Artificial intelligence and Big Data in the optimization of international supply chains: towards predictive and sustainable logistics

Abstract

This study examines the implementation of Artificial Intelligence (AI) and Big Data in the optimization of international supply chains in Ecuador and neighboring Andean countries, with the aim of developing a predictive and sustainable logistics model. The methodology employed is mixed, combining qualitative and quantitative analysis, including a systematic literature review. Direct observations were conducted at key points in the supply chains, and focus groups were organized with various stakeholders. The data was analyzed using quali-quantitative analysis techniques to identify key patterns and concepts. The main findings reveal an uneven adoption of AI and Big Data, with a clear gap between large multinational companies and local SMEs. Companies that successfully implemented these technologies experienced significant improvements in operational efficiency, demand forecasting accuracy, and environmental sustainability. Critical challenges were identified, such as the lack of adequate data infrastructure and the shortage of qualified personnel. Regional collaboration and proactive innovation policies proved crucial in accelerating technology adoption. The study concludes that the implementation of AI and Big Data has transformative potential for supply chains in the Andean region, but requires a comprehensive approach that addresses technical, organizational, and public policy aspects. The study underscores the need to develop specific strategies to support SMEs and foster cross-border collaboration. Furthermore, future research is suggested to explore the long-term impact of these technologies and their ethical and social implications in the context of emerging economies.