Supply chain 4.0: a machine learning-based Bayesian-optimized lightGBM model for predicting supply chain risk

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dc.contributor.authorSani, Shehu
dc.contributor.authorXia, Hanbing
dc.contributor.authorMilisavljevic-Syed, Jelena
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2023-09-06T10:37:34Z
dc.date.available2023-09-06T10:37:34Z
dc.date.issued2023-09-04
dc.description.abstractIn today’s intricate and dynamic world, Supply Chain Management (SCM) is encountering escalating difficulties in relation to aspects such as disruptions, globalisation and complexity, and demand volatility. Consequently, companies are turning to data-driven technologies such as machine learning to overcome these challenges. Traditional approaches to SCM lack the ability to predict risks accurately due to their computational complexity. In the present research, a hybrid Bayesian-optimized Light Gradient-Boosting Machine (LightGBM) model, which accurately forecasts backorder risk within SCM, has been developed. The methodology employed encompasses the creation of a mathematical classification model and utilises diverse machine learning algorithms to predict the risks associated with backorders in a supply chain. The proposed LightGBM model outperforms other methods and offers computational efficiency, making it a valuable tool for risk prediction in supply chain management.en_UK
dc.identifier.citationSani S, Xia H, Milisavljevic-Syed J, Salonitis K. (2023) Supply chain 4.0: a machine learning-based Bayesian-optimized lightGBM model for predicting supply chain risk. Machines, Volume 11, Issue 9, September 2023, Article number 888en_UK
dc.identifier.issn2075-1702
dc.identifier.urihttps://doi.org/10.3390/machines11090888
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20175
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmachine learningen_UK
dc.subjectsupply chain managementen_UK
dc.subjectbackorder risken_UK
dc.subjectpredictionen_UK
dc.subjectresilienceen_UK
dc.subjectlight gradient boosting machineen_UK
dc.subjectBayesian optimisationen_UK
dc.titleSupply chain 4.0: a machine learning-based Bayesian-optimized lightGBM model for predicting supply chain risken_UK
dc.typeArticleen_UK

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