In today's rapidly evolving economy, the adoption of artificial intelligence (AI) within supply chain management (SCM) has emerged as a key area of study. The integration of AI technologies into SCM systems presents a transformative opportunity for organizations seeking to enhance operational efficiency, reduce costs, and establish a competitive advantage in a dynamic market. AI technologies—ranging from machine learning algorithms to predictive analytics—serve as pivotal tools in addressing these challenges. By automating routine tasks, forecasting demand with greater accuracy, and facilitating real-time decision-making, AI enhances responsiveness and agility within supply chains. The economic benefits of incorporating AI into SCM frameworks are substantial. Implementing AI-driven solutions can lead to significant cost savings through improved inventory management, reduced waste, and enhanced resource allocation. For instance, machine learning models can predict stock requirements more accurately, minimizing excessive inventory and associated holding costs. Additionally, AI enhances supplier relationship management by analyzing vendor performance data, leading to more informed selection processes and negotiation strategies. As the field continues to evolve, it is crucial for professionals to engage with emerging technologies, ensuring that they remain competitive and responsive to the demands of an ever-changing market landscape. This study reviews the literature to determine why supply chain management (SCM) needs to adopt artificial intelligence (AI) in terms of integrative tactical planning, resource utilization and cost reduction, risk management, data management and inventory management. The aim of this study was to encourage professionals to investigate the possibilities of AI technology to enhance several elements of the supply chain.
| Published in | American Journal of Artificial Intelligence (Volume 8, Issue 2) |
| DOI | 10.11648/j.ajai.20240802.15 |
| Page(s) | 63-67 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Artificial Intelligence, Supply Chain Management, Economy, Industry 4.0, Risk Management, Data Management
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APA Style
Kumar, A., Divya, Kashyap, R., Kataria, P., Kumar, A. (2024). The Expected Contribution of Artificial Intelligence (AI) Adoption in Supply Chain Management. American Journal of Artificial Intelligence, 8(2), 63-67. https://doi.org/10.11648/j.ajai.20240802.15
ACS Style
Kumar, A.; Divya; Kashyap, R.; Kataria, P.; Kumar, A. The Expected Contribution of Artificial Intelligence (AI) Adoption in Supply Chain Management. Am. J. Artif. Intell. 2024, 8(2), 63-67. doi: 10.11648/j.ajai.20240802.15
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Download@article{10.11648/j.ajai.20240802.15,
author = {Aditya Kumar and Divya and Raina Kashyap and Pranav Kataria and Abhishek Kumar},
title = {The Expected Contribution of Artificial Intelligence (AI) Adoption in Supply Chain Management
},
journal = {American Journal of Artificial Intelligence},
volume = {8},
number = {2},
pages = {63-67},
doi = {10.11648/j.ajai.20240802.15},
url = {https://doi.org/10.11648/j.ajai.20240802.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20240802.15},
abstract = {In today's rapidly evolving economy, the adoption of artificial intelligence (AI) within supply chain management (SCM) has emerged as a key area of study. The integration of AI technologies into SCM systems presents a transformative opportunity for organizations seeking to enhance operational efficiency, reduce costs, and establish a competitive advantage in a dynamic market. AI technologies—ranging from machine learning algorithms to predictive analytics—serve as pivotal tools in addressing these challenges. By automating routine tasks, forecasting demand with greater accuracy, and facilitating real-time decision-making, AI enhances responsiveness and agility within supply chains. The economic benefits of incorporating AI into SCM frameworks are substantial. Implementing AI-driven solutions can lead to significant cost savings through improved inventory management, reduced waste, and enhanced resource allocation. For instance, machine learning models can predict stock requirements more accurately, minimizing excessive inventory and associated holding costs. Additionally, AI enhances supplier relationship management by analyzing vendor performance data, leading to more informed selection processes and negotiation strategies. As the field continues to evolve, it is crucial for professionals to engage with emerging technologies, ensuring that they remain competitive and responsive to the demands of an ever-changing market landscape. This study reviews the literature to determine why supply chain management (SCM) needs to adopt artificial intelligence (AI) in terms of integrative tactical planning, resource utilization and cost reduction, risk management, data management and inventory management. The aim of this study was to encourage professionals to investigate the possibilities of AI technology to enhance several elements of the supply chain.
},
year = {2024}
}
TY - JOUR T1 - The Expected Contribution of Artificial Intelligence (AI) Adoption in Supply Chain Management AU - Aditya Kumar AU - Divya AU - Raina Kashyap AU - Pranav Kataria AU - Abhishek Kumar Y1 - 2024/11/29 PY - 2024 N1 - https://doi.org/10.11648/j.ajai.20240802.15 DO - 10.11648/j.ajai.20240802.15 T2 - American Journal of Artificial Intelligence JF - American Journal of Artificial Intelligence JO - American Journal of Artificial Intelligence SP - 63 EP - 67 PB - Science Publishing Group SN - 2639-9733 UR - https://doi.org/10.11648/j.ajai.20240802.15 AB - In today's rapidly evolving economy, the adoption of artificial intelligence (AI) within supply chain management (SCM) has emerged as a key area of study. The integration of AI technologies into SCM systems presents a transformative opportunity for organizations seeking to enhance operational efficiency, reduce costs, and establish a competitive advantage in a dynamic market. AI technologies—ranging from machine learning algorithms to predictive analytics—serve as pivotal tools in addressing these challenges. By automating routine tasks, forecasting demand with greater accuracy, and facilitating real-time decision-making, AI enhances responsiveness and agility within supply chains. The economic benefits of incorporating AI into SCM frameworks are substantial. Implementing AI-driven solutions can lead to significant cost savings through improved inventory management, reduced waste, and enhanced resource allocation. For instance, machine learning models can predict stock requirements more accurately, minimizing excessive inventory and associated holding costs. Additionally, AI enhances supplier relationship management by analyzing vendor performance data, leading to more informed selection processes and negotiation strategies. As the field continues to evolve, it is crucial for professionals to engage with emerging technologies, ensuring that they remain competitive and responsive to the demands of an ever-changing market landscape. This study reviews the literature to determine why supply chain management (SCM) needs to adopt artificial intelligence (AI) in terms of integrative tactical planning, resource utilization and cost reduction, risk management, data management and inventory management. The aim of this study was to encourage professionals to investigate the possibilities of AI technology to enhance several elements of the supply chain. VL - 8 IS - 2 ER -
Department of Mechanical and Automation Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India
Department of Computer Science, Vanderbilt University, Nashville, United States of America.
Department of Distance and Continuing Education School of Open Learning (Department of Business and Financial Studies), University of Delhi, New Delhi, India.
Department of Computer Science, Bhaskaracharya College of Applied Sciences, New Delhi, India.
Department of Mechanical and Automation Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India
