Classification and Allocation of Suppliers to Customers in Resilince Supply Chains Using Machine Learning

Selection and allocation in the resilient supply chain, when disruption threatens the supply chain, has become a strategic decision and the focus of many researches; increase in the applications of machine learning in supply chain studies has led to the emergence of faster and reliable decision-making methods, however, in few studies, machine learning has been used to deal with the problem of selecting and assigning suppliers to customers in resilient mode. The purpose of this research is to take a step towards solving this gap by using machine learning algorithms on real world data from the automotive supply chain in Iran. the performance data of 441 suppliers and 7 customers in 1401 was used. In this research, two clustering algorithms have been used to generate labels based on the concept of resilience capacity; Then, since the interpretability of the results was a priority, based on the labeling of the clusters by the experts, the decision tree was used to classify the suppliers based on their performance. The results showed the K-means tree performs better than the DBSCAN tree and criteria such as on-time delivery, capacity, production line stoppage, quality alert, logistics performance and quality performance are effective on suppliers' resilience.