A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management

Meike Schroeder and Sebastian Lodemann
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Meike Schroeder: Institute of Business Logistics and General Management, Hamburg University of Technology, 21073 Hamburg, Germany
Sebastian Lodemann: Institute of Business Logistics and General Management, Hamburg University of Technology, 21073 Hamburg, Germany

Logistics, 2021, vol. 5, issue 3, 1-17

Abstract: The main objective of the paper is to analyze and synthesize existing scientific literature related to supply chain areas where machine learning (ML) has already been implemented within the supply chain risk management (SCRM) field, both in theory and in practice. Furthermore, we analyzed which risks were addressed in the use cases as well as how ML might shape SCRM. For this purpose, we conducted a systematic literature review. The results showed that the applied examples relate primarily to the early identification of production, transport, and supply risks in order to counteract potential supply chain problems quickly. Through the analyzed case studies, we were able to identify the added value that ML integration can bring to the SCRM (e.g., the integration of new data sources such as social media or weather data). From the systematic literature analysis results, we developed four propositions, which can be used as motivation for further research.

Keywords: supply chain risk management; machine learning; cases; propositions; supply chain (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:5:y:2021:i:3:p:62-:d:631062

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