An XGBoost-Based Forecasting Framework for Product Cannibalization

One of the major challenges in making such forecasts is taking the effect of product cannibalization into account. Product cannibalization occurs when demand for a certain product within the portfolio increases that may be due to launch of a new product. This consequently reduces the sales of older products. This interaction between different data samples leads to the fact that total demand of all products remains stable but with large variations in the demand of individual products within the portfolio. Machine learning allows us to model complex dynamics and capture large number of input variables over traditional statistical models. Generally, machine learning models try to optimize the cost function by using input features to the model and updating the model parameters accordingly. However, in product cannibalization the demand of a given product is being impacted by the demand of a different product that is not a part of the input feature set. In this work, the proposed framework is to make accurate sales forecast of old products that are cannibalized due to launch of newer products.