The findings indicate that machine learning is the dominant AI technology in SME supply chains, primarily used for forecasting, inventory management, process monitoring, logistics optimization, anomaly detection, and operational decision support, and economic and environmental sustainability dimensions receive substantially greater attention than social sustainability.
Abstract
The growing convergence of sustainability pressures, digital transformation, and supply chain complexity lead to a rising interest in how artificial intelligence (AI) can support sustainable supply chain management, particularly in small and medium-sized enterprises (SMEs). Despite the expanding literature on AI and sustainability, important gaps remain regarding how AI-enabled technologies contribute to economic, environmental, and social performance under the resource constraints typically faced by SMEs. Existing studies frequently address AI adoption through broad discussions of digital transformation, forecasting, or automation, while providing comparatively limited integration of sustainability and SME-specific organizational conditions. This research investigates the application of AI technologies in SME supply chains and their contribution to sustainability performance. The research adopts a systematic and bibliometric literature review approach based on the PRISMA methodology. Peer-reviewed journal articles indexed in Scopus and published between 2022 and 2026 were analyzed. Following a structured screening and eligibility assessment process, 49 articles were included in the final sample. The selected studies were examined through descriptive profiling, thematic coding, and bibliometric analyses covering AI technologies, sustainability dimensions, supply chain applications, implementation barriers, and emerging research trends. The findings indicate that machine learning is the dominant AI technology in SME supply chains, primarily used for forecasting, inventory management, process monitoring, logistics optimization, anomaly detection, and operational decision support. Natural language processing, large language models, and computer vision appear less frequently but are increasingly relevant for communication, information management, and intelligent operational analysis. The review also shows that economic and environmental sustainability dimensions receive substantially greater attention than social sustainability. Recurring barriers to AI adoption include financial constraints, weak digital infrastructures, fragmented data environments, limited analytical capabilities, and organizational readiness challenges. At the same time, managerial commitment, strategic alignment, technological partnerships, and policy support emerge as important enabling factors. This investigation contributes to the literature by integrating research on AI, sustainability, and SME supply chains through a combined systematic and bibliometric perspective. The findings highlight that the sustainability potential of AI depends not only on technological capabilities but also on organizational conditions, data governance, and SMEs’ ability to integrate AI-enabled decision support into operational processes. The study also identifies important theoretical, managerial, and methodological gaps and proposes directions for future research on AI-enabled sustainability in SME supply chains.
Artificial intelligence (AI) is increasingly embedded in sustainable supply chains through traceability systems, supplier scoring, risk analytics, demand forecasting, compliance monitoring and procurement platforms. These tools can improve transparency, reduce waste and strengthen resilience, but they can also convert sustainability into a data-intensive gatekeeping regime that disadvantages small and medium-sized enterprises (SMEs). This scoping review maps peer-reviewed evidence published between February 2021 and January 2026 on AI-enabled sustainable supply chain governance, with attention to SME participation and African or comparable emerging-market relevance. A PRISMA-aligned search identified 892 records, screened 624 titles and abstracts, assessed 186 full texts and retained 60 DOI-bearing journal studies. The synthesis finds that AI is most often framed as a performance and resilience tool, whereas governance questions of proportional evidence, explainability, supplier appeal rights and tiered compliance remain underdeveloped. The review proposes an inclusion-oriented framework linking digital infrastructure, algorithmic mechanisms and governance safeguards to sustainability and SME participation outcomes.
Ismail Sheik, J. Dubihlela, B. Chummun· International Journal of App...· 0 citations
The research is a systematic literature review of the use of artificial intelligence (AI) in sustainable supply chain management (SSCM) and comes up with a research agenda. Within a systematic literature review methodology that follows PRISMA guidelines, the review synthesizes the latest peer-reviewed works that were published in 2020-2026. The results demonstrate a rapid increase in the adoption of AI in SSCM over the last several years, as machine learning, predictive analytics, natural language processing, and automation have become commonly discussed topics. The literature is primarily centered around demand forecasting, optimization of logistics, inventory planning, resilience, and traceability. It is demonstrated that AI assists in maintaining the economy in terms of efficiency and cost reduction, the environment in terms of waste and emissions, as well as, to a lesser degree, social sustainability in terms of transparency and compliance. But significant impediments are also reported in the review, such as data quality, capability gap, complexity in integrating, high cost, and governance. On the whole, the research paper has concluded that AI has high potential to revolutionize SSCM, yet more empirical, theoretical, and socially oriented studies are required to improve the situation.
Muhammad Saleem· UCP Journal of Business Pers...· 0 citations
Artificial intelligence (AI) is increasingly recognised as an enabler of sustainability in industrial systems, yet existing research remains fragmented and strongly oriented towards technical optimisation. This systematic literature review examines how AI-enabled sustainability value creation has been conceptualised through the analysis of 75 peer-reviewed articles published between 2020 and 2025. The findings reveal a rapidly expanding field, with 54% of the reviewed studies published in 2024–2025. However, the evidence remains concentrated at process and plant levels: 69% of studies focus on operational applications, and 72% adopt technical, simulation-based, optimisation-oriented, or model-development approaches. Prediction, optimisation, monitoring, adaptive control, and decision support emerge as the dominant AI-enabled mechanisms, while social, governance, resilience, and systemic transformation dimensions remain comparatively underexplored. The review further shows that the literature is stronger in documenting operational sustainability outcomes than in explaining how sustainability value becomes organisationally embedded and sustained across industrial systems. In response, this study proposes a mechanism-based framework linking organisational antecedents, AI-enabled mechanisms, operational transformation, sustainability outcomes, and contextual contingencies. The framework conceptualises AI-enabled sustainability value creation as an organisationally embedded, contingent, and multilevel process rather than a direct outcome of technological deployment alone.
D. Martinho, P. Sobreiro, Filipa Martinho et al.· Sustainability· 0 citations
Background: Global logistics generates 16–25% of greenhouse gas emissions, yet small and medium-sized enterprises (SMEs) in developing economies lack the digital infrastructure to measure and improve their sustainability performance. Business intelligence (BI) systems can support data-driven sustainability decisions, but their application in SME logistics remains poorly understood, and no prior review has examined this intersection with a developing-economy focus. Methods: We conducted a systematic literature review following PRISMA 2020 guidelines, searching Scopus, Web of Science, and IEEE Xplore for peer-reviewed articles published between 2015 and April 2026. Two reviewers independently screened 412 records; 67 studies met inclusion criteria. Results: Five thematic clusters emerged: BI tools for logistics, sustainability KPI frameworks (triple bottom line [TBL], environmental–social–governance [ESG], Sustainable Development Goals [SDGs]), ERP integration, SME-specific barriers, and geographic gaps. Of the studies, 92% originate from developed economies; Africa and MENA remain almost entirely absent. No study combines open-source ERP with a validated TBL KPI framework for logistics SMEs. Conclusions: We propose a six-priority research agenda targeting empirical validation in developing economies and open-source BI solutions for SME logistics sustainability.
Amina Meskaoui, Hakim Nasaoui, Rania Rejjaoui et al.· Logistics· 0 citations
Despite growing interest in artificial intelligence (AI) applications in the construction industry, the literature still lacks a consolidated understanding of how AI functions across the full spectrum of construction supply chain processes. Existing studies are dispersed across different technologies, project stages, and application contexts, making it difficult to identify the intellectual structure of this field, the main areas of AI application, and the barriers that continue to constrain practical implementation. To address this gap, this study conducts a systematic review of AI applications in construction supply chains by combining scientometric analysis with qualitative content synthesis. A total of 212 journal articles retrieved from Scopus were analyzed using VOSviewer-based scientometric analysis and qualitative content synthesis. The scientometric analysis maps annual publication trends, keyword co-occurrence patterns, co-cited sources, influential documents, and collaboration networks. The qualitative synthesis further examines how AI supports construction supply chain management across three broad themes: procurement and production optimization, logistics and material management, and collaborative decision-making for resilience and sustainability. The findings show that AI has been primarily applied to demand forecasting, resource optimization, logistics coordination, contract and document processing, computer vision-based monitoring, and multi-agent decision support. However, its practical diffusion remains constrained by fragmented and low-quality data, limited empirical validation, high implementation costs, algorithmic opacity, cybersecurity risks, and unresolved governance and liability issues. Based on these findings, this study proposes a data-centric and phased research agenda that emphasizes benchmark datasets, human–AI collaboration, lifecycle economic evaluation, explainable AI, and multi-stakeholder governance. The study contributes to the literature by integrating fragmented AI-related research into a structured knowledge map and by clarifying future pathways for developing intelligent, transparent, and resilient construction supply chains.
Qiang Xu, Haitao Chen, Li Xu et al.· Buildings· 0 citations
Growing pressures for environmental responsibility have intensified the need for manufacturing SMEs to pursue sustainability as a core strategic priority, especially as these firms often operate under severe resource constraints. In this context, firms increasingly adopt advanced digital technologies such as big data analytics–artificial intelligence to strengthen their environmental, social, and economic performance. Guided by the Resource‐Based View (RBV), this study contributes to the ongoing discussion on how digital technologies support sustainable development by proposing the mediating roles of green supply chain management and green innovation in the relationship between BDA–AI capability and sustainable performance. The study further examines whether organizational green culture enhances the ability of BDA–AI to foster green capabilities. Using survey data from 388 Chinese manufacturing SMEs and structural equation modeling through SmartPLS, the findings show that BDA–AI significantly improves GSCM, GI, and sustainable performance. Mediation results confirm that GSCM and GI act as essential mechanisms through which BDA–AI generates environmental and operational benefits. However, the moderating influence of OGC is uneven: it significantly strengthens the pathway from BDA–AI to GI, whereas its effect on the BDA–AI to GSCM relationship remains insignificant. These insights highlight that digital adoption alone is insufficient; sustainability gains emerge when technological investment is accompanied by strong green capabilities and supportive cultural values.
Qing Chong, T. Ramayah· Business Strategy and the En...· 0 citations
