Article: AI-driven quality control techniques in manufacturing processes to enhance Six Sigma approach Journal: International Journal of Six Sigma and Competitive Advantage (IJSSCA) 2025 Vol.15 No.3 pp.291 - 314 Abstract: Six Sigma is a business strategy focused on reducing defects and variations in products and processes, thereby enhancing quality. The rise of Industry 4.0 technologies has increased data volumes in manufacturing, creating challenges for integrating Six Sigma methodologies. To adapt, we propose an enhanced Six Sigma approach incorporating AI technologies to optimise performance in this new landscape. This paper proposes a new methodology, the 5I method (identify, inspect, investigate, implement, improve) combines statistical methods and predictive analysis using machine learning, aiming to achieve data-driven predictive quality. To develop a robust model for detecting all types of steel plate defects, we propose a hybrid statistical sampling algorithm that combines the synthetic minority over-sampling technique (SMOTE) and edited nearest neighbour (ENN). Additionally, we applied a universal deep neural network (DNN) as a classifier of defects, achieving an impressive prediction accuracy of 99.05%, surpassing other machine learning algorithms. Inderscience Publishers - linking academia, business and industry through research

Title: AI-driven quality control techniques in manufacturing processes to enhance Six Sigma approach

Authors: Nadia Hoggas; Ouassila Hioual; Amel Hebboul

Addresses: ICOSI Laboratory, Abbes Laghrour University, Khenchela 40004, Algeria ' Abbes Laghrour University, Khenchela 40004, Algeria; LIRE Laboratory, Abdelhamid Mehri University, Constantine, Algeria ' BIOSTIM Laboratory, Salah Boubnider University of Constantine 3, Ali Mendjli Elkhroub, Constantine, Algeria; Ecole Normale Supérieure El Katiba Assia DjebarAssia Djebbar, Ali Mendjli Elkhroub, Constantine, Algeria

Abstract: Six Sigma is a business strategy focused on reducing defects and variations in products and processes, thereby enhancing quality. The rise of Industry 4.0 technologies has increased data volumes in manufacturing, creating challenges for integrating Six Sigma methodologies. To adapt, we propose an enhanced Six Sigma approach incorporating AI technologies to optimise performance in this new landscape. This paper proposes a new methodology, the 5I method (identify, inspect, investigate, implement, improve) combines statistical methods and predictive analysis using machine learning, aiming to achieve data-driven predictive quality. To develop a robust model for detecting all types of steel plate defects, we propose a hybrid statistical sampling algorithm that combines the synthetic minority over-sampling technique (SMOTE) and edited nearest neighbour (ENN). Additionally, we applied a universal deep neural network (DNN) as a classifier of defects, achieving an impressive prediction accuracy of 99.05%, surpassing other machine learning algorithms.

Keywords: Six Sigma; Industry 4.0; Quality 4.0; 5I; classification; synthetic minority over-sampling technique; SMOTE; edited nearest neighbour; ENN; deep neural network; DNN.

DOI: 10.1504/IJSSCA.2025.145624

International Journal of Six Sigma and Competitive Advantage, 2025 Vol.15 No.3, pp.291 - 314

Received: 01 Mar 2024
Accepted: 01 Nov 2024
Published online: 09 Apr 2025 *

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Title: AI-driven quality control techniques in manufacturing processes to enhance Six Sigma approach

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