using the Asset Administration Shell (AAS) standard to ensure interoperability. The architecture leverages Ethereum smart contracts for blockchain interaction and IPFS for off-chain decentralized storage. Two approaches for secure data sharing are implemented: Direct and Signature-based data sharing. Performance evaluation shows low latency for key operations like twin creation (167 ms) and data sharing (64 ms). By leveraging decentralization in DPPs, the proposed framework fosters collaboration, transparency, and circular economy practices, empowering stakeholders to access and share critical product data throughout the lifecycle.