Supporting an Expert-centric Process of New Product Introduction With Statistical Machine Learning
Keywords:Demand Forecasting, New Product Introduction, Statistical Machine Learning, Gradient Boosting, XGBoost
Industries that sell products with short-term or seasonal life cycles must regularly introduce new products. Forecasting the demand for New Product Introduction (NPI) can be challenging due to the fluctuations of many factors such as trend, seasonality, or other external and unpredictable phenomena (e.g., COVID-19 pandemic). Traditionally, NPI is an expertcentric process. This paper presents a study on automating the forecast of NPI demands using statistical Machine Learning (namely, Gradient Boosting and XGBoost). We show how to overcome shortcomings of the traditional data preparation that underpins the manual process. Moreover, we illustrate the role of cross-validation techniques for the hyper-parameter tuning and the validation of the models. Finally, we provide empirical evidence that statistical Machine Learning can forecast NPI demand better than experts.
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Copyright (c) 2021 Shima Zahmatkesh, Alessio Bernardo, Emanuele Falzone, Edgardo Di Nicola Carena, Emanuele Della Valle
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