Analysis of Artificial Intelligence on Warehouse Management in the Peruvian Pharmaceutical Sector: A Quantitative Study

Abstract

This study analyzes the impact of artificial intelligence (AI) in optimizing warehouse management in the Peruvian pharmaceutical sector. A quantitative, non-experimental, cross-sectional design was used, with a sample of 80 companies in the sector. Statistical analyzes were applied to evaluate the relationship between AI and logistics efficiency. The results indicate that AI significantly improves cost reduction and time optimization in the supply chain. Three key areas were identified: process automation, systems integration and data analysis. The strongest influence was found in data analysis, with a 44.6% impact on cost reduction and 46.6% on time optimization. Automation contributed 31% to cost reduction, while systems integration improved time by 26%. Additionally, it was observed that companies that implemented AI achieved a 20% reduction in frozen stock and an 18% decrease in delivery delays. The correlation between time optimization and cost reduction was high (85.8%), showing that the use of AI generates comprehensive improvements in logistics. It is concluded that AI is a key tool to improve the efficiency and competitiveness of the pharmaceutical sector, facilitating data-based decision making and optimizing warehouse management.