Navigating Uncertainty: Cutting-Edge Approaches in Process Control and Monitoring for Risk Mitigation in Supply Chain Management †
Engineering Proceedings, ISSN: 2673-4591, Vol: 67, Issue: 1
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In today’s dynamic and uncertain environment, supply chains are increasingly vulnerable to disruptions that can negatively impact operational efficiency, cost management, and customer satisfaction. Ensuring supply chain resilience and continuity has become a critical challenge for businesses. This review addresses the pressing issue of supply chain risk management by evaluating cutting-edge solutions that enhance visibility, agility, and responsiveness. Through a comprehensive analysis of literature from 2009 to 2024, sourced from Web of Science, Scopus, and Google Scholar, this study identifies key methodologies, technologies, and frameworks designed to mitigate supply chain risks. The findings of the study highlight the revolutionary potential of IoT sensors, machine learning algorithms, and digital twins for proactive risk assessment and mitigation, offering a pathway to safeguard supply networks in the face of uncertainty.
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