Machine Learning in Supply Chain Management

Authors

  • Dhruv Sharma Student, Department of Mechanical Engineering, Maharaja Agrasen Institute of Technology, Rohini Sector-22, Delhi-110086
  • Nitesh Student, Department of Mechanical Engineering, Maharaja Agrasen Institute of Technology, Rohini Sector-22, Delhi-110086
  • Abhirav Khanna Student, Department of Mechanical Engineering, Maharaja Agrasen Institute of Technology, Rohini Sector-22, Delhi-110086
  • Manish kumar Student, Department of Mechanical Engineering, Maharaja Agrasen Institute of Technology, Rohini Sector-22, Delhi-110086
  • Mohit Tomar Student, Department of Mechanical Engineering, Maharaja Agrasen Institute of Technology, Rohini Sector-22, Delhi-110086
  • Vipin Kumar Sharma Assistant Professor, Department of Mechanical Engineering, Maharaja Agrasen Institute of Technology, Rohini Sector-22, Delhi-110086

DOI:

https://doi.org/10.69996/jcai.2025010

Keywords:

Machine Learning, Maintenance, Inventory, Supply Chain, Predictive Analytics

Abstract

The study highlights the application of ML techniques like predictive analytics, optimization algorithms, and advanced demand forecasting in critical areas such as inventory management, supplier selection, logistics optimization, and predictive maintenance. By leveraging ML, businesses can anticipate customer demands with greater accuracy, minimize waste, and respond swiftly to potential disruptions. The findings demonstrate that ML not only enhances decision-making and operational efficiency but also fosters improved customer satisfaction and a stronger competitive edge. This research provides actionable insights into how organizations can harness ML to meet the dynamic demands of modern supply chains and navigate the complexities of a rapidly evolving business landscape. 

References

[1] Makkar, “Applications of Machine Learning Techniques in Supply Chain Optimization,” System Reliability, Quality Control, Safety, Maintenance and Management ICICCT(2019), pp.861-869, 2020

[2] Mallesham, “The Role of AI and ML in Revolutionizing Supply Chain Management,” International Journal of Scientific Research and Management (IJSRM), vol. 10, no.06, pp.918-928, 2022

[3] Mohamed-Iliasse, “Machine Learning in Supply Chain Management: A Systematic Literature Review,” International Journal of Supply and Operation Management (IJSOM), vol. 9, no.4, pp.398-416, 2022

[4] Lin, “An innovative machine learning model for supply chain management,” Journal of Innovation & Knowledge (JIK), vol. 7, no.4, 2022

[5] Srinivasa Sai Abhijit Challapalli, “Optimizing Dallas-Fort Worth Bus Transportation System Using Any Logic,” Journal of Sensors, IoT & Health Sciences, vol.2, no.4, pp.40- 55, 2024.

[6] M.I. Mahraz, L. Benabbou and A. Berrado, “Machine learning in supply chain management: A systematic literature review,” Int. J. Supply Oper. Manag, vol.2022, no.9, pp.398–416, 2022.

[7] E. Theodorou, E. Spiliotis and V. Assimakopoulos, “Optimizing inventory control through a data-driven and model-independent framework,” EURO J. Transp. Logist, vol. 12, pp.100103, 2023.

[8] Srinivasa Sai Abhijit Challapalli, “Sentiment Analysis of the Twitter Dataset for the Prediction of Sentiments,” Journal of Sensors, IoT & Health Sciences, vol.2, no.4, pp.1- 15, 2024.

[9] M. Bertolini, D. Mezzogori, M. Neroni and F. Zammori, “Machine Learning for industrial applications: A comprehensive literature review,” Expert Syst. Appl., vol. 175, pp.114820, 2021.

[10] A.C. Odimarha, S.A. Ayodeji and E.A. Abaku, “Machine learning’s influence on supply chain and logistics optimization in the oil and gas sector: A comprehensive analysis,” Comput. Sci. IT Res. J., vol. 5, pp.725–740, 2024.

[11] M. Elahi, S.O. Afolaranmi, J.L. Martinez Lastra and J.A. Perez Garcia, “A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment,” Discov. Artif. Intell., vol. 43, 2023.

[12] B. Venkateswarlu and Rekha Gangula, “Exploring the Power and Practical Applications of K-Nearest Neighbours (KNN) in Machine Learning,” Journal of Computer Allied Intelligence, vol.2, no.1, pp.8-15, 2024.

[13] B.Ashok Kumar, K.Vijayachandra, G.Naveen Kumar and V.N.Lakshmana Kumar, “Blockchain Technology Communication Technology Model for the IoT,” Journal of Computer Allied Intelligence, vol.2, no.4, pp.20-35, 2024.

[14] G.K.Uyanık and N. Güler, “A study on multiple linear regression analysis,” Procedia Soc. Behav. Sci., vol. 106, pp.234–240, 2013.

[15] K.P. Sinaga and M.S. Yang, “Unsupervised K-means clustering algorithm,” IEEE Access, vol. 8, pp.80716–80727, 2020.

Downloads

Published

2025-04-30

Issue

Vol. 3 No. 2 (2025): Volume: 3 Issue: 2 April

Section

Research Articles

License

Copyright (c) 2025 Journal of Computer Allied Intelligence(JCAI, ISSN: 2584-2676)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.

How to Cite

Dhruv Sharma, Nitesh, Abhirav Khanna, Manish kumar, Mohit Tomar, & , V. K. S. (2025). Machine Learning in Supply Chain Management. Journal of Computer Allied Intelligence(JCAI, ISSN: 2584-2676), 3(2), 28-36. https://doi.org/10.69996/jcai.2025010