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Modelling Supply Chain Information Collaboration Empowered with Machine Learning Technique

Naeem Ali1,*, Alia Ahmed1, Leena Anum2, Taher M. Ghazal3,4, Sagheer Abbas5, Muhammad Adnan Khan6,7, Haitham M. Alzoubi8, Munir Ahmad5

1 School of Business Administration, National College of Business Administration and Economics, Lahore, 54000, Pakistan
2 Department of Management Sciences, Lahore Garrison University, Lahore, 54000, Pakistan
3 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), Bangi, 43600, Malaysia
4 School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE
5 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
6 Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University Lahore Campus, Lahore 54000, Pakistan
7 Pattern Recognition and Machine Learning Lab, Department of Software Engineering, Gachon University, Seongnam, 13120, Republic of Korea
8 School of Business, Skyline University College, University City Sharjah, Sharjah, 1797, UAE

* Corresponding Author: Naeem Ali. Email: email

Intelligent Automation & Soft Computing 2021, 30(1), 243-257. https://doi.org/10.32604/iasc.2021.018983

Abstract

Information Collaboration of the supply chain is the domination and control of product flow information from the producer to the customer. The data information flow is correlated with demand fill-up, a role delivering service, and feedback. The collaboration of supply chain information is a complex contrivance that impeccably manages the efficiency flow and focuses on its vulnerable area. As there is always room for growth in the current century, major companies have shown a growing tendency to improve their supply chain’s productivity and sustainability to increase customer consumption in complying with environmental regulations. Therefore, in supply chain collaboration, it is a precarious problem to find the best approaches to achieving business intentions, and most organizations prefer to partner with reputable and viable firms. In this respect, machine learning methodology such as Support Vector Machine is used to jeopardize the supply chain information collaboration. More specific efficiency is obtained from the more productive device model. Simulation results show that by adopting the proposed model and applying the Support Vector Algorithm, 98.99 percent accuracy is obtained by training, 98.91 percent by testing, and 98.92 percent from validation. It is clinched that this model will revolutionize the supply chain information collaboration patterns and will provide a significant competitive edge for business sustainability.

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Cite This Article

APA Style
Ali, N., Ahmed, A., Anum, L., Ghazal, T.M., Abbas, S. et al. (2021). Modelling supply chain information collaboration empowered with machine learning technique. Intelligent Automation & Soft Computing, 30(1), 243-257. https://doi.org/10.32604/iasc.2021.018983
Vancouver Style
Ali N, Ahmed A, Anum L, Ghazal TM, Abbas S, Khan MA, et al. Modelling supply chain information collaboration empowered with machine learning technique. Intell Automat Soft Comput . 2021;30(1):243-257 https://doi.org/10.32604/iasc.2021.018983
IEEE Style
N. Ali et al., "Modelling Supply Chain Information Collaboration Empowered with Machine Learning Technique," Intell. Automat. Soft Comput. , vol. 30, no. 1, pp. 243-257. 2021. https://doi.org/10.32604/iasc.2021.018983



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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