Hybrid machine learning forecasting for resilient and sustainable pharmaceutical supply chains under regulatory and seasonal disruption
Saleh Al Dawsari
IntroductionDemand forecasting in pharmaceutical supply chains is not a simple task. In regulated markets it becomes more difficult, because seasonality, epidemic waves, and also policy changes can make demand behavior unstable. This study proposes a hybrid residual learning approach for forecasting pharmaceutical demand in Türkiye.MethodsThe model uses Support Vector Regression (SVR) together with Deep Neural Networks (DNN). In this structure, SVR estimates the main or baseline part of demand,