Analysis on the Implementation of Predictive, Cognitive and Generative AI in the Operational Efficiency of the Supply Chain in Cortés

Abstract

This study examines the implementation of predictive, cognitive, and generative artificial intelligence associated with operational efficiency within the supply chain. It aims to analyze the need to overcome the limitations of current systems that are not effectively adapted to the highly changeable logistics environment through systems based on artificial intelligence. A mixed approach combining quantitative and qualitative methods is used. The design is non-experimental and correlational, with the aim of identifying the magnitude of their relationship in a real-world context. The unit of analysis consists of companies from various sectors, while the unit of response includes professionals from different fields, technology managers, and supply chain managers through interviews as instruments. The techniques used include document analysis and correlation analysis to determine the strength of the relationship between variables, and interviews that allowed for contextualizing the interpretation of results. Findings indicate that cognitive and predictive artificial intelligence enable greater adaptability, more efficient data interpretation, and significant improvement in decision-making processes. However, the study recognizes the low level of adoption of these technologies in Honduras, as well as their economic limitations, as a constraint. In conclusion, the integration of advanced artificial intelligence technologies has a positive and significant impact on the operational efficiency of the supply chain.