MIT SCM Symposium 2024

Our capstone project focuses on forecasting sales of the sponsor company's Heat-not-Burn (HNB) products. We estimate future sales of consumables and kits in Italy by looking at monthly data between 2015 and 2023. Sales of the products have a solid positive trend, and regardless of any other parameter, we observe sales increase. We have received 117 features from the partner company, including but not limited to macroeconomic indicators, pricing of the sponsor company and competitors, and sales figures of the sponsor company and competitors. Our approach is first forecasting using traditional methods. After that, we apply different machine learning models. We compare the accuracies to see the difference between traditional and machine learning models. In addition to accuracy, we explore the explainability of the developed models. We use the SHAP algorithm to identify the features that contribute the most to the results.