demand forecasting model for pharmaceutical products using machine learning techniques with bayesian hyperparameter optimization

Demand forecasting is the basis of many planning activities in the supply chain. pharmaceutical industry, which deal with human health, require the implementation of an effective demand forecasting model. due to demand volatility, businesses find it challenging to forecast customer demand accurately using traditional models. in this study, a comparative analysis is performed based on machine learning techniques such as support vector regression (svr), random forest (rf), light gradient boosting machine (lgbm), and extreme gradient boosting (xgb) models for demand forecasting in pharmaceutical products. the effectiveness of machine learning models is greatly affected by choosing the appropriate hyperparameter configuration. therefore, bayesian optimization (bo) algorithm with the gaussian process (gp) is combined with time series cross-validation to determine the optimal combination of model hyperparameters. the results show that the extreme gradient boosting model outperforms the other forecasting models in terms of root mean squared error (rmse), mean absolute error (mae), and r^2 score. this method can effectively forecast future demand to improve pharmaceutical supply chain management.