Optimizing Pharmaceutical Inventory Control: Strategies for Classification and Seasonal Demand Forecasting

Effective pharmaceutical inventory management is critical for assuring the availability of necessary pharmaceuticals, avoiding the waste, and managing the costs in healthcare systems. The complexities of pharmaceutical inventory management are worsened by factors such as varied degrees of criticality, unexpected demand patterns, short shelf life, and resource constraint. This study looks at major inventory management frameworks including ABC, VED, and SDE analyses, which classify drugs based on their value, criticality, and supply risks, allowing for more accurate inventory calculations. Furthermore, the study emphasizes the use of machine learning models and time series forecasting approaches, notably SARIMA and LSTM, to predict seasonal demand fluctuations and improve inventory planning and decision-making processes. Although much research has been undertaken on the deployment of these approaches in a variety of industries, there is a significant gap in their application to pharmaceutical inventory management, particularly in resource-constrained contexts with insufficient historical data. This work seeks to close this gap by investigating how time series models might be modified to estimate demand seasonality in the absence of comprehensive historical data. The findings highlight the ability of SARIMA and LSTM to increase the forecasting accuracy and guide superior inventory managing methods, resulting in more efficient supply chains management and better decision-making in pharmaceutical contexts. The major goal is to use time series models to address seasonality difficulties in pharmaceutical inventory management, especially when data availability is limited.