The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing
Abstract
Abstract 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 these twins improve resilience. The proposed data-driven decision-making model for resilient supplier selection can be further exploited for design of risk mitigation strategies in supply chain disruption management models, re-designing the supplier base or investing in most important and risky suppliers.
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Metadata
- Authors
- Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D.
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 49
- Pages
- 86-97
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2947788863
- Cited by
- 527
- References
- 102
- FWCI
- 81.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
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- Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
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