Interpreting direct sales’ demand forecasts using SHAP values

Abstract

Paper aims: Several concerns regarding the lack of interpretability of machine learning models obstruct the implementation of machine learning projects as part of the demand forecasting process. This paper presents a methodology to support the introduction of machine learning into the forecasting process of a traditional direct sales company by providing explanations for the otherwise obscure results. We also suggest incorporating human knowledge inside the machine learning pipeline as an essential part of capturing the business logic and integrating machine learning into the existing processes.Originality: Using explainable machine learning methods on real-life company data demonstrates that machine learning techniques are functional beyond the academy and can be introduced to everyday companies' production. Research method:The project used real-world data from a company and followed a traditional machine learning pipeline to collect, preprocess, select and train a machine learning model, to conclude with the explanation of the model results through the implementation of SHAP Main findings: The results provided insights regarding the contribution of the features to the forecast. We analyzed individual predictions to understand the behavior of different variables, proving helpful when interpreting complex machine learning models. Implications for theory and practice:This study contributes to a discussion about adopting new technology and implementing machine learning models for demand forecasting. The methodology presented in this paper can be used to implement similar projects on interested companies.