Machine learning in supply chain management: A scoping review - Martin Brylowski; Meike Schröder; Sebastian Lodemann; Wolfgang Kersten (Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 31, 2021)
Purpose: Especially in supply chain management (SCM), data has become essential to the success of companies. Traditional analytical methods are being augmented by machine learning (ML), which is considered the foremost relevant branch of artificial intelligence. This article maps various ML use-cases and assigns them to the appropriate SCM tasks. Methodology: We applied a scoping review and checked scientific databases for relevant literature. Subsequently, the articles were assigned to different categories to map the research area. In the categorization, we considered, amongst others, the ML tasks and algorithms, data source and type, and the field of application. Findings: The results show that there are numerous ML use cases in SCM. These range from predictive demand forecasting and intelligent partner selection to the use of assistance systems for resource management. Various data sources, such as internal company data and publicly available data, are used for these applications. Originality: By mapping ML use cases in SCM, this complex and multifaceted field of research is presented in a transparent and structured way. Science and practice can deploy the results to improve existing ML use cases in SCM on the one hand and to identify promising areas of application on the other.
urn:nbn:de:gbv:830-882.0161734; hdl:10419/249608; https://econpapers.repec.org/RePEc:zbw:hiclpr:31
Keywords: 650; Artificial Intelligence; Blockchain