A Predictive analytics model for pharmaceutical inventory management

Musimbi, Patience Musanga
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Strathmore University
Inefficient inventory management is a factor that affects pharmacies in Kenya. The unpredictable nature of weather patterns during the traditional long and short rain seasons has resulted in seasons starting earlier or later than expected. Seasonal diseases such as flu may spike up when the temperatures decrease or when the rainy seasons begin, causing an increase in sales of drugs that cure and prevent the flu and vice versa. Due to this unpredictability, pharmacies may fail to stock up or down for different seasons due to unpreparedness and not knowing what to stock and when to stock. Ineffective drug management has a significant financial impact on pharmacies. Inventory management ensures that needed drugs or medicines are always available, in sufficient quantities, of the right type and quality, and are used rationally. An effective drug management process ensures the availability of drugs in the right type and amount in accordance with needs, thereby avoiding drug shortages and excesses. This research proposed a predictive analysis tool that would predict the required drugs or medicines prior to when they are needed, based on sales and seasonality. Another parameter for predictive analysis for this research was the period of the year when a certain disease could be common. This research discussed stocking and inventory management of pharmaceutical products and how predictive analytics with machine learning algorithms could be applied to improve the inventory management process in a pharmacy’s context. The purpose of the study was to examine the inefficient stocking of medicines in pharmacies and use predictive analysis to predict future stock. It reviewed various previous methods used for pharmaceutical inventory management and proposed the SARIMAX model with time series analysis for stock prediction. The result was a model that predicted the quantity of drugs to be stocked for the next six weeks. The six-week prediction model had a Root Mean Squared Error (RMSE) of 5.5.
Submitted in partial fulfilment of the requirements for the degree of Master of Science in Information Technology at Strathmore University