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Open Access Journal Article

Supply Chain Risk Management Using Predictive Analytics

by Sophia Thomas 1,*
1
Sophia Thomas
*
Author to whom correspondence should be addressed.
MAD  2023 5(2):43; https://doi.org/10.69610/j.mad.20230930
Received: 20 July 2023 / Accepted: 29 August 2023 / Published Online: 30 September 2023
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Abstract

This paper explores the application of predictive analytics in supply chain risk management (SCRM). The importance of effective risk management in supply chains cannot be overstated, as it directly impacts business continuity, cost optimization, and customer satisfaction. Predictive analytics, with its ability to analyze historical data and foresee future trends, offers a powerful tool for identifying, assessing, and mitigating potential risks. The study examines various predictive models and tools that can be employed in SCRM, such as time series analysis, machine learning algorithms, and simulation models. By integrating these techniques, companies can proactively manage risks, optimize supply chain performance, and enhance decision-making processes. The paper further discusses the challenges and limitations of predictive analytics in SCRM, such as data quality and availability, model accuracy, and the potential for over-reliance on technology. Overall, this research aims to provide insights into how predictive analytics can be leveraged to enhance supply chain risk management practices.

Keywords: Predictive Analytics; Supply Chain Risk Management (SCRM); Time series analysis; Machine Learning; Simulation Models.;
Copyright: © 2023 by Thomas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Thomas, S. (2023). Supply Chain Risk Management Using Predictive Analytics. Management Analytics and Decision, 5(2), 43. doi:10.69610/j.mad.20230930
ACS Style
Thomas, S. Supply Chain Risk Management Using Predictive Analytics. Management Analytics and Decision, 2023, 5, 43. doi:10.69610/j.mad.20230930
AMA Style
Thomas S. Supply Chain Risk Management Using Predictive Analytics. Management Analytics and Decision; 2023, 5(2):43. doi:10.69610/j.mad.20230930
Chicago/Turabian Style
Thomas, Sophia 2023. "Supply Chain Risk Management Using Predictive Analytics" Management Analytics and Decision 5, no.2:43. doi:10.69610/j.mad.20230930

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ACS Style
Thomas, S. Supply Chain Risk Management Using Predictive Analytics. Management Analytics and Decision, 2023, 5, 43. doi:10.69610/j.mad.20230930
AMA Style
Thomas S. Supply Chain Risk Management Using Predictive Analytics. Management Analytics and Decision; 2023, 5(2):43. doi:10.69610/j.mad.20230930
Chicago/Turabian Style
Thomas, Sophia 2023. "Supply Chain Risk Management Using Predictive Analytics" Management Analytics and Decision 5, no.2:43. doi:10.69610/j.mad.20230930
APA style
Thomas, S. (2023). Supply Chain Risk Management Using Predictive Analytics. Management Analytics and Decision, 5(2), 43. doi:10.69610/j.mad.20230930

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