Search Results (284)

Under China’s “Rural Revitalisation” strategy, contract farming faces challenges including farmers’ limited access to advanced technologies and high operational risks for agricultural support enterprises. The collaborative involvement of scientific research institutions offers potential solutions but remains underexplored. This study employs Stackelberg game theory to model a contract farming supply chain under two agricultural assistance modes: enterprise-led (EL) and collaborative assistance with scientific research institutions (CI). We further propose two government subsidy mechanisms: subsidies to enterprises and subsidies to scientific research institutions. The models analyze optimal decisions, supply chain performance, and subsidy efficiency, validated through numerical experiments. Key findings reveal the following: (1) The CI mode enhances agricultural output and farmer revenue but may reduce enterprise profits, deterring collaboration. (2) Government subsidies incentivize enterprise–institution collaboration. Subsidizing scientific research institutions typically improves agricultural productivity and economic benefits more effectively than subsidizing enterprises. (3) Synergistic effects exist among the government subsidy coefficient, cost coefficient of technical assistance, consumer preferences for agricultural quality, and profit-sharing ratio. The latter three parameters significantly influence subsidy model selection. This research provides policy insights for enhancing agricultural assistance efficiency and sustainable contract farming development. Full article

Healthcare supply chain management operates amid fluctuating patient demand, rapidly advancing biotechnologies, and unpredictable supply disruptions pose high risks and create an imperative for sustainable resource optimization. This study investigates the underlying mechanisms through which digital intelligence drives strategic decision optimization in healthcare supply chains. Drawing on the Resource-Based View and Dynamic Capabilities Theory, we develop a chain-mediated model, defined as the multistage indirect path whereby digital intelligence first bolsters innovation capability, which then activates supply chain resilience (absorptive, response, and restorative capability), to improve decision optimization. Data were collected from 360 managerial-level respondents working in healthcare supply chain organizations in China, and the proposed model was tested using structural equation modeling. The results indicate that digital intelligence enhances innovation capability, which in turn activates all three dimensions of resilience, producing a synergistic effect that promotes sustained decision optimization. However, the direct effect of digital intelligence on decision optimization was not statistically significant, suggesting that its impact is primarily mediated through organizational capabilities, particularly supply chain resilience. Practically, the findings suggest that in the process of deploying digital intelligence systems and platforms, healthcare organizations should embed technological advantages into organizational processes, emergency response mechanisms, and collaborative operations, so that digitalization moves beyond the technical system level and is truly internalized as organizational innovation capability and resilience, thereby leading to sustained improvement in decision-making performance. Full article

This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic behaviors. Anchored in the concept of technology trust, this study examines how different risk preferences affect BCT adoption decisions and freight rate strategies. A game-theoretic model is constructed using a mean-variance utility framework to analyze interactions between shipping companies and freight forwarders under three adoption scenarios: no adoption (NN), partial adoption (BN), and full adoption (BB). The results indicate that risk-seeking agents are more likely to adopt BCT early but face greater freight rate volatility in the initial stages. As the technology matures, strategic variability declines and the influence of adaptability on pricing becomes less pronounced. In contrast, risk-neutral and risk-averse participants tend to adopt more conservatively, resulting in slower but more stable pricing dynamics. These findings offer new insights into how technology trust and risk attitudes shape strategic decisions in digitally transforming supply chains. The study also provides practical implications for differentiated pricing strategies, BCT adoption incentives, and collaborative policy design among logistics stakeholders. Full article

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of “steady-state maintenance–dynamic adjustment–continuous evolution” from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the “carbon reduction–supply stability–efficiency improvement” goals, transforming them from policy vision to operational reality. Full article

The increasing scale of honey adulteration poses a significant challenge for modern food quality and safety management systems. Honey authenticity, defined as the conformity of products with their declared botanical and geographical origin, is challenging to verify solely through documentation and conventional physicochemical analyses. This study presents an integrated, process-oriented approach for digital honey authentication, building on initial findings from an interdisciplinary research and development project. The approach includes the creation of a comprehensive digital pollen database and the application of AI-driven image segmentation and classification methods. The developed system is designed to support decision-making processes in quality assessment and VACCP (Vulnerability Assessment and Critical Control Points) risk evaluation, enhancing the operational resilience of honey supply chains against fraudulent practices. This study aligns with current trends in the digitization of food quality management and the use of Industry 4.0 technologies in the agri-food sector, demonstrating the practical feasibility of integrating AI-supported palynological analysis into industrial workflows. The results indicate that the proposed approach can significantly improve the accuracy and efficiency of honey authenticity assessments, supporting the integrity and transparency of global honey markets. Full article

The Industrial Internet of Things (IIoT) offers transformative potential for supply chain management by enabling automation, real-time monitoring, and predictive analytics. However, fragmented standardization, interoperability challenges, and cybersecurity risks hinder its sustainable adoption. This study aims to develop and validate an ISO-based framework to optimize IIoT networks for sustainable supply chain operations. A quantitative time-series research design was employed, analyzing 150 observations from 10–15 industrial firms over five years. Analytical methods included ARIMA, structural equation modeling (SEM), and XGBoost for predictive evaluation. The findings indicate a 6.2% increase in system uptime, a 4.7% reduction in operational costs, a 2.8% decrease in lead times, and a 55–60% decline in security incidents following ISO standard implementation. Interoperability improved by 40–50%, and integration cost savings ranged from 35–40%, contributing to a 25% boost in overall operational efficiency. These results underscore the critical role of ISO frameworks such as ISO/IEC 30141 and ISO 50001 in enhancing connectivity, energy efficiency, and network security across IIoT-enabled supply chains. While standardization significantly improves key performance indicators, the persistence of lead time variability suggests the need for additional optimization strategies. This study offers a structured and scalable methodology for ISO-based IIoT integration, delivering both theoretical advancement and practical relevance. By aligning with internationally recognized sustainability standards, it provides policymakers, practitioners, and industry leaders with an evidence-based framework to accelerate digital transformation, enhance operational efficiency, and support resilient, sustainable supply chain development in the context of Industry 4.0. Full article

In the context of increasingly complex and dynamic maritime logistics, seaports serve as critical nodes for intermodal transport, energy distribution, and global trade. Ensuring the safe and uninterrupted operation of port infrastructure—particularly berths—is vital for maintaining supply chain resilience. This study explores the impact of multiple risk categories on berth efficiency in a seaport, aligning with the growing emphasis on maritime safety and risk-informed decision-making. A two-stage methodology is adopted. In the first phase, the DEA CCR input-oriented model is employed to assess the efficiency of selected berths considered as Decision Making Units (DMUs). In the second phase, the Analytical Hierarchy Process (AHP) is used to categorize and quantify the impact of four major risk classes—operational, technical, safety, and environmental—on berth efficiency. The results demonstrate that operational and safety risks contribute 63.91% of the composite weight in the AHP risk assessment hierarchy. These findings are highly relevant to contemporary efforts in maritime risk modeling, especially for individual ports and port systems with high berth utilization and vulnerability to system disruptions. The proposed integrated approach offers a scalable and replicable decision-support tool for port authorities, port operators, planners, and maritime safety stakeholders, enabling proactive risk mitigation, optimal utilization of available resources in a port, and improved berth performance. Its methodological design is appropriately suited to support further applications in port resilience frameworks and maritime safety strategies, being one of the bases for establishing collision avoidance strategies related to an individual port and/or port system, too. Full article

To address the issue of excessive pessimism caused by demand and supply uncertainties in the green hydrogen supply chain, this study develops a two-tier green hydrogen supply chain model comprising upstream hydrogen production stations and downstream hydrogen refueling stations. This research work investigates optimal ordering and production strategies under stochastic demand and supply conditions. Additionally, option contracts are introduced to share the risks associated with the stochastic output of green hydrogen. This study shows the following: (1) Under decentralized decision-making, the optimal ordering quantity when the hydrogen refueling station is excessively pessimistic is not necessarily lower than the optimal ordering quantity when it is in a rational state, and hydrogen production stations will only operate when the degree of excessive pessimism is relatively low. (2) The initial option ordering quantity is always larger than the minimum execution quantity under the option contract; higher first-order option prices and lower second-order option prices can help to increase the initial option ordering quantity. (3) The option contract is effective in circumventing the negative impact of excessive pessimism at hydrogen production stations on planned production quantities. This study addresses the gap in the existing research regarding excessively pessimistic behaviors and the application of option contracts within the green hydrogen supply chain, providing both theoretical insights and practical guidance for decision-making optimization. This advancement further promotes the sustainable development of the green hydrogen industry. Full article

Uncertainty in consumer purchasing behavior within online markets propels manufacturers to adopt blockchain for risk mitigation, reshaping supply chain operational dynamics. This study investigates the sales mode selection and blockchain adoption strategies of a risk-averse manufacturer in platform supply chain under uncertain market demand. By integrating Stackelberg game theory with mean-variance analysis, we analyze supply chain equilibrium across four scenarios: RN, RB, AN, and AB. Our findings highlight the significance of a critical commission rate threshold in the manufacturer’s sales mode choice, emphasizing that blockchain adoption enhances the preference for the agency mode. Importantly, highly risk-averse manufacturers are inclined to absorb higher costs associated with blockchain adoption, while those with lower risk aversion only consider it when costs are minimal. Notably, the “agency mode with blockchain adoption” (AB) creates mutual benefits under low adoption costs and risk aversion. When both parties exhibit risk aversion, the platform’s risk aversion significantly influences resale-mode decisions, leading to a transition from the scenario AN to the RB, thereby optimizing synchronized profits. Full article

Escherichia coli (E. coli) remains a major concern in poultry production due to its ability to incite foodborne illness and public health crisis, zoonotic potential, and the increasing prevalence of antibiotic-resistant strains. The contamination of poultry products with pathogenic E. coli, including avian pathogenic E. coli (APEC) and Shiga toxin-producing E. coli (STEC), presents risks at multiple stages of the poultry production cycle. The stages affected by E. coli range from, but are not limited to, the hatcheries to grow-out operations, slaughterhouses, and retail markets. While traditional detection methods such as culture-based assays and polymerase chain reaction (PCR) are well-established for E. coli detection in the food supply chain, their time, cost, and high infrastructure demands limit their suitability for rapid and field-based surveillance—hindering the ability for effective cessation and handling of outbreaks. Biosensors have emerged as powerful diagnostic tools that offer rapid, sensitive, and cost-effective alternatives for E. coli detection across various stages of poultry development and processing where detection is needed. This review examines current biosensor technologies designed to detect bacterial biomarkers, toxins, antibiotic resistance genes, and host immune response indicators for E. coli. Emphasis is placed on field-deployable and point-of-care (POC) platforms capable of integrating into poultry production environments. In addition to enhancing early pathogen detection, biosensors support antimicrobial resistance monitoring, facilitate integration into Hazard Analysis Critical Control Points (HACCP) systems, and align with the One Health framework by improving both animal and public health outcomes. Their strategic implementation in slaughterhouse quality control and marketplace testing can significantly reduce contamination risk and strengthen traceability in the poultry value chain. As biosensor technology continues to evolve, its application in E. coli surveillance is poised to play a transformative role in sustainable poultry production and global food safety. Full article

The sustainable utilization of forest biomass for bioenergy production is increasingly challenged by the variability and unpredictability of raw material availability. These challenges are particularly critical in regions like Central Portugal, where seasonality, dispersed resources, and wildfire prevention policies disrupt procurement planning. This study investigates two flexibility strategies—dynamic network reconfiguration and operations postponement—as policy relevant tools to enhance resilience in forest-to-bioenergy supply chains. A novel mathematical model, the mobile Facility Location Problem with dynamic Operations Assignment (mFLP-dOA), is proposed and solved using a scalable matheuristic approach. Applying the model to a real case study, we demonstrate that incorporating temporary intermediate nodes and adaptable processing schedules can reduce costs by up to 17% while improving operational responsiveness and reducing non-productive machine time. The findings offer strategic insights for policymakers, biomass operators, and regional planners aiming to design more adaptive and cost-effective biomass supply systems, particularly under environmental risk scenarios such as summer operation bans. This work supports evidence-based planning and investment in flexible logistics infrastructure for cleaner and more resilient bioenergy supply chains. Full article

The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human resource management. This study proposes a resilience-based AX strategy and implementation system that allows domestic container-terminal companies to proactively respond to the upcoming changes in the global supply chain, thus securing sustainable competitiveness. In particular, we aim to design an AI-based governance model to establish a trust-based logistics supply chain (trust value chain). As a research method, the core risk factors of AX processes were scientifically identified via text-mining and fault-tree analysis, and a step-by-step execution strategy was established by applying a backcasting technique based on scenario planning. Additionally, by integrating social control theory with new governance theory, we designed a flexible, adaptable, and resilience-oriented AI governance system. The results of this study suggest that the AI paradigm shift should be promoted by enhancing the risk resilience, trust, and recovery of organizations. By suggesting AX strategies and policy as well as institutional improvement directions that embed resilience to secure the sustainable competitiveness of AI-based smart ports in Korea, this study serves as a basis for establishing strategies for the domestic container-terminal industry and for constructing a global leading model. Full article

In the past two decades, particularly through the pandemic, the demand for real-time logistics has significantly increased. Cold chain logistics ensures specific temperature conditions for perishable goods such as food and pharmaceuticals, which is crucial for maintaining product quality, safety, and regulatory compliance. The integration of the Internet of Things (IoT) into cold chain logistics has transformed supply chain operations. The COVID-19 pandemic and the global urgency for vaccine distribution accelerated the adoption of cold chain technologies, emphasizing their role in preserving perishable goods’ integrity. IoT enables real-time monitoring, remote control, predictive analytics, and data-driven decision-making, all of which are essential for modern logistics. We conducted a bibliometric analysis of 50 publications from 1997 to 2024 to examine IoT’s role in cold chain management. Through co-occurrence and co-citation network analysis, core themes, influential works, and major contributors were identified. Thematic mapping highlighted the importance of temperature monitoring, logistics optimization, and risk management. Additionally, the transition from conventional logistics practices to IoT-driven methodologies was investigated in cold chain operations. The findings of this study provide a basis for understanding the structural properties of co-citation and co-occurrence networks in cold chain logistics and the evolving landscape of cold chain technology, and its impact on logistics, emphasizing the importance of intelligent, reliable, and sustainable cold chain systems to meet the growing demands in global supply chains. Full article

The global shift to low-carbon energy systems has significantly increased demand for critical raw materials like lithium, cobalt, nickel, rare earth elements, and copper. These materials are essential for renewable technologies and energy storage. However, their extraction and processing produce significant carbon emissions and face challenges from supply chain vulnerabilities and price volatility. This review examines the complex relationship between carbon pricing mechanisms—such as carbon markets and taxes—and raw material markets. It explores the strategic importance of these materials, recent policy developments, and the transmission of carbon pricing impacts through supply chains. The review also analyzes the systemic risks created by carbon pricing, including regulatory uncertainty, market volatility, and geopolitical tensions. We then discuss financial tools and corporate strategies for managing these risks, such as carbon-linked derivatives and supply chain diversification. Finally, this review identifies key challenges and suggests future research to improve the resilience and sustainability of raw material supply chains. Here, resilience is defined as the capacity to adapt to carbon pricing volatility, geopolitical disruptions, and regulatory shocks, while maintaining operations. The paper concludes that coordinated policies and flexible risk management are urgently needed to support a reliable and sustainable energy transition. Full article

Disruptions in a single supplier’s operations can trigger cascading effects across the entire supply chain, highlighting the critical importance of effective supplier-focused risk management. While supply chain risk management (SCRM) frameworks encompass diverse dimensions—such as supply, products, demand, and information—risks specifically related to suppliers demand tailored strategies and analytical focus. Despite the growing volume of publications on this topic, the literature still lacks updated conceptual guidance on how to manage these risks, particularly in light of emerging challenges and practices. This study addresses this gap, with the primary objective of developing a contemporary conceptual framework for supplier risk management, reflecting recent academic and practical advances. The research methodology combines bibliometric analysis, the PRISMA systematic review protocol, and visualization tools including CiteSpace and CitNet Explorer. Key findings include the evolution of thematic clusters over time, with “supplier selection” identified as the most dominant theme, and simulation as the prevailing research method. The automotive industry emerges as the most frequently studied empirical context. Moreover, the study expands existing frameworks by introducing two emerging dimensions—environmental, social, and governance (ESG) and information technology (IT)—as key factors in supplier risk management. This framework contributes to theory and practice by offering an updated lens for understanding supplier-related risks and providing decision-makers with structured insights to enhance resilience in complex supply networks. Full article