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Journal of Statistics and Management Systems cover

Journal of Statistics and Management Systems

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Indexed in ABDC Australia

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Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Journal of Statistics and Management Systems
Open Access Research Article

Statistical analysis in AI and IoT integration for real-time quality control in Industry 4.0

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pp. 443–453Vol. 29Issue 5May 2026DOI: 10.47974/JSMS-1522XML
Received:
01 Apr 2025
Published Online:
27 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1522
Pages:
443–453

Abstract

As industries evolve toward digital transformation, the integration of traditional quality management tools with emerging technologies becomes imperative. This paper presents a novel framework that synergizes Six Sigma methodologies with Artificial Intelligence (AI) to align with the principles of Quality 4.0. By leveraging machine learning, real-time analytics, and big data, the proposed model enhances the DMAIC (Define, Measure, Analyze, Improve, Control) cycle for continuous improvement in smart manufacturing and service environments. The framework introduces AI-powered tools for root cause analysis, predictive quality, and intelligent decision-making, thereby reducing process variation and enhancing operational efficiency. Case studies and simulations demonstrate the effectiveness of this integrated approach in driving superior quality outcomes and enabling agile responses to dynamic market demands. This research bridges the gap between statistical process control and AI-driven quality assurance, offering a scalable pathway for organizations to achieve excellence in the Industry 4.0 era.

Keywords

Six sigmaArtificial intelligence (AI)Quality 4.0Data-driven frameworkControl charts

Subject Classifications

62P30 Applications of statistics in engineering and industrycontrol charts

References

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