Version 1
: Received: 3 August 2024 / Approved: 5 August 2024 / Online: 5 August 2024 (13:47:00 CEST)
How to cite:
Wilson, G.; Johnson, O.; Brown, W. The Role of Machine Learning in Predictive Analytics for Supply Chain Management. Preprints2024, 2024080343. https://doi.org/10.20944/preprints202408.0343.v1
Wilson, G.; Johnson, O.; Brown, W. The Role of Machine Learning in Predictive Analytics for Supply Chain Management. Preprints 2024, 2024080343. https://doi.org/10.20944/preprints202408.0343.v1
Wilson, G.; Johnson, O.; Brown, W. The Role of Machine Learning in Predictive Analytics for Supply Chain Management. Preprints2024, 2024080343. https://doi.org/10.20944/preprints202408.0343.v1
APA Style
Wilson, G., Johnson, O., & Brown, W. (2024). The Role of Machine Learning in Predictive Analytics for Supply Chain Management. Preprints. https://doi.org/10.20944/preprints202408.0343.v1
Chicago/Turabian Style
Wilson, G., Oliver Johnson and William Brown. 2024 "The Role of Machine Learning in Predictive Analytics for Supply Chain Management" Preprints. https://doi.org/10.20944/preprints202408.0343.v1
Abstract
This study explores the transformative impact of machine learning on predictive analytics within supply chain management. As supply chains become increasingly complex and dynamic, traditional methods of forecasting and management are proving inadequate. Machine learning offers advanced algorithms capable of analyzing vast datasets to uncover patterns and trends that enhance decision-making processes. This research investigates how machine learning improves demand forecasting accuracy, optimizes inventory management, strengthens supplier relationships, and enhances risk management. The study employs a qualitative approach, analyzing data from industry case studies and expert interviews to identify key themes and challenges associated with the adoption of machine learning. Findings reveal that machine learning significantly improves forecasting precision, reduces inventory costs, and enables proactive supplier and risk management. However, challenges such as ensuring data quality, acquiring specialized skills, integrating new technologies with existing systems, and addressing ethical considerations are critical. The study also highlights the potential of emerging technologies like blockchain, IoT, and 5G to further enhance machine learning applications in supply chain management. This research contributes to a deeper understanding of how machine learning can drive innovation and efficiency in supply chains, while also addressing the hurdles that organizations must overcome. The results underscore the need for a balanced approach that incorporates technological advancements and ethical practices to fully leverage the benefits of machine learning.
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
