What question did this study set out to answer?

The research aims to investigate how machine learning is applied within supply chain risk management and the associated risks addressed.

September 8, 2021Open Access

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

Key Points

The research aims to investigate how machine learning is applied within supply chain risk management and the associated risks addressed.
Conducted a systematic literature review of existing studies on ML in SCRM
Analyzed case studies to identify risks and benefits of ML in supply chain contexts
Developed propositions based on literature analysis for future research
Identified that ML helps in early detection of production, transport, and supply risks.
Highlighted added value of ML integration, including use of new data sources like social media and weather data.
Proposed four directions for further investigation in the field.

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.

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