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

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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)

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.

Suggested Citation

  • Meike Schroeder & Sebastian Lodemann, 2021. "A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management," Logistics, MDPI, vol. 5(3), pages 1-17, September.
    • Handle: RePEc:gam:jlogis:v:5:y:2021:i:3:p:62-:d:631062
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    References listed on IDEAS

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    Cited by:

    1. Abeer Aljohani, 2023. "Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility," Sustainability, MDPI, vol. 15(20), pages 1-26, October.

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