PHARMACY SALES ANALYSİS AND SALES FORECASTİNG WİTH EXPLORATORY DATA ANALYSİS

Abstract

With the increase in daily use and accessibility of drugs, which have an important place in human health, their consumption has started to increase. As in all business types, it is important to know sales and to develop foresight for the future in pharmacies where the pharmaceutical industry is in contact with the end user. Drugs constitute a significant part of the sales of the pharmaceutical industry to individuals. Pharmaceutical sales are in a different position from consumer products. While consumer products are affected by the developing socio-economic situation and product promotion activities as much as their needs, health products are mainly consumption products that are made in obligatory situations. When the products consumed in the field the health sector are examined with exploratory analysis, it provides to reveal the change in public health in the relevant region. In terms of the efficiency of businesses, it depends on predicting future sales as much as possible. In this study, approximately three years of sales data of a pharmacy located in the Southeastern Anatolia region were examined with exploratory data analysis, and then the sales data for the next 15 days were tried to be estimated by machine learning and time series algorithms. The most successful result was obtained with the ARIMA method with a root mean square error (RMSE) of 23.