ArticleComputers & industrial engineering2023
Supply chain risk management with machine learning technology: A literature review and future research directions.
Article in Computers & industrial engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- O²RDL-net for joint risk classification and delay forecasting in logistics systems using interaction amplified deep.Scientific reports · 2026Article
- Technological risks disrupting trade stability in the global lithium supply chain network.iScience · 2025Article
- Increasing load factor in logistics and evaluating shipment performance with machine learning methods: A case from the automotive industry.Scientific reports · 2025Article
- Machine learning applications in risk management: Trends and research agenda.F1000Research · 2025Review
- A Study of the Adsorption Properties of Individual Atoms on the Graphene Surface: Density Functional Theory Calculations Assisted by Machine Learning Techniques.Materials (Basel, Switzerland) · 2024Article
- Resilience in the Face of Uncertainty: Navigating Supply Chain Challenges Through Proactive Risk Surveillance and Mitigation Strategies among SMEs in ASEAN countries.F1000Research · 2024Article
- Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations.Materials (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) has placed tremendous pressure on supply chain risk management (SCRM) worldwide. Recent technological advances, especially machine learning (ML) technology, have shown the possibility to prevent supply chain risk (SCR) by decreasing the need for human labor, increasing response speed, and predicting risk. However, the literature lacks a comprehensive analysis of the relationship between ML and SCRM. This work conducts a comprehensive review of the relatively limited literature in this field. An analysis of 67 shortlisted articles from 9 databases shows that this area is still in the rapid development stage and that researchers have shown extraordinary interest in it. The main purpose of this study is to review the current research status so that researchers have a clear understanding of the research gaps in this area. Moreover, this study provides an opportunity for researchers and practitioners to pay attention to ML algorithms for SCRM during the COVID-19 pandemic.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.