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.
Figueiredo, Bernardo J. R., Ferreira, Marco P. M., Matos, João, Cova, Marco
A new approach to asset management and traceability emerges upon the integration of Non-Fungible Tokens (NFTs) and Digital Twin (DT) technology. While NFTs are widely used in digital art and gaming, their potential for securing real-world assets in DT simulations remains under-explored. A generic NFT-driven DT simulation platform could transform asset management by enhancing traceability, optimizing operations, and fostering sustainability within and across industries. In livestock management, DTs can model individual animals in real time, capturing data on health and growth. Linking this data to NFTs ensures ownership and provenance, improving traceability and accountability. Similarly, in manufacturing, DTs can identify inefficiencies, reducing waste and energy use. It is pressing to improve decision-making and operational efficiencies throughout distinct contexts. A Systematic Literature Review (SLR) was conducted following PRISMA. The goal was to build a solid, unbiased foundation for our research, contribute lasting value to the community, and identify where our work can make the most impact. From an initial set of 114 papers, the authors screened and classified the most relevant. This led to a final selection of 8 papers for full-text reading and in-depth analysis. They are presented in detail and compared in order to depict the current state of the art in the field. Results reveal a significant gap concerning the topic, particularly highlighting the absence of simulation environments that align with the previous proposal presented by the authors: a comprehensive NFT-driven and DT simulation platform that transforms NFT-based asset management from static ownership records to dynamic, provides simulation operational tools, enables real-time monitoring, predictive maintenance, and performance optimization for real-world assets.
Against the backdrop of the Industry 5.0 transformation, traditional centralized Manufacturing Execution Systems (MES) struggle to meet the complex demands of flexible and small-batch production. This paper proposes a four-level distributed architecture oriented toward human-centric manufacturing, encompassing Manufacturing Units (UMS), Production Lines (PL-MES), Workshops (MOM), and Factories (MOM), supporting dynamic reconfiguration and resource optimization from the unit level to cross-factory operations. Building on this, the study designs a smart factory operating system architecture based on ‘cloud-edge-end’ collaboration, achieving vertical integration of equipment, systems, and business processes through an industrial internet platform. Leveraging intelligent connectivity gateways and modular microservices, the architecture constructs a decentralized decision-making chain. Innovatively integrating multi-agent systems and federated learning, the architecture significantly enhances the autonomous decision-making capabilities of manufacturing units and cross-level collaboration efficiency. Additionally, the system supports rapid deployment for small and medium-sized enterprises through low-code toolchains, lowering technical barriers. This research provides a theoretical framework and technical pathway for human-machine collaborative manufacturing in the Industry 5.0 era, driving the paradigm shift of manufacturing systems from rigid control to ecological self-organization, laying a solid foundation for the sustainable development of future smart factories
MediFlow is an advanced pharmaceutical supply-chain management platform leveraging blockchain technology to enhance efficiency and transparency in drug distribution. By integrating Ethereum blockchain with predictive analytics powered by the TIME LLM model, MediFlow facilitates secure and streamlined drug procurement and delivery processes. The system utilizes smart contracts to automate transactions, optimize vendor collaboration, and eliminate unnecessary intermediaries, leading to improved efficiency and stakeholder trust. Real-time inventory and shipment tracking capabilities reduce logistical delays and errors, while predictive demand analysis helps mitigate stock shortages and minimize waste. Furthermore, the immutable nature of blockchain technology ensures the authenticity of medicines, thereby reducing the prevalence of counterfeit drugs in the market. Implementation results indicate notable improvements in transparency, cost-effectiveness, and overall healthcare service delivery, making MediFlow a transformative solution for pharmaceutical supplychain management.
Alexander Grünewald, Patrick Stuckmann-Blumenstein, Patrick Keitzl, Larissa Krämer
Additive manufacturing processes such as 3D printing have seen significant progress in the industry in recent years and have become an integral part of Industry 4.0. This fourth industrial revolution is characterized by the increasing networking and automation of production systems and the use of large amounts of data. In this context, distributed ledger technologies (DLT), which include blockchain technology, offer promising opportunities to change production fundamentally. Production processes can be more secure and efficient by creating trust and transparency in data storage and eliminating dependence on centralized instances. However, the full potential of blockchain technology is often not realized due to the perceived complexity of its implementation. Overcoming this skepticism requires a better understanding of the application possibilities and, more importantly, successful practical examples demonstrating blockchain technology’s transformative power in the industry. This study explores how blockchain can be effectively integrated into additive manufacturing processes and offers a structured overview of existing blockchain-based business models within this domain. Hence, a systematic literature interview, Crunchbase review, and Workshop are performed to examine specific use cases of blockchain in additive manufacturing and analyze how these technologies interact with existing business models. In order to provide an overview of existing blockchain-based business models in the context of additive manufacturing, a taxonomy is developed in the underlying paper to identify characteristic features. The taxonomy is further demonstrated along different existing business models.
Unmanned Aerial Vehicles (UAVs) will play a vital role in the operation, management and service provisioning process of Next-Generation Networks (NGNs). Given that the Sixth Generation (6G) network is an AI-native cooperative ecosystem that relies on the resource and intelligent capabilities of every layer of the network, especially edge devices, UAVs are thus critical and a key player in 6G. This paper presents a UAVsupported framework for 6G that relies on a multi-tiered approach to guarantee autonomous and optimal network coverage and bandwidth. The UAV network adapts Federated Learning (FL) to maintain self-organization and support for real-time edge processing. The multi-tiered UAV network approach uses realtime adjustments in swarm membership and task allocation to enhance the network energy consumption. Blockchain is integrated into each swarm to maintain the integrity of the trained models, and provides a decentralized device authentication and swarm coordination mechanism. System evaluations reveal that the proposed framework provides high levels of task completion ratio and learning accuracy.
The manufacturing environment and activities with a large volume and variety of product data have put forward higher requirements for the proof and verification of identity information. Achieving decentralized digital identity management in the Industrial Internet of Things (IIoT) helps to improve the performance of relevant proofs and authentication. The Decentralized Identity (DID) system serves as a bridge between the physical and digital worlds, assigning digital identities to physical entities to facilitate their participation in online activities. However, faced with the huge number of manufacturing entities accessing the DID system, the number of DID documents in the system has proliferated. It is still a big challenge to improve the scalability of the system while ensuring the efficiency of information access and verification. In this paper, we propose a blockchain-based verifiable decentralized identity system for IIoT. First, we propose a blockchain-based system architecture with a specially designed storage structure for DID documents. Specifically, we design a structure based on Merkle Tree that visually summarises the physical associations of manufacturing entities and reduces access overhead. Second, we design a multiblock storage structure within the blockchain, which establishes inter-block jumps based on the associated DID, effectively improving the query efficiency of the system. Finally, we design a verification scheme that enables users to verify the integrity of the identity data of the proof provider. We implemented the system framework and conducted experiments to evaluate the performance of our system. The experimental results proved the effectiveness of the system.
As the U.S. healthcare system shifts toward value-based care, there is a growing need for patient-centered technologies that ensure data ownership, interoperability, and trust.This paper proposes a novel framework integrating neuro-symbolic artificial intelligence (AI) and zero-knowledge (ZK) blockchain to enable a secure, scalable, and ethically grounded digital-twin marketplace for patient data.The goal is to empower individuals to own and control their health digital twins-comprehensive, dynamic, AI-driven models representing real-time physiological, behavioral, and clinical states-while facilitating precision care and research collaborations.At a macro level, the framework leverages neuro-symbolic AI to enhance digital twin reasoning, enabling explainable predictions and treatment simulations across diverse datasets.This is paired with ZK-proof blockchain infrastructure to ensure privacy-preserving authentication, decentralized governance, and monetization of patient data without revealing sensitive health information.The integration addresses key challenges in trust, transparency, and consent in patient-provider and patient-researcher relationships.Zooming into operational layers, the paper outlines a decentralized application (dApp) architecture that supports smart contracts for patient-informed data sharing, automated payer-provider interactions, and regulatory compliance tracking.It also highlights how incentives within the marketplace can align with care quality metrics, promote social determinants of health inclusion, and advance equitable data access in underrepresented populations.Case scenarios in chronic disease management and clinical trials illustrate the feasibility of this patient-owned digital-twin ecosystem.Ethical considerations, including algorithmic fairness, data sovereignty, and digital consent protocols, are also critically examined.By combining symbolic logic, neural learning, and cryptographic assurance, this framework sets the foundation for a secure and equitable next-generation health economy.
Το Διαδίκτυο των Πραγμάτων (Internet of Things – IoT), όπου φυσικές συσκευές προσπελαύνονται μέσω του Διαδικτύου, επικοινωνούν μεταξύ τους και λειτουργούν αυτόνομα και χωρίς επίβλεψη, διαμορφώνει εκ νέου την κατάσταση του σύγχρονου Διαδικτύου και πολλών από τους υποκείμενους μηχανισμούς του. Το IoT αποτελεί ένα χαρακτηριστικό παράδειγμα που δέχεται αυξανόμενη προσοχή από την ερευνητική κοινότητα, λόγω του μεγάλου πλήθους σημαντικών και συχνά ευαίσθητων υπηρεσιών που παρέχει σε διάφορους τομείς. Ωστόσο, η εξάπλωση χρήσης του ΙοΤ, αυξάνει ραγδαία τον αριθμό των ΙοΤ συσκευών και εφαρμογών που απαιτούν συνεργασία μεταξύ ετερογενών συσκευών. Επίσης, αυξάνεται η ανταλλαγή (συχνά ευαίσθητων) πληροφοριών μεταξύ πολλαπλών οντοτήτων, που δεν εμπιστεύονται μεταξύ τους. Οπότε η ανάγκη για απρόσκοπτη διαλειτουργικότητα, ενισχυμένη εγγενή ασφάλεια, αποκέντρωση, καθώς και για αποτελεσματικό, αποκεντρωμένο και ευέλικτο έλεγχο πρόσβασης αυξάνεται. Επομένως, παρόλο που θεωρητικά το ΙοΤ φαίνεται να προσφέρει πολυάριθμα οφέλη, στην πράξη δημιουργούνται πολλά ζητήματα. Συγκεκριμένα, μία από τις πιο κρίσιμες προκλήσεις που καλείται να αντιμετωπίσει το IoT είναι η προστασία των συσκευών και των δεδομένων που παράγονται εντός αυτών των συστημάτων. Ωστόσο, οι περισσότερες συμβατικές λύσεις ελέγχου πρόσβασης, οι οποίες είναι σχεδιασμένες για κεντρικοποιημένα, λιγότερο δυναμικά περιβάλλοντα και για συσκευές χωρίς περιορισμούς πόρων, έχουν αποδειχθεί αναποτελεσματικές και ανεπαρκείς για τις απαιτήσεις και τις ιδιαιτερότητες του IoT. Καθίσταται, επομένως, σαφές ότι οι υπάρχουσες λύσεις για το ΙοΤ πρέπει να επανεκτιμηθούν και να αναπτυχθούν νέες λύσεις ελέγχου πρόσβασης. Η παρούσα διατριβή υποστηρίζει ότι οι τεχνολογίες των Αλυσίδων Καταχωρήσεων (Distributed Ledger Technologies – DLTs) μπορούν να ικανοποιήσουν τις απαιτήσεις για τον έλεγχο πρόσβασης στο ΙοΤ, αλλά και των ΙοΤ συστημάτων γενικότερα. Η συνεισφορά μας προς αυτή την κατεύθυνση είναι διττή. Αρχικά, αναλύουμε τη σχετική βιβλιογραφία και εντοπίζουμε βασικές απαιτήσεις που πρέπει να πληρούν οι μηχανισμοί ελέγχου πρόσβασης στο IoT. Με βάση αυτές, προτείνουμε καινοτόμες λύσεις ελέγχου πρόσβασης, βασισμένες σε Αλυσίδες Καταχωρήσεων, οι οποίες αξιοποιούν τα έξυπνα συμβόλαια (smart contracts) για την έκδοση και διαχείριση τεκμηρίων ελέγχου πρόσβασης (Access Control Tokens – ACTs). Επίσης, χρησιμοποιούμε τα έξυπνα συμβόλαια ως αποκεντρωμένα σημεία λήψης αποφάσεων (Policy Decision Points – PDPs) για τον έλεγχο πρόσβασης, προσφέροντας έτσι ισχυρά χαρακτηριστικά ασφάλειας. Για την επίδειξη της εφικτότητας των προτεινόμενων λύσεων, τις ενσωματώνουμε στο πρωτόκολλο OAuth 2.0. Επιπλέον, εξετάζουμε πως ο μηχανισμός συναίνεσης (consensus mechanism) του Hyperledger Fabric μπορεί να λειτουργήσεις ως σημείο λήψης απόφασης για έλεγχο πρόσβασης σε συνεργατικά ΙοΤ συστήματα. Στη συνέχεια, παρουσιάζουμε πώς η τεχνολογία των Αλυσίδων Καταχωρήσεων μπορεί να ενσωματωθεί σε συστήματα και αρχιτεκτονικές IoT, αναδεικνύοντας τα πλεονεκτήματά της σε περιπτώσεις, όπως τα ΙοΤ παιχνίδια και τα ψηφιακά δίδυμα (digital twins) συσκευών. Τέλος, δείχνουμε πώς αυτή η ενσωμάτωση, σε συνδυασμό με την αξιοποίηση του Ιστού των Πραγμάτων (Web of Things – WoT), μπορεί να ενισχύσει σημαντικά τα ΙοΤ συστήματα και τις εφαρμογές, ιδιαίτερα όσον αφορά την ασφάλεια και τη διαλειτουργικότητα.
The increasing digitization of the built environment, along with the growing demand for sustainable, resilient, and intelligent infrastructure, has led to the emergence of smart buildings as a critical domain of innovation. These buildings leverage Internet of Things (IoT) devices, building automation systems, data analytics, and artificial intelligence (AI) to optimize operations, reduce energy consumption, and enhance occupant comfort. However, the conventional design of smart building systems remains largely centralized. The reliance on centralized communication protocols and data architectures exposes building systems to single points of failure and cybersecurity threats, where a malfunction can disrupt its operational processes. Moreover, traditional facility management processes often predominantly rely on the centralized organizational structure which restricts participation from the building's stakeholders in the governance and operation of the facility, resulting in inefficiencies, misalignment with user needs, and missed opportunities for community participation. Furthermore, while traditional AI models and machine learning approaches have enabled a degree of building automation, their dependence on predefined rules or narrowly scoped training data limits their ability to reason adaptively in complex environments which is crucial in enabling autonomous building operations. This dissertation introduces a novel interdisciplinary framework that integrates generative artificial intelligence, digital twins, and distributed ledger technologies (DLTs) such as blockchain and decentralized autonomous organizations (DAOs) to develop a secure, decentralized, and autonomous building cyber-physical systems framework for building infrastructure. The overarching objective of this research is to transform how buildings are governed, operated, and maintained by shifting from centralized, administrator-led systems to inclusive, data-driven, and self-governing infrastructure. The research is structured around three primary goals: to enhance the security and resilience of IoT and digital twin data using decentralized communication protocols; to establish democratic and decentralized governance mechanisms for facility management; and to develop a prototype of an autonomous building cyber-physical system that integrates generative AI, digital twins, blockchain, and decentralized governance of building operation. To fulfill these objectives, the dissertation is composed of four interconnected studies. The first study focuses on enhancing the cybersecurity and resilience of smart building systems by developing blockchain-based protocols for transmitting IoT and digital twin data and building operation automation. The second study introduces a decentralized governance platform that allows building stakeholders to collectively participate in facility management decisions. With the addition of a real-time digital building twin and an AI assistant powered by a large language model, the platform promotes transparent, data-driven decision-making and greater inclusivity in building operations. Building on this foundation, the third study adds a blockchain-based incentivization model to encourage community involvement in the collective improvement and upkeeping of building infrastructure. The final study integrates the technological and governance components into a prototype of a Decentralized Autonomous Building Cyber-Physical System. This system brings together blockchain governance, digital twins, and AI agents/assistants capable of smart building appliances through natural human interaction and autonomous control of building operations. It also creates a collectively governed building infrastructure that functions autonomously with self-sustaining operation through blockchain-based protocols includes. The prototype was evaluated across several simulated and real-world scenarios, validating its effectiveness in enabling autonomous, secure, and community-governed building systems. This dissertation contributes to the growing body of knowledge at the intersection of smart buildings, blockchain governance, Generative AI-driven automation, and human-building interaction. By combining decentralized ledger technologies, AI, and digital twins, it lays the groundwork for a new paradigm of autonomous, secure, and community-driven building infrastructure. The research not only offers theoretical advancements in decentralized facility management and AI integration but also provides practical blueprints and open-source prototypes for broader adoption in the built environment.
Ruba Islayem, Ahmad Musamih, Khaled Salah, Raja Jayaraman · 5 authors
Medical digital twins (MDTs) are rapidly emerging as transformative tools in healthcare. They provide virtual representations of medical devices and systems that facilitate real-time analysis and enhance decision-making. However, challenges such as secure data management, access control, and the lack of immersive and intelligent patient interactions limit their effectiveness. In this paper, we propose a solution integrating blockchain technology, Non-Fungible Tokens (NFTs), and Large Language Models (LLMs) within a metaverse environment to enhance MDT functionality. Blockchain and NFTs ensure secure ownership and access control, while the metaverse offers an engaging platform for user interaction. An LLM-powered non-player character (NPC) enables intelligent real-time user interactions and personalized insights. We develop two blockchain smart contracts for user registration, NFT ownership, and access control, and utilize decentralized InterPlanetary File System (IPFS) storage for the metaverse, MDT metadata, and interaction logs. We present the system architecture, sequence diagrams, and algorithms, along with the implementation and testing details. We conduct cost, security, and response time analyses to evaluate the smart contracts and LLM performance and compare our solution with existing approaches. We discuss practical implications, as well as challenges and limitations of the proposed solution. Finally, we explore the generalization of our system for various applications. The smart contract code and metaverse files are publicly available on GitHub.
Financial disruptions, such as extended trade credit periods and reduced down payments, pose significant challenges to effective working capital management in supply chains. While the benefits of blockchain and digital twin technologies have been studied independently in supply chain finance, their combined potential to optimise working capital performance during disruptions remains underexplored. Our study addresses this research gap by proposing an integrated blockchain and digital twin framework to enhance financial resilience in disrupted supply chains. Blockchain facilitates secure, decentralised data sharing, providing visibility into product, order, and cash flows across supply chain stages. The digital twin complements blockchain by offering predictive capabilities and enabling dynamic adjustments to working capital policies in response to disruptions. Within this framework, discrete-event simulation assesses the impact of financial disruptions on working capital performance, while machine learning models generate decision rules for adaptive working capital management. The study highlights the critical role of inventory adjustments in mitigating financial disruptions and reducing working capital variability relative to demand fluctuations. This research provides actionable insights for supply chain managers seeking to improve working capital stability amid disruptions and offers a data-driven approach to financial resilience in supply chains.
The pharma supply chain has various issues, including counterfeiting, visibility, and product authenticity in the market. Some of the issues threatening the safety of the patients and the efficacy of the industry include those named above. Such issues are overcome by new technologies like Blockchain and Artificial Intelligence (AI) that are being offered. Blockchain enhances transparency since it is a distributed ledger technology that traces the drug through the supply chain and minimizes the risk of counterfeit drugs. In contrast, there is AI that extends beyond data acquisition and analytics to forecast supply chain disruptions, inventory controls, and even drug shortages to improve decision-making and performance. This paper seeks to focus on the issues that are overcome by Blockchain when integrated with Business Intelligence (BI) systems, such as drug authenticity and counterfeiting issues. This highlights the emphasis that there is a need to consider technology integration when there is a need to make a change, security considerations, and the integration of AI to make the supply chain in the pharma industries more efficient and reliable.
This article explores the transformative integration of generative AI capabilities with Data Mesh architecture to revolutionize enterprise analytics. Beginning with examining traditional data architectures' limitations, the discussion highlights how centralized proceeds towards creating bottlenecks that impede innovation and time-to-insight. The Data Mesh paradigm is presented as a fundamental shift that decentralizes data ownership while maintaining federated governance. The integration of generative AI within this framework enables natural language interfaces, synthetic data generation, automated documentation, and intelligent insight creation. Implementation strategies using Databricks platform capabilities demonstrate how organizations can balance domain autonomy with enterprise interoperability. The architecture delivers enhanced analytics through AutoML-powered data quality with generative explanations and event-driven processing that enables real-time, predictive intelligence. Together, these capabilities create a self-improving ecosystem that democratizes data access while ensuring governance, ultimately enabling organizations to move beyond traditional reporting toward autonomous, data-driven operations with cross-domain collaboration.
AI and DLT can play a transformative role when it comes to supply chain visibility and accountability. Artificial intelligence can be utilized to improve predictive analytics, demand forecasting, and automation of operations, resulting in real-time, data-driven decision-making. However, while DLT provides security, immutability, traceability, and decentralized control that enhance transparency, reduce the possible of fraud and build trust among diverse parties across the supply chain. This paper discusses the symbiosis of both technologies and will demonstrate how their emerging capabilities deliver end-to-end visibility, streamlining operations and improving goods traceability throughout supply chain logistics. Case studies from diverse industries including manufacturing, logistics, and retail highlight the tangible implementation and benefits of these technologies in real-world settings. The paper also emphasizes the difficulties and constraints encountered during the deployment of AI and DLT technologies, with specific mention to interoperability, scalability, and data privacy issues. Also it outlines a path for organizations to embrace such technologies and better integrate them into their supply chains, increasing efficiency, accountability, and competitive advantage.
Anniina Saari, Sarah Sinclair, Rebecca Leshinsky, Seppo Junnila
Digital transformation (DT) is complex and uncertain, particularly within traditional industries. Distributed ledger technology, such as blockchain, has driven DT across many global sectors. Despite its transformative potential, scaling blockchain solutions beyond pilot projects often fails due to technical, organisational and environmental barriers. Many failures stem from ineffective collaboration and strategic misalignment among ecosystem partners rather than from technology. This study examines collaborative processes and enabling capabilities required for successful DT by focusing on a longitudinal case study from Finland (2019–2023). The case follows a cross-industry, blockchain-driven DT initiative that achieved scale despite transitioning from private blockchain to centralised technologies after market entry. While blockchain's role in this success has been documented ( Saari et al., 2024 ), the ecosystem's collaboration mechanisms and strategies for tackling challenges remain unclear. Using the technology–organisation–environment (TOE) framework, this paper analyses how DT unfolded across these contexts, identifying best practice strategies and enabling capabilities. The study draws on 35 interviews with key stakeholders and thematic content analysis to identify critical scaling enablers. Our findings reveal that scaling blockchain-driven DT in cross-industry settings requires 1) industry-level mindset and shared vision, 2) formalised collaboration and public–private partnerships, 3) governance structure with a sustainable business model and funding, 4) complementary ecosystem roles and an early-stage champion, 5) stakeholder training and support and 6) flexibility in technology and agile implementation. These elements show that trust and strategic alignment can emerge even among direct competitors, offering valuable insights for enterprises exploring blockchain innovation in business-to-business contexts.
Under the “dual carbon” background, consumer electronics consumption has become deeply ingrained in people’s minds. However, consumers often distrust the sustainability claims of consumer electronics products. Artificial intelligence (AI) and blockchain technology can address this trust deficit through transparency and traceability mechanisms. This study integrates blockchain technology into traditional consumer electronics supply chains, considering consumers’ preferences and trust in these products. An AI-based game model is proposed to analyze the interactions among supply chain members before and after implementing blockchain technology, under varying Edge Computing-based power structures. This model quantitatively evaluates emission reduction and pricing strategies, aiming to optimize consumer surplus and total social welfare. By leveraging Lightweight AI and blockchain, smart wholesale and cost-sharing contracts are designed to establish reasonable ranges for wholesale prices and optimal cost-sharing ratios, enhancing enterprise operational efficiency and achieving supply chain coordination. Results demonstrate that when consumers exhibit a stronger preference for consumer electronics products, the adoption of Lightweight AI and blockchain delivers greater benefits across the supply chain. Furthermore, as consumer willingness to purchase these products increases, the advantages become more pronounced. Numerical analysis highlights that smart contracts can better coordinate the supply chain, particularly in retailer-dominated scenarios. Finally, empirical cases
The rapid expansion of digital twin (DT) applications in industrial settings has created a pressing need for robust interoperability solutions, particularly for blockchainsupported DT teams operating across heterogeneous platforms. Current blockchain frameworks struggle with seamless data exchange and operational efficiency due to interoperability limitations. In this study, we introduce an innovative cross-chain framework that leverages a relay-chain mechanism and dynamic non-fungible tokens (NFTs) to overcome these challenges. Through experimental validation on Ethereum and Hyperledger Fabric, we demonstrate the framework's effectiveness in ensuring scalability, security, and cost efficiency. The evaluation reveals minimal network latency for cross-chain transactions with a negligible impact on overall throughput. The comparative analysis further highlights the advantages of our approach over existing cross-chain solutions, offering enhanced interoperability while maintaining low deployment costs. By addressing key barriers to blockchain deployment, this work provides a practical solution for improving cross-chain collaboration and advancing industrial DT integration. Future research will focus on refining relay-chain consensus mechanisms and extending support to private blockchains to enhance adoption in multi-stakeholder environments.
Abstract Realizing its overarching potential in achieving digital transformation, leading organizations, such as Air New Zealand, have recently employed blockchain technology to manage 3D printing innovation. Despite the potential benefits, including the key role of blockchain as a supporting technology in Industry 5.0, these applications of blockchain technology in 3D printing are still at a nascent stage in practice and mostly limited to producer innovation. Additionally, 3D printing and blockchain technologies are often independently revolutionizing value capture and value creation, whereas the theoretical implications of their combined impacts on innovation management remain an underexplored domain. Limited digital transformation efforts have been made to employ blockchain technology to create a synergy between producer innovation and community innovation in relation to 3D printing innovation. To solve these issues, we introduce a novel concept, distributed ledger innovation platform (DLIP), which refers to a network of firms and individuals that create and capture value on a blockchain through digital transformation efforts. In this research, we draw upon transaction cost and social production theories to investigate the potential benefits of blockchain to solve the digital transformation challenges associated with 3D printing innovation. We utilize these perspectives to propose the concept of DLIP as a new governance structure for managing digital transformation activities with blockchain. Additionally, we examine illustrative digital transformation efforts via 3D printing innovation and applications of blockchain technology. Then, we present future research directions for innovation management scholars to investigate the benefits of DLIP in digital transformation efforts via 3D printing. We conclude by discussing the potential applications of DLIP to manage other enabling technologies of digital transformation, including big data, artificial intelligence, and the metaverse.
This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.
M. Ferreira, Bernardo J. R. Figueiredo, Alexandre Soares dos Santos, João Filipe Matos · 5 authors
Digital twins (DTs) are transforming industries by offering real-time virtual representations of physical assets, enabling smarter decision-making and optimization. However, as these systems become more complex and distributed, maintaining reliable traceability remains a significant challenge. To address this, we propose a multi-context, modular platform that leverages blockchain and distributed ledger technologies (DLTs) to enhance the traceability of digital twins. Our proposal will ensure secure, immutable, and transparent records of data interactions, fostering greater trust, accountability, and interoperability across various domains. The foundation of this work involved identifying the key Architecturally Significant Requirements (ASRs) that must be considered in the platform's design. Based on the first ASRs related to traceability and data flexibility, we developed a conceptual architecture supported by an ontology that addresses the multi-context traceability challenge. The proposed approach was validated through two distinct case studies: livestock management and shop-floor processes. Moving forward, our focus will be on addressing the remaining ASRs that were already identified, to further develop a high-performance, secure, and scalable modular platform capable of supporting diverse contexts and applications.
ABSTRACT The convergence of blockchain technology and the industrial metaverse is poised to revolutionize digital transformation in small‐ and medium‐sized manufacturing enterprises (SMMEs), addressing their unique challenges and unlocking new opportunities for innovation and competitiveness. This survey explores the fundamental concepts, state‐of‐the‐art advancements, and practical applications of blockchain and industrial metaverse technologies, focusing on their synergistic potential to reshape supply chain management, enhance production efficiency, and drive intelligent manufacturing. Key blockchain features such as decentralization, distributed ledgers, and smart contracts are analyzed alongside metaverse technologies like digital twins, virtual reality, and augmented reality, showcasing their transformative impact on data security, product traceability, and operational efficiency. Case studies and industry insights highlight the practical integration of these technologies in SMMEs, demonstrating their ability to overcome resource constraints and align with strategic goals. The survey also addresses the major challenges—technological, financial, and infrastructural—faced by SMMEs in digital transformation, providing actionable strategies and policy recommendations. By examining the interplay between blockchain and the industrial metaverse, this paper underscores the emerging trends and future trajectories of these technologies, offering a comprehensive roadmap for leveraging their potential to achieve sustainable growth and innovation in the manufacturing sector.