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 KonÄĂĄr, 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.
Smart Cities (SCs) leverage advanced technologies and data analytics to optimize infrastructure and services for economic and quality of life benefits. However, realizing the potential of SCs requires interoperability between different systems, which remains challenging due to fragmentation. Thus, unified architectures are needed to enable effective coordination through common languages and protocols. Digital Twin (DT) models, bidirectional virtual representations of physical assets, show immense promise for unifying SCs by integrating massive heterogeneous data streams. Although there have been numerous studies investigating unified models for SCs, in the context of DT, most studies narrowly focus on using DT for different systems within cities rather than citywide implementation. As a response, this study identified 34 recent papers investigating interoperability and unified models in SCs, out of which 19 papers were focused on developing unified models for SCs and 15 papers were focused on unified DT models in SCs. These 15 papers were systematically reviewed, identifying the key factors, benefits, and challenges of such models. To help city leaders and to make focused, context-aware decisions aligned to their objectives, whole factors were categorized into four groups, including relevance-based, influence-based, complexity-based, and risk-based. To guide future research, the study highlights edge computing and implementing blockchains as underrepresented areas within the realm of SCs.
Purpose This study aims to develop and evaluate a blockchain-enabled traceability framework capable of improving circular steel recovery and reducing embodied carbon within Tata Steelâs supply chain. Study Blockchain technology is increasingly being explored as a digital enabler for circular economy practices and low-carbon industrial supply chains. In the steel sector, fragmented documentation systems, limited material traceability and inefficient scrap recovery mechanisms continue to constrain circularity and embodied carbon reduction, particularly within the Indian construction industry. Despite growing interest in blockchain-enabled traceability, limited research has examined its application within steel supply chains using a case-grounded and quantitatively modelled approach linking traceability improvements with circular steel recovery and embodied carbon reduction. Design/Methodology/Approach A single-case explanatory research design combined with secondary-data-based scenario modelling was adopted using publicly available Tata Steel sustainability disclosures, industry benchmarks and validated emission factors. The proposed framework integrates QR/RFID-enabled batch identities, Internet of Things (IoT)-assisted verification systems, smart contracts and artificial intelligence (AI)-assisted dashboard visualisations to model improvements in traceability and closed-loop recycling performance. Findings Scenario modelling indicates that traceability coverage could improve from approximately 42% to 98%, while verified scrap reuse could increase from 60% to 88%. Using an emission avoidance factor of 1.35 tCO2 per tonne of recycled steel and a conservative annual scrap throughput assumption of 8.0 Mt, the framework estimates a feasibility-oriented embodied carbon reduction of approximately 3.0 MtCO2/year. Originality/Value The study proposes a Tata Steel-specific blockchain circularity framework demonstrating how digital provenance systems can strengthen transparent material governance, enhance circular steel recovery and support feasibility-oriented embodied carbon reduction pathways within industrial steel supply chains. Research Limitations/Implications The findings are based on secondary-data-driven scenario modelling and should therefore be interpreted as feasibility-oriented estimates rather than empirically validated operational outcomes. The framework provides a basis for future empirical assessment and sensitivity analysis under alternative industrial and policy conditions. Practical Implications The framework provides a conceptual decision-support approach for improving batch-level traceability, scrap verification, closed-loop recovery, environmental reporting and supply-chain accountability through blockchain-enabled systems. Social Implications Improved material provenance and verification can strengthen transparency and accountability among supply-chain actors and support more trustworthy circular material governance.
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 frameworkâs underlying architecture appears transferable across sectors, subject to industry-specific adaptation, supporting sustainable manufacturing, the circular economy, and low-carbon development.
Abstract The integration of Industry 4.0 technologies into supply chains (SCs) such as blockchain, artificial intelligence (AI), robotics, additive manufacturing and the Internet of Things (IoT), has transformed sustainability, operational efficiency and transparency. This enhances green supply chain managementâs (GSCM) integrity and resilience which enable the achievement of sustainability objectives in line with the 2030 United Nations Sustainable Development Goals (UNSDGs) agenda. However, the accelerated digital adoption post-COVID-19 in developing countries came with increased SC vulnerabilities which undermine its integrity, resilience and sustainability goals. This study aims to determine the main SC cybersecurity risks and to recognise strategies that can be adopted to counter SC cybersecurity risks in developing countries. A search for literature was done in the Scopus, Google Scholar and ProQuest databases between 2018 and 2025 using a systematic literature review (SLR). The common SC cyber vulnerabilities findings were phishing incidents, breach of confidentiality, SC software attacks owing compromised open-source components and data theft. Consequently, this chapter proposed the use of monitoring and intrusion detection systems, collaborations for risk intelligence sharing, developing standardised cyber security protocols, use of firewalls and data encryption. These cybersecurity measures aim to equip SC managers and practitioners in developing countries with tools to protect data integrity and support sustainable Industry 4.0 operations by seamlessly integrating cybersecurity protocols with sustainability strategies, thereby enhancing resilience against digital disruptions. This chapter ends with proposing areas for further research and conclusion.
Supply Chain Resilience and Risk Management
Digital Transformation in Industry
Infrastructure Resilience and Vulnerability Analysis
Abstract The quick implementation of technology in Industry 4.0 which includes the Internet of Things (IoT), artificial intelligence (AI), blockchain, and robotics leads to supply chain management (SCM) transformation as it alters workforce profiles. This chapter examines the fundamental links between human factors, like employee transition, and sustainable SCM in Industry 4.0 specifically for the United Nations Sustainable Development Goals (UNSDG) 2030 targets as well as the post-COVID-19 environment. It explores how technology transforms talents and promotes roboticâhuman teamwork as well as impacts workforce well-being. The analysis of job displacement challenges, ethical problems, and labour shortages, together with identifying opportunities for new skills development and resilient supply chain implementations. Special emphasis is placed on sustainable workforce solutions through actual implementations and practical frameworks for workforce protection through ongoing learning and efficient management of organizational change along with policy collaboration. It explores both ethical and social consequences through an examination of employee data security risks, social inequality problems, and employee mental health effects related to fast technology acceptance. The future research areas include AI autonomous driving vehicles in different fields, smart manufacturing, and health care sustainability. The extensive evaluation provides essential information that benefits scholars, policymakers, together with industrial leaders who need to understand human capital management in sustainable supply chains using Industry 4.0 technologies.
Raja Rehan, Mohd Hanafia Huridi, Aeshah Mohd Ali, Malik Shahzad Shabbir
Abstract This chapter explores how the synergy between digital transformation and sustainability initiatives offers businesses a powerful way to achieve economic growth while reducing environmental and social impacts to realize the United Nations Sustainable Development Goals (UNSDGs) by 2030. Clearly, businesses are rapidly adopting the UNSDGs program, which aims to alleviate poverty, hunger, and improve health for all, while building strong institutions. By leveraging digital technologies like Artificial Intelligence, blockchain, Internet of Things, fintech, and cloud computing for digital transformation, companies can improve energy efficiency, increase supply chain transparency, reduce carbon emissions, and support financial sustainability. Therefore, integrating digital transformation and new technologies into business strategies helps advance the UNSDGs toward their 2030 goals. As a result, companies need to adopt key strategies that merge sustainability principles with digital transformation to meet the UNSDG targets. Numerous metrics help measure how digital transformation contributes to achieving these sustainability goals. However, addressing challenges such as cost, data privacy, cybersecurity, resistance to change, lack of technical expertise, and workforce skills is essential for widespread adoption of UNSDGs. Digital transformation plays a vital role in overcoming these obstacles, often through offering innovative technological products that help raise funds for sustainability initiatives. This chapter concludes that businesses embracing digital transformation with a strong commitment to the UNSDGs will not only gain a competitive edge but also contribute significantly to building a more sustainable and resilient future.
Abstract The rapid integration of Industry 4.0 technologies is profoundly transforming global supply chains, impacting sustainability, economic efficiency, and competitiveness. This study economically analyzes the adoption of Industry 4.0-driven green supply chain management (GSCM) practices specifically within developing economies. Leveraging established economic theories, including transaction cost economics, resource-based view, and institutional theory, alongside empirical data and case studies, the authors assess how digital transformation enhances supply chain sustainability and economic performance. The authors employ a panel data regression model to examine the relationship between Industry 4.0 technologies (Internet of Things, blockchain, artificial intelligence, and additive manufacturing) and key sustainability metrics, including carbon footprint reduction, cost efficiency, and resilience. By leveraging case studies from developing nations, the authors highlight how digital adoption influences supply chain productivity and long-term economic gains while aligning with the United Nations Sustainable Development Goals (UNSDGs) 2030. The authorsâ findings provide policy recommendations for governments and firms to optimize digital infrastructure investments, mitigate adoption barriers, and enhance regulatory frameworks for sustainable economic growth. This chapter contributes to the literature by bridging economic analysis with the practical application of Industry 4.0 technologies for supply chain sustainability in developing economies. It offers novel insights into how these nations can achieve economic and environmental resilience amid evolving global trade dynamics, particularly in the post-COVID-19 era.
Abstract This chapter examines how Industry 4.0 technologies enhance adaptive performance within the framework of Green Supply Chain Management (GSCM), drawing on the theoretical perspectives of Dynamic Capabilities, the Resource-Based View and Circular Economy principles. A systematic review of peer-reviewed literature from Scopus and Web of Science (2018â2025) was conducted, using defined inclusion and exclusion criteria to identify 126 relevant studies. The analysis highlights how key technologies â including the Internet of Things (IoT), Artificial Intelligence (AI), Blockchain, Robotics and Additive Manufacturing â enable real-time responsiveness, transparency and resource efficiency in sustainable supply chains. Findings indicate that the integration of these technologies not only strengthens operational flexibility and resilience but also accelerates progress toward United Nations Sustainable Development Goals (SDGs), particularly Goals 9, 12 and 13. This chapter provides theoretical contributions by linking adaptive performance to strategic technological capabilities and practical recommendations for policymakers and industry leaders to align digital transformation with net-zero carbon targets. Future research directions are proposed to empirically assess these relationships using both quantitative and qualitative approaches.
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 quantumâclassical 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]â[28].
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. It should provide enough detail to help readers quickly understand the scope and value of the study while encouraging them to read the full paper. The recommended length is between 150 and 250 words; however, it may extend up to 500 words if necessary to clearly communicate the research objectives, methods, findings, and significance. Keywordsâ Sustainable Logistics; Green Supply Chain Management; Artificial Intelligence; Smart Transportation; Digital Twins; Blockchain; Energy-Efficient Logistics; Quantum Computing; Supply Chain Resilience; FKF Analysis.
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. Keywordsâ 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 humanâmachine 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, physicalâvirtual 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 humanâAI 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 SĂĄnchez, Augusto Renato PĂŠrez Mayo, NohemĂ 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%)âconstitute 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 (DRIâ4.06). 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 sensingâanalysisâintelligenceâsimulationâoptimization architecture provides an adaptive pathway for efficient and resilient next-generation logistics Keywordsâ 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, JoĂŁo 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.