Accurate sales forecasting is essential for effective inventory planning, resource
allocation, and customer service. This thesis evaluates machine learning methods for predicting sales using Molaris Biotech data from January 2014 to April 2024. We compare the performance of ElasticNet, Random Forest, XGBoost, and neural networks. Results show that ElasticNet and Random Forest deliver the highest performance, reducing MAPE by up to 30% relative to the firm’s current approach. These findings highlight the value of predictive analytics for improving operational efficiency and strengthening competitive positioning in the retail sector.
