A decision support system for classifying supplier selection criteria using machine learning and random forest approach

Supplier selection is an important process in supply chain management that sets a foundation for a long-term partnership with suppliers that can greatly contribute to the success or failure of a business. This study aims to identify, validate and propose a comprehensive list of supplier selection criteria applicable to most organizations. The proposed integrated framework comprises four widely used supervised machine learning (ML) models of Random Forest (RF) classifier and RF-based feature selection algorithm to identify a comprehensive list of critical criteria and their performance measures. We present a case study and show the RF classifier’s performance increased by 3.89% in accuracy and 5.17% in f-score after removing non-critical criteria. Nine criteria are identified as critical among 30 potential criteria…