Search Results (157)

The increasing dependence of financial supply chains on digital infrastructures has made it more necessary to design secure, resilient, and reliable networks than ever before. This research presents a self-healing framework based on blockchain and digital twins for multi-objective fuzzy optimization of financial supply chains under uncertainty. The proposed model, focusing on minimizing financial cost, cyber risk, and recovery time while simultaneously maximizing the level of trust and resilience, enables intelligent decision-making in the face of cyber threats. By combining real-time monitoring, secure transaction validation, fuzzy risk assessment, and automated recovery, the framework identifies the role of each component in maintaining the financial and operational stability of the network. The results showed that the complete model achieved an overall performance score of 0.944 in the component elimination study and increased the level of trust and resilience to 0.95 and 0.96, respectively. The cyber risk index was also maintained at 0.118, indicating the framework’s ability to control threats and maintain network stability. The findings show that the convergence of blockchain, digital twin, fuzzy logic, and self-healing mechanism can provide an effective basis for the development of smart, secure, and resilient financial supply chains. Full article

Background: Managing agrologistics supply chains under infrastructure scarcity requires integrative, spatially explicit decision-support tools. This study develops a macro-level digital twin of the multimodal agricultural supply chain in Kazakhstan’s Almaty region to optimize freight allocation and guide strategic investment planning. Methods: Our methodology integrates Earth observation data (ESA WorldCover 10 m) with a large-scale multimodal road–rail graph network (1.39 million nodes) to identify 135 crop production clusters. Using linear programming in MATLAB, we optimize the regional distribution of 322.2 thousand tons of seasonal maize, wheat, and soybeans while localizing new storage silos using Green Field Analysis. Results: The baseline simulation reveals a critical storage capacity deficit, yielding a Capacity Coverage Ratio of only 23.8%. However, implementing optimal multimodal rail-road routing mathematically reduces the Logistics Cost Index from 8,642,195 to 4,716,175 units, achieving overall cost savings of 45.4%. Conclusions: The proposed digital twin and its performance metrics provide a scientifically grounded, data-driven toolkit for public–private partnerships, ensuring robust infrastructure investment localization and facilitating the transition toward the Agriculture 4.0 paradigm. Full article

Construction materials move in large volumes, over varying distances and through fragmented logistics chains. Yet EN 15804 Module A4, which covers transport to the building site, is still often calculated with static assumptions rather than verified transport data. This creates a gap between declared and project-specific cradle-to-site emissions, especially when circular strategies shift flows from factory-to-site towards site-to-site, site-to-processing and processing-to-site logistics. This paper focuses on dynamic A4 assessment, using macro-level analysis to identify the material flows, spatial patterns, transport modes and digital data needed for traceable logistics-based carbon accounting. The framework connects construction material-flow analysis with Digital Product Passports, ISO 14083 logistics data, GS1 Digital Link, GS1 EPCIS and digital-twin concepts. Building-permit data are used as spatial indicators of material sinks and transport-demand hotspots. The analysis uses Slovenian road, rail and maritime freight statistics, operational railway data for construction-related material categories, CDW data and building-permit data for 2020–2024. Results show that domestic road transport accounts for 93–95% of transported construction-material tonnage, while the smaller international freight volume generates nearly half of total ton-kilometers. Macro-level analysis is essential for locating material demand, potential transport hotspots and 5R strategies: refuse, reduce, reuse, repurpose and recycle. This study defines key information needs for product identification, transported mass, transport legs, distance, mode, energy pathway, load factor, empty running, logistics events and traceability. The results support policymakers, standardization bodies, contractors, logistics providers, manufacturers, researchers and software developers. Full article

Low-emission zones (LEZs) require cities to define policy rules, predict effects before deployment, and verify outcomes afterwards, yet mobility, emissions, meteorology, exposure, data governance, and public communication are commonly handled in separate systems. This paper presents envair360, a Physical Intelligence architecture and a four-stage, evidence-gated LEZ methodology connecting project definition, baseline feasibility, digital-twin design, deployment, and verified impact closure. A design-science method is combined with an operational case study of Cartagena, Spain, because the research object is both a socio-technical artefact and a context-dependent municipal deployment. The technology chain is selected to bridge complementary scales and functions: SUMO for link- and vehicle-level traffic, WRF and CHIMERE for meteorology and regional chemistry, MUNICH and street-canyon parameterisation for computationally tractable street resolution, model-output calibration anchored to measurements, and FIWARE/NGSI-LD for governed context exchange. The manuscript distinguishes city observations, peer-reviewed component validation, demonstrated platform capabilities, and policy or engineering targets. A Murcia component study reports lower hourly than daily agreement after deep-learning calibration (NO₂: $r=0.79$ hourly and $0.94$ daily; O₃: $r=0.85$ hourly and $0.97$ daily), illustrating the importance of temporal aggregation and transfer limits. Digitisation of the prior Madrid ozone-density figure indicates modal shifts of approximately +32.0 and +27.8 source-axis units at two stations; the supplied source does not permit a numerical NO_x bias estimate. A separate six-city export audit covers 24,384 records and 4064 street segments and demonstrates a common model-output schema, not predictive validation. In Cartagena, project documentation reports elevated PM₁₀/PM_2.5, urban heat and solar-radiation stress, and a plausible role for dry-climate dust resuspension, supporting a superblock-oriented LEZ proposal with a long-term 30% vehicular CO₂ reduction target. The paper’s specific contribution is the governed orchestration, evidence taxonomy, quality gates, reproducible lineage, explicit policy-scenario representation, and portable city-onboarding protocol; it does not claim that the individual scientific models, the Cartagena deployment, or the cited project targets originated in this manuscript. Full article

Humanitarian supply chains often face sudden demand surges, disrupted transportation, limited inventory visibility, and fragmented coordination under disaster uncertainty. Digital twins can improve real-time visibility and adaptive resource allocation, but their effectiveness depends on stable collaboration among public, private, and platform actors. This study develops a digital twin-oriented tripartite evolutionary game and simulation framework involving the public sector, private logistics/material enterprises, and a digital twin platform provider. The model examines how digital twin-oriented governance, active resource and data sharing, and high-quality platform operation co-evolve under bounded rationality. Stability analysis identifies the conditions for convergence to the desired collaborative equilibrium. Numerical simulations, including global sensitivity analysis, stability-region analysis, scenario comparison, stochastic evolutionary dynamics, and Monte Carlo tests, show that enterprise sharing costs and platform operation costs are key barriers, whereas subsidies, platform payments, credible constraints, digital twin visibility, collaboration synergy, and disaster uncertainty promote collaboration. Scenario comparison further indicates that technology-only digital twin construction is insufficient, while adaptive incentive-compatible governance generates shorter convergence time and lower collaboration loss. The findings highlight digital twins as socio-technical coordination infrastructure requiring incentive-compatible governance in humanitarian supply chains. Full article

Digital Twins are increasingly viewed as key enablers of real-time decision-making in supply chain management and logistics, yet the literature remains fragmented across application domains, decision problems, methodological approaches, and technological implementations. This systematic review examines how Digital Twins support real-time decision-making, understood not as a fixed response time threshold but as the temporal alignment between data refresh, decision generation, and system evolution. Following the PRISMA 2020 framework, a Scopus search conducted on 16 March 2026 identified studies in which Digital Twins were a central component, incorporated dynamically updated data, supported real-time or near-real-time decision-making, and addressed supply chain management or logistics problems. A total of 57 peer-reviewed studies were retained and synthesized using descriptive analysis, cross-tabulation, and thematic coding across decision problems, application contexts, solution methods, enabling technologies, implementation challenges, and future research directions. The findings show that real-time Digital Twin applications are concentrated mainly in production scheduling and planning, followed by routing and dispatching, resource allocation, disruption management, and inventory management. Methodologically, most studies adopt hybrid approaches combining simulation, optimization, and/or machine learning, reflecting the complexity of real-time operational decision-making. Enabling technologies were grouped into six functional layers, with data acquisition technologies receiving the greatest attention, while higher-level integration and enterprise system layers remain less developed. Reported challenges cluster around data, methodological, and systemic issues. The review concludes that real-time Digital Twins are emerging as integrated decision environments, but further research is needed on computational efficiency, interoperability, validation, and application in underexplored logistics domains. Full article

The metalworking industry faces growing regulatory pressure to quantify and verify its product carbon footprint (PCF), driven by frameworks such as the European Union’s Carbon Border Adjustment Mechanism (CBAM) and emissions trading schemes (ETS). Traditional static accounting methods, based on generic emission factors and annual averages, are insufficient given the dynamic, multi-stakeholder nature of the sector’s supply chains. This study presents a systematic review conducted under the PRISMA 2020 protocol to identify, classify and critically evaluate the digital tools and technologies used to calculate, manage and verify PCF in this sector. From 495 records screened, 53 thematically relevant studies were analyzed and 5 sector-specific cases examined in depth. The results indicate that the Internet of Things (IoT) and smart sensor networks constitute the primary data-capture layer, while Machine Learning, Big Data and Digital Twins are the predominant processing technologies. Blockchain and verifiable digital credentials emerge as governance mechanisms that ensure the transparency, auditability and immutability of emissions inventories, enabling compliance through Digital Product Passports (DPPs). We conclude that digital decarbonization requires interoperable architectures integrating real-time capture, distributed traceability and common semantic standards; viability in small and medium-sized enterprises (SMEs) and interoperability across heterogeneous platforms remain the main research gaps. Full article

Artificial intelligence (AI) is increasingly reshaping production management by enabling data-driven optimization, predictive decision-making, and more adaptive operational systems across manufacturing. With research activity in this area growing rapidly, the literature is fragmented; thus, understanding the current reconfigurations of production management driven by AI has become difficult, though necessary. This study offers a comprehensive analysis of scientific publications (n = 439) on AI in production management published between 2016 and 2026. Research was conducted through an adapted selection process guided by Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to ensure transparency, while data were analyzed using VOSviewer and complementary statistics, combining keyword co-occurrence mapping, subject area classification, and country and author collaboration networks. Findings indicate an increase in research activity, with computer science, engineering and telecommunications emerging as the primary foundations of the field. The analysis identifies three interconnected thematic directions: AI-enabled smart manufacturing and operational optimization, including scheduling, production planning, predictive maintenance, and quality control; sustainability- and resilience-oriented production and supply chain transformation; and digital twin-based, human-centered, and knowledge-driven production systems. Moreover, international collaboration patterns show a highly globalized research landscape led by Germany, France, Italy, Sweden, Norway and South Korea. Overall, bibliometric patterns suggest an emerging framing of AI in production management research which needs more attention and further development. Full article

The accelerating transition toward renewable electricity generation has elevated grid-scale electrochemical energy storage from an ancillary grid service to a foundational infrastructure requirement. This review provides a comprehensive account of rechargeable battery technologies for stationary grid applications, spanning advanced lithium-ion systems, sodium-ion and post-lithium multivalent chemistries, vanadium and organic flow batteries, solid-state architectures, and high-energy-density future systems such as lithium-sulfur and metal-air cells. The techno-economic context of grid-scale storage is systematically examined, including performance metrics, market drivers, and regulatory frameworks. Each battery chemistry is analyzed with respect to electrochemical mechanism, cycle life, energy density, safety profile, material availability, and commercial readiness. Non-electrochemical storage technologies are discussed as system-level alternatives. Battery safety engineering, thermal management system design, thermal runaway mechanisms and prevention, and failure containment strategies are examined in depth, followed by analysis of critical material supply-chain vulnerabilities, life-cycle assessment, and recycling pathways. The expanding role of artificial intelligence, machine learning, and digital twin frameworks in optimizing performance and enabling predictive maintenance is reviewed. Key challenges, including material bottlenecks, manufacturing scalability, long-duration storage gaps, and the absence of harmonized performance standards, are identified, and the review concludes with a techno-economic roadmap toward cost-competitive, resilient, and low-carbon grid storage. Full article

In light of the growing prominence of environmental issues, the frequent occurrence of unexpected incidents, and the dynamic challenges of a changing market environment, suppliers must possess comprehensive capabilities that encompass both green and sustainable development as well as resilience to risks. Consequently, green supplier selection has emerged as a critical research topic. By integrating virtual and physical systems, digital twin technology enhances supply chain transparency and efficiency—a capability that plays a significant role in advancing sustainable supply chain development. In view of this, this study incorporates risk factors into the green supplier evaluation system, introduces indicators related to digital twin technology, and proposes a stochastic slack-based measure data envelopment analysis method, namely SSBM, for evaluating green suppliers. This approach expands and refines the existing evaluation criteria and the decision-making model. Finally, a numerical case study is conducted to validate the feasibility of the proposed method. This research provides more systematic and scientific decision support for green supplier selection, enriching the theoretical and practical applications in the fields of green supply chain and multi-criteria decision-making. Full article

Forest bioenergy holds significant potential for North American decarbonization and energy security, yet persistently high logistics costs, feedstock quality variability, and geographic dispersion of biomass resources continue to constrain commercial viability. This review asks what it will take for forest bioenergy supply chains to achieve economic and operational lift-off, identifying key bottlenecks and the most promising pathways to scale. We systematically review 237 peer-reviewed studies and technical reports with the majority published between 2000 and 2025, covering feedstock types ranging from logging residues and woody biomass to short rotation woody crops, and end-products spanning solid biofuels, heat and power, thermochemical products, and sustainable aviation fuel. The literature consistently identifies delivered cost, feedstock quality control, and the geographic mismatch between biomass supply and conversion facility location as the three primary barriers to sector viability. Depot-based preprocessing, cascading utilization strategies, and participatory landowner contracting emerge as the most effective near-term solutions for improving supply chain economics and mobilizing economically recoverable biomass. At the frontier, AI-enabled optimization, digital twin modeling, and integrated biorefinery configurations show strong potential to manage spatial variability and unlock the scale economies on which commercial viability depends. Translating these advances into practice will require stable, long-term policy signals and coordinated investment across the full supply chain. Full article

Operational Technology (OT) and Industrial Control Systems (ICSs) along the NATO-EU eastern flank face escalating hybrid threats, yet existing cyber-resilience metrics remain IT-centric, lacking OT-specific constraints and geopolitical exposure dimensions. This paper presents a Design Science Research contribution: the development and simulation-based feasibility demonstration of two interconnected artefacts. The first is the AI-enabled Cyber-Resilience Index (ACRI)—a composite 0–100 metric operationalized through 16 indicators across four domains (detection performance, operational continuity, governance maturity, supply-chain risk), aggregated as a three-term convex combination of capability domains with a linear subtractive supply-chain exposure penalty, weighted via AHP-based illustrative sector-reference profiles. The second is the Unified Compliance Framework (UCF), a structured R → C → E → SLO mapping linking 47 atomic regulatory requirements (NIS2, DORA, CER, AI Act, CRA) to standards (IEC 62443, ISO/IEC 27001) and auditable evidence artifacts, with a Continuous Assurance Loop operationalizing continuous control monitoring. Feasibility is demonstrated through digital twin simulation under three OT-representative threat scenarios (energy SCADA APT, railway supply-chain compromise, manufacturing ransomware). Results in simulated environments show ACRI improvement from Moderate-Risk baselines (45–61) to Adequate-Resilience thresholds (65–73); the proposed federated autoencoder–LSTM detector attains a composite $D_{perf}$ of 0.883 versus 0.510 for a static ±3$\sigma$ threshold baseline (a 73% relative improvement at the domain level). Sensitivity analysis confirms classification robustness ($\pm 7.3\%$ weight perturbation; coefficient of variation below 9.1% across 10,000 Monte Carlo iterations). Critical limitations are explicit: simulation-only evidence ($n=12$ scenario instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. The contribution is positioned as a proof-of-concept design artifact establishing methodological foundations for OT-centric resilience assessment and compliance-to-engineering traceability, not as a field-validated operational system. Full article

Disruptions threaten supply chains, creating a need for more resilient manufacturing networks. Manufacturing-as-a-Service (MaaS) has emerged as a promising Industry 4.0 approach to address this challenge. Yet, its effectiveness relies on interoperable digital twins (DTs), enabling the standardized exchange of manufacturing capabilities across organizational boundaries. The Asset Administration Shell (AAS) standards can be used to meet this requirement. However, modeling AAS-compliant DTs is considered challenging due to the standard’s complexity. This paper, therefore, investigates the automatic generation of AAS-compliant DTs for representing manufacturing capabilities. Requirements from MaaS use cases in two research projects reveal limitations in current approaches. To address these limitations, this paper introduces an automated, LLM-supported generation process that leverages ontologies as a domain-specific knowledge base. The approach is operationalized in a modular software architecture and demonstrated through two use cases. Full article

Quantum computing is increasingly seen as a disruptive technology capable of expanding the computational limits of advanced manufacturing systems within the emerging Industry 5.0 framework. By utilizing quantum mechanical principles such as superposition, entanglement, and quantum parallelism, quantum computation enables new approaches to solving complex optimization, simulation, and data-intensive problems that are challenging or impractical for classical computers. This paper offers a comprehensive and critical review of the potential impacts of quantum computing on advanced manufacturing, focusing on intelligent production planning, supply chain optimization, materials discovery, predictive maintenance, and human–machine collaboration, key aspects of Industry 5.0. The originality of this review lies in its integrated analysis of quantum computing alongside artificial intelligence, digital twins, and cyber–physical systems, highlighting how these technologies, when combined, improve decision-making speed, process efficiency, and sustainability. Despite these opportunities, the integration of quantum computing into Industry 5.0 systems faces critical challenges, including hardware limitations, algorithm scalability, data security concerns, workforce readiness, and the complexity of integrating quantum solutions with existing industrial infrastructures. The role of hybrid quantum-classical architectures is examined as a feasible and transitional approach for near-term manufacturing applications. By critically assessing both technological strengths and practical constraints, this review positions quantum computing as a promising enabler of resilient, human-centered, and sustainable manufacturing ecosystems. The insights aim to assist researchers, industry players, and policymakers in strategically managing the integration of quantum technologies as manufacturing systems advance toward Industry 5.0. Full article

This research presents an integrated framework for supply chain finance in which digital twin, blockchain, and multi-objective fuzzy optimization are used in synergy to improve financial decision-making in dynamic and uncertain environments. In this framework, the digital twin acts as a real-time monitoring and forecasting layer, blockchain acts as a trust and transparency infrastructure, and the optimization model acts as the decision-making core. To evaluate the proposed framework, a scenario-based mathematical model was developed and analyzed using a combination of real-world and simulated data. The results showed that the proposed framework was able to reduce the total cost by 18.6% and increase the return on investment to 12.4%. Also, the use of the digital twin framework significantly reduced financial risks and delays, while the integration of blockchain improved the transparency, traceability, and reliability of transactions and reduced operational errors. Overall, the findings show that this framework has high potential for developing smart, transparent, and resilient financial systems in the supply chain context. Full article