Published 29-11-2023
Keywords
- Supply Chain Resilience,
- AI-Powered Supply Chain,
- Supply Chain
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Supply chain processes are complex, global, and unpredictable. Over the past decade, advances in AI technologies have enabled companies to rethink their traditional supply chain strategies and consider paradigms leading to more adaptive and responsive supply chains. As such, many recent works have shown a growing interest in the development of AI-driven supply chains. When it comes to addressing resilience quantitatively, the proposed solution can generally be categorized according to the level of information available to supply chain actors. If historic sales data is available, AI models enable the subjective process to be semi-automated, feedforward, and extrapolated from the training data. The bottom line is that AI reduces uncertainty by increasing business information and supports decision-makers in making more profitable arrangements.
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References
- S. Kumari, “AI-Driven Cybersecurity in Agile Cloud Transformation: Leveraging Machine Learning to Automate Threat Detection, Vulnerability Management, and Incident Response”, J. of Art. Int. Research, vol. 2, no. 1, pp. 286–305, Apr. 2022
- Tamanampudi, Venkata Mohit. "A Data-Driven Approach to Incident Management: Enhancing DevOps Operations with Machine Learning-Based Root Cause Analysis." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 419-466.
- Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.
- Singh, Jaswinder. "Sensor-Based Personal Data Collection in the Digital Age: Exploring Privacy Implications, AI-Driven Analytics, and Security Challenges in IoT and Wearable Devices." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 785-809.
- Tamanampudi, Venkata Mohit. "Natural Language Processing for Anomaly Detection in DevOps Logs: Enhancing System Reliability and Incident Response." African Journal of Artificial Intelligence and Sustainable Development 2.1 (2022): 97-142.
- J. Singh, “How RAG Models are Revolutionizing Question-Answering Systems: Advancing Healthcare, Legal, and Customer Support Domains”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 850–866, Jul. 2019
- Tamanampudi, Venkata Mohit. "AI and NLP in Serverless DevOps: Enhancing Scalability and Performance through Intelligent Automation and Real-Time Insights." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 625-665.