Optimizing Smart Factories: A Data-Driven Approach

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Janne Heilala
Janne Heilala
2
Antti Salminen
Antti Salminen
3
Wallace Moreira Bessa
Wallace Moreira Bessa
4
Jussi Kantola
Jussi Kantola

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Since the first industrial revolution, the leading role of emerging technologies has been highlighted in modernizing the industry and developing the workforce. This study explores the impact of Industry 4.0 digital technologies on manufacturing competitiveness, focusing on Finnish SMEs within the EU with a sample (n = 123). It utilizes extensive 2022 European Manufacturing Survey (EMS22) data. Advanced statistical techniques reveal complex connections between automation, competitive edge on services, and innovation models, among other factors. Robust statistical methods, including component and reliability analyses, reinforced the findings. The conclusion offers critical insights and identifies areas for further research in combining innovative manufacturing practices with technology education.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Janne Heilala. 2026. \u201cOptimizing Smart Factories: A Data-Driven Approach\u201d. Global Journal of Research in Engineering - G: Industrial Engineering GJRE-G Volume 23 (GJRE Volume 23 Issue G3): .

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Optimizes industrial processes using data analytics for smart factory efficiency.
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Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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GJRE-G Classification: FOR Code: 0915
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v1.2

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January 10, 2024

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English

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Since the first industrial revolution, the leading role of emerging technologies has been highlighted in modernizing the industry and developing the workforce. This study explores the impact of Industry 4.0 digital technologies on manufacturing competitiveness, focusing on Finnish SMEs within the EU with a sample (n = 123). It utilizes extensive 2022 European Manufacturing Survey (EMS22) data. Advanced statistical techniques reveal complex connections between automation, competitive edge on services, and innovation models, among other factors. Robust statistical methods, including component and reliability analyses, reinforced the findings. The conclusion offers critical insights and identifies areas for further research in combining innovative manufacturing practices with technology education.

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Optimizing Smart Factories: A Data-Driven Approach

Janne Heilala
Janne Heilala
Antti Salminen
Antti Salminen
Wallace Moreira Bessa
Wallace Moreira Bessa
Jussi Kantola
Jussi Kantola

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