A Machine Learning-Based System for Predicting Service-Level Failures in Supply Chains
References
- (2016) Xgboost: A scalable tree boosting system. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 785–794.Google Scholar
- (2001) Greedy function approximation: A gradient boosting machine. Ann. Statist. 29(5):1189–1232.Google Scholar
- (2015) An analytical framework for supply network risk propagation: A Bayesian network approach. Eur. J. Oper. Res. 243(2):618–627.Google Scholar
- (2015) A critical review on supply chain risk—definition, measure and modeling. Omega 52:119–132.Google Scholar
- (2003) Supply chain risk management: Outlining an agenda for future research. Internat. J. Logist. Res. Appl. 6(4):197–210.Google Scholar
- (2017) Supply Chain Risk Management (Springer, Singapore).Google Scholar
- (2010) Minimisation of supply chain cost with embedded risk using computational intelligence approaches. Internat. J. Prod. Res. 48(13):3717–3739.Google Scholar
- (2017) A unified approach to interpreting model predictions. Adv. Neural Inform. Processing Systems 30:4765–4774.Google Scholar
- (2018) Consistent individualized feature attribution for tree ensembles. Preprint, submitted February 12, https://arxiv.org/abs/1802.03888.Google Scholar
- (2018) Big data analytics in supply chain management: A state-of-the-art literature review. Comput. Oper. Res. 98:254–264.Google Scholar
- (2018) Bayesian network modelling for supply chain risk propagation. Internat. J. Prod. Res. 56(17):5795–5819.Google Scholar
- (2015) Understanding natural disasters as risks in supply chain management through web data analysis. Internat. J. Comput. Comm. Engrg. 4(2):126–133.Google Scholar
- (2017) The role of Big Data in explaining disaster resilience in supply chains for sustainability. J. Cleaner Prod. 142:1108–1118.Google Scholar
- (2017) A quantitative model for disruption mitigation in a supply chain. Eur. J. Oper. Res. 257(3):881–895.Google Scholar
- (2011) Strategies for customer service level protection under multi-echelon supply chain disruption risk. Transportation Res. Part B Methodological 45(8):1266–1283.Google Scholar
- (1953) A value for n-person games. Contributions Theory Games 2(28):307–317.Google Scholar
- (2018) Data models for service failure prediction in supply chain networks. Preprint, submitted October 20, https://arxiv.org/abs/1810.09944.Google Scholar
- (2015) Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces 45(5):375–390.Link, Google Scholar
- , eds. (2002) Supply Chain Management and Advanced Planning: Concepts, Software, Models, and Case Studies. 4th ed. (Springer, Berlin).Google Scholar

