Optimizing facility location and inventory management in food supply chains is essential for reducing costs, prevent spoilage of perishable products, and ensuring equitable food distribution across rural and urban districts. To deal with this issue, this paper proposes a comprehensive model for the integrated optimization of facility location and inventory management within a three-tier hierarchical hub network architecture. The network topology is a complete-star-star structure, with fully interconnected central hub nodes at the highest level. The intermediate and lowest tiers consist of star-shaped subnetworks, where end nodes, including manufacturers, connect to non-central hubs. Given the NP-complete nature of the problem, we propose a hybrid algorithm combining an exact solution with a meta -heuristic genetic algorithm. These algorithms are implemented in GAMS and MATLAB software. Sensitivity analysis is conducted on model’s parameters. The results show that decreasing the costs of establishing the hub by more than 75% increases the number of median hubs. Production quantity and inventory levels remain steady with cost variations up to −50%, but decrease with production cost increases up to 50%, where inventory levels drop to zero

A functionalized photocrosslinked organic-inorganic hybrid hydrogel for strawberry preservation and multimodal sensing.

Traditional computer vision-based quality perception lacks depth information, limiting its application to reliable food quality management in the post-harvest supply chain. Three-dimensional (3D) reconstruction technology captures detailed surface geometry and internal structural information for reliable non-destructive quality inspection. Combined with emerging artificial intelligence (AI) technologies, 3D data-driven adaptive management brings potential for next-generation post-harvest quality management. This review analyzed 90 major related studies during 2015–2025, covering high-throughput 3D in-line inspection, high-resolution tomography, portable 3D sensing, and AI-driven 3D reconstruction technologies. Post-harvest supply chain application scenarios are mainly distributed in post-harvest processing (33 studies), manufacturing (29 studies), distribution (15 studies), and consumption (13 studies). Among them, fruit and vegetable products are the most intensively researched, highlighting the suitability and potential benefits of 3D reconstruction for these types of products. Besides, multiple 3D reconstruction technologies have been validated for postharvest evaluation, with X-ray CT dominating postharvest processing, manufacturing, and distribution, and portable RGB imaging devices dominating application in consumption. Besides, relevant 3D reconstruction analysis is evolving from geometry-driven to AI-enhanced analysis and management, highlighting the growing role of 3D reconstruction technology in intelligent, traceable, and sustainable post-harvest supply chain quality management. In the future, by integrating digital twins, IoT, and blockchain technologies, it is expected to build a transparent, tamper-proof quality traceability and control system across the global food supply chain. • 3D reconstruction significantly improves non-destructive food quality inspection. • AI integration expands 3D reconstruction applications in food supply chains. • Applications span post-harvest, processing, logistics, and consumer evaluation. • Enables automation in grading, manufacturing, cold chain, and dietary assessment. • Future trends include sensor fusion, XR, and digital twin for food quality control.

Ordering the chaos of sustainable supply chains: A q-rung fuzzy hypersoft framework for multi-attribute green supplier selection

Harnessing machine learning for supply chain management: A systematic review and future research framework

The growing use of disposable equipment in operating rooms (ORs) underscores the need for efficient storage systems that ensure reliable access while minimizing waste. Multiple organizational models exist, including open rack storage, Omnicell cabinets, and PAR Excellence weight-based scales. This study evaluated the effectiveness of the current storage model in the urology suite at a single academic institution. We first identified the storage systems used in the operating rooms (OR) and Omnicell storage room in the urology suite. We then obtained stored item data from our Supply Chain manager, containing quantities of each item and costs. Using our medical record system, EPIC, we kept track of the frequency of use for each stored item in academic year 2022-2023. Also, during this time, the PI surgeon and OR staff made note of any unavailable or expired equipment. At our institution, we use a combination of open rack and Omnicell storage. From August-September 2022, there were 3 out-of-stock items noted. In April 2023, there were 7 expired items noted. Of the 237 different disposable items stocked, 117 (49.37%) were unused over the study year, accounting for $21,812.26 (15.98%) of total on-hand inventory cost. The current organizational system demonstrated notable inefficiencies, including unavailable equipment, expired items, and substantial cost tied to unused supplies. These challenges highlight the limitations of manual or partially automated storage models and emphasize the need for more reliable, datadriven systems. Institutions may benefit from reevaluating their storage workflows to improve equipment availability, reduce waste, and support more sustainable financial and clinical practices.

Global supply chains are increasingly exposed to multi-source uncertainties, ranging from geopolitical tensions to climate extremes, making the accurate and interpretable prediction of disruptions an urgent operational priority. Existing predictive models often rely on either shallow statistical learners, which struggle with high-dimensional interactions, or deep neural networks, which trade off interpretability for marginal performance gains. To address this gap, we propose an interpretable machine learning framework that couples a feature-attention mechanism with a gradient-boosted decision tree ensemble for early warning of shipment-level disruption events. First, a dedicated attention module is trained to assign importance weights to 14 heterogeneous risk factors, generating an interpretable feature ranking that highlights pivotal signals such as lead-time volatility and geopolitical risk. The reweighted features are then fed into a gradient boosting classifier, which effectively captures non-linear patterns and interaction effects. Evaluated on a publicly available dataset of 5000 international freight records available on Kaggle, the proposed framework achieves an AUC of 0.8213 (±0.0002 over three independent runs), matching the best-performing baseline (standard gradient boosting, 0.8212 ± 0.0001) and surpassing logistic regression (0.777), random forest (0.806), and a standalone feature-attention network (0.805). The attention module preserves full predictive accuracy while adding an interpretability layer that conventional black-box implementations lack. Notably, the framework preserves the predictive accuracy of gradient boosting while enhancing interpretability through attention-based feature ranking and dual-perspective importance analysis, achieving a precision of 0.770 and a balanced F1-score of 0.781. The convergence of attention-based interpretability and ensemble learning efficiency provides supply chain managers with a transparent decision-support tool—distinguishing genuine “tipping points” from “false alarms” and enabling targeted risk mitigation under deep uncertainty.

The rising demand for a sustainable industrial development has led to the implementation of digitalization and Circular Economy (CE) methods. This review examines the combination of Industry 4.0 technologies (i.e., Internet of Things (IoT), Artificial Intelligence (AI), big data analytics, cyber-physical systems, blockchain, additive manufacturing, and cloud computing) with CE principles to enhance the Triple Bottom Line (TBL) performance in industrial firms. This research explores the impact of digital technology on resource efficiency, waste reduction, closed-loop production, prolonging product life, and sustainable supply chain management. The results demonstrate that the synergistic integration of Industry 4.0 and CE provides a significant enhancement in environmental sustainability by reducing emissions and resource consumption, economic performance improvement by increasing operational efficiency and value recovery, and social sustainability improvement by improving workplace safety, transparency, and skill development. The assessment also points to important implementation challenges, including technological complexity, cybersecurity concerns, significant investment requirements, and gaps in labour capability. Finally, future research directions including empirical TBL assessment, digital product passports, human-centric industry 5.0 techniques and policy frameworks are outlined. The report provides an integrated framework for industries to achieve sustainable, resilient and competitive growth through digital-circular transformation.

ABSTRACT Against the backdrop of increasing uncertainty and sustainability pressures, sustainable supply chain management (SSCM) has become critical for balancing economic, environmental, and social performance. Failure mode and effect analysis (FMEA) is widely used in SSCM, yet traditional FMEA is limited in handling uncertain linguistic information, deriving reasonable risk factor weights, and capturing intrinsic correlations among failure modes. To address these gaps, this study proposes a novel three‐stage FMEA‐based decision model for SSCM risk assessment. In the first stage, trapezoidal interval type‐2 fuzzy sets (TrIT2FSs) are employed to represent uncertain expert assessments. In the second stage, an integrated deck of cards with decision‐making trial and evaluation laboratory (DOC‐DEMATEL) method is developed to determine risk factor weights by considering dual interactions among risk factors and experts. In the third stage, a TrIT2FS‐based grey relational analysis (Tr‐GRA) method is constructed to rank failure modes while capturing their intrinsic relationships. Finally, an SSCM case is analyzed, followed by sensitivity and comparative analyses to validate the model. Results show that operation complexity, opportunity loss, and lack of trust are the highest‐priority failure modes. The proposed model outperforms traditional FMEA and multi‐criteria decision‐making methods in robustness and rationality. These findings provide clear managerial insights to help enterprises strengthen risk detection, improve supply chain collaboration, and optimize operational processes toward sustainable development.

Non-destructive ripeness classification of apricot (Prunus armeniaca L.) using a physically informed deep learning approach.

ABSTRACT Climate change anxiety is increasingly shaping consumer behavior, yet its role in luxury consumption remains underexplored. This study investigates how climate action perception (climate change anxiety) affects sustainable luxury consumer experiences, focusing on the moderating role of digital environmental concern and the mediating role of consumer‐centric green supply chain management (GSCM). Survey data from 865 respondents were analyzed within the framework of the Theory of Planned Behavior, Stakeholder Theory, and Ecological Modernization Theory. Results show that climate change anxiety significantly influences sustainable luxury experiences. GSCM mediates this relationship, transforming eco‐anxiety into increased consumer trust and satisfaction. Digital environmental concern negatively moderates the relationship between climate change anxiety and GSCM, indicating that higher digital concern weakens the effect of anxiety on demand for supply chain transparency. For luxury managers, this means designing transparent, ethical supply chains while also directly engaging emotionally with climate‐anxious consumers, especially those with high digital environmental concern.

Accurate district-level wheat yield forecasts are critical for food security planning, supply-chain management, and agricultural policy in India, the world's second-largest wheat producer. We benchmark nine model classes for this task on a 23-year (2001-2023) dataset of 275 districts across India's seven largest wheat-producing states, which together account for ∼95% of national production. The benchmark covers Random Forest, XGBoost, LightGBM, a 1D-CNN, an LSTM, a BiLSTM, a single-stream Transformer encoder, the recently proposed Parallel CNN-LSTM-Attention design, and our hybrid CNN-BiLSTM-Attention with modality-specific routing (a 1D-CNN over the vertically structured soil profile and a BiLSTM with self-attention over the meteorological and remote-sensing time series). The proposed model is the best entry, achieving a Mean Absolute Error (MAE) of 273.2 kg/ha and an [Formula: see text] of 0.795 on the held-out test set - a 43.8% MAE reduction over the Random Forest baseline, a ∼28% reduction over the gradient-boosted baselines, and a ∼4% reduction over the next-best deep model. A simple persistence forecast ([Formula: see text]) however, achieves an MAE of 274.9 kg/ha, essentially tying the proposed model on average. We show that the architectural value-add concentrates in anomalous years: in the dry 2023 sowing season the model improves MAE by 7.2% and RMSE by 11.2% over persistence, and SHAP attribution localises the temporal contribution to the February-March grain-filling window led by EVI and NDVI signal - consistent with the well-documented sensitivity of wheat grain-filling to moisture and temperature stress in that window. Together, these results position persistence-aware, modality-specific deep learning as a practical framework for stress-sensitive yield forecasting in data-scarce agricultural regions.

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation.

The pharmaceutical supply chain continues to face significant challenges, including the circulation of counterfeit medicines, limited traceability, and insufficient transparency among stakeholders. To address these issues, this study presents a blockchain- and IPFS-based traceability framework designed to improve the secure tracking and verification of pharmaceutical products. The proposed system combines role-based smart contracts with decentralised off-chain storage to maintain product history and enable integrity validation using unique digital identifiers. The prototype was implemented in Ethereum-compatible environments. Repeated workflow measurements were obtained in the Remix VM environment, while Sepolia was used for deployment-level validation and public testnet verification. The system demonstrated the complete workflow, including participant registration, product enrollment, manufacturing, multi-stage transfer of ownership, and authenticity verification. Analysis of the execution outputs indicates that the core operations exhibit consistent gas consumption across repeated runs, offering clear insight into computational cost and system behaviour. The findings suggest that the proposed framework can serve as a viable foundation for improving traceability in pharmaceutical supply chains at the prototype level. However, further investigation is required to assess system performance under real-world deployment conditions, particularly with respect to scalability, latency, and large-scale operational constraints.

This study develops a centralized three echelon open loop supply chain optimization model that integrates circular input adoption with coordinated pricing and cooperative advertising decisions. In the proposed setting, recycled materials and usable components are supplied through an external recycling market and become available for procurement by supply chain members as circular inputs. Circularity is represented through echelon specific circular input utilization decision variables at the supplier, manufacturer, and retailer levels, allowing the model to capture the economic and demand side effects of circular input adoption under market availability constraints. Customer demand is formulated as a function of selling price, cooperative advertising effort, and average circular input utilization, thereby linking market response with sustainability oriented operational decisions. Key cost parameters are modeled as triangular fuzzy numbers and transformed into crisp equivalents using the signed distance method. The resulting nonlinear constrained optimization problem maximizes total supply chain profit subject to advertising budget, inventory capacity, recycled input availability, and circularity related feasibility constraints. Sequential Quadratic Programming is used as the main solution approach and is compared with genetic algorithm, pattern search, and minimax methods. The numerical results show that the SQP based solution achieves the highest profit while maintaining feasible and balanced decision values across pricing, advertising, and circular input utilization variables. Sensitivity analysis further indicates that profitability is most affected by recycled input availability and circular cost adjustment, while incentive strength provides a secondary but consistent improvement. The proposed framework provides a quantitative decision support tool for firms seeking to coordinate pricing, advertising, and circular input adoption decisions while maintaining profitability under realistic market and policy conditions.

The European Green Deal and related EU regulatory initiatives have intensified sustainability requirements for industrial firms, prompting transformative changes in supply chains toward circularity. These regulatory pressures require companies to adopt sustainability-oriented innovations (SOI) that integrate economic, environmental, and social dimensions into business and innovation processes. This paper explores how sustainability-oriented innovations are adopted at the supply chain level through an in-depth qualitative case study of a leading Baltic food manufacturing company operating globally. The study examines supply chain management practices that include implementation of sustainability-oriented innovation that supports sustainability outcomes. The results suggest how product and process SOI enables companies to create sustainable value, respond to regulatory pressures, and promote circular supply chain practices, also highlights the role of a supply chain lead firm, which orchestrates the entire supply chain, in creating SOI. By adopting a contextual supply chain approach, this paper enriches understanding of SOI in the food industry and offers managerial insights relevant to firms navigating sustainability-driven transformation under EU regulatory frameworks.

Globally, chronic wounds resulting from diverse etiologies impose a significant physical, psychosocial, and economic burden and have remained a highly neglected public health challenge. Yet, people in rural Ethiopia have limited access to quality and comprehensive wound care. There is a marked paucity for an integrated, holistic care model in the primary healthcare settings. Hence, this study aimed to develop a context-tailored, integrated, holistic, chronic wound care package in Ethiopia. A multi-method approach was applied, including a scoping review, formative assessments, Theory of Change workshops and a qualitative validation study. Purposive sampling was employed to recruit participants. Collected data were transcribed, coded, and thematically analysed to generate insights for refining the intervention package. A total of 49 stakeholders participated in the Theory of Change (ToC) workshops, while 36 participants were included in the qualitative study. The intervention package is structured around six core thematic components: awareness-raising and stigma reduction, capacity-building, active case detection and follow-up, program and supply chain management, Institutional and community-based rehabilitation, monitoring, and evaluation. Implementation and scale-up of these components are designed to cascade across three levels of the existing primary healthcare system, specifically the health organization, the health facility level, and the community level. This study introduced a context-driven, integrated, and holistic wound care package aimed at managing chronic wounds alongside their associated mental health and psychosocial challenges. The package encompasses interventions that address the physical, psychological, and social impacts of chronic wounds in individuals affected by neglected tropical diseases of the skin and related conditions. It employs multilevel implementation strategies targeting individuals, communities, and the health system to reduce morbidity, disability, and the economic and psychosocial burdens linked to chronic wounds. This standardized, scalable, and sustainable care model provides a promising approach for Ethiopia and other low- and middle-income countries.

ABSTRACT There is great concern for sustainable development to ensure that the future of unborn generations is not sacrificed for the market model of profit maximization by firms. Prior studies have investigated the attitude of individuals toward recycling, firms’ green supply chain management and sustainable performance. However, how public environmental policy influences recycling practices at the firm level and the mediation of recycling practices between environmental policy regimes and the triple bottom line of economic, social and environmental performance have not been adequately examined. Accordingly, the focus of this paper, through the theoretical lens of the Natural Resource Based View, investigated the nexus between environmental policy, recycling practices and sustainable performance of firms. The study adopted an analytical survey design. A sample of 174 managers was selected through a stratified sampling procedure, and data were collected using a structured questionnaire. The data were analyzed with structural equation modeling using Smart PLS software version 4.1.4 The result indicated that there was a positive relationship between environmental policy and recycling of waste materials; there was a positive relationship between recycling and sustainable social and environmental performance of manufacturing firms. The paper concluded that recycling practices reduced production costs, fostered eco-efficiency and enhanced sustainable performance. The paper recommended that managers of manufacturing firms need to invest in recycling systems to integrate eco-design and reverse logistics architecture and promote sustainability outcomes.

A two-warehouse inventory model is introduced with the manufacturer and retailer. They both use two warehouses for storing and selling products, with their own and a rental warehouse. The manufacturer produces both perfect and imperfect products. Defective products returned from the retailer are sent for remanufacturing. All remanufacturing products are shifted to the secondary retail stores. Every product has a lifespan, and to reduce deterioration, the manufacturer and the retailer invest in preservation technology. Both players offer trade credits to their customers. To achieve environmental sustainability, both the manufacturer and the retailer calculate and pay for their carbon emissions. The retailer advertises products in the market, as advertising policy is a significant policy for enhancing any business by attracting customers. A sustainable supply chain management model is developed to optimize the production rate, remanufacturing rate, and cycle time. A numerical experiment is performed to validate the study, and a sensitivity analysis is conducted to provide insights to managers. From numerical findings, if the holding cost increases to 20%, the profit decreases to 0.03%. The numerical results indicate the importance of remanufacturing, carbon emissions, and product quality control within a sustainable supply chain.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-026-43484-x.

The Supply Chain and Pharmacy breakout session focused on participants' real-world experience with supply chain and pharmaceutical needs for their alternate care facility (ACF). This included details around acquisition and utilization of durable medical equipment, personal protective equipment (PPE), consumables, and pharmacy stock. Participants were encouraged to discuss supply chain management considerations, resource allocation and acquisition, and influencing factors that impacted these topics. Participants explored various approaches to obtain medical supplies and pharmaceuticals, including leveraging existing vendor relationships and establishing climate-controlled storage spaces, while emphasizing the importance of electronic tracking systems and documentation. The overarching goal was to focus on what participants did and how deficiencies were managed. The discussion covered challenges in healthcare coordination during disasters, including supply chain issues, the management of unsolicited donations and volunteers, and the need for innovative solutions like drones and alternative medical applications, concluding with reflections on regulatory oversight and opportunities for process and systems improvements. The group identified several key gaps in emergency response systems, particularly the disconnect between emergency management agencies and healthcare systems, while emphasizing the need for qualified liaisons, just-in-time training, and systematic approaches to ensure effective medical resource management during crises.