The paper investigates the relationship between big-data-driven supply chains (BDSC) and sustainable supply chain performance (SSCP), examining the proposed mediating role of green supply chain management (GSCM) and the moderating role of big data analytics capability (BDAC). Drawing on the Resource-Based View and Dynamic Capabilities View, the study aims to close an important gap in understanding how digitalised supply-chain infrastructures result in environmental and operational sustainability gains, particularly in developing countries. Survey data were collected from a sample of 76 companies listed on the Amman Stock Exchange, and the structural relationships were tested using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that BDSC have a significant positive effect on SSCP and greatly facilitate GSCM. However, GSCM did not directly affect SSCP, indicating that green practices require digital and analytical supplements that afford measurable sustainability. The moderating analysis shows that BDAC strengthens the relationship between BDSC and GSCM, confirming that firms with stronger analytics capabilities are better positioned to translate data-driven supply chain initiatives into cleaner and more environmentally oriented supply chain practices. The paper contributes to theory by combining digital capabilities with a sustainability supply chain focus and offers practical guidance for firms to utilize analytics to promote improved environmental performance. Implications, limitations and future research opportunities are discussed.
The operational integration of Life Cycle Assessment (LCA) within Sustainable Supply Chain Management (SCM) is critical for driving global decarbonisation, yet its academic literature remains methodologically fragmented. This study conducts a systematic literature review and bibliometric analysis of 96 peer-reviewed articles to map the current state, structural boundaries, and systemic gaps within the LCA-SCM discipline. The empirical findings reveal a highly uneven sectoral landscape, with agri-food systems emerging as the dominant frontier representing 31.2% of the surveyed corpus, followed by manufacturing and waste management, whilst carbon-intensive sectors like logistics remain underexplored. Methodologically, the field is governed by powerful regional path dependencies, with SimaPro and Ecoinvent serving as the primary technical platforms characterising over three-quarters of the analysed literature. Furthermore, this review exposes a widespread crisis of methodological rigour and transparency across global value chains: 55% of the examined studies truncate their system boundaries to a cradle-to-gate scope, functional units are frequently conflated with simple mass-based declared units, and 47% completely omit both sensitivity and uncertainty analyses. This lack of statistical validation severely undermines the auditable reliability of environmental disclosures. To transition LCA from a retrospective diagnostic tool into a proactive sourcing enabler, this study provides a cross-sectoral decision guide for practitioners and outlines a strategic future research agenda. This framework prioritises the standardisation of allocation metrics, the expansion of Social LCA, the institutionalisation of dynamic inventory modelling, and the deployment of Industry 4.0 digital technologies like blockchain and artificial intelligence.
Vaccines are central to global health, yet the logistics that support their distribution in developing countries face significant sustainability challenges. Strict cold chain requirements, time-sensitive delivery, and extensive single-use packaging generate substantial waste and greenhouse gas emissions (GHG). This study conducts a systematic literature review guided by the PRISMA framework. Using Scopus, Web of Science, and Google Scholar, it examines how circular economy (CE) principles can be applied to vaccine logistics. The review identifies major barriers, including fragmented policy environments, limited technological capacity, financial constraints, cultural resistance, and low environmental awareness. It also highlights key enablers, including policy harmonisation, stronger stakeholder collaboration, advances in eco-design, digital traceability, and committed leadership. Real-world cases, including UNICEF's sea-shipment of vaccines to Côte d'Ivoire, demonstrate that CE practices can significantly reduce emissions, waste, and costs. This study provides one of the earliest comprehensive syntheses of CE adoption in vaccine supply chains and presents a framework explaining how contextual factors, barriers, and enablers shape progress toward circular vaccine logistics. The findings emphasize the need for coordinated policy reform, investment in renewable energy for cold-chain storage, capacity building, and cross-sector collaboration. Future research should employ quantitative modelling and organisational-behaviour perspectives to assess trade-offs and guide the practical integration of CE practices in resource-constrained health systems.
Despite recent advances in the deployment of artificial intelligence technologies for building resilient industries and sustainable development, existing literature largely overlooks the dual mediation role of green innovation and supply chain resilience in how AI-enabled dynamic capabilities relate to the sustainable performance of supply chains. In this study, we extend the dynamic capabilities framework by integrating AI-enabled dynamic capabilities and these two mediator variables in influencing sustainable supply chain performance. Drawing on survey data collected from 634 managers in 423 Chinese construction firms, our empirical validation demonstrates that AI-enabled dynamic capabilities amplify businesses' capacity to sense, seize, and reconfigure resources more rapidly in complex environments. Both green innovation and supply chain resilience exert significant mediation effects. Our theoretical contribution lies in showing that green innovation proactively enhances environmental performance through sustainable practices while supply chain resilience ensures reactive adaptability amid disruptions. These findings offer important implications for how construction industry firms should strategically invest in AI-enabled dynamic capabilities to meet new regulatory requirements, foster sustainable competitive advantages and build resilient supply chains.
The food supply chain requires special attention due to its importance in people's lives, its environmental and social effects, and the numerous sources of uncertainty it involves. Managers are worried about balancing sustainability and resilience goals in the event of a disruption. This article's model combines sustainability and resilience indicators. The amount of CO2 emissions and wastewater treatment are considered environmental indicators. Job creation, regional development, and employee health are regarded as social indicators. The model objectives to maintain resilience by using three strategies: increasing capacity, developing horizontal cooperation in the food industry for the first time, and backup suppliers. Finally, the presented model has been evaluated and implemented using data from the food industry with the Rolling Time Horizon algorithm. The simulation results suggest that among the resilience policies, the horizontal cooperation strategy has eliminated the negative competition between the production centers and improved income by 63.5% under disruption conditions, while also reducing lost sales by 26.82%. A 9% increase in construction costs is a result of the construction of a water treatment plant from an environmental perspective. In exchange, there is a 7% reduction in the cost of buying fresh water. In the social dimension, constructing a distribution center led to an 11% increase in employment, of which 4% was attributed to transportation in the chain, and with a 12% increase in regional development compared to current conditions. The results presented above demonstrate that the combination of resilience and sustainability is a key factor in improving chain performance.
The purpose of our study is to understand the nexus between stakeholders and the success of the local food supply chains (LFSCs). We conducted a systematic literature review (SLR) of 89 empirical papers to attain the research aim. Drawing on stakeholder theory, we have identified 12 distinct types of stakeholders that influence the development of LFSCs. We further categorized them into four groups (Players, Context setters, Subjects and Crowds) based on the power-interest matrix. Our analysis suggests that “Players” are the more significant stakeholders, followed by “Context setters”. Furthermore, we have identified five roles - “initiator”, “investor”, “educator”, “regulator”, and “facilitator” that stakeholders play in the successful development of LFSCs. However, their role enactment is triggered by drivers (self-centric and altruistic) while delayed by barriers (endogenous and exogenous). To satisfy drivers and vanquish barriers, stakeholders engage with each other. All these aspects are symbiotic; disregard or obliviousness of any of these leads to the failure of such chains. This is the first SLR that has adopted stakeholder theoretical lens in categorizing stakeholders of LFSCs; thus, this research has broadened the scope of the theory and widened the knowledge base of the investigated phenomena. By categorizing roles and showing what roles are unique and shared, this research clarifies level of contributions of stakeholders in developing LFSCs. Additionally, this study contributes to practice. The identification of stakeholders, especially the categorization, provides insights into whom to prioritize and why. Thus, guides focal firm in managing stakeholders effectively. This research offers directories of roles, drivers, and barriers by different stakeholders and types of chains that focal firm can consider while developing LFSCs.
This study examines how resource-constrained Moroccan manufacturing SMEs transform external environmental knowledge into green innovation within supply chain relationships. These firms face increasing pressure from international sustainability standards, customers, suppliers, and institutional actors. Yet limited resources often restrict their ability to implement cleaner products, processes, and operational practices. Using absorptive capacity as a theoretical lens, this study investigates three Moroccan SMEs operating in the textile, plastics, and cosmetics sectors. Data were collected through semi-structured interviews, on-site observations, and document analysis. The analysis identifies five mechanisms through which SMEs absorb and operationalize environmental knowledge: strategic knowledge filtering, hybrid knowledge synthesis, leadership-driven knowledge exploitation, organizational agility under resource constraints, and bifurcated absorption strategies. These strategies combine global sustainability requirements with local market realities. The analysis suggests that green innovation in emerging-economy SME supply chains depends not only on access to external environmental knowledge. It also depends on firms' ability to selectively interpret, adapt, and apply knowledge circulating across supply chain networks. This study contributes to sustainable supply chain management literature by explaining how SMEs convert supply-chain-mediated environmental knowledge into green innovation. It also extends absorptive capacity theory by conceptualizing knowledge absorption as a boundary-spanning process shaped by suppliers, customers, institutional actors, and sustainability pressures.
Ports have emerged as pivotal actors in the global sustainability transition as climate imperatives, digitalization, and stakeholder expectations increasingly reshape port-centric logistics systems. Despite the growing prominence of port sustainability in academic discourse, existing studies offer limited empirical insight into how sustainability is implemented in practice. This study addresses this gap by providing a comprehensive assessment of sustainability adoption across global ports. Extracting and thoroughly reviewing 302 sustainability projects reported in the World Ports Sustainability Program (WPSP) database between 2014 and January 2025, the study systematically identifies and classifies 45 sustainable port management practices (SPMPs) within the triple bottom line (TBL) framework, comprising environmental, social and economic dimensions. A mixed-method approach is employed, integrating thematic analysis with exploratory data analysis and visual analytics using RapidMiner to examine global adoption patterns, spatial distribution, and dominant sustainability trajectories. The results reveal a clear prioritization of environmental sustainability practices, particularly energy management and pollution control, reflecting the regulatory and decarbonization pressures faced by ports. Social sustainability initiatives, centred on community engagement and workforce well-being, constitute the second most prominent dimension, while economic sustainability practices are increasingly driven by smart port technologies and digital transformation. However, the diffusion of economically oriented practices across port-centric logistics systems remains uneven. To enhance managerial relevance, the study develops a sustainability driver matrix linking 45 SPMPs to regulatory compliance, operational efficiency, and social commitment, providing a practical decision-support framework for port authorities. By systematically mapping real-world sustainability practices, this study advances empirical understanding of TBL-based port sustainability and offers a scalable foundation for benchmarking, prioritization, and strategic planning toward more sustainable and resilient port operation.
This study addresses the challenge of improving the performance of waste-to-value investments by identifying the most critical strategies and operational criteria under limited resources. The main problem in this field is the lack of analytical frameworks that prioritize investment opportunities by incorporating queuing theory based operational performance indicators, such as congestion and waiting related measures, into strategic decision making. Although existing studies examine waste-to-value systems, the integration of queuing theory parameters into strategic decision making remains limited, creating a significant gap in the literature. To overcome this limitation, the study proposes a novel fuzzy decision-making model based on an extensive literature review and expert evaluations. Expert importance weights are determined using a Manhattan distance-based centrality approach, criteria weights are calculated through the cognitive maps technique to reflect interdependencies, and investment alternatives are ranked using an orthogonal metric robust aggregation method, while uncertainty is handled via cipher fuzzy sets. The results reveal that waiting time is the most influential queuing theory-based performance indicator, and plastic bottle remanufacturer and cardboard box recycler are the most essential waste-to-value investment opportunities. These findings highlight the importance of strategies that reduce operational delays and prioritize recyclable packaging waste, and the proposed model offers a structured and robust framework to support effective waste-to-value investment planning.
This study presents a novel integrated optimization model for designing a green supply chain network (GSCN). The model aims to minimize total cost and carbon emissions while maximizing service level. It operates across two geographically distinct regions, Region A and Region B, each subject to different carbon policies. The main contribution is the development of a comprehensive framework that combines economic efficiency, environmental sustainability, and customer satisfaction within a single mixed-integer nonlinear programming (MINLP) model. This framework incorporates a carbon cap mechanism in Region A and a carbon tax mechanism in Region B into the network structure. The proposed approach integrates cost, emission reduction, and service performance across heterogeneous regulatory zones. The model is implemented in GAMS and solved using an exact solution method. Numerical experiments with various problem sizes validate the model's applicability and robustness. Additionally, a real-world case study demonstrates the practical applicability and managerial relevance of the proposed model in a supply chain (SC) context. The results highlight important trade-offs among cost, carbon emissions, and service level. Quantity-based (cap) and price-based (tax) carbon controls influence network decisions differently. Overall, the model supports decision-makers in achieving sustainability goals without compromising profitability. This study provides practical managerial insights for designing environmentally responsible and customer-oriented SCs operating under dual‑carbon regulatory regimes.
The UK seafood supply chain's heavy reliance on international trade, with 81% of consumption imported and 70% of production exported, creates vulnerability to global disruptions. While COVID-19 impacts on seafood production are well-documented, the processing sector remains under-researched despite its vital role connecting producers to consumers. This paper examines how UK seafood processing businesses responded to COVID-19 and Brexit disruptions, focusing on flexibility, diversity, and connectivity as key characteristics of resilience. We conduct semi-structured interviews with shellfish companies, fish processors, mixed seafood processors, and industry experts across the UK. We find that during the crisis, UK processors demonstrated flexibility through proactive management, financial stability, and rapid business model pivoting. Businesses with diversified products, customers, labour force, and suppliers showed greater resilience. Local social capital and digital connectivity also strengthened responses, whilst severed international connections particularly challenged export-dependent shellfish processors. To build adaptive capacity, we recommend strengthening industry forums to support geographically isolated processors, developing UK processing capacity to reduce dependence on overseas supply chains, and expanding digital infrastructure for direct-to-consumer sales to counteract future disruptions from labour shortages, consolidation in aquaculture, and volatile export markets. This paper makes a theoretical contribution by further refining conceptualisations of resilience in the context of seafood systems and by characterising three firm-level antecedents for adaptations. It also highlights the relational nature of resilience noting that resilience for one business may not increase the resilience of the system as a whole.
Manufacturing small and medium-sized enterprises (SMEs) play a vital role in economic development; however, they continue to face substantial sustainability bottlenecks due to limited resources, weak operational capabilities, and the lack of an integrated framework that links sustainability with operational excellence. This study addresses this gap by developing a sustainability performance measurement framework that systematically incorporates operational excellence dimensions within sustainability assessment for manufacturing SMEs. A multi-method approach combining fuzzy delphi method (FDM), partial least squares structural equation modeling (PLS-SEM), and fuzzy full consistency method (fuzzy FUCOM) was employed to identify, validate, and prioritize critical key performance indicators (KPIs). Initially, 43 KPIs were screened using FDM, resulting in 22 critical indicators across operational excellence, economic, environmental, and social sustainability dimensions. The PLS-SEM results reveal that cost efficiency, equipment effectiveness, human resource management, and quality significantly enhance sustainability performance, whereas flexibility and responsiveness demonstrate insignificant effects. Subsequently, fuzzy FUCOM was applied to determine KPI weights and develop an integrated sustainability performance index framework. The findings indicate that economic sustainability and operational excellence are the most influential dimensions for manufacturing SMEs, while profitability, quality, cost efficiency, reliable customer relationships, and return on investment emerge as the highest-priority KPIs. Moreover, the framework was further validated through sensitivity analysis and case studies involving five manufacturing SMEs, demonstrating its practical applicability in benchmarking sustainability performance and identifying improvement priorities. The study contributes new knowledge by proposing a quantitatively validated and operationally integrated sustainability assessment framework tailored specifically for resource-constrained manufacturing SMEs in developing economies.
The effectiveness of policies in smallholder agri-food supply chains is crucial for guaranteeing sustainability. However, there is a research gap in empirically evaluating the effectiveness of policies in this sector. This study proposes a fuzzy-logic-based risk assessment framework to evaluate policies. A fuzzy inference system (FIS) based risk assessment framework is developed to analyse policy performance under inherent uncertainty and complexity. The framework evaluates the risk of policy failure by modelling the relationship between the probability of failure and its consequences using fuzzy inference mechanisms. To our knowledge, this is the first FIS framework designed expressly for assessing policy interventions aimed at smallholder farmers. It enables more effective and data-driven decisions that directly support poverty reduction (SDG1) and improved food security and sustainable agriculture (SDG2) by identifying and mitigating high-risk policy failures. Applying this framework to the case study revealed that value chain development initiatives (PS10) pose the highest risk of policy failure. This framework will be particularly useful for policymakers who may not be familiar with risk assessment, assisting them to better understand and managing the complexities inherent in policy implementation.
The accelerating transition to sustainable systems has intensified demand for platinum, a critical material in automotive catalytic converters. Platinum's strategic importance is underscored by its limited global supply, geographical concentration, and the technical challenges associated with its extraction and recovery. While recycling is essential to mitigating supply risks, significant inefficiencies persist, such as theft of catalytic converters at their end-of-life, converters exported through informal channels, or their loss due to improper recycling practices. Those inefficiencies result in substantial material waste and weaken supply chain resilience. This research introduces a blockchain-enabled framework designed to transform the traceability and recyclability of platinum across the catalytic converter supply chain. By creating an immutable, transparent, and secure digital record of material flows, the framework is intended to support greater accountability and help reduce risks of theft and loss. A central part of the solution is the deployment of digital passports that assign verifiable digital identities to Platinum containers and catalytic converters, thereby supporting ownership verification and lifecycle tracking. The proposed system offers a novel integration of blockchain technology and digital passports to address an important challenge in critical raw material management. Beyond supporting improved Platinum recovery, this approach has the potential to strengthen supply chain integrity, advance circular economy objectives, and contribute to more sustainable and resilient automotive systems.
The rapid adoption of electric vehicle (EV) has intensified the challenge of managing End-of-Life (EoL) components, particularly the batteries and motors of the EV, which contain both valuable resources and environmentally hazardous materials. This review synthesises emerging tools for the autonomous triaging of the EoL EV components. We critically examine the disconnect between established diagnostic methods (fault diagnosis / condition assessment) and Circular Economy (CE) decision-making. Key findings indicate that while fault detection accuracy is high (over 90% for data-driven methods), the industry lacks standardised thresholds to map these diagnostics to specific CE pathways. To address this, we propose a unified ‘Diagnostic-to-CE Decision Framework’ and a novel autonomy taxonomy to guide the transition from manual inspection to fully automated triaging systems.
Industry 5.0 (I5.0) emerged in response to the limitations of Industry 4.0. It emphasizes sustainability, resilience, and a human-centric approach. This transition is particularly relevant in agri-food supply chains (AGSCs) due to challenges related to food security, climate disruptions, and social inclusion. This study presents a systematic literature network analysis (SLNA) in accordance with the PRISMA protocol. A total of 100 peer-reviewed studies published after 2020 were analyzed using Bibliometrix, VOSviewer, BERTopic-LSA, to map research trends and identify implementation barriers. The results reveal that existing literature predominantly focuses on sustainability and resilience, typically operationalized through digital technologies. However, the human-centric pillar is understudied, with limited empirical validation and an absence of standardized metrics across AGSCs contexts. Multi-Attribute Decision-Making (MADM) approaches—particularly Analytic Hierarchy Process (AHP), TOPSIS, Best-Worst Method (BWM), and fuzzy models—emerge as the most effective analytical tools. However, significant barriers persist: (i) prohibitive implementation costs for SMEs, (ii) lack of standardized resilience metrics, (iii) insufficient empirical validation of theoretical frameworks, and (iv) weak governance and cultural misalignment among stakeholders. The review also highlights fragmented approaches that address the I5.0 pillars separately, which limits the development of integrated methodologies. These findings underscore the need for multi-level frameworks that connect technological, organizational, and social enablers to create cohesive and adaptive AGSCs. This study contributes to the body of knowledge on I5.0 by offering policymakers, researchers, and industry leaders an evidence-based synthesis and a structured research agenda to foster innovation, sustainability, and human-centered value creation in agri-food systems.
Food loss and waste remain the most pressing global sustainability challenges with enormous environmental, social, and economic consequences. Addressing this issue requires not only technological innovation but also a deeper understanding of how innovations diffuse and interact across supply chains. This study develops a multi-agent diffusion simulation model, calibrated with empirical data and stakeholder inputs, to assess three innovations across different product chains from 2025 to 2050. Innovations such as smart packaging for meat, AI-driven demand forecasting for fish, and AI-based quality for fruit, enabling the quantification of waste, costs, and greenhouse gas emissions under varying adoption pathways.The results show that innovation's impacts are powerfully context dependent. Smart packaging in the meat chain brings steady waste reductions of 10–15% in 2050, primarily by extending shelf life and reducing expiries. AI demand forecasting for fish achieves the most transformative outcomes, reducing waste by up to 27% together with marked decreases in costs and emissions. AI quality recognition for fruit offers smaller yet valuable gains, shifting consumer acceptance and improving retailer sell-through imperfect produce. Faster and broader adoption leads to larger benefits, highlighting the need for supportive policies, incentives, and consumer engagement to improve diffusion.By integrating diffusion theory with supply chain simulation, this study offers methodological and applied insights, serving as a decision-support tool for stakeholders to strategically deploy innovations and inform policy design. It contributes to the evidence required to achieve the EU's 2030 food waste reduction targets and promote sustainable food systems.
The forthcoming Ecodesign for Sustainable Products Regulation (ESPR) requires improved lifecycle data availability to support Product Carbon Footprint (PCF) and Corporate Carbon Footprint (CCF) calculations. Digital Product Passports (DPPs) are mandatory for structuring and sharing sustainability-relevant product information across value chains in selected domains, such as batteries. However, DPP integration in intralogistics remains underexplored, especially for Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). At the same time, interoperability standards such as the Asset Administration Shell (AAS) and existing AGV/AMR data models predominantly address operational information and provide limited coverage of product-management and ecological data required by DPPs. This study draws on 15 semi-structured expert interviews across the intralogistics ecosystem (manufacturers, software providers, integrators, suppliers, associations, and operators) to investigate challenges and perceived benefits of integrating DPPs into AGV/AMR-based intralogistics systems. The findings identify three interrelated challenges: inconsistent standardisation across interfaces and semantics, heterogeneous IT security requirements, and fragmented lifecycle information bases, particularly in brownfield environments. DPP value is primarily associated with operational improvements and lifecycle-extension use cases, including maintenance planning, predictive services, retrofit decision support, fleet-level transparency, and data-enabled pay-per-use models. Building on these insights, we propose a conceptual framework that integrates DPP management on a data platform with AAS-aligned semantic linking across component, vehicle, and fleet levels to enable scalable, inter-organisational data sharing beyond compliance.
This study explores various vehicle configurations and waste classification strategies to address the Capacitated Vehicle Routing Problem with Time Constraints (CVRPTC) in waste management. By integrating practical factors such as labor costs and working hours, and utilizing simulated residential and GIS data, the research employs cluster balancing and simulated annealing (CB-SA) algorithms to identify cost-effective solutions. The findings highlight the importance of selecting appropriate vehicle types and routing strategies tailored to specific operational demands. In addition, the study underscores the impact of operational parameters on overall costs and vehicle requirements. These insights provide valuable guidance for waste management companies aiming to enhance efficiency and sustainability in their collection operations through advanced optimization techniques and strategic vehicle selection. From a managerial perspective, the results advocate for region-specific vehicle and routing strategies to optimize resource utilization and cost management. Furthermore, the study emphasizes the necessity of continuously monitoring operational parameters to maintain cost-effectiveness and adapt to evolving waste management needs.
The European Union’s (EUs) Critical Raw Materials Act (CRMA) aims to strengthen the EUs resource resilience by increasing the autonomy of Critical Raw Material (CRM) supply through EU-based extraction, processing, and recycling, thereby reducing external dependencies and promoting a circular economy. Copper, a key CRM, faces growing demand that cannot be fulfilled by mining alone. This research analyzes the European copper supply chain under the CRMA and evaluates the role of recycling in meeting its requirements. An initial qualitative analysis suggests that recycling is the most promising solution to comply with the CRMA. The study develops a Mixed Integer Linear Programming (MILP) model to optimize the European copper recycling network under the CRMA recycling requirement and to validate the potential of recycling in a quantitative way. Results identify four optimal facility locations spread through Europe based on geographic centrality weighted by supply and demand quantities. Although the collection of European waste can feasibly satisfy the minimum recycling requirements as set out by the CRMA, changing regulations, disruptions, or changes in demand and external supply may lead to shortages. Improving recycling efficiency has therefore been marked as an important direction of future research.
