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

AI assimilation for sustainable and resilient supply chains: Role of green ambidexterity and cybersecurity awareness

Addressing post-harvest potato losses and advancing value addition through agro-processing for sustainable food supply chain: A review

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.

A hybrid blockchain–AI–IoT framework for low-cost digital MRV in sustainable bioethanol supply chains

A novel decision support framework for unveiling critical determinants in resilient–sustainable supply chain performance measurement

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.

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.

Advances and challenges in alternative proteins: From biotechnology to sustainable food production.

ABSTRACT This study employs a qualitative case study approach to explore how collaborative logistics pooling enhances last-mile delivery performance and strengthens supply chain resilience in the retail sector. Data were collected through semi-structured interviews, roundtable discussions, and on-site observations with stakeholders involved in a real-world pooling initiative. By analysing a real-world logistics pooling initiative, the research identifies how shared logistics resources, such as transportation and warehousing, address key challenges in modern supply chains, including cost efficiency, environmental sustainability, and adaptability to disruptions. The study reveals significant cost savings, enhanced flexibility, improved visibility, and stronger collaboration among stakeholders. Consolidated deliveries and optimized routes reduced transportation costs and carbon emissions. Shared digital platforms enabled real-time coordination, ensuring swift responses to disruptions and fluctuating demand. These findings highlight the strategic value of pooling in building adaptive, efficient, and sustainable supply chains. The study’s focus on practical and strategic implications, offers valuable insights for addressing modern supply chain challenges in competitive and dynamic environments.

Purpose This study aims to investigates how social norms influence behavioral intentions and the perceived effectiveness of interventions aimed at promoting sustainable procurement practices, specifically within the context of small and medium-sized enterprises (SMEs) in Ghana’s sachet water industry. Design/methodology/approach A quantitative survey methodology was used, targeting procurement and supply chain personnel within Ghanaian SMEs. Data from 245 respondents were collected and analyzed using structural equation modeling (SEM) to test a conceptual framework linking descriptive norms (DN), injunctive norms (IN) and personal norms (PN) to behavioral intentions and advocacy (BIA), as well as the effectiveness of social norm interventions (ESNI). Findings The results reveal that PN are the strongest predictor of BIA (β = 0.41), outperforming both injunctive (β = 0.27) and DN (β = 0.32). Furthermore, BIA was found to be a central mechanism, significantly mediating the relationship between the norm constructs and the perceived ESNI (β = 0.19). Research limitations/implications While the study identifies individual-level psychological drivers (PN, DN, IN) as central to sustainable procurement intentions, future research could delve deeper into organizational-level factors that may moderate or mediate these relationships. For instance, how does organizational culture influence the internalization of PN? How do leadership styles and top-management support affect the translation of behavioral intentions into actual procurement practices. Practical implications Practically, this suggests that interventions should prioritize strategies that activate PN, supplemented by leveraging DN once initial intentions are formed, to achieve more effective and sustainable supply chain practices, particularly in informal sector SMEs with limited regulatory pressure. Originality/value The study not only highlights the primacy of internalized PN but also demonstrates how the interplay of DN, IN and PN is reshaped by Ghana’s collectivist culture and the informal governance structures typical of SME-dominated sectors. By revealing these context-contingent mechanisms, the research advances norm theory beyond Western, formal-sector assumptions and offers a culturally grounded framework for sustainable procurement in similar Global South settings.

Grounded in social exchange and social network theories, we study whether and how buyer firms’ social sustainability draws on their suppliers’ digital technology-oriented open innovation (DTOI). We focus on two dimensions of buyer social sustainability, namely societal and workforce sustainability, and examine the moderating role of buyers’ director network structure. Using panel data on Chinese listed firms from 2014 to 2024, we find that both dimensions are positively associated with supplier DTOI. These relationships are stronger for buyers whose director networks feature higher degree centrality, betweenness centrality, and structural holes, but not closeness centrality. We further explore how these relationships vary across relational, ownership, and industry contexts. Our findings extend digital innovation research to supply chains and clarify when supplier spillovers reach buyers.

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.

Nickel is a critical metal with broad applications in stainless steel and nickel-based alloys. In recent years, its rapidly growing use in secondary batteries has become particularly important, underscoring its strategic role in enabling low-carbon, green, and sustainable societal development. As high-grade nickel sulfide resources become increasingly scarce and global demand for nickel accelerates-fueled by the expansion of clean energy technologies-there is a pressing need to develop environmentally benign extraction strategies for low-grade nickel sulfide ores to safeguard the stability and sustainability of the global nickel supply chain. Here we report a low-temperature predominantly solid-state process for extracting nickel from unconventional, low-grade ultramafic ores, estimated to contain approximately 45 million tonnes of untapped nickel. The method leverages cheap metallic iron as a nickel getter offering a sustainable pathway for high-value nickel recovery aligned with decarbonized metal production. The process produces ferronickel alloys (16-24% nickel) with several key advantages, including rapid processing time ( ~ 3 hours), low operational temperatures ( < 950 °C), and the elimination of SO2 emissions. Temperature, atmosphere, and iron addition were tailored to create favorable thermodynamic conditions within the reactor, enabling selective partitioning of nickel into metallic alloys while effectively sequestering sulfur as stable solid sulfide phases. Controlled tuning of alloy particle size and morphology facilitates efficient separation from gangue, yielding a ferronickel which can be converted to battery-grade nickel through conventional refining. This method, verified to mini-plant scale broadens the technological landscape of nickel extraction and contributes to a more equitable and resilient global nickel supply chain.

ABSTRACT This study examines whether the relationship between digital transformation and digital supply chains enhances sustainability. Given fragmented evidence and inconsistent findings in prior research, it aims to provide a clearer understanding of this linkage. A two-phase research design was adopted. First, a systematic literature review using the Scopus database explored how digital transformation contributes to supply chain sustainability. Second, a meta-analysis was conducted to evaluate whether digital transformation influences sustainability outcomes directly or indirectly. The findings indicate a significant positive relationship between digital transformation and sustainable supply chain performance. However, existing empirical studies report mixed results, limiting the ability to draw definitive conclusions. Although the evidence suggests that digital transformation can strengthen sustainability, current support remains largely theoretical and insufficiently validated. This study highlights the need for more robust empirical investigations from diverse perspectives to confirm the causal mechanisms and advance the development of sustainable digital supply chains in practice.

The development of sustainable agricultural supply chains is critical for improving operational efficiency, coordination, and resilience under increasing demand uncertainty, climate variability, and resource constraints. These supply chains involve complex planning and control decisions across interconnected stages, including pre-production, production, post-harvest processing, and distribution. However, existing research on artificial intelligence (AI) and machine learning (ML) remains fragmented and lacks an integrated planning and control perspective. This study addresses this gap by developing a conceptual decision-support framework that positions ML as a decision-intelligence layer embedded within planning, coordination, and control processes. Using a structured literature synthesis, the study maps ML techniques to key operational decision contexts across the supply chain. The framework demonstrates how ML-enabled decision support can enhance yield planning, resource allocation, logistics coordination, quality control, and waste reduction, while linking these decisions to sustainability outcomes such as environmental efficiency, economic resilience, and transparency. The study also identifies key implementation challenges, including data availability, interoperability, infrastructure constraints, and organizational readiness. The study contributes a decision-centric framework that integrates ML with supply chain planning and control, providing actionable insights for improving performance and sustainability in agricultural supply chains.

Industrial hemp (Cannabis sativa L.) has emerged as a sustainable source of bioactive compounds, with increasing interest in cosmeceutical applications for acne management. This systematic review synthesises evidence on cannabinoid-containing hemp extracts, particularly cannabidiol (CBD), with emphasis on anti-inflammatory and sebostatic mechanisms, alongside formulation considerations and supply-chain sustainability. Reporting followed PRISMA 2020 guidelines and encompassed preclinical and clinical evidence relevant to acne-associated outcomes. The review protocol was registered prospectively with PROSPERO (CRD420251272093). Across cell-based, ex vivo and early clinical studies, CBD modulated key inflammatory mediators, including TNF-α, IL-1β, IL-6 and IL-8; normalised sebocyte activity and attenuated Cutibacterium acnes (Propionibacterium acnes)-induced inflammatory signalling. Preliminary clinical observations indicate reductions in lesion counts and erythema, with generally favourable short-term tolerability; however, interpretation is limited by small sample sizes, predominantly non-randomised designs, heterogeneous formulations and frequent co-formulation with additional active ingredients. Evidence supporting direct antimicrobial efficacy and durable clinical benefit remains limited. Lipid-rich hemp seed-derived products were considered only in a contextual capacity for barrier-supportive and nutritional properties and were excluded from efficacy synthesis unless cannabinoid content was verified. Sustainability analyses highlight hemp’s low water requirements, carbon sequestration potential and relevance to Sustainable Development Goal 3 (SDG 3: Good Health and Well-Being) and Sustainable Development Goal 12 (SDG 12: Responsible Consumption and Production), supporting its role in environmentally responsible cosmeceutical development. Overall, CBD-containing hemp extracts show biologically plausible and clinically promising adjunctive potential for mild-to-moderate inflammatory acne, but current evidence remains preliminary. This review highlights the need for methodologically rigorous and transparent clinical studies, standardised formulations, validated outcome measures and the integration of sustainability metrics to strengthen evidence synthesis, clarify clinical relevance and guide responsible cosmeceutical development.

The rapid growth of e-commerce has increased the demand for efficient and timely order fulfilment. Same-day delivery has become essential for meeting customer expectations and maintaining profitability. Traditional methods often struggle with balancing fast delivery and cost-effectiveness, especially when handling diverse product assortments and fluctuating demand patterns. Our approach leverages AI-based models to predict order patterns, enabling dynamic and real-time order consolidation strategies that reduce overall operational costs. The AI-based decision-making model dynamically consolidates orders based on proximity and delivery windows by analyzing historical data and real-time customer behaviour. The experimental analysis demonstrates that the proposed approach not only reduces the operational costs but also decreases the number of delivery trips required by consolidating orders, and contributes to a more sustainable and cost-effective supply chain. The online grocery retailing data set used to validate our approach shows that the proposed model can capture almost 90 % of multi-orders and decrease order fulfilment costs by 4.29 % against the company's existing threshold policy, which transforms to over 15 million USD annually. This study provides a comprehensive analysis of the system's performance, highlighting its potential to revolutionise same-day delivery logistics in the online sector.

ABSTRACT This study used the coffee supply chain (SC) to explore the perceptions of organisational capabilities (OCs) held by suppliers (producers) and buyers (roasters). We conducted a thematic analysis to provide insights into synergies and discrepancies in OC perceptions, sustainability, and buyer – seller collaboration. Semi-structured interviews were conducted with 10 coffee suppliers in Brazil and 8 coffee buyers in Portugal. Interview transcripts were analysed using a mixed-methods content analysis, and a visual tool was used to generate conceptual cluster maps, supported by Leximancer software. For thematic analysis, the interviews were coded using MAXQDA. The results provide insights beyond the dyadic supplier – buyer perspective on OCs. We show that buyers and suppliers are not yet fully aligned to implement a sustainable SC due to OC-related limitations, a perceived lack of collaboration, and limited transparency in relationships and negotiations. Our findings indicate that, in long-term buyer – supplier relationships, both parties benefit from social capital. This study contributes to sustainable SC management by providing insights into OCs.