Search Results (295)

Current research on automotive supply networks predominantly examines single-type entities connected through one relationship type, resulting in oversimplified, single-layer network structures. This conventional approach fails to capture the complex interdependencies that exist among mineral resources, intermediate components, and finished products throughout the automotive industry. To overcome these analytical limitations, this study implements a multilayer network framework for examining global automotive supply chains spanning 2017 to 2023. The research particularly emphasizes the identification of critical risk sources through both static and dynamic analytical perspectives. The static analysis employs multilayer degree and strength centralities to illuminate the pivotal roles that countries such as China, the United States, and Germany play within these multilayer automotive supply networks. Conversely, the dynamic risk propagation model uncovers significant cascade effects; a disruption in a major upstream supplier can propagate through intermediary layers, ultimately impacting over 85% of countries in the finished automotive layer within a short temporal threshold. Furthermore, this study investigates how individual nations’ anti-risk capabilities influence the overall resilience of multilayer automotive supply networks. These insights offer valuable guidance for policymakers, enabling strategic topological modifications during disruption events and enhanced protection of the most vulnerable risk sources. Full article

Maritime transportation accounts for approximately 80 percent of global trade volume, with modern vessels increasingly reliant on Software-Defined Radio (SDR) technologies for communication and navigation. However, the very flexibility and reconfigurability that make SDRs advantageous also introduce complex radio frequency vulnerabilities exposing ships to threats that jeopardize vessel security, and this disrupts global supply chains. This survey paper systematically examines the security landscape of maritime SDR systems through a threat modeling lens. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we analyzed 84 peer-reviewed publications (from 2002 to 2025) and applied the STRIDE framework to identify and categorize maritime SDR threats. We identified 44 distinct threat types, with tampering attacks being most prevalent (36 instances), followed by Denial of Service (33 instances), Repudiation (30 instances), Spoofing (23 instances), Information Disclosure (24 instances), and Elevation of Privilege (28 instances). These threats exploit vulnerabilities across device, software, network, message, and user layers, targeting critical systems including Global Navigation Satellite Systems, Automatic Identification Systems, Very High Frequency or Digital Selective Calling systems, Electronic Chart Display and Information Systems, and National Marine Electronics Association 2000 networks. Our analysis reveals that maritime SDR threats are multidimensional and interdependent, with compromises at any layer potentially cascading through entire maritime operations. Significant gaps remain in authentication mechanisms for core protocols, supply chain assurance, regulatory frameworks, multi-layer security implementations, awareness training, and standardized forensic procedures. Further analysis highlights that securing maritime SDRs requires a proactive security engineering that integrates secured hardware architectural designs, cryptographic authentications, adaptive spectrum management, strengthened international regulations, awareness education, and standardized forensic procedures to ensure resilience and trustworthiness. Full article

The widening digital divide within the Global Critical Mineral Resource Supply Chain (GCMRS) 4.0 creates significant barriers to cross-border governance and operational efficiency. To quantify and address this disparity, this study identifies 20 Critical Success Factors (CSFs) through expert interviews with 15 industry specialists in South Korea. A hybrid multi-criteria decision-making framework integrating Fuzzy DEMATEL, Analytic Network Process (ANP), and the Choquet integral is developed to map causal relationships and determine factor weights. The empirical results reveal a distinct ‘technology-first’ dependency. Specifically, Scalable Technical Solutions and Cloud Computing Access emerge as the primary driving forces with the highest global weights, while Digital Investment Subsidies serve as the central hub for resource allocation. Unlike generic governance models, this study provides a quantifiable decision-making basis for policymakers. It demonstrates that bridging the hard infrastructure gap is a prerequisite for the effectiveness of soft collaborative mechanisms in the critical mineral sector. Full article

As the global food industry expands and consumers demand higher food safety and quality standards, high-throughput detection technology utilizing digital intelligent optical sensors has emerged as a research hotspot in food testing due to its advantages of speed, precision, and non-destructive operation. Integrating cutting-edge achievements in optics, electronics, and computer science with machine learning algorithms, this technology efficiently processes massive datasets. This paper systematically summarizes the construction principles of intelligent optical sensors and their applications in food inspection. Sensors convert light signals into electrical signals using nanomaterials such as quantum dots, metal nanoparticles, and upconversion nanoparticles, and then employ machine learning algorithms including support vector machines, random forests, and convolutional neural networks for data analysis and model optimization. This enables efficient detection of target substances like pesticide residues, heavy metals, microorganisms, and food freshness. Furthermore, the integration of multiple detection mechanisms—including spectral analysis, fluorescence imaging, and hyperspectral imaging—has significantly broadened the sensors’ application scenarios. Looking ahead, optical sensors will evolve toward multifunctional integration, miniaturization, and intelligent operation. By leveraging cloud computing and IoT technologies, they will deliver innovative solutions for comprehensive monitoring of food quality and safety across the entire supply chain. Full article

Currently, factors such as geopolitical conflicts, frequent extreme weather events, and power struggles among major countries are threatening the stability of the global supply chain. Building a more resilient supply chain has received international consensus. Today, new quality productivity (NQP), spawned by disruptive innovation, is an important way for China to enhance its agricultural product supply chain resilience (SCR). However, studies often overlook the “time lag” problem of the panel data adopted, and their empowering paths require further investigation. Therefore, this study firstly constructs NQP and agricultural product SCR indicators. Based on the panel data produced by 31 Chinese provinces from 2011 to 2022, we solved the “time lag” problem by integrating a Backpropagation Neural Network (BPNN) with an Autoregressive Integrated Moving Average (ARIMA) model to predict the NQP level. Subsequently, the empowering paths through NQP-enhancing agricultural product SCR were explored via entropy weight TOPSIS and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method. Foundations: China’s agricultural product SCR exhibits a spatial differentiation characteristic of “prominent in the central region and weak in the western region”. A single factor is not a necessary condition for high resilience, and its improvement depends on the synergy of multiple factors. Three differentiated driving paths have been identified: “autonomous endogenous driving type”, “environment-enabled driving type”, and “system architecture driving type”. NQMP has become the bottleneck for improving agricultural product SCR, and the threshold of each factor has increased significantly as the resilience target is raised. High resilience stems from the synergy and functional compensation of core factors, while low resilience is mostly caused by the concurrent absence of key conditions or structural mismatch, showing distinct “multiple concurrencies” and “causal asymmetry” characteristics. Full article

Palm oil (Elaeis guineensis), one of the most widely used vegetable oils globally, originates from West Africa and has played a significant role in food, health care, and historical trade networks. It holds cultural, historical, ecological and symbolic significance in Bahia, Brazil. Unlike industrial monocultures, Bahia’s dendê economy is rooted in biodiverse landscapes, maintained through artisanal methods and generational knowledge. Yet, this traditional system faces mounting pressures from deforestation, declining artisanal production, and the industrialization of palm oil supply chains. Parallel to these ecological and economic threats is the abandonment of Bahia’s historical processing infrastructure: many traditional mills and industrial heritage sites have been lost, eroding both tangible and intangible cultural landscapes. These shifts have profound implications for the Baianas do Acarajé, the iconic street vendors who embody the matriarchal cultural lineage and rely on high-quality, traditionally produced dendê for their Afro-Brazilian cuisine. The increasing cost and reduced availability of artisanal oil compromise not only their livelihoods but also the authenticity of comidas de azeite, diminishing a sensory and symbolic culinary tradition. This study adopts a rigorous interdisciplinary methodology, synthesizing ethnography, heritage science, and engineering principles to explore how these artisanal practices can help us solve modern industrial sustainability problems. This article argues that Bahia’s palm oil heritage embodies a multifaceted heritage, spanning religious, economic, ecological, and cultural dimensions, that remains under-recognized and vulnerable. Drawing from UNESCO’s framework of intangible cultural heritage, the study not only details how these practices are transmitted across generations through the matriarchal culinary lineage, but ultimately advances three core contributions, analyzing artisanal performance, proposing a transferable sustainability framework, and outlining actionable pathways, to demonstrate that local communities are co-designers of solutions whose heritage offers a proven blueprint to address contemporary industrial sustainability challenges, calling for informed recognition and support to safeguard this essential component of Brazil’s Afro-descendant cultural identity. Full article

Building a resilient and efficient supply chain system is critical for sustaining firm operations in an increasingly uncertain global environment. This study examines whether the firm-level exposure to industry-wide robot penetration mitigates firm-level supply chain risks. By adopting Bartik’s instrumental variable approach to decompose industry-level robot data to the firm level (from the International Federation of Robotics, IFR), and using a novel text-mining-based supply chain risk index, constructed via a tailored “supply chain risk” dictionary, to quantify sentences containing both keywords from firms’ annual report MD&A sections, we apply a fixed effects model, and find that robot adoption significantly reduces supply chain risk by enhancing firms’ discourse power and improving supply chain coordination. The effect is more pronounced in firms with higher capital intensity, greater international exposure, stronger regulatory oversight, and better ESG (Environmental, Social, and Governance) performance. By integrating automation adoption with supply chain risk management, this study extends the literature on production economics and supply chain resilience. Our findings reveal that industrial robots, beyond enhancing productivity, function as a risk-mitigating technology that strengthens supply chain stability and operational continuity in volatile global production networks. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a growing global health challenge, driven in part by how antibiotics are accessed, distributed, and used within complex value chains. In peri-urban India, these supply chains involve a range of formal and informal actors and practices, making them a critical yet underexamined focus for antimicrobial stewardship efforts. While much research has focused on the manufacturing and regulatory end, less is known about how antibiotics reach consumers in rural and peri-urban settings. This study aimed to map the human antibiotic value chain in West Bengal, India, and to analyse how formal and informal governance structures influence antibiotic use and stewardship. Methods: This qualitative study was conducted in two Gram Panchayats in South 24 Parganas district, West Bengal, India. Semi-structured interviews were carried out with 31 key informants, including informal providers, medical representatives, wholesalers, pharmacists, and regulators. Interviews explored the structure of the antibiotic value chain, actor relationships, and regulatory mechanisms. Data were analysed thematically using a value chain governance framework and NVivo 12 for coding. Results: The antibiotic value chain in rural West Bengal is highly fragmented and governed by overlapping formal and informal rules. Multiple actors—many holding dual or unofficial roles—operate across four to five tiers of distribution. Informal providers play a central role in both prescription and dispensing, often without legal licences but with strong community trust. Informal norms, credit systems, and market incentives shape prescribing behaviour, while formal regulatory enforcement is inconsistent or absent. Conclusions: Efforts to promote antibiotic stewardship must move beyond binary formal–informal distinctions and target governance structures across the entire value chain. Greater attention should be paid to actors higher up the chain, including wholesalers and pharmaceutical marketing networks, to improve stewardship and access simultaneously. This study highlights how fragmented governance structures, overlapping actor roles, and uneven regulation within antibiotic value chains create critical gaps that must be addressed to design effective antimicrobial stewardship strategies. Full article

Pharmaceutical manufacturing and logistics rely on accurate prediction and decision making to safeguard product quality, delivery reliability, and patient outcomes. Despite rapid advances in artificial intelligence (AI) and machine learning (ML), few studies benchmark model performance across the diverse operational demands of global pharmaceutical supply chains. Predictive setbacks contribute to financial losses, reduced supply chain efficacy, and potential adverse health consequences, yet understanding these failures offers firms opportunities to refine strategy and strengthen resilience. Drawing on 1.2 million shipments spanning 39 countries, we compare traditional statistical models (ARIMA), ensemble methods (random forests, gradient boosting), and deep neural networks (LSTM, GRU, CNN, ANN) across pricing, demand forecasting, vendor management, and shipment planning. Gradient boosting produced the strongest pricing performance, while ARIMA delivered the lowest demand-forecasting errors but with limited explanatory power; neural networks captured nonlinear demand shocks and achieved superior maintenance-risk classification. We also identified three vendor performance clusters—high-performing, cost-efficient, and mixed-reliability vendors—enabling firms to better align shipment criticality with vendor capabilities by prioritizing high performers for urgent deliveries, leveraging cost-efficient vendors for non-urgent volumes, and managing mixed performers through targeted oversight. These insights highlight the value of our evidence-based roadmap for selecting algorithms in high-stakes healthcare logistics, in rapidly evolving, technologically complex global contexts where increasing algorithmic sophistication elevates the standards for safer, smarter pharmaceutical supply chains. Full article

Ports are vital components of global and regional supply chains, supporting trade, transport connectivity, and socio-economic development. However, their functionality is increasingly threatened by climatic hazards such as sea-level rise and heat stress, both of which are projected to intensify under future climate change. This study presents a comprehensive framework for assessing the criticality of ports within a national network, demonstrated through its application to the Greek port system, which encompasses a multitude of ports of all types from large international hubs to small island ones. The framework combines openly accessible geospatial and socio-economic data with projections of exposure to sea-level rise and extreme heat within a structured multi-criteria decision-making (MCDM) approach, enabling the identification of critical ports and the prioritization of adaptation needs. Results show that large mainland ports dominate in socio-economic importance and network centrality, while smaller island ports are vital locally due to limited redundancy and high exposure to climatic hazards. By 2100, nearly all ports are projected to experience freeboard reductions below operational thresholds and increased heat-related stress. These results highlight the need for targeted adaptation measures, including engineering interventions for mainland ports and redundancy-enhancing actions for island ports. The proposed framework provides a replicable, data-driven tool to guide evidence-based prioritization of adaptation investments and strengthen climate-resilient maritime transport and coastal management, thereby contributing to the achievement of Sustainable Development Goals (SDGs) 1.5, 9 and 13. Full article

Small- and Medium-sized Enterprises (SMEs) play a crucial role in the global economy, accounting for approximately two-thirds of global employment and contributing significantly to the GDP of developed countries. Despite the availability of various cybersecurity standards and frameworks, SMEs remain highly vulnerable to cyber threats. Limited resources and a lack of expertise in cybersecurity make them frequent targets for cyberattacks. It is essential to identify the challenges faced by SMEs and explore effective defensive strategies to enhance the implementation of cybersecurity measures. The study aims to bridge the gap and help these organizations in implementing cost-effective and practical cybersecurity approaches through a systematic mapping study (SMS) conducted, where 73 articles were thoroughly reviewed. This research will shed light on the current cybersecurity approaches (practices) posture for different SMEs, along with the threats they are facing, which have stopped them from deciding, planning, and implementing cybersecurity measures. The study identified a wide range of cybersecurity threats, including phishing, social engineering, insider threats, ransomware, malware, denial of services attacks, and weak password practices, which are the most prevalent for SMEs. This study identified defensive practices, such as cybersecurity awareness and training, endpoint protection tools, incident response planning, network segmentation, access control, multi-factor authentication (MFA), access controls, privilege management, email authentication and encryption, enforcing strong password policies, cloud security, secure backup solutions, supply chain visibility, and automated patch management tools, as key measures. The study provides valuable insights into the specific gaps and challenges faced by SMEs, as well as their preferred methods of seeking and consuming cybersecurity assistance. The findings can guide the development of targeted defensive practices and policies to enhance the cybersecurity posture of SMEs for successful software development. This SMS will also provide a foundation for future research and practical guidelines for SMEs to improve the process of secure software development. Full article

CCUS-EOR combines emission reduction with economic benefits, making it one of the key technologies for addressing global climate change. Addressing the lack of systematic comparative studies on the differences in geological endowments and engineering conditions between China and North America in existing literature, this paper systematically reviews the progress of North American CO₂-EOR in areas such as gas source structure transformation, capture technologies, and pipeline network construction, based on a self-constructed database of typical projects. It then conducts a quantitative comparative analysis of typical projects in China and the United States from three dimensions: reservoir geological endowment, gas source composition, and infrastructure. The study reveals that the advancement of U.S. CO₂-EOR projects benefits from increasing industrial CO₂ supply and the construction of cross-regional pipeline networks. Comparative analysis indicates that North American projects primarily feature miscible displacement in medium-to-low temperature and light oil reservoirs. This contrasts fundamentally with the characteristics of China’s continental reservoirs, which exhibit “strong heterogeneity, high viscosity, and high minimum miscibility pressure (MMP)”. Currently, China’s CCUS-EOR is transitioning from engineering demonstration to commercial application. However, gaps persist compared to more mature international systems in areas such as low-concentration CO₂ capture, pipeline network construction for source-sink matching, and suitability for continental reservoir EOR. Moving forward, China can draw on U.S. CCUS-EOR development experience, accelerate research on relevant technologies tailored to its continental reservoir characteristics, and establish a differentiated whole-industry-chain CCUS-EOR technology system. Full article

Global supply chains face unprecedented complexity as organizations must simultaneously achieve sustainability objectives and operational resilience amid evolving risk landscapes. Despite extensive research, the absence of systematic knowledge synthesis has limited understanding of how these dual imperatives intersect. This study conducts the first comprehensive scientometric analysis of global supply chain risk management research, examining 1228 peer-reviewed articles from major databases published from 2016 to June 2025. The study employed co-occurrence analysis, temporal burst detection, and network visualization to map the intellectual structure and evolutionary dynamics of this field. Our study reveals four distinct research clusters: risk factor identification (traditional and unconventional threats), environmental and social sustainability integration, technology-driven challenges, and innovative risk management methodologies. Temporal analysis demonstrates significant research acceleration post-2020, driven by pandemic disruptions, with emerging focus on cyberattacks, geopolitical conflicts, and ESG compliance challenges. The findings reveal critical gaps at the sustainability-resilience intersection, particularly paradoxical tensions where short-term resilience measures may compromise long-term sustainability goals. We propose four priority research directions: digital transformation frameworks balancing sustainability-resilience trade-offs, ESG-integrated early warning systems, adaptive governance mechanisms for unconventional risks, and policy frameworks addressing regulatory complexity. This systematic knowledge mapping provides theoretical foundations for future research and practical guidance for supply chain managers navigating dual sustainability-resilience objectives in an uncertain global environment. Full article

Digital transformation (DT) has become a strategic priority for global ports; however, many in developing countries, including Indonesia, face challenges in translating digital initiatives into measurable business performance (BP). This study examines the impact of DT on BP through the mediating roles of supply chain integration (SCI) and operational performance (OP) within Indonesian ports, using the Dynamic Capabilities Theory (DCT) framework. A quantitative survey of 128 operational managers from state-owned ports was analyzed using partial least squares structural equation modeling. The findings reveal that DT significantly improves SCI and OP, both of which positively influence BP. Moreover, SCI and OP jointly mediate the DT–BP relationship, highlighting that digital technologies create value only when integrated into coordinated processes and operational routines. The study underscores that DT should be managed as a strategic transformation aligning technology, operations, and interorganizational collaboration. For port managers, strengthening digital connectivity across internal and external networks, supported by governance and incentive mechanisms, is essential to enhance visibility, responsiveness, and resilience. Theoretically, this research advances DCT by demonstrating how DT functions as a reconfiguring capability realized through SCI and OP, providing empirical insights from developing-country port contexts. Full article

With the increasing globalization of supply chains, ensuring food safety has become more complex, necessitating advanced approaches for risk assessment. This study aims to review the transformative role of machine learning (ML) and deep learning (DL) in enabling intelligent food safety management by efficiently analyzing high-quality and nonlinear data. We systematically summarize recent advances in the application of ML and DL, focusing on key areas such as biotoxin detection, heavy metal contamination, analysis of pesticide and veterinary drug residues, and microbial risk prediction. While traditional algorithms including support vector machines and random forests demonstrate strong performance in classification and risk evaluation, unsupervised methods such as K-means and hierarchical cluster analysis facilitate pattern recognition in unlabeled datasets. Furthermore, novel DL architectures, such as convolutional neural networks, recurrent neural networks, and transformers, enable automated feature extraction and multimodal data integration, substantially improving detection accuracy and efficiency. In conclusion, we recommend future work to emphasize model interpretability, multi-modal data fusion, and integration into HACCP systems, thereby supporting intelligent, interpretable, and real-time food safety management. Full article