Artificial intelligence-based proposal to optimize logistics processes in the commercial sector in northern Peru (#480)

Authors

Balarezo Zelada, Andrew Gabriel

Granda Pretel, Fatima Alexandra

Escurra Lagos, Jean Carlos

Abstract

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. Results indicate that the system significantly improves operational efficiency, reduces logistical errors, and supports 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.