Demand Prediction: sales forecasting for over-the-counter (OTC) medicines
A major pharmaceutical company approached us to optimise the planning of its marketing campaigns for over-the-counter medicines. The main challenge is to accurately forecast demand in a highly dynamic market influenced by external variables such as seasonality, epidemic trends, and changing consumer behaviour. The goal is to maximise the availability of medicines during periods of peak demand, avoiding waste or shortages and improving the effectiveness of promotional activities.
We developed a forecasting model based on Artificial Intelligence and Machine Learning algorithms, integrated with mathematical modelling techniques. This system analysed large amounts of qualitative data (such as changes in consumer behaviour) and quantitative data (such as historical sales and seasonal variables) to predict demand more accurately. The model was designed to be flexible and adaptable, to simulate different scenarios and quickly respond to market changes.
