Search Results (2,199)

Medicine and vaccine supply chains in Nigeria are socio-technical systems exposed to persistent uncertainty and disruption. Existing studies rarely integrate systems thinking with uncertainty-aware decision tools to jointly prioritize challenges and policy responses. This study asks which policy mix most effectively strengthens these supply chains while balancing multiple, conflicting criteria and stakeholder judgments. We develop a two-stage Fermatean fuzzy framework that first weights 35 challenges using Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) and then ranks four policy alternatives via Fermatean Fuzzy VIšeKriterijumska Optimizacija I Kompromisno Resenje (FF-VIKOR), based on expert elicitation and linguistic assessments. Results identify interruption of drug supplies, limited vaccine funding, cold-chain potency loss, human resource shortages, and product damage as the most critical challenges. FF-VIKOR prioritizes Effective Implementation of Existing Policies as the best alternative, followed by Improving Access to Medicines and Vaccines, indicating that governance quality and access-enabling infrastructure are complementary levers for resilience. To further enhance robustness, we embed the VIKOR outcomes into a policy-oriented game-theoretic analysis, where strategic weighting scenarios (e.g., cost-focused, infrastructure-driven, human-capital focused) interact with policy choices. The equilibrium results reveal that a mixed strategy combining Effective Implementation of Existing Policies and Strengthening Distribution and Storage Systems guarantees the best compromise performance across adversarial scenarios. The proposed framework operationalizes systems thinking for uncertainty-aware and strategically robust policy design and can be extended with real-time data integration, scenario planning, and regional replication to guide adaptive supply chain governance. Full article

This study examines the role of the supply chain finance (SCF) ecosystem as an innovative financial framework in driving the high-quality development of rural industries. Using panel data from 31 Chinese provinces (2004–2022), we employ a fixed-effects model to analyze this relationship, confirming that the SCF ecosystem has a significant promoting effect. Mechanism analysis reveals that this positive effect operates primarily through two channels: enhancing rural industrial integration and stimulating technological innovation. Furthermore, we identify significant regional heterogeneity, with the most substantial positive spillover effects observed in the Southwest and South China. These results underscore the critical importance of the SCF ecosystem in rural revitalization and provide a basis for formulating regionally tailored financial policies. Full article

Ensuring food safety and quality has become increasingly critical due to the complexities introduced by globalization, industrialization, and extended supply chains. Traditional analytical methods for food quality control, such as chromatography and mass spectrometry, while accurate, face limitations including high costs, lengthy analysis times, and limited suitability for on-site rapid monitoring. Electrochemical sensors integrated with molecularly imprinted polymers (MIPs) have emerged as promising alternatives, combining high selectivity and sensitivity with portability and affordability. MIPs, often termed ‘plastic antibodies,’ are synthetic receptors capable of selective molecular recognition, tailored specifically for target analytes. This review comprehensively discusses recent advancements in MIP-based electrochemical sensing platforms, highlighting their applications in detecting various food quality markers. It particularly emphasizes the detection of antioxidants—both natural (e.g., vitamins, phenolics) and synthetic (e.g., BHA, TBHQ), artificial sweeteners (e.g., aspartame, acesulfame-K), colorants (e.g., azo dyes, anthocyanins), traditional contaminants (e.g., pesticides, heavy metals), and toxicants such as mycotoxins (e.g., aflatoxins, ochratoxins). The synthesis methods, including bulk, precipitation, surface imprinting, sol–gel polymerization, and electropolymerization (EP), are critically evaluated for their effectiveness in creating highly selective binding sites. Furthermore, the integration of advanced nanomaterials, such as graphene, carbon nanotubes, and metallic nanoparticles, into these platforms to enhance sensitivity, selectivity, and stability is examined. Practical challenges, including sensor reusability, regeneration strategies, and adaptability to complex food matrices, are addressed. Finally, the review provides an outlook on future developments and practical considerations necessary to transition these innovative MIP electrochemical sensors from laboratory research to widespread adoption in industry and regulatory settings, ultimately ensuring comprehensive food safety and consumer protection. Full article

Background: This study investigates how varying levels of digital interoperability affect coordination and performance in Indonesia’s decentralized air cargo system, reflecting the inefficiencies typical of fragmented digital infrastructures in developing economies. Methods: An Agent-Based Model (ABM) was developed to simulate interactions among shippers, freight forwarders, airlines, ground handlers, and customs agents along the CGK–SIN/HKG export corridor. Six simulation scenarios combined varying levels of digital adoption, operational friction, and behavioral adaptivity to capture emergent coordination patterns and threshold dynamics. Results: The simulation identified a distinct interoperability threshold at approximately 60%, beyond which performance improvements became non-linear. Once this threshold was surpassed, clearance times decreased by more than 40%, and capacity utilization exceeded 85%, particularly when adaptive decision rules were implemented among agents. Conclusions: Digital transformation in fragmented logistics systems requires both technological connectivity and behavioral adaptivity. The proposed hybrid framework—integrating Autonomous Supply Chains (ASC), Graph-Based Digital Twins (GBDT), and interoperability thresholds—provides a simulation-based decision-support tool to determine when digitalization yields system-wide benefits. The study contributes theoretically by linking behavioral adaptivity and digital interoperability within a unified modeling approach, and practically by offering a quantitative benchmark for policymakers and practitioners seeking to develop efficient and resilient logistics ecosystems. Full article

The rise of Industry 4.0 has made digital transformation a critical element of modern supply chain management, offering organizations a pathway to competitive advantage. While the prior literature has examined aspects of digitalization, few studies present a comprehensive view of the transformation process through to its practical application. This study proposes and aims to validate a digital transformation model developed by the author, based on a systematic literature review of 284 articles from Scopus and Web of Science. The model outlines a progression from data management to the integration of enabling technologies, culminating in enhanced supply chain decision-making. It introduces three key metrics—readiness, adoption, and Digital Maturity—to guide companies through distinct stages of transformation. Validation will be conducted using a mixed-methods approach, combining expert interviews and a quantitative survey with SCM professionals. This study offers both a theoretical framework and practical roadmap to support organizations in evolving their digital transformation strategies. Full article

Modern companies often rely on integrating an extensive network of suppliers to organize and produce industrial artifacts. Within this process, it is critical to maintain sustainability and flexibility by analyzing and managing information from the supply chain. In particular, there is a continuous demand to automatically analyze and infer information from extensive datasets structured in various forms, such as natural language and domain-specific models. The advancement of Large Language Models (LLM) presents a promising solution to address this challenge. By leveraging prompts that contain the necessary information provided by humans, LLM can generate insightful responses through analysis and reasoning over the provided content. However, the quality of these responses is still affected by the inherent opaqueness of LLM, stemming from their complex architectures, thus weakening their trustworthiness and limiting their applicability across different fields. To address this issue, this work presents a framework to leverage the graph-based LLM to support the analysis of supply chain information by combining the LLM and domain knowledge. Specifically, this work proposes an integration of LLM and domain knowledge to support an analysis of the supply chain as follows: (1) constructing a graph-based knowledge base to describe and model the domain knowledge; (2) creating prompts to support the retrieval of the graph-based models and guide the generation of LLM; (3) generating responses via LLM to support the analysis and reason about information across the supply chain. We demonstrate the proposed framework in the tasks of entity classification, link prediction, and reasoning across entities. Compared to the average performance of the best methods in the comparative studies, the proposed framework achieves a significant improvement of 59%, increasing the ROUGE-1 F1 score from 0.42 to 0.67. Full article

Plastic recycling is critical to transitioning toward a circular economy (CE), yet traceability systems for recycled plastics remain unevenly adopted. While effective traceability supports transparency, compliance, and supply chain accountability, its implementation is shaped not only by technological readiness but also by organisational behaviours and strategic priorities. This study explores how traceability adoption is influenced by company size, internal CE strategy, and perceptions of cost, risk, and regulatory demand. A survey of 65 Australian industry stakeholders reveals that 76% of companies with a CE strategy have implemented traceability systems, compared to 42% without. Larger firms report higher adoption rates than small and medium enterprises, largely due to resource advantages and differing interpretations of traceability’s value. Key barriers include high perceived costs, lack of standardised frameworks, and scepticism toward digital tools. Conversely, motivations such as reputational benefits, regulatory alignment, and inter-organisational trust were identified as enablers, alongside emerging technologies like blockchain and chemical tracers. The findings underscore the role of organisational context in shaping traceability practices and highlight the need for tailored interventions. Recommendations include financial incentives, harmonised standards, and sector-specific guidance that address not only technical gaps but behavioural and structural factors limiting uptake. Positioning traceability as an integrated organisational strategy may accelerate its adoption and support broader circular economy outcomes across the plastics value chain. Full article

Consumers and businesses are often connected online in today’s digitally connected world. Fast and barrier-free communication, easier and faster operation, and automation and networking of robots and production offer many competitive advantages. Recognizing the limiting factors of new technology, such as the significant dependency on technology and the vulnerability of IT devices, is crucial. As digitalization might increase the competitiveness of companies and have an impact on both the supply and value chains, we need to consider and assess their vulnerability from an information security perspective. Consequently, competitive advantage is not only about creating value more cost-efficiently and with higher quality but also about extracting the correct information from big data, interpreting and integrating it into business operations, and protecting it. This study proposes a fishbone model to help identify and overcome these challenges. It allows companies to identify the root cause of each information security incident. Full article

The paper manufacturing industry faces increasing challenges in balancing operational costs with service quality under uncertain market conditions. This research presents a bi-objective credibility-based expected value model for integrated production–distribution planning that simultaneously minimizes total costs and maximizes service-level performance. The model considers multiple paper grades, production facilities, warehouses, and customer zones while handling demand uncertainty through credibility theory. Three additional constraints are introduced: service time limitations, capacity expansion decisions, and quality assurance requirements. The Torabi–Hassini (TH) method is employed to solve the bi-objective optimization problem effectively. Computational experiments demonstrate the model’s capability to provide balanced trade-off solutions between cost efficiency and service quality, achieving service-level improvements of 8–13% with cost increases of 5–9% compared to cost-only optimization, and cost reductions of 10–15% compared to service-only optimization. The results show that the credibility-based expected value approach provides robust and practical solutions for paper manufacturing supply chain optimization. Full article

This study develops a comprehensive decision-making approach to sustainable product selection for chemical industry supply chains under uncertainty. Five product categories -enamel, ceramics, pigments, non-stick coatings, and glass- were evaluated through fifteen criteria along environmental, economic, and social sustainability dimensions. The hybrid methodology combines Fuzzy SWARA, which weights criteria based on expert opinion, with Fuzzy ARAS, which ranks the alternatives accordingly. The study found that occupational health and safety, consumer safety and health, and water usage are the most important criteria, reflecting a human-centered approach to sustainability decision-making. Ceramics had the best performance score, followed by enamel and non-stick coating. Sensitivity analysis confirmed the robustness of these rankings across various weighting scenarios. The findings indicate that decision-makers in the chemical industry prioritize worker and consumer protection alongside environmental resource stewardship. This framework provides practitioners with a structured method for integrating sustainability considerations into supply chain product portfolio decisions, balancing environmental impact, economic performance, and social responsibility. Full article

Modular shipbuilding, as a cutting-edge ship construction paradigm, enables parallel manufacturing across workshops and stages—a core advantage that significantly shortens the total shipbuilding cycle, making it pivotal for modern shipyards to enhance productivity. However, this mode decomposes the integrated shipbuilding project into a large number of interdependent sub-activities spanning three key stages (fabrication, logistics, and assembly). Further, the duration of these sub-activities is inherently uncertain, primarily due to the extensive manual operations, variable on-site conditions, and supply chain fluctuations inherent in shipbuilding. These characteristics collectively pose a formidable challenge to project planning that pursues both high efficiency and low cost. To address this challenge, this paper proposes a Strategy-Group Evolution algorithm. First, the modular shipbuilding process scheduling problem is mathematically formulated as a resource-constrained three-stage multi-objective optimization model, where triangular fuzzy numbers are employed to characterize the uncertain sub-activity durations. Second, a two-layered Strategy-Group Evolution algorithm is designed for solving this model: the inner layer comprises 12 practical priority rules tailored to modular shipbuilding’s multi-stage features, while the outer layer adopts a genetic algorithm-based evolution policy to schedule and optimize the assignment of inner-layer rules to activity groups. The core of the Strategy-Group Evolution algorithm lies in dynamically assigning suitable strategies to different activity groups and evolving these assignments toward optimality—this avoids the limitation of a single priority rule for all stages, thereby facilitating the search for global optimal solutions. Finally, validation tests on real cruise ship construction projects and benchmark datasets demonstrate the efficacy and superiority of the proposed Strategy-Group Evolution algorithm. Full article

Disposable diapers contribute significantly to municipal solid waste, with non-biodegradable polymers such as low-density polyethylene (LDPE) persisting in landfills for centuries. Biodegradable alternatives, including polylactic acid (PLA), poly(butylene adipate-co-terephthalate) (PBAT), bamboo, and organic cotton, offer reduced environmental persistence, although challenges remain regarding cost, mechanical performance, and scalability. This review synthesizes current literature on these materials, highlighting their properties, biodegradation mechanisms, environmental performance, and commercial feasibility. In addition, we examine emerging biodegradable superabsorbent polymers (SAPs), such as polysaccharide-based hydrogels, chitosan, and nanocellulose, essential for fully compostable diapers. Our review uniquely integrates material performance, tropical high-humidity degradation, cost considerations, and consumer acceptance, providing insights into both technological advances and barriers to adoption. Key challenges include high production costs, supply chain limitations, and maintaining performance parity with conventional diapers. Finally, we discuss sustainable waste management strategies, including industrial composting, and identify future research directions focused on optimizing biopolymer properties, safety, and life-cycle impacts. This synthesis informs researchers, industry stakeholders, and policymakers seeking to advance environmentally responsible diaper products. Full article

The meat industry faces significant economic losses and environmental impacts due to spoilage and waste, much of which results from inadequate, delayed, or inefficient quality assessment. Traditional methods used for assessing meat quality are often time-consuming, labor-intensive, and lack the ability to provide real-time information, making them insufficient for modern supply chains that demand safety, freshness, and minimal waste. Recent advances in nanotechnology position nanofibers (NFs) as promising materials for addressing these challenges through smart sensing and active packaging. NFs, characterized by their high surface-to-volume ratio, tunable porosity, and small diameter, enable superior encapsulation and immobilization of sensing agents. These features improve the efficiency of colorimetric indicators, electronic noses, biosensors and time–temperature indicators. Electrospun NFs functionalized with metallic nanoparticles can detect contaminants such as antibiotics and hormones, while polymeric NFs embedded with reduced graphene oxide act as electrodes for advanced biosensing. Freshness indicators based on pH and nitrogenous compounds demonstrate real-time spoilage detection through visible color changes. This review explores nanofiber fabrication methods, their integration into sensing systems, and their potential to advance rapid, sustainable, and cost-effective meat quality monitoring. Full article

Since the beginning of the 21st century, major epidemics and pandemics such as SARS, H1N1pdm09, Ebola, and COVID-19 have repeatedly challenged global systems of disease diagnostics and control. These crises exposed the weaknesses of traditional diagnostic models, including long turnaround times, uneven resource distribution, and supply chain bottlenecks. As a result, there is an urgent need for more advanced diagnostic technologies and integrated diagnostics strategies. Our review summarizes key lessons learned from four recent major outbreaks and highlights advances in diagnostic technologies. Among these, molecular techniques such as loop-mediated isothermal amplification (LAMP), transcription-mediated amplification (TMA), recombinase polymerase amplification (RPA), and droplet digital polymerase chain reaction (ddPCR) have demonstrated significant advantages and are increasingly becoming core components of the detection framework. Antigen testing plays a critical role in rapid screening, particularly in settings such as schools, workplaces, and communities. Serological assays provide unique value for retrospective outbreak analysis and assessing population immunity. Next-generation sequencing (NGS) has become a powerful tool for identifying novel pathogens and monitoring viral mutations. Furthermore, point-of-care testing (POCT), enhanced by miniaturization, biosensing, and artificial intelligence (AI), has extended diagnostic capacity to the front lines of epidemic control. In summary, the future of epidemic and pandemic response will not depend on a single technology, but rather on a multi-layered and complementary system. By combining laboratory diagnostics, distributed screening, and real-time monitoring, this system will form a global diagnostic network capable of rapid response, ensuring preparedness for the next global health crisis. Full article

The Dutch horticultural supply chain is characterised by substantial uncertainty resulting from ongoing organisational changes, such as the transformation from an auction-cooperative system to a sales organisation-based structure. This uncertainty causes strategic behaviour among all supply-chain members (including producers), which often disadvantages primary producers. This study investigates how uncertainty shapes trading behaviour and decision-making using Transaction Cost Theory as a theoretical framework. Specifically, it examines the relationship between environmental and behavioural uncertainty, trading behaviour and strategic responses. Employing a multimethod approach involving interviews, simulation sessions and debriefings to collect data, this study integrates a qualitative and quantitative analysis. The findings reveal: (1) how uncertainty influences trader behaviour and strategic decision-making, and demonstrates the need for more effective coordination mechanisms and strategies to reduce opportunism and inefficiencies in horticultural trade, (2) the diversity of strategic responses to uncertainty and (3) the factors that influence uncertainty and their relationship. Thes factors, include the current supply-chain structure that upholds uncertainty and strategic behaviour such as the deliberate exploitation of the absence or lack of information (asymmetric information). By combining methodological triangulation with theoretical insight, this study provides a foundational understanding of strategic behaviour under uncertainty in agri-food supply chains. Full article