Pharmaceutical Inventory Optimization Using Predictive Machine Learning and Business Intelligence Techniques

Chirinos Gonzales H.C.; Vereau Jacobo E.W. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/leird2025.1.1.438

Resumen

Inadequate management of pharmaceutical inventories generates stockouts in 69% of establishments and economic losses of 3.5-8.3% of annual revenue, affecting the continuity of medical treatments. This systematic review analyzes the effectiveness of Machine Learning and Business Intelligence techniques to optimize demand forecasting in small and medium-sized pharmacies with limited technology. PRISMA methodology was implemented to identify relevant studies in Scopus between 2020-2025, using structured PICOC criteria. From 1,327 initial records, 40 studies meeting specific inclusion criteria for pharmaceutical predictive analysis were selected. Data were extracted through a standardized protocol, evaluating predictive accuracy, operational impact, and implementation barriers. Results demonstrate a 30.5% improvement in predictive accuracy compared to traditional methods. Neural networks showed the highest effectiveness with 85.3% accuracy, followed by ensemble models with 83.7%. A 28.5% reduction in stockouts and 25.7% inventory optimization were achieved. Economic analysis reveals viability with investments of $2,500-45,000 USD and ROI of 16-24 months. Main barriers include technical training limitations in 58% of staff and inadequate infrastructure in 43% of establishments. The techniques represent scalable solutions that foster intrapreneurship, being viable in resource-limited contexts, contributing to economic sustainability and public health improvement. © LEIRD 2025.All rights reserved.

Instituciones: Universidad Tecnológica del Perú