Subjective Prediction and Models for Forecasting Sales in Pharmaceutical Market Demand

A business's utilization of historical data can support decision-making and is a necessary process for modern enterprises. Enterprises today can estimate short-term sales by using appropriate forecasting techniques and leveraging sales data. In some cases, however, and in some markets such as the pharmaceutical demand market, the targets for sales as well as the temporary or extraordinary demand that can be created for specific medicines are very important factors that influence these forecasts. In this paper, a five-year sales data of a pharmaceutical company operating in Greece is used for three medicines and a combined sales forecasting model is used. The selected medicines have different characteristics so that various factors such as seasonality, extraordinary events, and randomness are taken into account for generalizability purposes by similar enterprises. The predictions of this model are compared with actual sales data. They are also compared with the subjective forecasts of the company executives with the sales targets. The process of estimating demand can contribute to more effective decision-making and avoid financial losses for the organization. The proposed model, if used appropriately, can help particular industry predictions can be made with greater accuracy and less chance of error. It also can help similar companies that may be active in sectors outside the pharmaceutical industry.