Predicting late delivery in Supply chain 4.0 using feature selection: a machine learning model

Late delivery is a common issue that can affect organizational performance and growth of any Supply Chain. The ability to predict delivery status would be an invaluable tool for any organization seeking to retain customers and predict their future behavior. This transition of Supply Chain performance in the context of industry 4.0 is called Supply chain 4.0. This study employed machine learning (ML) algorithms to predict whether a customer's order will be delivered late. It is presented as a comparative performance combination of Random Forest Classifier algorithms and three Feature Selection scenarios. In this experiment, the best predictors were identified using the SelectKBest and the predictive models' performance was evaluated using several metrics of Anova method's f_classif() function. The empirical results have demonstrated that feature selection has increased the model accuracy from 97.9% to 99.38%.