AI integration in supply chain and operations management: Enhancing efficiency and resilience

Daren Zhang

doi:10.54254/2755-2721/90/2024MELB0060

Research Article

Open access

Published on 27 August 2024

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Zhang,D. (2024). AI integration in supply chain and operations management: Enhancing efficiency and resilience. Applied and Computational Engineering,90,8-13.

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AI integration in supply chain and operations management: Enhancing efficiency and resilience

Daren Zhang *^,1,

¹ University of New South Wales, Sydney, Australia.

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/90/2024MELB0060

Abstract

Artificial intelligence (AI) has become a transformative force in supply chain and operations management, offering significant enhancements in efficiency and resilience. This paper examines the integration of AI technologies such as machine learning, predictive analytics, and real-time data processing in demand forecasting, inventory management, logistics, and risk mitigation. By analyzing diverse data sources, AI improves demand forecasting accuracy, reduces inventory costs, optimizes logistics routes, and enhances supply chain visibility. Case studies and data-driven insights demonstrate how AI-driven systems enable companies to adapt to market dynamics, prevent disruptions, and achieve substantial cost savings. The findings suggest that embracing AI is essential for businesses aiming to optimize their supply chain operations and build robust, resilient frameworks capable of withstanding future challenges.

Keywords

Artificial Intelligence, Supply Chain Management, Operations Management, Machine Learning, Predictive Analytics

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References

[1]. Mohsen, Baha M. "Impact of artificial intelligence on supply chain management performance." Journal of Service Science and Management 16.1 (2023): 44-58.

[2]. Hendriksen, Christian. "Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption?." Journal of Supply Chain Management 59.3 (2023): 65-76.

[3]. Rathor, Ketan. "Impact of using Artificial Intelligence-Based Chatgpt Technology for Achieving Sustainable Supply Chain Management Practices in Selected Industries." International Journal of Computer Trends and Technology 71.3 (2023): 34-40.

[4]. Hatamlah, H., et al. "The role of artificial intelligence in supply chain analytics during the pandemic." Uncertain Supply Chain Management 11.3 (2023): 1175-1186.

[5]. Nwagwu, Urenna, et al. "The influence of artificial intelligence to enhancing supply chain performance under the mediating significance of supply chain collaboration in manufacturing and logistics organizations in Pakistan." Traditional Journal of Multidisciplinary Sciences 1.02 (2023): 29-40.

[6]. Jauhar, Sunil Kumar, et al. "How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chains." International Journal of Production Research (2023): 1-25.

[7]. Gupta, Shivam, et al. "Influences of artificial intelligence and blockchain technology on financial resilience of supply chains." International Journal of Production Economics 261 (2023): 108868.

[8]. Venkatesh, Viswanath, Raji Raman, and Frederico Cruz-Jesus. "AI and emerging technology adoption: a research agenda for operations management." International Journal of Production Research 62.15 (2024): 5367-5377.

[9]. Meredith, Jack R., and Scott M. Shafer. Operations and supply chain management for MBAs. John Wiley & Sons, 2023.

[10]. Panneerselvam, R. Operations research. PHI Learning Pvt. Ltd., 2023.

[11]. Jaboob, Ali Said, Ali Mohsin Ba Awain, and Khairul Anuar Mohd Ali. "Introduction to Operation and Supply Chain Management for Entrepreneurship." Applying Business Intelligence and Innovation to Entrepreneurship. IGI Global, 2024. 52-80.

Cite this article

Zhang,D. (2024). AI integration in supply chain and operations management: Enhancing efficiency and resilience. Applied and Computational Engineering,90,8-13.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://2024.confcds.org/

ISBN：978-1-83558-609-9(Print) / 978-1-83558-610-5(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Ammar Alazab

Series: Applied and Computational Engineering

Volume number: Vol.90

ISSN：2755-2721(Print) / 2755-273X(Online)

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