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

• Published in: International journal of information management 2019-12, Vol.49, p.86-97
• Main Authors: Cavalcante, Ian M., Frazzon, Enzo M., Forcellini, Fernando A., Ivanov, Dmitry
• Format: Article
• Language: English
• Subjects: Allocations; Analytics; Artificial intelligence; Component and supplier management; Computer simulation; Data-driven decision-making support; Decision making; Digital data; Digital supply chain; Digital supply chain twin; Disruption; Machine learning; Manufacturing; Reliability analysis; Reliability aspects; Resilience; Risk analysis; Risk assessment; Simulation; Supplier selection; Suppliers; Supply chains
• ISSN: 0268-4012; 1873-4707
• DOI: 10.1016/j.ijinfomgt.2019.03.004

•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. There has been an increased interest in resilient supplier selection in recent years, much of it focusing on forecasting the disruption probabilities. We conceptualize an entirely different approach to analyzing the risk profiles of supplier performance under uncertainty by utilizing the data analytics capabilities in digital manufacturing. Digital manufacturing peculiarly challenge the supplier selection by the dynamic order allocations, and opens new opportunities to exploit the digital data to improve sourcing decisions. 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. The results suggest that a combination of supervised machine learning and simulation, if utilized properly, improves the delivery reliability. Our approach can also be of value when analyzing the supplier base and uncovering the critical suppliers, or combinations of suppliers the disruption of which result in the adverse performance decreases. The results of this study advance our understanding about how and when machine learning and simulation can be combined to create digital supply chain twins, and through t