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

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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Enhancing Supply Chain Risk Management by Applying Machine Learning to Identify Risks

Business Information Systems - Lecture Notes in Business Information Processing ◽

10.1007/978-3-030-20482-2_16 ◽

2019 ◽

pp. 191-205 ◽

Cited By ~ 3

Author(s):

Ahmad Pajam Hassan

Keyword(s):

Machine Learning ◽

Risk Management ◽

Supply Chain ◽

Supply Chain Risk Management ◽

Supply Chain Risk

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Exploring Progress with Supply Chain Risk Management during the First Year of the COVID-19 Pandemic

Logistics ◽

10.3390/logistics5040070 ◽

2021 ◽

Vol 5 (4) ◽

pp. 70

Author(s):

Remko van Hoek

Keyword(s):

Risk Management ◽

Supply Chain ◽

Risk Mitigation ◽

First Year ◽

Supply Chain Risk Management ◽

Supply Chain Risk ◽

Supply Chain Resilience ◽

Data Points ◽

Supply Risks ◽

One Year

Background: In response to calls for actionable research that considers ongoing pandemic risk dynamics, we explore how risks experienced and risk mitigation techniques used have changed during the first year of the pandemic. Methods: We used a survey and studied six cases; data were collected both at the start of the pandemic and one year into the pandemic. This paper offers the first empirical exploration of the first full year of the pandemic and provides data points from both early and one year into the pandemic. Results: Our findings indicate that not only are pandemic risks far from mitigated, several types of risks have also increased in severity. Multifaceted and multidirectional approaches have been adopted, going well beyond demand and supply risks (the risks most widely considered in the literature) and much more work remains for supply chain managers to mitigate risks and improve supply chain resilience. Conclusions: We find that in addition to the risk management techniques, considering behavioral aspects is key for navigating a pathway towards risk mitigation.

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