Optimizing Retail Pharmacy Inventory via Descriptive and Predictive Analysis

Abstract

Effective inventory management is crucial for retail pharmacies to balance supply and demand while minimizing costs. This study proposes an integrated approach combining predictive and descriptive data mining to optimize inventory decisions using sales data from pharmacies in Western Province, Sri Lanka. For predictive analysis, Seasonal Auto-Regressive Moving Average with exogenous variables (SARIMAX) was initially employed but showed limitations in capturing nonlinear trends. Thus, Recurrent Neural Networks (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), were implemented, achieving superior accuracy (lowest RMSE: 0.16 for GRU, 0.34 for LSTM). Hybrid models (RNN LSTM + SARIMAX) and ensemble techniques (Bagging, XGBoost) further enhanced performance. For descriptive analysis, K-Medians clustering categorized drugs into 7 clusters based on dosage, price, and weekly sales, revealing key patterns: Cluster 4 contained widely sold essential medications, Cluster 3 comprised high-cost, low-volume specialty drugs, while Cluster 6 represented niche medications with unique demand patterns. The FP-Growth algorithm identified strong purchase associations (support >0.8) between commonly co-prescribed medications. Results demonstrated that RNN-based models outperformed traditional approaches (reducing RMSE by up to 94% versus SARIMAX), while clustering and association rules provided actionable insights for inventory segmentation. This dual analytical framework offers a data-driven solution for inventory management.