Search Results (388)

The application of Artificial Intelligence and Machine Learning in the supply chain fields is significantly changing the way businesses manage their operations, forecast their demand, manage their inventory, optimize their logistics, and increase their level of resilience. This research explores, through a bibliometric literature review, how the integration of these technologies can support the implementation of a demand-driven supply chain approach in the global healthcare and pharmaceutical supply chains, which are facing remarkable challenges in ensuring demand-driven operations, especially in light of sudden disruptions such as the COVID-19 pandemic. Full article

The eyeglass lens manufacturing industry has become increasingly vulnerable to supply chain risks due to overlapping global disruptions, including the COVID-19 pandemic, the Suez Canal blockage, the Russia–Ukraine conflict, Red Sea shipping insecurity, and recent U.S. import tariffs. These events have challenged inventory planning, supplier coordination, and cost control across the industry. This study aims to evaluate how five operational constructs—stock system, inventory optimization, standardized methodology, production capability, and logistics performance—influence inventory resilience during global crises. Using an empirical case study, data were collected from 215 supply chain professionals at a multinational lens manufacturer in Southeast Asia and analyzed via Structural Equation Modeling (SEM). The results show that inventory optimization (β = 0.93) is the most influential factor in mitigating supply–demand imbalances, followed by logistics performance and production capability. This study offers practical recommendations, including real-time demand tracking, modular production systems, and scalable logistics strategies, to enhance inventory resilience. These findings contribute to both theory and practice by providing a validated framework tailored to high-precision manufacturing under persistent global risk. Full article

Multimodal freight transport systems, integrating maritime, rail, and road modes, play a vital role in modern logistics but face elevated operational, human, and environmental risks due to their complexity and interdependencies. To address the limitations of conventional risk assessment methods, this study proposes a hybrid risk modeling framework that integrates fault tree analysis (FTA), dynamic fault trees (DFTs), and fuzzy logic reasoning. This approach supports the modeling of sequential failures and captures qualitative uncertainties such as human fatigue and inadequate training. The framework incorporates reliability metrics, including Mean Time to Failure (MTTF) and Mean Time Between Failures (MTBF), enabling the quantification of system resilience and identification of critical failure pathways. Application of the model revealed human error, particularly procedural violations, insufficient training, and fatigue, as the dominant risk factor across transport modes. Road transport exhibited the highest probability of risk occurrence (p = 0.9960), followed by rail (p = 0.9937) and maritime (p = 0.9900). By integrating probabilistic reasoning with qualitative insights, the proposed model offers a flexible decision support tool for logistics operators and policymakers, enabling scenario-based risk planning and enhancing system robustness under uncertainty. Full article

Patent licensing is essential for sustainable technological diffusion, fostering innovation and strengthening industrial resilience. However, the determinants influencing patent licensing decisions remain underexplored. This study investigates these factors at both the enterprise and patent levels, emphasizing their role in promoting sustainable industrial innovation and knowledge transfer. Given the low proportion of licensed patents, this research proposes a measurement framework to identify thematically similar but unlicensed patents and applies a conditional logistic regression model to analyze the factors affecting licensing decisions. Using patent abstracts from the intelligent connected vehicles (ICVs) sector, topic modeling is conducted to classify technological themes, and Kullback–Leibler divergence is applied to measure differences between licensed and unlicensed patents. The results indicate that technological prestige and depth negatively influence licensing, whereas technological breadth, advancement, and stability have a positive effect. From a sustainability perspective, enterprises should optimize technology management to support responsible knowledge transfer and green innovation. Universities should enhance patent quality and innovation impact to contribute more effectively to sustainable development. Policymakers should refine patent licensing frameworks to foster an efficient, inclusive, and sustainable intellectual property ecosystem, thereby facilitating cross-sectoral technology diffusion, advancing eco-friendly industrial transformation, and promoting sustainable economic growth. Full article

In this article, we explored how open-source software serves as a strategic enabler for safety and security in the digital transformation of logistics systems. Open source is examined across multiple dimensions, including transparency, community collaboration, digital sovereignty, and long-term infrastructure resilience. The analysis focuses on the logistics domain, where interoperability, critical infrastructure protection, and supply chain continuity are essential. Key elements of open-source development—such as modular architectures, legal and licensing frameworks, and peer-reviewed codebases—support rapid vulnerability management, increased transparency, and the creation of sustainable digital ecosystems. Emphasis is placed on the role of open-source models in strengthening institutional trust, reducing dependency on proprietary vendors, and enhancing responsiveness to cyber threats. Our findings indicate that open source is not merely a technical alternative, but a strategic decision with legal, economic, and political implications, shaping secure, sovereign, and adaptive digital environments—particularly in mission-critical sectors. Full article

South American coffee producers face growing challenges due to external trade dependencies and climate-induced disruptions. This study investigates the role of export market diversification as a sustainability strategy for four major regional exporters of roasted non-decaffeinated coffee: Brazil, Colombia, Peru, and Ecuador. A quantitative and comparative methodology was applied over a ten-year period using the Herfindahl–Hirschman Index (HHI) to evaluate export market concentration and the Revealed Comparative Advantage (RCA) Index—including its normalized variant—to assess international competitiveness by destination. The results reveal substantial disparities: Brazil and Colombia exhibit moderate to high diversification and relative competitiveness in select markets, while Peru and Ecuador remain dependent on a few strategic buyers, with limited or declining comparative advantages. The findings emphasize that sustained export performance in the coffee sector requires not only a broader destination portfolio but also improved positioning through trade agreements, infrastructure development, and climate-resilient innovation. This study concludes with a strategic proposal based on three pillars—commercial, logistical, and technological—to support structural transformation and enhance the long-term sustainability of the coffee trade in South America. Full article

This study presents a novel artificial intelligence-driven architecture for real-time sensor fusion in autonomous vehicles (AVs), leveraging Apache Kafka and MongoDB for synchronous and asynchronous data processing to enhance resilience against sensor failures and dynamic conditions. We introduce Chaotic AdaBoost (CAB), an advanced variant of AdaBoost that integrates a logistic chaotic map into its weight update process, overcoming the limitations of deterministic ensemble methods. CAB is evaluated alongside k-Nearest Neighbors (kNNs), Artificial Neural Networks (ANNs), standard AdaBoost (AB), Gradient Boosting (GBa), and Random Forest (RF) for speed and acceleration prediction using CARLA simulator data. CAB achieves a superior 99.3% accuracy (MSE: 0.018 for acceleration, 0.010 for speed; MAE: 0.020 for acceleration, 0.012 for speed; R²: 0.993 for acceleration, 0.997 for speed), a mean Time-To-Collision (TTC) of 3.2 s, and jerk of 0.15 m/s³, outperforming AB (98.5%, MSE: 0.15, TTC: 2.8 s, jerk: 0.22 m/s³), GB (99.1%), ANN (98.2%), RF (97.5%), and kNN (87.0%). This logistic map-enhanced adaptability, reducing MSE by 88% over AB, ensures robust anomaly detection and data fusion under uncertainty, critical for AV safety and comfort. Despite a 20% increase in training time (72 s vs. 60 s for AB), CAB’s integration with Kafka’s high-throughput streaming maintains real-time efficacy, offering a scalable framework that advances operational reliability and passenger experience in autonomous driving. Full article

The rapid evolution of wireless communications has introduced new possibilities for the digital transformation of maritime operations. As 5G begins to take shape in selected nearshore and port environments, the forthcoming 6G promises to unlock transformative capabilities across the entire maritime domain, integrating Terrestrial/Non-Terrestrial Networks (TN/NTN) to form a space-air-ground-sea-underwater system. This paper presents a comprehensive review of how 6G-enabling technologies can be adapted to address the unique challenges of Maritime Communication Networks (MCNs). We begin by outlining a reference architecture for heterogeneous MCNs and reviewing the limitations of existing 5G deployments at sea. We then explore the key technical advancements introduced by 6G and map them to maritime use cases such as fleet coordination, just-in-time port logistics, and low-latency emergency response. Furthermore, the critical Artificial Intelligence/Machine Learning (AI/ML) concepts and algorithms are described to highlight their potential in optimizing maritime functionalities. Finally, we propose a set of resource optimization scenarios, including dynamic spectrum allocation, energy-efficient communications and edge offloading in MCNs, and discuss how AI/ML and learning-based methods can offer scalable, adaptive solutions. By bridging the gap between emerging 6G capabilities and practical maritime requirements, this paper highlights the role of intelligent, resilient, and globally connected networks in shaping the future of maritime communications. Full article

This study presents a structured multi-agent system (MAS) architecture aimed at optimizing operational efficiency in roll-on/roll-off (Ro-Ro) terminal management through intelligent coordination and decentralized decision-making. The proposed framework enhances space allocation, route planning, traffic control, and boarding coordination, enabling real-time decision-making and adaptive operational strategies. Through structured MAS architecture, agents interact dynamically to optimize vehicle flow, reducing congestion and improving overall efficiency. The study evaluates the system’s potential benefits compared to traditional port management models, highlighting improvements in transit time reduction, resource utilization, and operational resilience. The findings suggest that MAS-based automation can enhance decision-making, sustainability, and integration with Industry 4.0 paradigms, driving the transition toward intelligent, efficient, and scalable port logistics. Full article

Background: Growing environmental concerns and the vulnerability of global supply chains to disruptions, such as pandemics, natural disasters, and logistical failures, necessitate the design of sustainable and resilient supply chains. Methods: A novel multi-period mixed-integer linear programming model is developed with the objective of maximizing supply chain profit to design a complete citrus supply chain, which incorporates the production of citrus fruit and juice, and accommodates resilience and sustainability perspectives. Results: A comprehensive citrus supply chain scenario is presented to support the applicability of the proposed model, leveraging real data from citrus supply chain stakeholders in Egypt. Moreover, an actual case study involving a citrus processing company in Egypt is demonstrated. Gurobi software is used to solve the developed model. To build a resilient supply chain which can cope with different disruptions, different scenarios are modeled and strategies for having multiple suppliers, backup capacity, and alternative logistics routes are evaluated. Conclusions: The findings underscore the critical role of resilience in supply chain management, particularly in the agri-food sector. Moreover, the proposed model not only maximizes supply chain profitability but also equips stakeholders with the tools necessary to navigate challenges effectively. Full article

Blockchain technology (BT) is increasingly recognized as a transformative digital infrastructure for advancing environmental, economic, and social sustainability. However, academic research on its sustainability potential remains fragmented, with limited integration of theoretical models, sector-specific applications, and system-level impacts. This study addresses these gaps by conducting a systematic literature review of 131 peer-reviewed articles published between 2015 and early 2025, guided by the PRISMA 2020 framework. The analysis is structured around the three pillars of sustainability, exploring the mechanisms through which blockchain enables transparent governance, ethical consumption, resilient infrastructure, and inclusive development. Anchored in Institutional and Stakeholder theories, the review develops an integrative dual-framework that overlays four technical components of BT (data, network, consensus, and application) onto institutional pressures and stakeholder-engagement dynamics. The framework shows how BT enhances resource efficiency, supply-chain traceability, and social inclusion across sectors such as renewable energy, agriculture, healthcare, education, and logistics. The study makes two principal contributions. First, it unifies previously dispersed findings into a holistic model that links BT’s technical capabilities with organizational and societal conditions. Second, it provides actionable guidance: policymakers should harmonize cross-border standards and incentivize energy-efficient consensus protocols, while managers should co-design stakeholder-inclusive pilots to scale sustainable BT solutions. Collectively, these insights map a research and practice agenda for leveraging blockchain to accelerate progress toward the Sustainable Development Goals. Full article

Background: This study examines the application of Artificial Intelligence (AI) and Big Data Analytics (BDA) in enhancing humanitarian supply chain resilience, focusing on Ghana and South Africa. Despite their potential, AI-BDA applications are underexplored in disaster response, particularly in developing economies. Methods: An explanatory research design using a quantitative approach was employed, analyzing data from 200 supply chain professionals in both nations. Structured questionnaires assessed the implementation of four key AI-BDA techniques: Time-Series Forecasting (TSF), Early Warning Systems (EWS), Logistics Optimization (LO), and Real-time Monitoring (RTM). Exploratory factor analysis and regression analysis were conducted to evaluate the relationship between these techniques and supply chain resilience, controlling for organizational size and technological readiness. Results: The findings indicate that AI-BDA techniques significantly improve humanitarian supply chain resilience, with TSF and LO demonstrating the highest predictive power. Additionally, technological readiness facilitates the adoption of these techniques. Conclusions: While AI-BDA offers substantial benefits, opportunities for greater adoption remain, particularly in real-time monitoring and predictive analytics. Humanitarian organizations should invest in capacity-building initiatives, enhance data quality, and foster multi-stakeholder partnerships to maximize the impact of AI-BDA. Full article

The rapid expansion of online grocery shopping has intensified the need for strategically located distribution hubs that ensure efficient and sustainable operations. Traditional location models emphasize economic and logistical factors but often neglect energy efficiency and environmental sustainability. This paper proposes a hybrid decision-making model that integrates the analytic hierarchy process (AHP) and the spherical fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS) to address the complexities of delivery hub location selection. The AHP is used to determine the relative importance of key decision-making criteria, including cost, accessibility, infrastructure, competition, and sustainability, while SFTOPSIS ranks the candidate locations based on their proximity to the ideal solution. Spherical fuzzy sets allow for a more nuanced treatment of uncertainty, improving decision-making accuracy in dynamic environments. The results demonstrate that this hybrid approach effectively manages incomplete and uncertain data, delivering a robust ranking of candidate locations. By incorporating sustainability as a key factor, this study provides a structured and adaptive framework for businesses to optimize logistics operations in the post-pandemic landscape. The proposed methodology not only enhances decision-making in location selection but contributes to the development of more resilient and sustainable supply chain strategies. Full article

This study evaluates the potential of spent coffee grounds (SCGs) as a co-substrate to improve anaerobic co-digestion (AcD) performance, with a focus on biogas yield, methane (CH₄) content, and the removal of volatile solids (VS) and total chemical oxygen demand (TCOD). Biochemical methane potential (BMP) tests were conducted in two stages. In Stage I, SCGs were blended with active sludge (AS) and the organic fraction of municipal solid waste (OFMSW) at varying ratios. The addition of 25% SCGs increased biogas production by 24.47% (AS) and 20.95% (OFMSW), while the AS50 mixture yielded the highest methane yield (0.302 Nm³/kg VS, 66.42%). However, SCG concentrations of 75% or higher reduced process stability. In Stage II, we evaluated the impact of mixing. The AS25 configuration maintained stable biogas under varying mixing conditions, showing system resilience, whereas OFMSW25 showed slight improvement. Biogas production kinetics were modeled using modified Gompertz, logistic, and first-order equations, all of which demonstrated high predictive accuracy (R² > 0.97), with the modified Gompertz model offering the best fit. Overall, SCGs show promise as a sustainable co-substrate for the improvement of methane recovery and organic matter degradation in AcD systems when applied at optimized concentrations. Full article

Background: This study aimed to assess patient satisfaction with the quality of healthcare services at selected public primary healthcare facilities in Almaty during the COVID-19 pandemic and to identify associated demographic and facility-related factors. Methods: A cross-sectional, quantitative study was conducted over a period of 6 months, from 30 June to 31 December 2021, through a web-based survey. An adapted questionnaire was employed to survey the respondents. In total, 1035 respondents participated in the study. To examine the relationship between demographic and facility characteristics and patient satisfaction, we utilized the proportional odds model for ordinal logistic regression. Results: A total of eight primary healthcare organizations from the public sector in Almaty participated in the survey. The analysis identified significant demographic predictors of patient satisfaction, such as marital status, social status, self-perceived health, and the use of online consultations. Among the facility-related factors, only the availability of a cross-ventilation system emerged as a significant predictor. Conclusions: This study provides evidence for the factors influencing patient satisfaction with primary healthcare services in Almaty during the COVID-19 pandemic. Both demographic characteristics and facility-level attributes were found to significantly affect satisfaction levels. These findings underscore the need for targeted structural and organizational improvements in primary healthcare settings, especially during public health emergencies. Addressing these gaps through infrastructural upgrades, enhanced preparedness, and the integration of patient-centered care models can help to bolster trust and resilience within Kazakhstan’s healthcare system. Full article