Optimal Strategy for Supplier Selection in a Global Supply Chain Using Machine Learning Technique

This paper proposes an optimization strategy for the best selection process of suppliers. Based on recent literature reviews, the paper assumes a selection of commonly used variables for selecting suppliers and using logistic regression algorithm technique to build a model of optimization that learns from customer requirements and supplier data and then makes predictions and recommendations for best suppliers. The supplier selection process can quickly at times turn into a complex task for decision-makers to deal with the growing number of suppliers. But logistics regression technique makes the process easier in the ability to efficiently fetch customer requirements with the entire supplier base list by predicting a list of potential suppliers meeting the actual requirements. The selected suppliers make up the recommendation list for the best suppliers for the requirements. And finally, graphical representations are given to showcase the framework analysis, variable selection, and other illustrations about the model analysis.