Open Access

ARTICLE

Dynamic Voting Classifier for Risk Identification in Supply Chain 4.0

by Abdullah Ali Salamai¹, El-Sayed M. El-kenawy², Ibrahim Abdelhameed^3,*

1 Community college, Jazan University, Jazan, Kingdom of Saudi Arabia
2 Delta Higher Institute of Engineering and Technology, Mansoura, Egypt
3 Faculty of Engineering, Mansoura University, Mansoura, Egypt

* Corresponding Author: Ibrahim Abdelhameed. Email:

(This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 69(3), 3749-3766. https://doi.org/10.32604/cmc.2021.018179

Received 28 February 2021; Accepted 05 May 2021; Issue published 24 August 2021

Abstract

Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems, which integrate the supply chain’s manufacturing operations, information technology, and telecommunication processes. Although supply chain 4.0 aims to improve supply chains’ production systems and profitability, it is subject to different operational and disruptive risks. Operational risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT systems. This paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group (SCDG) algorithm. Exploration and exploitation mechanisms of the basic Sine Cosine Algorithm (CSA) are adjusted and controlled by two groups of agents that can be changed dynamically during the iterations. External and internal features were collected and analyzed from different data sources of service level agreements and transaction data from various KSA firms to validate the proposed algorithm’s efficiency. A balanced accuracy of 0.989 and a Mean Square Error (MSE) of 0.0476 were achieved compared with other optimization-based classifier techniques. A one-way analysis of variance (ANOVA) and Wilcoxon rank-sum tests were performed to show the superiority of the proposed SCDG algorithm. Thus, the experimental results indicate the effectiveness of the proposed SCDG algorithm-based voting classifier.

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Keywords

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