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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: email

Intelligent Automation & Soft Computing 2022, 31(3), 1671-1687. https://doi.org/10.32604/iasc.2022.019892

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.

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Cite This Article

N. Ali, T. M. Ghazal, A. Ahmed, S. Abbas, M. A. Khan et al., "Fusion-based supply chain collaboration using machine learning techniques," Intelligent Automation & Soft Computing, vol. 31, no.3, pp. 1671–1687, 2022.

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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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