Supporting an Expert-centric Process of New Product Introduction With Statistical Machine Learning

Shima Zahmatkesh; Alessio Bernardo; Emanuele Falzone; Edgardo Di Nicola Carena; Emanuele Della Valle

doi:10.52825/bis.v1i.57

Authors

Shima Zahmatkesh Politecnico di Milano https://orcid.org/0000-0002-7832-0288
Alessio Bernardo Politecnico di Milano https://orcid.org/0000-0002-3492-0345
Emanuele Falzone Politecnico di Milano
Edgardo Di Nicola Carena Abstract s.r.l.
Emanuele Della Valle Politecnico di Milano https://orcid.org/0000-0002-5176-5885

DOI:

https://doi.org/10.52825/bis.v1i.57

Keywords:

Demand Forecasting, New Product Introduction, Statistical Machine Learning, Gradient Boosting, XGBoost

Abstract

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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