Jul 2026· Aposta: Revista de Ciencias Sociales· Vol 24, pp. e1353· 0 citations· 34 references
Abstract
This study explored the relationship between Artificial Intelligence (AI) and the performance of Supply Chain (SC) in logistics firms within the United Kingdom. The aim was to quantify the impact of AI technologies on the environmental, economic and social sustainability goals of the logistics sector. The quantitative cross sectional survey design was used, involving 400 respondents who were measured with a structured questionnaire on four-point Likert scale. Descriptive statistics, Pearson Product Moment Correlation (PPMC) and linear regression analysis were used for data analysis. Results showed that the level of AI adoption was high (grand mean = 3.37) and the sustainable supply chain performance was also high (grand mean = 3.33). Correlation analysis revealed that adoption of AI had significant positive correlations with environmental sustainability (r = 0.71, p < 0.05), economic sustainability (r = 0.76, p < 0.05) and social sustainability (r = 0.69, p < 0.05). The results from linear regression also showed that AI adoption significantly predicted the sustainable supply chain performance (R² = 0.61, β = 0.78, F = 374.62, p<0.05). The research suggests that AI is playing a pivotal role in boosting efficiency, sustainability, and social impact within logistics operations. It finds that AI is the key to transforming the logistics sector toward sustainability in the UK. The study calls for greater investment in AI technologies, training workforce digitally and supportive government policies to improve sustainability results.
The growing demand for sustainability in manufacturing and the inefficiencies of traditional methods in the supply chain highlight the importance of finding smarter solutions. Even with the growing availability of digital tools and AI technologies, organizations are yet to fully utilize them to aid sustainability efforts, causing inefficient use of resources, waste, and environmental damage. The chapter examined AI supply chains optimizing for sustainability: evidence from Nigeria manufacturing industry. The chapter utilized a survey research design with a population of 950 supply chain employees in Nigerian Breweries, Lagos State, with an estimated sample size of 281 using the Yamane (1967) formula. Data was collected using a structured questionnaire with a five-point Likert scale. Descriptive statistics and multiple regression analysis were used to analyze the data in SPSS version 27. The findings showed that AI-driven demand forecasting (coefficient = 4.287) and AI-based inventory optimization (coefficient = 3.549) have a positive significant on sustainability, with 56.7% of the variance in sustainability outcomes. The chapter concluded that AI-driven supply chain optimization plays a significant role in minimizing waste, optimizing resources, and meeting market demand. The study recommended that Nigerian Breweries should utilize AI-powered demand forecasting tools for production planning and should implement AI-based inventory optimization systems to ensure that inventory levels are optimized and reducing excess stock.
Dolapo Stephen Akinwumi, A. Salau· Al-Zaytoonah University Jour...· 0 citations
This research examines the combination of artificial intelligence (AI) and Blockchain (BC), two transformative technologies of Industry 4.0, with the potential to reshape the agri-food sector. It highlights their role in enhancing supply network resilience and promoting sustainability in the agri-food industry, particularly in emerging countries such as Peru. This study is grounded in the Technology–Organization–Environment framework and organizational information processing theory as its theoretical foundation. The best–worst method and the Decision-Making Trial and Evaluation Laboratory were employed to rank the identified technological, organizational, and environmental drivers and to analyze their causal relationships, followed by a sensitivity analysis to ensure the robustness of results. Data from coffee practitioners in the Peruvian region is used to analyze the combination of AI and BC (Digital Technologies) to improve supply chain resilience, supply chain sustainability at the supply network, and agri-food. In conclusion, the study demonstrates that AI and BC technologies can significantly enhance transparency and traceability in the agri-food supply chain, thereby improving decision-making and operational efficiency. These insights reflect expert perspectives from the Peruvian coffee supply chain and are context-specific.
Peace Aludogbu, Melissa Chávez, Pradeep Kumar Tarei et al.· IEEE transactions on enginee...· 0 citations
This study examines the role of Artificial Intelligence (AI) in enhancing supply chain project management and operational performance in a dynamic business environment. As supply chains become increasingly complex, data-intensive, and disruption-prone, organizations are adopting AI-driven tools to improve forecasting accuracy, optimize inventory, streamline logistics, and strengthen decision-making. The purpose of this research is to assess the level of AI adoption, identify key application areas, and examine the relationship between AI familiarity and AI adoption while considering the broader roles of organizational readiness and governance mechanisms. A quantitative research design was employed using a structured questionnaire administered to 42 respondents, including supply chain professionals, project managers, data/AI analysts, students, and other business or technology-related participants. Data were analyzed using descriptive statistics, correlation analysis, and regression techniques. The findings indicate that approximately 57% of respondents reported current AI adoption within their organizations, while the mean AI familiarity score was 3.6 on a five-point scale, reflecting moderate awareness. Correlation analysis revealed a positive relationship between AI familiarity and AI adoption (r = 0.61), suggesting that increased knowledge supports adoption behavior. The results also highlight the perceived importance of AI training, organizational preparedness, and governance frameworks in maximizing implementation benefits. This study contributes to business analytics, operations management, and decision sciences by providing empirical insight into AI-enabled supply chain transformation. The findings offer practical implications for managers, policymakers, and industry stakeholders seeking to strengthen AI readiness, improve operational efficiency, and promote responsible AI adoption for sustainable supply chain excellence.
Denise Nalini, Dr. S.Barathi, Dr. Rubidhadevi· The Journal of Theoretical A...· 0 citations
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.
L. Fonseca, Luca Esposito, T. Murino et al.· Management & Marketing· 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
Sustainability has become a mission critical goal for organizations, especially in the context of supply chains where data on the environment are important in the management of carbon emissions, resources and regulatory compliance. While more and more companies have access to environmental information and more and more ESG reporting frameworks, it's hard for many companies to translate sustainability information into concrete actions. This gap indicates that other than data availability, other factors might influence decision making towards sustainable development. This research aims at understanding how cognitive biases, organizational pressure and the problem of information overload affect sustainable decision-making of supply chain practitioners. The study has been targeted towards employees from Malaysia's Manufacturing & Logistics industry. Data will be gathered from 384 respondents, who have relevant experience in SC decision making and have access to environmental information, using purposive sampling. This research aims to determine if there is a gap between the maximum possible and actual use of environmental information for supply chain decision-making purposes due to other limitations such as psychological and organizational factors. The findings make an impact on the sustainability and supply chain management literature as they provide insights into the obstacles facing firms to translate environmental knowledge into sustainable actions. The findings could help organisations and policymakers create more robust decision-support systems and sustainability initiatives to improve performance in the long run of their supply chains.
Nurhanan Syafiah Abdul Razak, W. A. Madhoun, Tasya Aspiranti· Veredas do Direito· 0 citations