Reviewing the role of AI and machine learning in supply chain analytics
1 Independent Researcher, UK.
2 Independent Researcher, Nigeria.
3 Department of Chemical, Biochemical and Environmental Engineering, University of Maryland, Baltimore County, Baltimore, Maryland, USA.
4 Independent Researcher, Nigeria.
5 Independent Researcher, Seattle, Washington, USA.
6 Department of Information Management, Ahmadu Bello University, Zaria, Nigeria.
7 Independent Researcher, UK.
Review Article
GSC Advanced Research and Reviews, 2024, 18(02), 312–320.
Article DOI: 10.30574/gscarr.2024.18.2.0069
Publication history:
Received on 02 January 2024; revised on 12 February 2024; accepted on 14 February 2024
Abstract:
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learning algorithms, allows supply chain professionals to forecast demand more accurately, identify potential disruptions, and optimize inventory levels. This not only improves overall efficiency but also reduces costs and minimizes the risk of stockouts or overstock situations. Furthermore, the integration of AI-driven automation in supply chain management has streamlined routine tasks, such as order processing, inventory replenishment, and route optimization. This automation not only accelerates processes but also mitigates the risk of human errors, enhancing overall reliability. The ability of AI to continuously learn from historical data and adapt to evolving market conditions contributes to a more agile and responsive supply chain ecosystem. In the context of supply chain risk management, AI and ML play a pivotal role in identifying vulnerabilities and providing proactive strategies to mitigate potential disruptions. Sentiment analysis and predictive modeling enable organizations to assess geopolitical, economic, and environmental factors, thereby enhancing the resilience of their supply chains. However, the adoption of AI and ML in supply chain analytics is not without challenges. This review explores the ethical considerations, data security concerns, and the need for skilled personnel in managing these advanced technologies. Additionally, it delves into the importance of explainability and transparency in AI-driven decision-making processes, emphasizing the need for a balance between automation and human oversight. This review underscores the transformative impact of AI and ML on supply chain analytics, emphasizing their potential to revolutionize traditional practices, enhance efficiency, and fortify resilience in an increasingly complex and dynamic business environment.
Keywords:
AI; Machine Learning; Supply Chain; Analytics; Review
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
