A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing

Highlights：

• We conceptualize a new approach to analyzing the risk profiles of supplier performance under uncertainty by utilizing the data analytics capabilities in digital manufacturing.

• We develop a hybrid technique, combining simulation and machine learning and examine its applications to data-driven decision-making support in resilient supplier selection.

• We consider on-time delivery as an indicator for supplier reliability, and explore the conditions surrounding the formation of resilient supply performance profiles.

• We theorize the notions of risk profile of supplier performance and resilient supply chain performance.

• We show that the associations of the deviations from the resilient supply chain performance profile with the risk profiles of supplier performance can be efficiently deciphered by our approach.