A hybrid hw-rfr forecasting model: case of moroccan pharmaceutical sector

Abstract

Sales forecasting is an essential element of effective supply chain management, particularly in the pharmaceutical sector where continuous availability of drugs is crucial. This article examines sales forecasts for fluoxetine, an antidepressant available on the Moroccan market under six trade names and 14 different forms. The main objective of this study is to compare the effectiveness of four forecasting models, namely Prophet Facebook, ARIMA, GRU and Holt-Winters through their accuracy, and to propose a hybrid model that will contribute to improving the accuracy of demand forecasts. Each model was applied individually to predict future sales, and evaluated using MAPE, MAE and RMSE metrics. Next, a hybrid model, integrating Holt-Winters and Random Forest Regressor methods, was developed to leverage the robustness of traditional models while improving predictive performance through machine learning techniques. The results of the study show that traditional models, such as ARIMA and Holt-Winters, offer a solid basis for sales forecasting. However, the hybrid HW-RFR (Holt-Winters Random Forest Regressor) model stands out for a significant improvement in forecast accuracy, demonstrating great robustness to fluctuations in fluoxetine demand. This article highlights the potential of hybrid models for forecasting pharmaceutical sales. The improved forecast accuracy achieved with the HW-RFR model provides stakeholders with more reliable information, enabling them to make informed decisions to optimize pharmaceutical supply chain management