Enhancing Operational Efficiency in the Supply Chain: A Systematic Review on Artificial Intelligence Applications

Cochachin-Maguiña M.I.; Lopez-Santacruz J.A.; Coaquira-Torres E.; Gómez-Montoya J.P.; Rojas-Rimbaldo J.M. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/laccei2025.1.1.1410

Resumen

Currently, supply chains have become increasingly complex due to globalization, which has driven companies to seek innovative alternatives to maintain their competitive advantage. In this context, artificial intelligence (AI) has emerged as a fundamental resource for refining logistical processes and increasing operational efficiency. The main purpose of the Systematic Literature Review (SLR) is to examine how the implementation of AI contributes to improving operational efficiency in the supply chains of industrial companies. To carry out this analysis, the PIOC methodology (Problem, Intervention, Outcomes, Context) was used, which facilitated the formulation of precise research questions and guided the selection of relevant studies in academic databases such as Scopus. The analysis of twenty scientific publications showed that the use of AI in demand forecasting and inventory management represents the most relevant applications, contributing 25% and 20% to overall performance, respectively. In addition, route optimization and predictive maintenance also demonstrated a considerable effect, with 15%. The results suggest that adopting AI not only increases efficiency by reducing costs and downtime, but also helps companies respond quickly to environmental changes. This study expands knowledge about the positive impact of AI on the supply chain, highlighting its importance in the current digital era.

Instituciones: Universidad Tecnológica del Perú