Artificial intelligence-based proposal to optimize logistics processes in the commercial sector in northern Peru

Andrew Gabriel B.Z.; Fatima Alexandra G.P.; Jean Carlos E.-L. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/leird2025.1.1.480

Resumen

This article presents the design of a prototype system based on artificial intelligence aimed at optimizing logistics processes in the commercial sector of Trujillo, Peru. The proposed architecture includes predictive modules and a conversational interface (chatbot) to address common issues such as stockouts, overstocking, and empirical decision-making. The system conceptually applies mathematical models for demand forecasting and automated recommendations. Key data sources were identified, performance metrics were defined, and a cost structure feasible for small and medium-sized enterprises was estimated. The results show that the prototype system could be a solution that contributes to significantly improving operational efficiency, reducing logistical errors, and facilitating data-driven decision-making. The conclusions confirm the applicability of the system in regional digital transformation efforts, demonstrating its potential to enhance commercial competitiveness through accessible, scalable, and context-aware technological solutions. © LEIRD 2025.All rights reserved.

Instituciones: Universidad Privada del Norte