Open Access

ARTICLE

Modelling Supply Chain Information Collaboration Empowered with Machine Learning Technique

by Naeem Ali^1,*, Alia Ahmed¹, Leena Anum², Taher M. Ghazal^3,4, Sagheer Abbas⁵, Muhammad Adnan Khan^6,7, 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:

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

Received 28 March 2021; Accepted 30 April 2021; Issue published 26 July 2021

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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Keywords

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