This paper proposes a novel approach to software supply chain security management leveraging the inherent characteristics of blockchain technology. The core claim is to build a robust management system capable of guaranteeing the integrity and traceability of software components throughout their lifecycle. The proposed mechanism utilizes a blockchain network to record critical data points related to the software supply chain, including code commits, build processes, and security audits. Smart contracts are then employed to automate security checks, enforce access control, and trigger alerts based on predefined rules. This approach addresses the escalating risks associated with compromised software supply chains by providing an immutable and auditable record of all activities. The research highlights the potential of blockchain to significantly enhance software security and trust within complex, distributed development environments. The key contribution lies in the systematic application of blockchain and smart contracts specifically tailored for supply chain security, offering a verifiable and resilient solution.
Martin Končár, Adel Aazami, Sebastian Kummer, Navid Mohammadi
Digital technologies are widely expected to reshape supply chains, yet the conditions under which they deliver operational and sustainability-related value in practice remain insufficiently understood. This study examines how digital technologies influence supply chain operations and performance, where performance is operationalized through demand forecasting accuracy, cost, lead time, visibility, and inter-organizational collaboration, and asks how these technologies can support more resilient, resource-efficient, and sustainable supply chain operations. The study adopts an exploratory qualitative design based on semi-structured interviews with eight supply chain professionals at manager level or above, drawn from the aluminum, elevator, railway, food, and consumer goods industries in Austria, Slovakia, and the Czech Republic, and conducted between March and June 2025. A structured literature review complements the interview evidence. Within this exploratory sample, Artificial Intelligence and Data Analytics were the most widely adopted technologies (five of eight participants each), followed by the Internet of Things (four of eight) and Automation (five of eight), while no participant reported active Blockchain deployment. Reported benefits concentrated on forecasting accuracy, operational efficiency, visibility, and collaboration, whereas high implementation costs, legacy system integration, skill shortages, and regulatory uncertainty formed the principal barriers. The central finding is a conditional relationship between adoption and competitiveness: internal operational gains did not automatically translate into competitive advantage among the professionals interviewed, but appeared to require strategic alignment, cross-functional integration, and performance measurement. Because digitally enabled forecasting, inventory positioning, and resource optimization also reduce waste and improve resource utilization, the findings link digital supply chain transformation to sustainable development objectives. This exploratory study of eight participants therefore provides practitioner-level propositions and a research agenda for digital and sustainable supply chain transformation rather than statistically generalizable findings.
The integration of artificial intelligence (AI), the Internet of Things (IoT), and blockchain may provide a viable approach to tackle persistent operational and informational challenges in cleaner production. This conceptual review synthesizes existing literature and presents an integrated AI-IoT-blockchain framework mapped across the four sequential stages of cleaner production: source reduction, process control, end-of-pipe treatment and recycling, and full-chain traceability. The literature indicates that IoT enables real-time sensing, AI drives predictive and prescriptive analytics, and blockchain ensures tamper-proof record-keeping and stakeholder trust. Together, these technologies may help address long-standing barriers including fragmented data, delayed responses, and a lack of verifiability. Despite challenges such as high costs, technical fragmentation, and organizational resistance, several emerging strategies have been proposed in the literature to address these challenges. These include modular deployment, federated learning, permissioned blockchains, and regulatory sandboxes. The framework’s underlying architecture appears transferable across sectors, subject to industry-specific adaptation, supporting sustainable manufacturing, the circular economy, and low-carbon development.
The convergence of AI, Blockchain, IoT, Digital Twins, quantum computing, and FKF spectral methods constitutes a decisive research direction for next-generation logistics, especially when examined through the lens of Smart Mobility and Intelligent Transportation Systems [9]. Future work must prioritize real-time fusion of connected-vehicle and smart-infrastructure data streams, construction of scalable multi-resolution Digital Twin environments that span both supply chains and urban mobility networks, development of trustworthy AI models for predictive and prescriptive control, realization of practical hybrid quantum–classical optimizers for the combinatorial problems that dominate intelligent transportation and logistics, and computationally efficient multi-scale spectral analysis of complex temporal dynamics. Empirical validation across transportation, inventory, energy, and disruption scenarios incorporating advances in battery technologies [5], [10], solar-assisted and hybrid vehicle architectures [12], [28], and Industry 5.0 human-centric automation [27]will be indispensable. Realizing these advances will transform intelligent logistics from a conceptual integration into operational, sustainable, and resilient supply-chain systems that fully exploit the emerging capabilities of smart mobility ecosystems [1]–[28].
A convergent supply-chain architecture is proposed by integrating Digital Twins with Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), quantum computing, and FKF spectral analysis to address the growing complexity, uncertainty, and disruption risks in modern logistics. Digital Twins enable real-time virtual representation, simulation, monitoring, and disruption-response analysis, while AI extracts predictive and prescriptive intelligence from continuous IoT-generated data. Blockchain strengthens data integrity, transparency, security, and end-to-end traceability across supply-chain transactions. Quantum annealing supports computationally intensive logistics optimization problems, including routing, resource allocation, scheduling, and ULD configuration. FKF-based spectral features provide a mathematical representation of shifts, modulation effects, lead-time behavior, transient variations, and multiscale supply-chain dynamics. The integration of these complementary technologies enables information to flow from real-time sensing and trusted data management to spectral analysis, predictive intelligence, simulation, and advanced optimization. Together, the proposed architecture provides an adaptive, intelligent, sustainable, and resilient framework for monitoring supply-chain conditions, anticipating disruptions, evaluating alternative decisions, and improving overall logistics performance.
This study introduces an FKF-enabled intelligent supply-chain framework that integrates Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), Digital Twins, and quantum optimization into a unified architecture. The FKF transform provides a mathematical spectral representation of supply-chain signals, enabling the identification of temporal shifts, modulation effects, multiscale patterns, demand fluctuations, and lead-time dynamics. These spectral features can be supplied to AI and machine-learning models to improve forecasting, anomaly detection, disruption prediction, and resilience assessment. IoT devices continuously provide real-time operational data from transportation, inventory, production, and logistics processes, while Blockchain supports secure data sharing, traceability, and transaction transparency across supply-chain participants. Digital Twins complement these technologies by creating dynamic virtual representations of physical supply-chain systems, allowing alternative scenarios, disruptions, and recovery strategies to be simulated before implementation. Quantum annealing is incorporated to address selected computationally intensive combinatorial decisions, such as routing, scheduling, resource allocation, and logistics configuration. By connecting FKF-based mathematical spectral intelligence with AI-driven analytics, trusted digital infrastructure, simulation capabilities, and emerging quantum optimization, the proposed framework provides an integrated pathway toward more predictive, adaptive, transparent, sustainable, and resilient supply-chain management. The content should be logically organized in a single paragraph, maintaining coherence and clarity throughout. Ensure that the abstract captures the research context, problem statement, approach, key results, and final conclusions in a balanced manner. Keywords— Quantum Computing; Quantum Annealing; Logistics Optimization; Unit Load Device Configuration; Supply Chain Management; Artificial Intelligence; Digital Twins; Blockchain; Supply Chain Resilience; FKF Transform.
This study proposes a convergent framework integrating AI, Blockchain, IoT, Digital Twins, quantum computing, and FKF spectral analysis for sustainable and resilient supply chains. AI supports prediction and optimization, Blockchain improves transparency and traceability, IoT enables real-time sensing, and Digital Twins facilitate simulation and adaptive disruption management. Quantum annealing addresses selected combinatorial logistics problems, including ULD configuration, while FKF methods characterize temporal shifts, modulation, multiscale dynamics, and lead-time variations. The integration establishes a closed-loop architecture linking sensing, spectral analysis, intelligence, simulation, trust, and optimization for adaptive logistics decision-making.
Digital-twin-enabled human–machine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physical–virtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and human–AI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance.
Pablo Guerrero Sánchez, Augusto Renato Pérez Mayo, Nohemí Roque Nieto
The adoption of central bank-issued digital currencies introduces significant risks, such as misuse, international distrust, and financial contagion. These dynamics generate higher levels of uncertainty and volatility in the short term, which influences the ability of organizations to develop resilient innovation. These factors especially affect the flexible management of supply chains and the agile integration of B2B processes, impacting SMEs differently according to their innovation models. Likewise, digital currencies and the digitalization of the economic environment function as elements that can reduce or intensify operational turbulence, depending on the degree of organizational maturity. Decision-making and business performance are influenced by e-commerce, digital business intensity, and organizational agility. These effects vary between sectors: agriculture, manufacturing and services show unequal capacities to adapt and respond to a digital market in constant transformation. Together, these factors determine the ability of companies to manage uncertainty and take advantage of the opportunities of the digital economy.
Accurately quantifying enterprise digital capability is fundamental to evaluating industrial modernization, yet conventional empirical inquiries predominantly rely on single-dimensional proxy variables or unweighted keyword counts, failing to capture the multidimensional integration of digital assets. Moving beyond causal regression paradigms and reductionist metrics, this inquiry develops a comprehensive, objective Digital Readiness Index (DRI) for physical manufacturing enterprises using an information-theoretic Entropy Weight Method (EWM). Grounded in multi-source text-mining disclosures and corporate balance sheets across 41,756 firm-year observations of Chinese A-share listed manufacturing enterprises spanning 2000 to 2025, the evaluation framework integrates eight discrete operational indicators across three dimensions: Technical Depth (AI, Big Data, Cloud Computing, Blockchain, and Digital Applications), Intangible Capital Endowments, and Governance Oversight. Objective entropy weighting demonstrates that specialized frontier technologies, particularly Blockchain (w=37.81%), Artificial Intelligence (w=15.95%), Big Data Analytics (w=15.93%), and Cloud Computing (w=15.66%)—constitute the primary sources of informational divergence across manufacturing firms. Longitudinal trajectory evaluation reveals a sustained upward trajectory in mean digital readiness, accelerating markedly after the 2015 macroeconomic policy inflection point. Non-parametric Gaussian Kernel Density Estimation uncovers a distinct dynamic polarization pattern, characterized by a shifting rightward distribution and an elongating upper tail. Cross-sectional decomposition establishes substantial structural disparities: high-tech sectors such as Computers and Electronics exhibit the highest mean digital readiness (DRI=16.28), whereas chemical and pharmaceutical sectors display persistent digital inertia (DRI≈4.06). Furthermore, non-state-owned enterprises (Non-SOEs) systematically outperform state-owned enterprises (SOEs) across all asset scale tiers. These findings provide an objective measurement tool and benchmark for corporate technology auditing and industrial policy calibration.
This study proposes a unified framework integrating AI, Blockchain, IoT, Digital Twins, quantum computing, and FKF spectral analysis for sustainable, transparent, and resilient supply chains. AI enables prediction and optimization; Blockchain ensures trusted traceability; IoT provides real-time sensing; Digital Twins support simulation; and quantum annealing addresses complex logistics optimization. FKF analysis captures temporal shifts, modulation, multiscale dynamics, and lead-time variations for AI-based spectral intelligence. The resulting sensing–analysis–intelligence–simulation–optimization architecture provides an adaptive pathway for efficient and resilient next-generation logistics Keywords— Artificial Intelligence; Sustainable Logistics; Green Supply Chain; Blockchain; Digital Twins; Internet of Things; Quantum Computing; Quantum Annealing; FKF Transform; Supply Chain Resilience.
Open access
Supply Chain Resilience and Risk Management
Digital Transformation in Industry
Infrastructure Resilience and Vulnerability Analysis
Felipe Bastos dos Reis, Adriana Marotti de Mello, João Valsecchi Ribeiro de Souza
Industry 4.0 technologies are increasingly recognized as important contributors to the transition toward a circular economy, yet their association with circular business models (CBMs) remains poorly understood. This paper examines how Industry 4.0 technologies contribute to CBM implementation. Drawing on a systematic literature review of 34 papers and an analysis of 13 illustrative cases from the Circular X and Ellen MacArthur Foundation databases, the study maps the relationship between technologies, applications, and CBM patterns. The findings identify seven core technologies, Artificial Intelligence, Big Data, Cloud Computing, Internet of Things, Additive Manufacturing, Augmented Reality, and Blockchain, and 14 key applications linked to circular business models, such as data integration, predictive maintenance, smart energy management, and supply chain traceability. The results suggest that technology adoption alone is not necessarily associated with circular outcomes. Most applications correspond to efficiency-oriented strategies, particularly the reduction of material and energy consumption, indicating that current uses of Industry 4.0 in CBMs remain strongly oriented toward digital optimization. When technologies are not aligned with circular value propositions, efficiency gains may generate rebound effects, potentially increasing environmental impacts. This study advances the circular economy literature by providing a configurational perspective on the implementation of Industry 4.0 technologies within CBMs and by mapping how digital capabilities contribute to specific circular applications. Practically, the findings offer guidance for managers seeking to prioritize technology investments and move circular business strategies beyond operational efficiency toward circular transformation. Overall, the study contributes to a more comprehensive understanding of how the association between Industry 4.0 technologies and circular transformation, rather than digital optimization alone, appears stronger when their applications align with CBM patterns and circular value propositions.
Vasuk M., Anjay Kumar Mishra, A Dinesh Kumar, Shila Mishra · 6 authors
The rapid advancement of digital technologies, Artificial Intelligence (AI), robotics, automation, data analytics, biotechnology, and intelligent manufacturing has fundamentally transformed the global economy and the nature of work. As industries continue to evolve beyond traditional production systems, education must also undergo a significant transformation to prepare learners for emerging economic, technological, and societal challenges. Industry 5.0 represents the next stage of industrial development, emphasizing collaboration between humans and intelligent technologies while promoting sustainability, resilience, creativity, and human well-being. Unlike previous industrial revolutions that primarily focused on technological efficiency and automation, Industry 5.0 places human values at the centre of technological innovation. Consequently, future educational systems must equip learners not only with advanced technical competencies but also with creativity, ethical reasoning, emotional intelligence, adaptability, lifelong learning abilities, and interdisciplinary problem-solving skills required in the evolving global workforce.Education for Industry 5.0 seeks to develop highly skilled professionals capable of working effectively alongside intelligent machines while maintaining the uniquely human qualities that technology cannot replace. Future graduates will be expected to integrate technical expertise with innovation, collaboration, leadership, critical thinking, and social responsibility. Educational institutions therefore play a crucial role in preparing learners to thrive within technologically advanced workplaces where human creativity and machine intelligence complement one another to improve productivity, innovation, and sustainable development.The transition toward Industry 5.0 requires significant changes in curriculum design, teaching methodologies, assessment practices, and institutional strategies. Traditional educational models that emphasize memorization and routine procedural knowledge are insufficient for preparing learners to succeed in highly dynamic and technology-driven environments. Modern curricula increasingly incorporate interdisciplinary learning, experiential education, project-based learning, competency-based education, digital literacy, entrepreneurship, innovation management, and sustainability principles. These educational reforms enable learners to acquire both technical competencies and transferable skills that remain valuable throughout rapidly changing professional careers.Artificial Intelligence has become one of the most influential technologies shaping Education for Industry 5.0. AI-powered intelligent tutoring systems, adaptive learning platforms, predictive learning analytics, automated assessment, virtual laboratories, and personalized educational environments enable institutions to deliver individualized learning experiences that respond to each learner's abilities, interests, and learning pace. Rather than replacing educators, AI enhances teaching effectiveness by supporting instructional planning, identifying learning gaps, providing timely feedback, and enabling data-driven educational decision-making. Human educators continue to play indispensable roles as mentors, facilitators, ethical guides, and innovators who nurture critical thinking, creativity, empathy, and responsible citizenship.Industry 5.0 also emphasizes the development of a workforce capable of continuous learning and adaptation. Technological innovations rapidly transform professional knowledge, requiring individuals to update their competencies throughout their careers. Educational institutions increasingly promote lifelong learning through flexible degree programmes, online learning platforms, micro-credentials, professional certification courses, industry partnerships, and continuing education initiatives. Lifelong learning enables professionals to remain competitive, adapt to technological changes, and contribute effectively to innovation across diverse industrial sectors.Collaboration between educational institutions and industry has become increasingly important in preparing learners for Industry 5.0. Universities, research institutions, industries, government agencies, and technology organizations work together to design relevant curricula, establish innovation laboratories, provide internships, support collaborative research, and develop practical learning experiences aligned with evolving workforce requirements. Industry-academia partnerships expose students to real-world challenges while fostering innovation, entrepreneurship, leadership, and problem-solving competencies that enhance graduate employability and economic competitiveness.Sustainability represents another central dimension of Education for Industry 5.0. Future industrial systems seek to balance economic growth with environmental protection and social responsibility by promoting sustainable manufacturing, resource efficiency, renewable energy, circular economy principles, and ethical technological development. Educational programmes increasingly integrate sustainability across engineering, business, healthcare, agriculture, information technology, and social sciences, enabling learners to develop innovative solutions that address global environmental and societal challenges while supporting sustainable economic development.Digital transformation further reshapes educational delivery and workforce preparation. Cloud computing, the Internet of Things (IoT), big data analytics, blockchain technology, digital twins, Virtual Reality (VR), Augmented Reality (AR), and extended reality technologies provide learners with immersive educational experiences that closely simulate modern industrial environments. Virtual laboratories, intelligent simulations, remote experimentation, collaborative digital workspaces, and AI-supported learning environments enable students to acquire practical competencies while overcoming geographical and infrastructural limitations. These technologies strengthen experiential learning and prepare graduates for digitally integrated workplaces.Human-centred education remains the defining characteristic of Industry 5.0 despite rapid technological advancement. Future educational systems recognize that qualities such as creativity, emotional intelligence, ethical judgement, cultural awareness, communication, leadership, resilience, and collaborative problem-solving distinguish human capabilities from automated systems. Educational institutions therefore emphasize holistic learner development by integrating technical education with social sciences, humanities, ethics, communication, and emotional well-being. Such balanced educational approaches prepare graduates to utilize technology responsibly while maintaining human dignity, inclusiveness, and social responsibility.Despite its numerous opportunities, Education for Industry 5.0 also presents several challenges. Educational institutions must continuously update curricula, invest in advanced technological infrastructure, train educators in emerging technologies, strengthen industry collaboration, ensure equitable access to digital learning resources, and address ethical concerns associated with Artificial Intelligence, automation, cyber security, and data privacy. Bridging disparities in digital access and ensuring that technological advancements benefit all learners remain essential priorities for achieving inclusive educational transformation.The future of Education for Industry 5.0 will be shaped by continuous technological innovation, interdisciplinary collaboration, intelligent learning ecosystems, personalized education, sustainable development, and global cooperation. Artificial Intelligence will increasingly support adaptive learning, competency assessment, intelligent career guidance, and workforce forecasting. Emerging technologies such as quantum computing, advanced robotics, immersive digital environments, autonomous systems, and human-machine interfaces will further redefine educational practices and professional competencies. Educational institutions that successfully integrate technological innovation with human-centred values will prepare graduates capable of leading sustainable industrial transformation while addressing complex global challenges.In conclusion, Education for Industry 5.0 represents a transformative approach that aligns educational systems with the demands of future industries while preserving the central role of human creativity, ethics, collaboration, and innovation. By integrating advanced technologies, interdisciplinary learning, sustainability, lifelong education, and strong industry partnerships, educational institutions can develop a resilient, intelligent, and adaptable workforce capable of thriving in rapidly evolving technological environments. As Industry 5.0 continues to redefine global production, innovation, and employment, education will remain the primary driver for preparing individuals to contribute responsibly, creatively, and effectively to the future of work.
The paper provides the Abelian and Tauberian theorems for the generalized Mellin-Whittaker transform. The asymptotic results obtained here are also relevant to emerging interdisciplinary applications that rely on transform methods for the analysis of complex dynamical systems, including smart logistics ecosystems that converge the Internet of Things, artificial intelligence and quantum computing, human-centric automation under the Industry 5.0 paradigm, resilient and adaptive global supply chains enabled by digital-twin technology, and quantum-assisted optimization frameworks for transportation and logistics. Further connections are drawn to battery-management algorithms for electric and hybrid vehicles, blockchain-secured supply-chain transparency, and systematic reviews of supply-chain resilience in the era of digital transformation. In the quantum-computing literature the short-time embedding of continuous dynamics into discrete Ising or QUBO Hamiltonians is a recognized bottleneck. The initial-value theorem guarantees that the leading order asymptotic of the physical signal is correctly represented by the lowest-order terms of the quantum Hamiltonian, thereby improving the quality of the solutions returned by quantum annealers and variational quantum algorithms alike. Key-words: edge computing, IoT-enabled logistics, multi-agent logistics, multi-commodity flows, edge computing, IoT-enabled networks, real-time decision support, digital-twin consistency, supply-chain resilience, Industry 5.0 automation, blockchain audit trails, smart mobility, battery residual capacity, green logistics.
Efficient material traceability and lifecycle management are essential for achieving circular utilization in indoor renovation projects. This study proposes a reversible decoration framework based on a Digital Material Cycle Map (DMCM) to support the tracking, recovery, and reuse of construction materials throughout their service lifecycle. The framework integrates Digital Product Passport concepts, RFID- and QR-based identification, distributed ledger technology, and lifecycle data management to establish a unified material information architecture. A modular and detachable construction strategy is further developed using standardized interfaces and non-destructive disassembly mechanisms, enabling efficient component recovery and reuse. In addition, a material traceability workflow is introduced to support condition assessment, lifecycle auditing, and recycling decision-making based on dynamically updated records. The framework promotes information sharing among manufacturers, designers, contractors, and recycling organizations through standardized data interfaces. By combining material identification, information transmission, and lifecycle monitoring, the proposed approach provides an engineering-oriented solution for digital material governance, intelligent sensing, and distributed infrastructure management.
Industry 5.0 emphasises human-centric technologies (HCTs) as essential drivers of sustainable and resilient production. However, their specific contributions to Circular Economy (CE) strategies and the associated skill requirements are not well-defined. This paper investigates how HCTs support Circular Economy practices (CEPs) and which skills and competencies are needed for their effective implementation. A systematic literature review was conducted using Scopus and Web of Science, following established guidelines. The search employed a string that links Industry 5.0, human-centricity, and the 10 R framework of CE. After a multi-stage screening and snowballing process, 41 peer-reviewed contributions published between 2015 and 2025 were selected for analysis through a combination of bibliometric and qualitative content analysis. The review maps the main HCTs, such as AI, digital twin, XR, robotics, blockchain, and IoT, to CEPs and specific 10 R strategies. It identifies seven clusters of skills ranging from analytical and decision-making abilities to human-machine collaboration, CE-specific expertise, and green human resource management practices. A Sankey diagram visualises the primary linkages between technology and strategy. Then, the authors developed a framework (TSC framework) that links skill clusters, CE practices, and enabling technologies and validated it through an illustrative case study. Interpreting the findings through the Resource-Based View, the paper argues that value arises from socio-technical bundles that integrate technologies, circular practices, and human capabilities. The study concludes with implications for policymakers, educators, and practitioners and outlines potential avenues for future research on skills for human-centred circularity.
Muhammad Farooq Shaikh, S. Hamza Hassan, Jawwad Shamsi, Alessia Maccaro · 5 authors
Background and objective The integration of blockchain and digital twin (DT) technologies is increasingly recognised as a promising approach for improving healthcare data integrity, interoperability, privacy, and clinical decision support. While digital twins enable dynamic patient modelling and predictive healthcare applications, blockchain provides secure data governance through decentralised trust, auditability, and access control. However, existing research remains fragmented, with limited synthesis of the architectural integration, regulatory readiness, ethical governance, and interoperability of blockchain-enabled healthcare digital twin systems. This systematic scoping review addresses these gaps by providing a comprehensive architectural and compliance-oriented analysis of the current evidence. Methods A systematic scoping review was conducted following PRISMA 2020 guidelines using Scopus, PubMed, and Web of Science. From 148 identified records, 55 eligible studies published between 2020 and 2025 were included after duplicate removal and eligibility screening. Data were extracted on digital twin functionality, blockchain architecture, healthcare application domains, consensus mechanisms, privacy-preserving strategies, and regulatory and ethical alignment. Structured Python-based visual mapping and comparative analyses were performed to identify architectural, governance, and compliance patterns across the literature. Results The findings demonstrate that blockchain is predominantly employed to provide access control, audit logging, data integrity, consent management, and secure data provenance within healthcare digital twin ecosystems. Patient-level and EHR-centred digital twins represented the most mature application areas, whereas cross-domain and infrastructure-level frameworks dominated early architectural exploration. The review identifies recurring compliance-oriented architectural patterns while revealing substantial gaps in clinically validated deployments, interoperability with established healthcare standards, decentralised governance models, and formal implementation of GDPR- and HIPAA-compliant engineering practices. Comparative heatmap analyses further highlight the uneven maturity of ethical governance and regulatory integration across blockchain functionalities. Conclusion This review provides the first comprehensive compliance-oriented architectural synthesis of blockchain-enabled healthcare digital twin systems by integrating technical architecture, regulatory readiness, ethical governance, and privacy-preserving design patterns within a unified analytical framework. The proposed architectural mapping identifies critical research gaps in interoperability, governance engineering, consensus optimisation, and real-world clinical validation, providing a foundation for the development of trustworthy, GDPR/HIPAA-aligned, FHIR-compatible, and clinically interoperable healthcare digital twin ecosystems.
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 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.
Artificial Intelligence (AI) is reshaping contemporary fashion by transforming design processes, production systems, and sustainability strategies in the textile and apparel sector. Amid growing concerns over overconsumption, environmental degradation, carbon emissions, and social inequities, AI has emerged as both a technological enabler and a subject of ethical scrutiny. This study examines the influence of AI on creative practice, circular design implementation, and responsible innovation in fashion. Drawing on a qualitative synthesis of systematic literature, design theory, and industry case analyses, this study proposes a framework that situates AI within sustainable fashion discourse. Findings indicate that generative design tools, virtual prototyping, digital twins, and predictive analytics support waste reduction, virtual sampling, demand-responsive production, and informed material selection. AI-enabled resale systems, automated textile sorting, and blockchain-based traceability strengthen circular economy initiatives by extending product lifecycles and improving transparency. However, algorithmic decision-making challenges authorship, craftsmanship, dataset neutrality, and labor structures. Concerns over bias, intellectual property ambiguity, digital energy consumption, and workforce displacement complicate sustainability narratives. The study argues that sustainable transformation requires a human-centered governance approach in which AI augments rather than replaces creative agency and is supported by ethical regulation and critical design education. By integrating sustainability theory, computational creativity, and AI ethics, this research contributes a holistic framework for responsible AI adoption in fashion systems.
In modern business and trade, digital transformation (DT) has become a key factor in gaining a competitive edge. Rapid developments in blockchain, big data analytics, cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) are changing company models, value generation workflows, and organizational strategies. By combining organizational, strategic, and technological viewpoints, this study offers a multifaceted examination of digital transformation. Secondary data from peer-reviewed literature, international industry publications, and corporate disclosures of top companies, such as Amazon, Alibaba Group, Microsoft, and Tesla, Inc., were analyzed using a descriptive and analytical research design. The study creates a conceptual framework that connects performance results, transformation processes, and digital drivers. The results indicate that ecosystem integration, organizational agility, digital capability development, and strategic alignment are necessary for a successful digital transformation. The paper contributes to digital transformation literature by combining findings from several sectors and putting forth an integrated strategic model that can be empirically validated in further studies; the paper adds to the body of knowledge on digital transformation.
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.