Search Results (87)

Global supply chains face intensifying pressures from disruption, regulatory complexity, and sustainability mandates, requiring a shift toward more resilient and adaptive coordination. While artificial intelligence (AI) and blockchain have been recognised as complementary enablers, their implementation remains largely fragmented, existing as isolated tools linked by manual data exchange rather than integrated, programmable logic. This paper addresses this orchestration gap by proposing the Dynamic Resource Orchestration Framework for AI-Blockchain Integrated Supply Chains (DROF-AIBC). Grounded in Resource Orchestration Theory (ROT) and Dynamic Capabilities Theory (DCT), the framework provides a theoretical foundation for the strategic and autonomous orchestration of digital resources. Unlike classic supply chain orchestration, which focuses on the linear coordination of physical assets and legacy systems, DROF-AIBC conceptualises an “intelligent conductor” as a coordination mechanism combining AI-driven analytics and smart contract-based execution. This mechanism supports the configuration, optimisation, and monitoring of resources in response to changing external signals, effectively closing the loop between analytical insights and verifiable execution. The paper further substantiates how this autonomous capability serves as a foundational roadmap for the Industry 5.0 paradigm, embedding human-centricity through Explainable AI (XAI) to provide a “provenance of logic”, promoting circular economy sustainability, and fostering systemic resilience in turbulent environments. The framework aims to provide both a theoretical foundation and a practical roadmap for orchestrating AI and blockchain to advance resilient, sustainable and adaptive supply chains. Full article

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

The adoption of circular economy (CE) practices and artificial intelligence (AI) in the supply chain (SC) has become extremely significant in manufacturing organizations. The CE seeks to facilitate sustainable growth by managing the flow of materials and energy within closed-loop systems. The CE has resulted in the development of sustainable business models. AI capabilities transform work activities, data flows, and organizational processes. Therefore, the present study aims to develop a framework to improve circular supply chain (CSC) adoption in the automobile manufacturing sector by identifying and analyzing CE practices and AI-enabled digital strategies. The proposed framework was analyzed by employing a hybrid approach of Prioritized Weighted Average–Criteria Importance Through Intercriteria Correlation–Preference Ranking Organization Method for Enrichment Evaluations-II (PWA-CRITIC-PROMETHEE-II) under an Interval-Valued Fermatean Fuzzy (IVFF) environment. IVFF-CRITIC was employed to determine the CE practices’ weights, while IVFF-PROMETHEE-II was utilized to establish the relative index of AI-enabled digital strategies to enhance the CSC adoption. The key findings of the current study indicate that “AI-enabled infrastructure configuration for circular economy adoption in the supply chain”, “AI-integrated equipment to facilitate adaptability and mass personalization”, and “Robotics and AI-driven manufacturing and material reclamation” are the most significant AI-based digital strategies that support CE practices to enhance the adoption of a CSC and encourage case example manufacturing organizations to align their operations with AI and CE. Moreover, the outcomes of the study will deliver a comprehensive evaluation of CE practices and AI-enabled digital strategies for SC managers, based on the relative indexing obtained through the implementation of the hybrid approach. Full article

Artificial intelligence (AI)-enabled unmanned aerial vehicles (UAVs) are rapidly emerging as transformative technologies for sustainable healthcare logistics, particularly in remote and infrastructure-constrained regions. Despite growing implementation, the environmental, social, and governance (ESG) implications of these systems remain insufficiently synthesized in the literature. This study conducts a PRISMA-ScR-guided Systematic Review of 37 peer-reviewed studies selected from 333 records across six major scientific databases (2015–2026). The analysis reveals a sharp acceleration of research after 2021, with over 80% of publications produced between 2021 and 2024, indicating increasing global interest in AI-supported autonomous medical logistics. Evidence demonstrates that AI-enabled drones can substantially reduce delivery times; expand access to blood, vaccines, and essential medicines; and enhance emergency response capacity in rural and disaster-affected environments. From a sustainability perspective, AI-driven route optimization and autonomous navigation may reduce transport-related emissions, supporting climate-responsive healthcare supply chains. However, large-scale deployment remains constrained by regulatory fragmentation, cybersecurity risks, operational limitations, and challenges with social acceptance. This review proposes an ESG-oriented framework linking technological innovation, ethical governance, and equitable healthcare access while identifying key research gaps in lifecycle sustainability assessment, cost-effectiveness modeling, and real-world implementation aligned with the Sustainable Development Goals (SDGs). Full article

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

As Unmanned Aerial Vehicles (UAVs) become integral to critical infrastructure, ranging from precision agriculture to emergency disaster recovery, their security becomes a matter of systemic resilience. This paper provides a comprehensive thematic survey of the security landscape for unmanned devices, bridging the gap between low-level hardware vulnerabilities and high-level mission failures. We propose a multidimensional taxonomy that categorizes challenges into hardware roots of trust, swarm intelligence threats, and domain-specific applications. A primary focus is placed on the Resource–Security Paradox, where the energy cost of heavy cryptographic or AI defenses directly reduces flight endurance, creating a trade-off that adversaries exploit through battery-exhaustion attacks. Beyond standard threats, we analyze emerging risks in additive manufacturing supply chains, the “Sim-to-Real” gap in AI-driven perception, and the legal necessity of Digital Forensic Readiness (DFR) for post-incident attribution. Through a systematic review of defensive frameworks, including lightweight encryption, Mamba-KAN anomaly detection, and blockchain-anchored logging, we evaluate the effectiveness of current solutions against complex adversarial models. Finally, we identify critical research gaps, providing a roadmap for security-by-design in the next generation of critical infrastructure swarms. Full article

The kapok (Ceiba pentandra) fiber industry plays a vital role in Indonesia’s rural bioeconomy, particularly in regions with high production intensity such as Pasuruan Regency. Despite its economic potential and alignment with the green economy agenda, the industry faces increasing volatility due to seasonal harvest cycles, climate-induced disruptions, global demand fluctuations, and exchange rate instability. These conditions necessitate an adaptive and predictive approach to supply chain risk governance. We evaluated the performances of predictive analytics models, including linear regression, random forest, gradient boosting, XGBoost 3.2.0 libraries, K-nearest neighbors, and stacking regressor. Using multi-year monthly data on production volume, residual stock, and exchange rates, the stacking regressor was the most accurate model, achieving the lowest root mean square error and highest R² values. The results bridge the gap by applying predictive analytics to a resource-based, seasonal small industry sector. Practically, the results also enable leveraging AI in strengthening the long-term sustainability of agribusiness supply chains. Full article

Environmental challenges, such as climate change, resource scarcity, and pollution, increasingly demand organizational strategies that integrate artificial intelligence (AI) into sustainable innovation. This study examines how employee-level artificial intelligence capabilities (AIC) enable digital green innovation, a strategic approach that leverages AI-powered digital technologies to enhance green product development, green processes, and sustainable supply chains. Drawing on knowledge-based view (KBV) and the dynamic capability view (DCV), this study develops a theoretical framework linking AIC, knowledge-based dynamic capabilities (KBDC), and digital green innovation. Using survey data from 299 employees in Chinese High-Tech firms, results show that higher employee AIC strengthens KBDC, which in turn facilitates effective digital green innovation. The findings contribute theoretically by extending the antecedents of digital green innovation to the individual level and clarifying the multilevel mechanism through which AIC translates into organizational environmental performance, thereby enhancing both theories’ explanatory power in digital environments. Practically, the study highlights the importance for environmental managers of strengthening employee AIC and organizational KBDC to implement AI-driven sustainability strategies more effectively. Full article

Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport difficult, impacting logistical efficiency and the viability of bioenergy and bioproduct production. This study analyzes how combining artificial intelligence (AI) with multimodal transport can optimize and improve efficiency, as well as reduce costs, in biomass logistics. Methods: The study uses a tiered research framework that encompasses the physical domain (biomass limitations), the structural domain (mathematical modeling for multimodal transport), the intelligence domain (AI-based decision making), and the strategic approach. Results: The outcomes indicate that while truck transport is ideal for short distances, integrating rail and water transport through AI-driven optimization reduces costs and greenhouse gas emissions for long-distance travel. AI technologies, such as digital twins and machine learning, improve demand forecasting, real-time routing, and cargo consolidation, leading to enhanced prediction accuracy for transport costs. Conclusions: The integration of AI and multimodal networks builds resilient and sustainable biomass supply chains. However, full implementation requires addressing data fragmentation and investing in digital infrastructure to enable seamless coordination between supply chain stakeholders. Full article

Conventional food security strategies have largely been formulated under assumptions of population growth, abundant agricultural labor, and stable global trade. However, many advanced economies—particularly in East Asia—are entering a shrinking-society context characterized by population decline, rapid aging, and regional depopulation. This paper argues that demographic shrinkage should be understood not as a peripheral trend but as a landscape-level structural pressure that destabilizes incumbent agri-food systems. Drawing on the Multi-Level Perspective (MLP), the study conceptualizes demographic shrinkage as a cumulative force that erodes the labor base, productive capacity, and institutional stability of food systems, thereby weakening regime path dependence. Building on this framework, it advances Food Security 3.0 as a theory-driven contribution to sustainability research. Food Security 3.0 reconceptualizes food security under shrinkage conditions as a problem of systemic resilience rather than production expansion or import diversification, and theorizes food technology—including smart and automated agriculture, alternative proteins, and AI-enabled supply chains—as transitional infrastructure enabling regime reconfiguration under structural constraints. By integrating demographic change, socio-technical transitions, and governance, the study reframes food security as a question of resilience-oriented system design, strategic self-reliance, and integrated food-system governance. While anchored in the East Asian experience, the framework offers theoretical and policy-relevant insights for shrinking societies confronting overlapping demographic, climatic, and geopolitical pressures. Full article

The manufacturing sector drives industrialisation and contributes substantially to economic growth and employment creation. Despite this, it faces the challenges of diminishing size and lack of competitiveness, mainly due to operational uncertainty. The study developed an approach to managing operational uncertainty using Industry 4.0 and 5.0 technologies. It employed a multimethod quantitative design based on the post-positivist paradigm, with data collected from 22 experts and 262 responses from a manufacturing firms’ survey. The study employed an integrated fuzzy decision-making trial and evaluation laboratory (DEMATEL) with partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The fuzzy DEMATEL results reveal that growing geopolitical tension, cost-of-living-driven consumer behavioural change, pandemic turbulence, lack of energy stability and security, and the entrenched power of large firms are causal dimensions of operational uncertainty. Industry 4.0 and 5.0 technologies, with capabilities for scenario planning and supply chain integration, flexible production and mass customisation, real-time system and process monitoring and response, root cause analysis, and sustainable solutions, can manage operational uncertainty. These technologies include artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and, to a lesser extent, advanced robotics, blockchain, and augmented and virtual reality (AR/VR). This study advanced configuration theory and a new integrated methodology (fuzzy-DEMATEL-PLS-SEM-fsQCA) to develop solutions for sustained performance during operational uncertainty in manufacturing. This research offers valuable information to advance the subject, make meaningful changes in day-to-day manufacturing operations, and promote practical real-world problem solving. Full article

Background: This study examines the influence of AI-driven supply chains on the adoption of automation and robotics within Jordanian manufacturing firms, emphasizing the role of supply chain adaptability and agility as mediators and market turbulence as a moderator. Methods: Drawing on dynamic capabilities theory and institutional theory, the study develops a conceptual model and tests it using data collected from 337 managers through an online survey. The analysis was carried out through partial least squares structural equation modeling (PLS-SEM). Results: The results show that AI-driven supply chains significantly enhance both adaptability and agility. However, only agility has a direct and significant effect on the adoption of automation and robotics, while market turbulence significantly moderates the connection between supply chain adaptability and the adoption of automation and robotics, but not the relationship between agility and adoption. Conclusions: Theoretically, the study provides insight into the interplay among internal dynamic capabilities in shaping technology adoption under external uncertainty. These results provide actionable implications for managers operating in developing economies like Jordan, highlighting the significance of building agile capabilities and adopting AI technologies to support innovation. The study is limited by its focus on a single country and sector; future research should explore other industries and incorporate additional moderating or mediating variables. Full article

Ongoing global disruptions, including pandemics, geopolitical tensions, and climate-driven events, have exposed vulnerabilities in healthcare supply chains (HSCs). This study examines how artificial intelligence (AI) is reshaping HSCs to improve agility, resilience, and sustainable performance. Using a systematic literature review with PRISMA-style screening across Scopus and Web of Science, the study is complemented by bibliometric analysis and latent Dirichlet allocation topic modeling to analyze peer-reviewed articles. The results indicate an exponential increase in AI-enabled HSC research, concentrated in a small number of journals and spanning a globally diverse author community. Three dominant thematic clusters emerged: (1) sustainability-oriented supply chain design, (2) disruption and resilience management, and (3) healthcare-focused digital transformation. Across these themes, AI, digital twins, Internet of Things, and simulation are evolving from efficiency tools to strategic enablers of decision intelligence, supporting real-time sensing, scenario analysis, and proactive risk mitigation. The study highlights a convergence of “triple transformation” in which digitalization, resilience, and sustainability are increasingly co-dependent capabilities in HSCs. However, persistent barriers exist, including data quality issues, legacy systems, workforce skill gaps, limited model interpretability, and incomplete governance frameworks, which constrain large-scale adoption. The findings indicate a need for longitudinal and multi-method studies on human–AI collaboration, trust calibration, and leadership in AI-enabled HSCs. This study provides practical guidance for healthcare organizations looking to leverage AI in developing agile, resilient, and sustainable supply chain ecosystems. Full article

The rapid growth of electric vehicles (EVs) has created an urgent need for sustainable end-of-life battery management systems. This paper presents a novel AI-driven framework for designing resilient reverse logistics networks that optimize the collection, testing, repurposing, and recycling of EV batteries within a circular economy context. We develop a bi-level optimization model in which the upper level determines strategic facility location decisions under disruption uncertainty, and the lower level employs deep reinforcement learning (DRL) to make dynamic operational decisions including battery routing, State-of-Health (SoH)-based sorting, and inventory management. The model simultaneously optimizes three objectives: total supply chain cost minimization, carbon emission reduction, and resilience maximization. A novel Fuzzy-Robust Stochastic programming approach with Conditional Value-at-Risk (FRS-CVaR) handles hybrid uncertainty from demand variability, supply disruptions, and material price volatility. We propose an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III) integrated with Proximal Policy Optimization (PPO) for an efficient solution. The framework is validated through a comprehensive case study of the Gulf Cooperation Council (GCC) region, demonstrating that the AI-driven approach reduces total costs by 18.7%, decreases carbon emissions by 23.4%, and improves supply chain resilience by 31.2% compared to traditional optimization methods. Ablation studies across 10 independent runs with different random seeds confirm the robustness of these findings (95% confidence intervals within ±2.3% for all metrics). Sensitivity analysis reveals that battery SoH prediction accuracy and facility redundancy levels significantly impact network performance. This research contributes to both methodology and practice by providing decision-makers with an intelligent, adaptive tool for sustainable EV battery lifecycle management. Full article

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article