Wenzel, HannahHannahWenzelSmit, DanielDanielSmitSardesai, SaskiaSaskiaSardesai2019-11-112019-11-112019-09-26Hamburg International Conference of Logistics (HICL) 27: 413-441 (2019)http://hdl.handle.net/11420/3742Purpose: In recent years, a number of practical logistic applications of machine learning (ML) have emerged, especially in Supply Chain Management (SCM). By linking applied ML methods to the SCM task model, the paper indicates the current applications in SCM and visualises potential research gaps. Methodology: Relevant papers with applications of ML in SCM are extracted based on a literature review of a period of 10 years (2009-2019). The used ML methods are linked to the SCM model, creating a reciprocal mapping. Findings: This paper results in an overview of ML applications and methods currently used in the area of SCM. Successfully applied ML methods in SCM in industry and examples from theoretical approaches are displayed for each task within the SCM task model. Originality: Linking the SC task model with current application areas of ML yields an overview of ML in SCM. This facilitates the identification of potential areas of application to companies, as well as potential future research areas to science.enhttps://creativecommons.org/licenses/by-sa/4.0/Supply chain managementMachine learningLiterature reviewPredictive analyticsInformatikWirtschaftHandel, Kommunikation, VerkehrConference Paperurn:nbn:de:gbv:830-882.05434510.15480/882.2478https://www.epubli.de/shop/buch/Artificial-Intelligence-and-Digital-Transformation-in-Supply-Chain-Management-Christian-M-Ringle-Thorsten-Blecker-Wolfgang-Kersten-9783750249479/9209510.15480/882.2478Conference Paper