A multi-level time-series analysis for forecasting and operational planning in pharmaceutical services: A case study

Background The efficient allocation of resources is a core challenge in health services management. In pharmacy services, accurate forecasting is fundamental to ensuring the availability of essential medicines, managing operational costs, and improving service quality. However, simple forecasts are often insufficient for robust operational planning. This study demonstrates a multi-level analytical framework to provide deeper, more actionable insights for the management of pharmaceutical resources. Methods A retrospective analysis was conducted using a public dataset of daily and weekly pharmaceutical sales from a single pharmacy (2014–2019). We analyzed three drug classes with distinct demand patterns: M01AE (anti-inflammatories), N02BE (analgesics), and R06 (antihistamines). A comparative analysis of SARIMA and Prophet forecasting models was performed on weekly data to guide strategic inventory planning. To provide deeper operational insights, we integrated three additional analyses: Change Point Detection to identify structural shifts in demand, Volatility Analysis to quantify inventory risk, and Market Basket Analysis to uncover product purchasing associations from daily transaction data. Volatility metrics were translated into a Safety Stock Index to inform inventory policy. Results The analysis confirmed that the optimal forecasting model depends on the characteristics of the service demand data. The SARIMA model performed best for the N02BE (RMSE: 57.46) and R06 (RMSE: 9.24) classes, while the Prophet model was more accurate for the M01AE class (RMSE: 9.52). Change Point Detection identified a significant structural break in demand for N02BE, which also exhibited the highest volatility (Std. Dev: 76.07) and thus the greatest inventory risk. The Market Basket Analysis revealed no strong purchasing associations between the selected drug classes (highest Lift: 1.01), suggesting that co-promotion strategies would be ineffective. Conclusions A multi-level analytical framework provides a more comprehensive evidence base for the management of pharmacy services than forecasting alone. By integrating analyses of demand volatility, structural breaks, and purchasing behavior, health service managers can develop more resilient and efficient resource allocation strategies, ultimately improving service delivery and operational performance.