Jul 2026· Al-Zaytoonah University Journal of Business· Vol 2, pp. 22-31· 0 citations· 27 references
Abstract
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.
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.
Odeh Odeson Osaiyekemwen, F. Bekun, S. C. Cavdar et al.· Aposta: Revista de Ciencias...· 0 citations
The purpose of this study was to examine the influence of green supply chain management on organizational sustainability, where technology innovation and environmental performance are taken as mediating factors. The focus of this study was on the increasing need for better protection of the environment and the increasing demands of the sustainable development of industries, especially in developing countries, where there are limited empirical data available on sustainability activities. The collected sample comprised 387 managers and technologists from Libyan manufacturing companies using a survey instrument and a stratified random sampling technique. Validity scales were used, and the measurements were done using the five-point Likert scale. The data analysis involved the use of PLS-SEM, where SmartPLS 4.0 was used. It entailed the analysis of the measurement model as well as the structural model, with the bootstrapping being done for 5000 samples. The findings show that GSCM significantly affects the OS. Specifically, it has a positive impact on environmental performance (EP) and technological innovation (TI), which, in turn, enhance the OS. Among the mediating factors, the EP was found to be the strongest predictor and mediator of the OS, followed by TI. The developed model proved to have a high explanatory power, with a coefficient of determination (R2) of 0.660. Thus, the integration of GSCM with innovation and environmental activities can lead to the successful achievement of sustainability objectives among manufacturing enterprises. The study’s limitations are associated with its cross-sectional design and its focus on managers. Future research could employ longitudinal designs and examine other contexts, such as digital transformation, the institutional environment, and the organizational culture.
Mohamed Alakrod, Sami Mohammad· Sustainability· 0 citations
The apparel industry faces persistent sustainability challenges arising from overproduction, inefficient resource utilization, textile waste, and increasingly complex global supply chains. Artificial intelligence (AI) has emerged as a promising technology for improving operational decision-making; however, evidence of its contribution to sustainability remains fragmented across research on forecasting, production, waste management, and the circular economy. This narrative review synthesizes current knowledge on AI-enabled optimization, waste reduction, and circular economy practices within apparel supply chains. Drawing on recent literature, the review examines AI applications in demand forecasting, production planning, supplier selection, logistics, risk management, and explainable decision support, and evaluates their role in reducing textile waste and supporting circular strategies such as product life extension, traceability, reverse logistics, and material recovery. The review finds that AI contributes to sustainability primarily by improving the quality of operational decisions rather than through automation alone. Although AI can enhance resource efficiency, reduce waste, and strengthen circular supply chains, these benefits depend on effective governance, high-quality data, organizational capability, and integration with circular economy practices. Based on this synthesis, an integrated conceptual framework is proposed that positions AI as a decision-support capability linking operational decisions with sustainability outcomes through continuous feedback and governance mechanisms. The review also identifies key research gaps, including the need for industrial validation, apparel-specific datasets, and responsible AI implementation. The findings provide researchers and practitioners with an integrated perspective on how AI can support the transition towards more sustainable and circular apparel supply chains.
Nadira Kulsum Papri· Frontiers in Computer Scienc...· 0 citations
Green supply chain management (GSCM) has become a strategic requirement for industrial firms seeking to reduce environmental harm while improving long-term sustainability performance. Despite growing interest in green procurement, eco-design, green manufacturing, and green distribution, limited empirical evidence explains how these practices are converted into sustainability performance through collaborative environmental action in the Sultanate of Oman. Guided by the Natural Resource-Based View, this study examines the effects of four GSCM practices on environmental collaboration and investigates the mediating role of environmental collaboration in the relationship between GSCM practices and sustainability performance. A quantitative cross-sectional survey was conducted among procurement, manufacturing, logistics, and supply chain professionals working in Omani industries. From 520 questionnaires distributed, 342 valid responses were retained for analysis. The data were analyzed using Smart PLS 4.0 and partial least squares structural equation modelling. The measurement model demonstrated acceptable reliability, convergent validity, and discriminant validity. The structural results show that eco-design, green procurement, green manufacturing, and green distribution have significant positive effects on environmental collaboration. Environmental collaboration, in turn, has a strong positive effect on sustainability performance. The indirect effects also support the mediation of the relationships between the GSCM practices and the sustainability performance, confirming the mediation chain. The results indicated that green practices are not enough; they have to be integrated with the collaboration of suppliers, customers, logistics and other functions within the company to make green practices produce results in terms of performance. The study adds to GSCM and sustainability literature by providing a relational mechanism that determines the contribution of green practices to the industrial sustainability of the Oman context.
Shahzad Ahmad Khan, D. Muniyanayaka, Khalifa Al Adabi et al.· Journal of Intelligent Decis...· 0 citations
This study evaluated the impact of descriptive data analytics on the supply chain performance of agritech companies in Kenya. The study was anchored on the Resource Dependence Theory and adopted a descriptive research design. The target population comprised 315 supply chain and information technology officers, from whom a sample of 172 respondents was selected using Yamane's (1967) formula. Primary data were collected using structured questionnaires and analyzed using correlation and linear regression techniques. The findings revealed that descriptive data analytics had a positive and statistically significant effect on fresh food supply chain performance. The study concludes that descriptive data analytics enhances supply chain performance by improving visibility, monitoring, and evidence-based decision-making within agritech firms. The study recommends that agritech companies invest in integrated data management systems, strengthen data quality practices, and build employees' analytical capabilities to maximize the benefits of descriptive analytics and improve overall supply chain performance. JEL: M11, L23, L14, R41
Omuyoyi Maureen Matanga, Nurwin Fozia, Denis Ouma· European Journal of Economic...· 0 citations
This research is motivated by the increasing demand for carbon emission transparency, particularly Scope 3 emissions, which reflect the environmental impacts of a company's entire supply chain, but are still not optimally disclosed in Indonesia. This study aims to analyze the effect of Scope 3 Emission Disclosure on Sustainability Performance, with Sustainable Supply Chain Performance as a mediator. This study uses a quantitative approach with an explanatory research type. Data were obtained from annual reports and sustainability reports of companies listed on the Indonesia Stock Exchange for the 2021–2024 period using a purposive sampling technique, resulting in 55 companies or 220 observations (firm-year). Data analysis was performed using the Partial Least Squares-based Structural Equation Modeling (SEM-PLS) method. The results show that Scope 3 Emission Disclosure has a positive and significant effect on Sustainable Supply Chain Performance and Sustainability Performance. In addition, Sustainable Supply Chain Performance also has a positive effect on Sustainability Performance and is able to mediate the relationship between Scope 3 Emission Disclosure and Sustainability Performance. These findings suggest that carbon emissions disclosure will be more effective in improving sustainability performance if accompanied by the implementation of sustainable supply chain practices. This research contributes to the development of supply chain-based green accounting and offers practical implications for companies in improving the quality of sustainability reporting and management