AI-Powered Business Intelligence in Manufacturing: A Technical Overview

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) and Business Intelligence (BI) integration in manufacturing environments within the Industry 4.0 framework. The article examines the implementation methodologies, architectural considerations, and performance metrics across various manufacturing domains, including predictive maintenance, quality control, and supply chain optimization. The article demonstrates significant improvements in operational efficiency, cost reduction, and product quality through AI-powered systems. Through detailed analysis of edge computing integration, advanced AI capabilities, and enterprise system architecture, the article provides insights into both current implementations and future technological considerations, offering a roadmap for manufacturing organizations pursuing digital transformation.

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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-03-09
DOI
https://doi.org/10.32628/cseit25112411
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Business Intelligence in Manufacturing: A Technical Overview

Indraneel Madabhushini
1 links
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Big Data and Business Intelligence
article

AI-Powered Business Intelligence in Manufacturing: A Technical Overview

Indraneel Madabhushini
article en

Abstract

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) and Business Intelligence (BI) integration in manufacturing environments within the Industry 4.0 framework. The article examines the implementation methodologies, architectural considerations, and performance metrics across various manufacturing domains, including predictive maintenance, quality control, and supply chain optimization. The article demonstrates significant improvements in operational efficiency, cost reduction, and product quality through AI-powered systems. Through detailed analysis of edge computing integration, advanced AI capabilities, and enterprise system architecture, the article provides insights into both current implementations and future technological considerations, offering a roadmap for manufacturing organizations pursuing digital transformation.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(2)
University of Southern Queensland (AU)
Openalex Percentile: Top 94%
Big Data and Business Intelligence
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