Vol.31, No.3, 2022, pp.1671-1687, doi:10.32604/iasc.2022.019892
OPEN ACCESS
ARTICLE
Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques
  • Naeem Ali1, Taher M. Ghazal2,3, Alia Ahmed1, Sagheer Abbas4, M. A. Khan5, Haitham M. Alzoubi6, Umar Farooq7, Munir Ahmad4, Muhammad Adnan Khan8,*
1 School of Business Administration, National College of Business Administration and Economics, Lahore, 54000, Pakistan
2 Center for Cyber Security, Faculty of Information Science and Technology, University Kebansaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia
3 School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE
4 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
5 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University Lahore Campus, Lahore, 54000, Pakistan
6 School of Business, Skyline University College, University City Sharjah, 1797, Sharjah, UAE
7 Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
8 Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, Gyeonggido, 13120, Korea
* Corresponding Author: Muhammad Adnan Khan. Email:
Received 30 April 2021; Accepted 21 July 2021; Issue published 09 October 2021
Abstract
Supply Chain Collaboration is the network of various entities that work cohesively to make up the entire process. The supply chain organizations’ success is dependent on integration, teamwork, and the communication of information. Every day, supply chain and business players work in a dynamic setting. They must balance competing goals such as process robustness, risk reduction, vulnerability reduction, real financial risks, and resilience against just-in-time and cost-efficiency. Decision-making based on shared information in Supply Chain Collaboration constitutes the recital and competitiveness of the collective process. Supply Chain Collaboration has prompted companies to implement the perfect data analytics functions (e.g., data science, predictive analytics, and big data) to improve supply chain operations and, eventually, efficiency. Simulation and modeling are powerful methods for analyzing, investigating, examining, observing and evaluating real-world industrial and logistic processes in this scenario. Fusion-based Machine learning provides a platform that may address the issues/limitations of Supply Chain Collaboration. Compared to the Classical probable data fusion techniques, the fused Machine learning method may offer a strong computing ability and prediction. In this scenario, the machine learning-based Supply Chain Collaboration model has been proposed to evaluate the propensity of the decision-making process to increase the efficiency of the Supply Chain Collaboration.
Keywords
Business intelligence; k-nearest neighbor; machine learning; simulation; supply chain collaboration; support vector machine
Cite This Article
Ali, N., Ghazal, T. M., Ahmed, A., Abbas, S., Khan, M. A. et al. (2022). Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques. Intelligent Automation & Soft Computing, 31(3), 1671–1687.
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