Medication Control System Based on Data Analytics for Decision-Making in Community Pharmacies

Abstract

Medication management in pharmaceutical establishments faces significant challenges arising from the absence of automated systems capable of ensuring efficient inventory control and compliance with proper storage conditions, particularly regarding product expiration and demand variability. This study analyzes the implementation of a data analytics-based medication control system, using a community pharmacy in La Maná, Ecuador, as a case study. A mixed-methods approach was applied, combining quantitative data from structured surveys administered to 100 frequent customers and qualitative data from staff interviews, alongside the analysis of operational records. This methodology enabled the evaluation of current inventory practices, identification of consumption patterns, and detection of structural process inefficiencies. Results show that 68% of customers visit the pharmacy weekly or biweekly, 42% reported having received near-expiry medications at least once, and all staff members expressed the need for automated management tools. Based on this diagnosis, an information system integrating data analytics tools was designed and implemented, incorporating six functional modules: medication registration, inventory control, expiry and low-stock alerts, predictive consumption analysis (ARIMA model), exportable reporting, and role-based access control (RBAC). The system reduced dependence on manual processes, automated inventory monitoring, and strengthened data-driven decision-making through Business Intelligence tools. These results contribute to safer medication dispensing and improved service quality in community pharmacy settings, demonstrating that accessible technological solutions can generate significant operational impact in low-digitalization contexts.